Embracing uncertainty in applied ecology.

PubMed

Milner-Gulland, E J; Shea, K

2017-12-01

Applied ecologists often face uncertainty that hinders effective decision-making.Common traps that may catch the unwary are: ignoring uncertainty, acknowledging uncertainty but ploughing on, focussing on trivial uncertainties, believing your models, and unclear objectives.We integrate research insights and examples from a wide range of applied ecological fields to illustrate advances that are generally underused, but could facilitate ecologists' ability to plan and execute research to support management.Recommended approaches to avoid uncertainty traps are: embracing models, using decision theory, using models more effectively, thinking experimentally, and being realistic about uncertainty. Synthesis and applications . Applied ecologists can become more effective at informing management by using approaches that explicitly take account of uncertainty.

An empirical application of transaction-costs theory to organizational design characteristics.

PubMed

Williams, S

2000-01-01

The environmental uncertainty component of transaction-costs theory was used to predict the organizational structural characteristics of size (number of employees) and horizontal differentiation (number of vice presidents) using financial and management information from the COMPACT DISCLOSURE data base (which contains the most recent annual and periodic reports for more than 12,000 public companies). Organizations were categorized as low- or high-uncertainty industries according to Dess and Beard's (1984) Dynamism Scale, and net sales volume was controlled. As predicted, high-uncertainty companies had significantly higher horizontal differentiation than low-uncertainty firms, a finding that supports the transaction-costs expectation that organizations may require more departments or personnel to cope with increasing uncertainty. Surprisingly, low-uncertainty firms were found to have significantly more employees than high-uncertainty organizations, which is the opposite of what transaction-costs theory predicts. Possible explanations for this unexpected finding and further potential limitations are discussed.

Management applications of discontinuity theory

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Barichievy, Chris; Eason, Tarsha; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance H.; Knutson, Melinda; Nash, Kirsty L.; Nelson, R. John; Nystrom, Magnus; Spanbauer, Trisha; Stow, Craig A.; Sundstrom, Shana M.

2015-01-01

Human impacts on the environment are multifaceted and can occur across distinct spatiotemporal scales. Ecological responses to environmental change are therefore difficult to predict, and entail large degrees of uncertainty. Such uncertainty requires robust tools for management to sustain ecosystem goods and services and maintain resilient ecosystems.We propose an approach based on discontinuity theory that accounts for patterns and processes at distinct spatial and temporal scales, an inherent property of ecological systems. Discontinuity theory has not been applied in natural resource management and could therefore improve ecosystem management because it explicitly accounts for ecological complexity.Synthesis and applications. We highlight the application of discontinuity approaches for meeting management goals. Specifically, discontinuity approaches have significant potential to measure and thus understand the resilience of ecosystems, to objectively identify critical scales of space and time in ecological systems at which human impact might be most severe, to provide warning indicators of regime change, to help predict and understand biological invasions and extinctions and to focus monitoring efforts. Discontinuity theory can complement current approaches, providing a broader paradigm for ecological management and conservation.

Management applications of discontinuity theory | Science ...

EPA Pesticide Factsheets

1.Human impacts on the environment are multifaceted and can occur across distinct spatiotemporal scales. Ecological responses to environmental change are therefore difficult to predict, and entail large degrees of uncertainty. Such uncertainty requires robust tools for management to sustain ecosystem goods and services and maintain resilient ecosystems. 2.We propose an approach based on discontinuity theory that accounts for patterns and processes at distinct spatial and temporal scales, an inherent property of ecological systems. Discontinuity theory has not been applied in natural resource management and could therefore improve ecosystem management because it explicitly accounts for ecological complexity. 3.Synthesis and applications. We highlight the application of discontinuity approaches for meeting management goals. Specifically, discontinuity approaches have significant potential to measure and thus understand the resilience of ecosystems, to objectively identify critical scales of space and time in ecological systems at which human impact might be most severe, to provide warning indicators of regime change, to help predict and understand biological invasions and extinctions and to focus monitoring efforts. Discontinuity theory can complement current approaches, providing a broader paradigm for ecological management and conservation This manuscript provides insight on using discontinuity approaches to aid in managing complex ecological systems. In part

"I Don't Want to Be an Ostrich": Managing Mothers' Uncertainty during BRCA1/2 Genetic Counseling.

PubMed

Fisher, Carla L; Roccotagliata, Thomas; Rising, Camella J; Kissane, David W; Glogowski, Emily A; Bylund, Carma L

2017-06-01

Families who face genetic disease risk must learn how to grapple with complicated uncertainties about their health and future on a long-term basis. Women who undergo BRCA 1/2 genetic testing describe uncertainty related to personal risk as well as their loved ones', particularly daughters', risk. The genetic counseling setting is a prime opportunity for practitioners to help mothers manage uncertainty in the moment but also once they leave a session. Uncertainty Management Theory (UMT) helps to illuminate the various types of uncertainty women encounter and the important role of communication in uncertainty management. Informed by UMT, we conducted a thematic analysis of 16 genetic counseling sessions between practitioners and mothers at risk for, or carriers of, a BRCA1/2 mutation. Five themes emerged that represent communication strategies used to manage uncertainty: 1) addresses myths, misunderstandings, or misconceptions; 2) introduces uncertainty related to science; 3) encourages information seeking or sharing about family medical history; 4) reaffirms or validates previous behavior or decisions; and 5) minimizes the probability of personal risk or family members' risk. Findings illustrate the critical role of genetic counseling for families in managing emotionally challenging risk-related uncertainty. The analysis may prove beneficial to not only genetic counseling practice but generations of families at high risk for cancer who must learn strategic approaches to managing a complex web of uncertainty that can challenge them for a lifetime.

Adaptive management of rangeland systems

USGS Publications Warehouse

Allen, Craig R.; Angeler, David G.; Fontaine, Joseph J.; Garmestani, Ahjond S.; Hart, Noelle M.; Pope, Kevin L.; Twidwell, Dirac

2017-01-01

Adaptive management is an approach to natural resource management that uses structured learning to reduce uncertainties for the improvement of management over time. The origins of adaptive management are linked to ideas of resilience theory and complex systems. Rangeland management is particularly well suited for the application of adaptive management, having sufficient controllability and reducible uncertainties. Adaptive management applies the tools of structured decision making and requires monitoring, evaluation, and adjustment of management. Adaptive governance, involving sharing of power and knowledge among relevant stakeholders, is often required to address conflict situations. Natural resource laws and regulations can present a barrier to adaptive management when requirements for legal certainty are met with environmental uncertainty. However, adaptive management is possible, as illustrated by two cases presented in this chapter. Despite challenges and limitations, when applied appropriately adaptive management leads to improved management through structured learning, and rangeland management is an area in which adaptive management shows promise and should be further explored.

Health information seeking and the World Wide Web: an uncertainty management perspective.

PubMed

Rains, Stephen A

2014-01-01

Uncertainty management theory was applied in the present study to offer one theoretical explanation for how individuals use the World Wide Web to acquire health information and to help better understand the implications of the Web for information seeking. The diversity of information sources available on the Web and potential to exert some control over the depth and breadth of one's information-acquisition effort is argued to facilitate uncertainty management. A total of 538 respondents completed a questionnaire about their uncertainty related to cancer prevention and information-seeking behavior. Consistent with study predictions, use of the Web for information seeking interacted with respondents' desired level of uncertainty to predict their actual level of uncertainty about cancer prevention. The results offer evidence that respondents who used the Web to search for cancer information were better able than were respondents who did not seek information to achieve a level of uncertainty commensurate with the level of uncertainty they desired.

Integral control for population management.

PubMed

Guiver, Chris; Logemann, Hartmut; Rebarber, Richard; Bill, Adam; Tenhumberg, Brigitte; Hodgson, Dave; Townley, Stuart

2015-04-01

We present a novel management methodology for restocking a declining population. The strategy uses integral control, a concept ubiquitous in control theory which has not been applied to population dynamics. Integral control is based on dynamic feedback-using measurements of the population to inform management strategies and is robust to model uncertainty, an important consideration for ecological models. We demonstrate from first principles why such an approach to population management is suitable via theory and examples.

Assessing and managing freshwater ecosystems vulnerable to global change

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Birge, Hannah E.; Drakare, Stina; McKie, Brendan G.; Johnson, Richard K.

2014-01-01

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.

A review of uncertainty research in impact assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leung, Wanda, E-mail: wanda.leung@usask.ca; Noble, Bram, E-mail: b.noble@usask.ca; Gunn, Jill, E-mail: jill.gunn@usask.ca

2015-01-15

This paper examines uncertainty research in Impact Assessment (IA) and the focus of attention of the IA scholarly literature. We do so by first exploring ‘outside’ the IA literature, identifying three main themes of uncertainty research, and then apply these themes to examine the focus of scholarly research on uncertainty ‘inside’ IA. Based on a search of the database Scopus, we identified 134 journal papers published between 1970 and 2013 that address uncertainty in IA, 75% of which were published since 2005. We found that 90% of IA research addressing uncertainty focused on uncertainty in the practice of IA, includingmore » uncertainty in impact predictions, models and managing environmental impacts. Notwithstanding early guidance on uncertainty treatment in IA from the 1980s, we found no common, underlying conceptual framework that was guiding research on uncertainty in IA practice. Considerably less attention, only 9% of papers, focused on uncertainty communication, disclosure and decision-making under uncertain conditions, the majority of which focused on the need to disclose uncertainties as opposed to providing guidance on how to do so and effectively use that information to inform decisions. Finally, research focused on theory building for explaining human behavior with respect to uncertainty avoidance constituted only 1% of the IA published literature. We suggest the need for further conceptual framework development for researchers focused on identifying and addressing uncertainty in IA practice; the need for guidance on how best to communicate uncertainties in practice, versus criticizing practitioners for not doing so; research that explores how best to interpret and use disclosures about uncertainty when making decisions about project approvals, and the implications of doing so; and academic theory building and exploring the utility of existing theories to better understand and explain uncertainty avoidance behavior in IA. - Highlights: • We identified three main themes of uncertainty research in 134 papers from the scholarly literature. • The majority of research has focused on better methods for managing uncertainty in predictions. • Uncertainty disclosure is demanded of practitioners, but there is little guidance on how to do so. • There is limited theoretical explanation as to why uncertainty is avoided or not disclosed. • Conceptual, practical and theoretical guidance are required for IA uncertainty consideration.« less

ENVIRONMENTAL SYSTEMS MANAGEMENT, SUSTAINABILITY THEORY, AND THE CHALLENGE OF UNCERTAINTY

EPA Science Inventory

Environmental Systems Management is the management of environmental problems at the systems level fully accounting fo rthe multi-dimensional nature of the environment. This includes socio-economic dimensions as well s the usual physical and life science aspects. This is important...

An inventory-theory-based interval-parameter two-stage stochastic programming model for water resources management

NASA Astrophysics Data System (ADS)

Suo, M. Q.; Li, Y. P.; Huang, G. H.

2011-09-01

In this study, an inventory-theory-based interval-parameter two-stage stochastic programming (IB-ITSP) model is proposed through integrating inventory theory into an interval-parameter two-stage stochastic optimization framework. This method can not only address system uncertainties with complex presentation but also reflect transferring batch (the transferring quantity at once) and period (the corresponding cycle time) in decision making problems. A case of water allocation problems in water resources management planning is studied to demonstrate the applicability of this method. Under different flow levels, different transferring measures are generated by this method when the promised water cannot be met. Moreover, interval solutions associated with different transferring costs also have been provided. They can be used for generating decision alternatives and thus help water resources managers to identify desired policies. Compared with the ITSP method, the IB-ITSP model can provide a positive measure for solving water shortage problems and afford useful information for decision makers under uncertainty.

School management and contingency theory: an emerging perspective.

PubMed

Hanson, E M

1979-01-01

In an article written for educational administrators, Hanson explains the assumptions, framework, and application of contingency theory. The author sees contingency theory as a way for organizations to adapt to uncertainty by developing a strategic plan with alternative scenarios. He urges school administrators to join businessmen and public managers in using a technique described as "the most powerful current sweeping over the organizational field." The theory assumes that: (1) a maze of goals govern the development of events; (2) different management approaches may be appropriate within the same organization; and (3) different leadership styles suit different situations. Contingency planning helps the organization to respond to uncertainty in the external environment by identifying possible events that may occur and by preparing alternative stratgies to deal with them. Hanson describes the purpose of this process as providing "a more effective match between an organization and its environment." He explains that contingency theory analyzes the internal adjustments of the organization (e.g., decision making process, structure, technology, instructional techniques) as it seeks to meet the shifting demands of its external or internal environments. According to the author, the intent of contingency theory is to establish an optimal "match" between environmental demands (and support) and the response capabilities of the organization including its structure, planning process, and leadership style.

Psychological Entropy: A Framework for Understanding Uncertainty-Related Anxiety

ERIC Educational Resources Information Center

Hirsh, Jacob B.; Mar, Raymond A.; Peterson, Jordan B.

2012-01-01

Entropy, a concept derived from thermodynamics and information theory, describes the amount of uncertainty and disorder within a system. Self-organizing systems engage in a continual dialogue with the environment and must adapt themselves to changing circumstances to keep internal entropy at a manageable level. We propose the entropy model of…

Introducing Risk Analysis and Calculation of Profitability under Uncertainty in Engineering Design

ERIC Educational Resources Information Center

Kosmopoulou, Georgia; Freeman, Margaret; Papavassiliou, Dimitrios V.

2011-01-01

A major challenge that chemical engineering graduates face at the modern workplace is the management and operation of plants under conditions of uncertainty. Developments in the fields of industrial organization and microeconomics offer tools to address this challenge with rather well developed concepts, such as decision theory and financial risk…

When do business units benefit more from collective citizenship behavior of management teams? An upper echelons perspective.

PubMed

Liu, Wu; Gong, Yaping; Liu, Jun

2014-05-01

Drawing upon the notion of managerial discretion from upper echelons theory, we theorize which external contingencies moderate the relationship between collective organizational citizenship behavior (COCB) and unit performance. Focusing on business unit (BU) management teams, we hypothesize that COCB of BU management teams enhances BU performance and that this impact depends on environmental uncertainty and BU management-team decision latitude, 2 determinants of managerial discretion. In particular, the positive effect of COCB is stronger when environmental uncertainty or the BU management-team decision latitude is greater. Time-lagged data from 109 BUs of a telecommunications company support the hypotheses. Additional exploratory analysis shows that the positive moderating effect of environmental uncertainty is further amplified at higher levels of BU management-team decision latitude. Overall, this study extends the internally focused view in the micro OCB literature by introducing external contingencies for the COCB-unit-performance relationship. (c) 2014 APA, all rights reserved.

Bayesian-information-gap decision theory with an application to CO 2 sequestration

DOE PAGES

O'Malley, D.; Vesselinov, V. V.

2015-09-04

Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and non-probabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information-Gap Decision Theory (IGDT) to addressmore » model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real-world engineering problems related to the enumeration of possible outcomes. In the case of zero non-probabilistic uncertainty, the method reduces to a Bayesian method. Lastly, to illustrate the approach, we apply it to a site-selection decision for geologic CO 2 sequestration.« less

The Way of Openness: Moral Sphere Theory, Education, Ethics, and Classroom Management

ERIC Educational Resources Information Center

Bullough, Robert V., Jr.

2014-01-01

Noting the challenges of radical pluralism and uncertainty to ethics and education, the author describes, then explores Moral Sphere Theory (MST) developed by the philosopher Robert Kane and in relationship to insights drawn from American pragmatism. The argument is that MST offers fresh ways for thinking about education and the profound…

How to manage future groundwater resource of China under climate change and urbanization: An optimal stage investment design from modern portfolio theory.

PubMed

Hua, Shanshan; Liang, Jie; Zeng, Guangming; Xu, Min; Zhang, Chang; Yuan, Yujie; Li, Xiaodong; Li, Ping; Liu, Jiayu; Huang, Lu

2015-11-15

Groundwater management in China has been facing challenges from both climate change and urbanization and is considered as a national priority nowadays. However, unprecedented uncertainty exists in future scenarios making it difficult to formulate management planning paradigms. In this paper, we apply modern portfolio theory (MPT) to formulate an optimal stage investment of groundwater contamination remediation in China. This approach generates optimal weights of investment to each stage of the groundwater management and helps maximize expected return while minimizing overall risk in the future. We find that the efficient frontier of investment displays an upward-sloping shape in risk-return space. The expected value of groundwater vulnerability index increases from 0.6118 to 0.6230 following with the risk of uncertainty increased from 0.0118 to 0.0297. If management investment is constrained not to exceed certain total cost until 2050 year, the efficient frontier could help decision makers make the most appropriate choice on the trade-off between risk and return. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using info-Gap Decision Theory for Water Resources Planning Under Severe Uncertainty

NASA Astrophysics Data System (ADS)

Korteling, B.; Brazier, R.; Kapelan, Z.; Dessai, S.

2012-12-01

Water resource managers are required to develop comprehensive water resource plans based on severely uncertain information of the effects of climate change on local hydrology and future socio-economic changes on localised demand. In England and Wales, current water resource planning methodologies include a headroom estimation process that quantifies uncertainty based on only one point of an assumed range of deviations from the expected climate and projected demand 25 years into the future. There are many situations where there is not enough knowledge to be able to estimate a representative probability of occurrence, or to be confident that the tails of an assumed probability distribution will not exhibit unexpected skewness, or that the kurtosis of a distribution differs from the norm. These situations can be considered severely uncertain. Information-Gap decision theory offers a method to sample a wider range of uncertainty than with traditional methods, and as a result, compare the robustness of various water resource management options under conditions of severe uncertainty. A more robust management option is one that delivers the same level of performance as other options at higher levels of uncertainty. A case study is based on a Water Supply Area that encompasses the county of Cornwall in southwest England containing 17 reservoirs and 19 demand nodes. The performance success of management options are evaluated primarily by measures of water availability including a reservoir risk measure that tests the probability and magnitude that strategic reservoir storage levels fall below the drought management curve under adverse conditions and also a safety margin deficit that tests how quickly reservoir levels can return to optimum operating levels in favourable conditions. Multi-Criteria Decision Analysis (MCDA) is used to test the effectiveness of different management options with different weightings for metrics other than water availability including; capital and operating costs, costs to customers, carbon emissions, environmental impact and social acceptability. Findings show that beyond the uncertainty range explored with the traditional headroom method, preference reversals can occur, i.e. some management options that underperform at lower uncertainties, outperform at higher levels of uncertainty. This study also shows that when 50% or more of the population adopts demand side management, efficiency related measures and innovative options such as rainwater collection can perform equally well or better than some supply side options. The additional use of MCDA shifts the focus away from reservoir expansion options that perform best with respect to water availability, to combined strategies that include innovative demand side management actions of rainwater collection and greywater reuse as well as efficiency measures and additional regional transfers. This research illustrates how an Info-Gap based approach can offer a comprehensive picture of potential supply/demand futures and a rich variety of information to support adaptive management of water systems under severe uncertainty.

Unconscious Vigilance: Worldview Defense Without Adaptations for Terror, Coalition, or Uncertainty Management

PubMed Central

Holbrook, Colin; Sousa, Paulo; Hahn-Holbrook, Jennifer

2012-01-01

Individuals subtly reminded of death, coalitional challenges, or feelings of uncertainty display exaggerated preferences for affirmations and against criticisms of their cultural in-groups. Terror management, coalitional psychology, and uncertainty management theories postulate this “worldview defense” effect as the output of mechanisms evolved either to allay the fear of death, foster social support, or reduce anxiety by increasing adherence to cultural values. In 4 studies, we report evidence for an alternative perspective. We argue that worldview defense owes to unconscious vigilance, a state of accentuated reactivity to affective targets (which need not relate to cultural worldviews) that follows detection of subtle alarm cues (which need not pertain to death, coalitional challenges, or uncertainty). In Studies 1 and 2, death-primed participants produced exaggerated ratings of worldview-neutral affective targets. In Studies 3 and 4, subliminal threat manipulations unrelated to death, coalitional challenges, or uncertainty evoked worldview defense. These results are discussed as they inform evolutionary interpretations of worldview defense and future investigations of the influence of unconscious alarm on judgment. PMID:21644809

Understanding the ways in which health visitors manage anxiety in cross-cultural work: a qualitative study.

PubMed

Cuthill, Fiona

2014-10-01

This paper is a report of part of a study that explored the ways in which health visitors manage uncertainty and anxiety when working with clients across cultures. Internationally health care professionals are required to deliver a high standard of culturally appropriate care to increasingly diverse communities and yet problems persist. Research evidence informing cultural 'competence' is focused largely around student experience and consequently little is known about the day-to-day experiences of health professionals in diverse community settings. Anxiety and uncertainty are increasingly recognised as important emotions experienced by a variety of health care professionals when working across cultures and yet the ways in which anxiety and uncertainty is managed in practice is less well understood. Grounded theory methodology was used and 21 semi-structured interviews were conducted with participating health visitors in the North East of England between May 2008 and September 2009. All participants described themselves as white. This study identified three different positions adopted by the health visitors to manage uncertainty and anxiety in their work across cultures. Identified as, 'Fixing a culture', 'Reworking the equality agenda' and 'Asserting the professional self', these strategies identify the ways in which health visitors try to manage the uncertainty and anxiety they feel when working in diverse communities. All of these strategies attempt in different ways to negate cultural difference and to render culture as static and known. Given that health professionals report anxiety and uncertainty when working across diverse community settings, identification of the strategies used by health visitors to manage that anxiety is important for both policy and practice. New strategies need to be developed to help health professionals to manage uncertainty and anxiety in ways that promote both cultural safe care and health equity.

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

PubMed Central

2012-01-01

Background Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. Aims We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Methods Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. Results We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. Conclusions The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals. PMID:23249291

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty.

PubMed

Ben-Haim, Yakov; Dacso, Clifford C; Zetola, Nicola M

2012-12-19

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

Applying principles from the game theory to acute stroke care: Learning from the prisoner's dilemma, stag-hunt, and other strategies.

PubMed

Saposnik, Gustavo; Johnston, S Claiborne

2016-04-01

Acute stroke care represents a challenge for decision makers. Decisions based on erroneous assessments may generate false expectations of patients and their family members, and potentially inappropriate medical advice. Game theory is the analysis of interactions between individuals to study how conflict and cooperation affect our decisions. We reviewed principles of game theory that could be applied to medical decisions under uncertainty. Medical decisions in acute stroke care are usually made under constrains: short period of time, with imperfect clinical information, limit understanding about patients and families' values and beliefs. Game theory brings some strategies to help us manage complex medical situations under uncertainty. For example, it offers a different perspective by encouraging the consideration of different alternatives through the understanding of patients' preferences and the careful evaluation of cognitive distortions when applying 'real-world' data. The stag-hunt game teaches us the importance of trust to strength cooperation for a successful patient-physician interaction that is beyond a good or poor clinical outcome. The application of game theory to stroke care may improve our understanding of complex medical situations and help clinicians make practical decisions under uncertainty. © 2016 World Stroke Organization.

Matrix management in hospitals: testing theories of matrix structure and development.

PubMed

Burns, L R

1989-09-01

A study of 315 hospitals with matrix management programs was used to test several hypotheses concerning matrix management advanced by earlier theorists. The study verifies that matrix management involves several distinctive elements that can be scaled to form increasingly complex types of lateral coordinative devices. The scalability of these elements is evident only cross-sectionally. The results show that matrix complexity is not an outcome of program age, nor does matrix complexity at the time of implementation appear to influence program survival. Matrix complexity, finally, is not determined by the organization's task diversity and uncertainty. The results suggest several modifications in prevailing theories of matrix organization.

Adaptive management for ecosystem services (j/a) | Science ...

EPA Pesticide Factsheets

Management of natural resources for the production of ecosystem services, which are vital for human well-being, is necessary even when there is uncertainty regarding system response to management action. This uncertainty is the result of incomplete controllability, complex internal feedbacks, and non-linearity that often interferes with desired management outcomes, and insufficient understanding of nature and people. Adaptive management was developed to reduce such uncertainty. We present a framework for the application of adaptive management for ecosystem services that explicitly accounts for cross-scale tradeoffs in the production of ecosystem services. Our framework focuses on identifying key spatiotemporal scales (plot, patch, ecosystem, landscape, and region) that encompass dominant structures and processes in the system, and includes within- and cross-scale dynamics, ecosystem service tradeoffs, and management controllability within and across scales. Resilience theory recognizes that a limited set of ecological processes in a given system regulate ecosystem services, yet our understanding of these processes is poorly understood. If management actions erode or remove these processes, the system may shift into an alternative state unlikely to support the production of desired services. Adaptive management provides a process to assess the underlying within and cross-scale tradeoffs associated with production of ecosystem services while proceeding with manage

Uncertainty after treatment for prostate cancer: definition, assessment, and management.

PubMed

Yu Ko, Wellam F; Degner, Lesley F

2008-10-01

Prostate cancer is the second most common type of cancer in men living in the United States and the most common type of malignancy in Canadian men, accounting for 186,320 new cases in the United States and 24,700 in Canada in 2008. Uncertainty, a component of all illness experiences, influences how men perceive the processes of treatment and adaptation. The Reconceptualized Uncertainty in Illness Theory explains the chronic nature of uncertainty in cancer survivorship by describing a shift from an emergent acute phase of uncertainty in survivors to a new level of uncertainty that is no longer acute and becomes a part of daily life. Proper assessment of certainty and uncertainty may allow nurses to maximize the effectiveness of patient-provider communication, cognitive reframing, and problem-solving interventions to reduce uncertainty after cancer treatment.

Evaluation of Uncertainty in Runoff Analysis Incorporating Theory of Stochastic Process

NASA Astrophysics Data System (ADS)

Yoshimi, Kazuhiro; Wang, Chao-Wen; Yamada, Tadashi

2015-04-01

The aim of this paper is to provide a theoretical framework of uncertainty estimate on rainfall-runoff analysis based on theory of stochastic process. SDE (stochastic differential equation) based on this theory has been widely used in the field of mathematical finance due to predict stock price movement. Meanwhile, some researchers in the field of civil engineering have investigated by using this knowledge about SDE (stochastic differential equation) (e.g. Kurino et.al, 1999; Higashino and Kanda, 2001). However, there have been no studies about evaluation of uncertainty in runoff phenomenon based on comparisons between SDE (stochastic differential equation) and Fokker-Planck equation. The Fokker-Planck equation is a partial differential equation that describes the temporal variation of PDF (probability density function), and there is evidence to suggest that SDEs and Fokker-Planck equations are equivalent mathematically. In this paper, therefore, the uncertainty of discharge on the uncertainty of rainfall is explained theoretically and mathematically by introduction of theory of stochastic process. The lumped rainfall-runoff model is represented by SDE (stochastic differential equation) due to describe it as difference formula, because the temporal variation of rainfall is expressed by its average plus deviation, which is approximated by Gaussian distribution. This is attributed to the observed rainfall by rain-gauge station and radar rain-gauge system. As a result, this paper has shown that it is possible to evaluate the uncertainty of discharge by using the relationship between SDE (stochastic differential equation) and Fokker-Planck equation. Moreover, the results of this study show that the uncertainty of discharge increases as rainfall intensity rises and non-linearity about resistance grows strong. These results are clarified by PDFs (probability density function) that satisfy Fokker-Planck equation about discharge. It means the reasonable discharge can be estimated based on the theory of stochastic processes, and it can be applied to the probabilistic risk of flood management.

Unconscious vigilance: worldview defense without adaptations for terror, coalition, or uncertainty management.

PubMed

Holbrook, Colin; Sousa, Paulo; Hahn-Holbrook, Jennifer

2011-09-01

Individuals subtly reminded of death, coalitional challenges, or feelings of uncertainty display exaggerated preferences for affirmations and against criticisms of their cultural in-groups. Terror management, coalitional psychology, and uncertainty management theories postulate this "worldview defense" effect as the output of mechanisms evolved either to allay the fear of death, foster social support, or reduce anxiety by increasing adherence to cultural values. In 4 studies, we report evidence for an alternative perspective. We argue that worldview defense owes to unconscious vigilance, a state of accentuated reactivity to affective targets (which need not relate to cultural worldviews) that follows detection of subtle alarm cues (which need not pertain to death, coalitional challenges, or uncertainty). In Studies 1 and 2, death-primed participants produced exaggerated ratings of worldview-neutral affective targets. In Studies 3 and 4, subliminal threat manipulations unrelated to death, coalitional challenges, or uncertainty evoked worldview defense. These results are discussed as they inform evolutionary interpretations of worldview defense and future investigations of the influence of unconscious alarm on judgment. (PsycINFO Database Record (c) 2011 APA, all rights reserved). PsycINFO Database Record (c) 2011 APA, all rights reserved.

Combination of uncertainty theories and decision-aiding methods for natural risk management in a context of imperfect information

NASA Astrophysics Data System (ADS)

Tacnet, Jean-Marc; Dupouy, Guillaume; Carladous, Simon; Dezert, Jean; Batton-Hubert, Mireille

2017-04-01

In mountain areas, natural phenomena such as snow avalanches, debris-flows and rock-falls, put people and objects at risk with sometimes dramatic consequences. Risk is classically considered as a combination of hazard, the combination of the intensity and frequency of the phenomenon, and vulnerability which corresponds to the consequences of the phenomenon on exposed people and material assets. Risk management consists in identifying the risk level as well as choosing the best strategies for risk prevention, i.e. mitigation. In the context of natural phenomena in mountainous areas, technical and scientific knowledge is often lacking. Risk management decisions are therefore based on imperfect information. This information comes from more or less reliable sources ranging from historical data, expert assessments, numerical simulations etc. Finally, risk management decisions are the result of complex knowledge management and reasoning processes. Tracing the information and propagating information quality from data acquisition to decisions are therefore important steps in the decision-making process. One major goal today is therefore to assist decision-making while considering the availability, quality and reliability of information content and sources. A global integrated framework is proposed to improve the risk management process in a context of information imperfection provided by more or less reliable sources: uncertainty as well as imprecision, inconsistency and incompleteness are considered. Several methods are used and associated in an original way: sequential decision context description, development of specific multi-criteria decision-making methods, imperfection propagation in numerical modeling and information fusion. This framework not only assists in decision-making but also traces the process and evaluates the impact of information quality on decision-making. We focus and present two main developments. The first one relates to uncertainty and imprecision propagation in numerical modeling using both classical Monte-Carlo probabilistic approach and also so-called Hybrid approach using possibility theory. Second approach deals with new multi-criteria decision-making methods which consider information imperfection, source reliability, importance and conflict, using fuzzy sets as well as possibility and belief function theories. Implemented methods consider information imperfection propagation and information fusion in total aggregation methods such as AHP (Saaty, 1980) or partial aggregation methods such as the Electre outranking method (see Soft Electre Tri ) or decisions in certain but also risky or uncertain contexts (see new COWA-ER and FOWA-ER- Cautious and Fuzzy Ordered Weighted Averaging-Evidential Reasoning). For example, the ER-MCDA methodology considers expert assessment as a multi-criteria decision process based on imperfect information provided by more or less heterogeneous, reliable and conflicting sources: it mixes AHP, fuzzy sets theory, possibility theory and belief function theory using DSmT (Dezert-Smarandache Theory) framework which provides powerful fusion rules.

Adaptive management for ecosystem services.

PubMed

Birgé, Hannah E; Allen, Craig R; Garmestani, Ahjond S; Pope, Kevin L

2016-12-01

Management of natural resources for the production of ecosystem services, which are vital for human well-being, is necessary even when there is uncertainty regarding system response to management action. This uncertainty is the result of incomplete controllability, complex internal feedbacks, and non-linearity that often interferes with desired management outcomes, and insufficient understanding of nature and people. Adaptive management was developed to reduce such uncertainty. We present a framework for the application of adaptive management for ecosystem services that explicitly accounts for cross-scale tradeoffs in the production of ecosystem services. Our framework focuses on identifying key spatiotemporal scales (plot, patch, ecosystem, landscape, and region) that encompass dominant structures and processes in the system, and includes within- and cross-scale dynamics, ecosystem service tradeoffs, and management controllability within and across scales. Resilience theory recognizes that a limited set of ecological processes in a given system regulate ecosystem services, yet our understanding of these processes is poorly understood. If management actions erode or remove these processes, the system may shift into an alternative state unlikely to support the production of desired services. Adaptive management provides a process to assess the underlying within and cross-scale tradeoffs associated with production of ecosystem services while proceeding with management designed to meet the demands of a growing human population. Copyright © 2016 Elsevier Ltd. All rights reserved.

Complexities and Challenges of Singapore Nurses Providing Postacute Home Care in Multicultural Communities: A Grounded Theory Study.

PubMed

Wong, Alfred Ka-Shing; Ong, Shu Fen; Matchar, David Bruce; Lie, Desiree; Ng, Reuben; Yoon, Kirsten Eom; Wong, Chek Hooi

2017-10-01

Studies are needed to inform the preparation of community nurses to address patient behavioral and social factors contributing to unnecessary readmissions to hospital. This study uses nurses' input to understand challenges faced during home care, to derive a framework to address the challenges. Semistructured interviews were conducted to saturation with 16 community nurses in Singapore. Interviews were transcribed verbatim and transcripts independently coded for emergent themes. Themes were interpreted using grounded theory. Seven major themes emerged from 16 interviews: Strained social relationships, complex care decision-making processes within families, communication barriers, patient's or caregiver neglect of health issues, building and maintaining trust, trial-and-error nature of work, and dealing with uncertainty. Community nurses identified uncertainty arising from complexities in social-relational, personal, and organizational factors as a central challenge. Nursing education should focus on navigating and managing uncertainty at the personal, patient, and family levels.

Decision making with epistemic uncertainty under safety constraints: An application to seismic design

USGS Publications Warehouse

Veneziano, D.; Agarwal, A.; Karaca, E.

2009-01-01

The problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. Simple approximations are often used whereby future variations in epistemic uncertainty are ignored or worst-case scenarios are postulated. These strategies tend to produce sub-optimal decisions. We develop a general framework based on Bayesian decision theory and exemplify it for the case of seismic design of buildings. When temporal fluctuations of the epistemic uncertainties and regulatory safety constraints are included, the optimal level of seismic protection exceeds the normative level at the time of construction. Optimal Bayesian decisions do not depend on the aleatory or epistemic nature of the uncertainties, but only on the total (epistemic plus aleatory) uncertainty and how that total uncertainty varies randomly during the lifetime of the project. ?? 2009 Elsevier Ltd. All rights reserved.

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a modelmore » is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.« less

Risk-based principles for defining and managing water security

PubMed Central

Hall, Jim; Borgomeo, Edoardo

2013-01-01

The concept of water security implies concern about potentially harmful states of coupled human and natural water systems. Those harmful states may be associated with water scarcity (for humans and/or the environment), floods or harmful water quality. The theories and practices of risk analysis and risk management have been developed and elaborated to deal with the uncertain occurrence of harmful events. Yet despite their widespread application in public policy, theories and practices of risk management have well-known limitations, particularly in the context of severe uncertainties and contested values. Here, we seek to explore the boundaries of applicability of risk-based principles as a means of formalizing discussion of water security. Not only do risk concepts have normative appeal, but they also provide an explicit means of addressing the variability that is intrinsic to hydrological, ecological and socio-economic systems. We illustrate the nature of these interconnections with a simulation study, which demonstrates how water resources planning could take more explicit account of epistemic uncertainties, tolerability of risk and the trade-offs in risk among different actors. PMID:24080616

Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories

NASA Astrophysics Data System (ADS)

Ghodsi, Seyed Hamed; Kerachian, Reza; Estalaki, Siamak Malakpour; Nikoo, Mohammad Reza; Zahmatkesh, Zahra

2016-02-01

In this paper, two deterministic and stochastic multilateral, multi-issue, non-cooperative bargaining methodologies are proposed for urban runoff quality management. In the proposed methodologies, a calibrated Storm Water Management Model (SWMM) is used to simulate stormwater runoff quantity and quality for different urban stormwater runoff management scenarios, which have been defined considering several Low Impact Development (LID) techniques. In the deterministic methodology, the best management scenario, representing location and area of LID controls, is identified using the bargaining model. In the stochastic methodology, uncertainties of some key parameters of SWMM are analyzed using the info-gap theory. For each water quality management scenario, robustness and opportuneness criteria are determined based on utility functions of different stakeholders. Then, to find the best solution, the bargaining model is performed considering a combination of robustness and opportuneness criteria for each scenario based on utility function of each stakeholder. The results of applying the proposed methodology in the Velenjak urban watershed located in the northeastern part of Tehran, the capital city of Iran, illustrate its practical utility for conflict resolution in urban water quantity and quality management. It is shown that the solution obtained using the deterministic model cannot outperform the result of the stochastic model considering the robustness and opportuneness criteria. Therefore, it can be concluded that the stochastic model, which incorporates the main uncertainties, could provide more reliable results.

Incorporating uncertainty of management costs in sensitivity analyses of matrix population models.

PubMed

Salomon, Yacov; McCarthy, Michael A; Taylor, Peter; Wintle, Brendan A

2013-02-01

The importance of accounting for economic costs when making environmental-management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population-management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost-efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making. © 2013 Society for Conservation Biology.

Representation of analysis results involving aleatory and epistemic uncertainty.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Helton, Jon Craig; Oberkampf, William Louis

2008-08-01

Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for themore » representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.« less

Adaptive Delta Management: cultural aspects of dealing with uncertainty

NASA Astrophysics Data System (ADS)

Timmermans, Jos; Haasnoot, Marjolijn; Hermans, Leon; Kwakkel, Jan

2016-04-01

Deltas are generally recognized as vulnerable to climate change and therefore a salient topic in adaptation science. Deltas are also highly dynamic systems viewed from physical (erosion, sedimentation, subsidence), social (demographic), economic (trade), infrastructures (transport, energy, metropolization) and cultural (multi-ethnic) perspectives. This multi-faceted dynamic character of delta areas warrants the emergence of a branch of applied adaptation science, Adaptive Delta Management, which explicitly focuses on climate adaptation of such highly dynamic and deeply uncertain systems. The application of Adaptive Delta Management in the Dutch Delta Program and its active international dissemination by Dutch professionals results in the rapid dissemination of Adaptive Delta Management to deltas worldwide. This global dissemination raises concerns among professionals in delta management on its applicability in deltas with cultural conditions and historical developments quite different from those found in the Netherlands and the United Kingdom where the practices now labelled as Adaptive Delta Management first emerged. This research develops an approach and gives a first analysis of the interaction between the characteristics of different approaches in Adaptive Delta Management and their alignment with the cultural conditions encountered in various delta's globally. In this analysis, first different management theories underlying approaches to Adaptive Delta Management as encountered in both scientific and professional publications are identified and characterized on three dimensions: The characteristics dimensions used are: orientation on today, orientation on the future, and decision making (Timmermans, 2015). The different underlying management theories encountered are policy analysis, strategic management, transition management, and adaptive management. These four management theories underlying different approaches in Adaptive Delta Management are connected to Hofstede's (1983) cultural dimensions, of which uncertainty avoidance and long-term orientation are of particular relevance for our analysis. Our conclusions comment on the suitability of approaches in Adaptive Delta Management rooted in different management theories are more suitable for specific delta countries than others. The most striking conclusion is the unsuitability of rational policy analytic approaches for The Netherlands. Although surprising this conclusion finds some support in the process dominated approach taken in the Dutch Delta Program. In addition, the divergence between Vietnam, Bangladesh and Myanmar, all located in South East Asia, is striking. References Hofstede, G. (1983). The cultural relativity of organizational practices and theories. Journal of international business studies, 75-89. Jos Timmermans, Marjolijn Haasnoot, Leon Hermans, Jan Kwakkel, Martine Rutten and Wil Thissen (2015). Adaptive Delta Management: Roots and Branches, IAHR The Hague 2015.

Managing uncertainty: a grounded theory of stigma in transgender health care encounters.

PubMed

Poteat, Tonia; German, Danielle; Kerrigan, Deanna

2013-05-01

A growing body of literature supports stigma and discrimination as fundamental causes of health disparities. Stigma and discrimination experienced by transgender people have been associated with increased risk for depression, suicide, and HIV. Transgender stigma and discrimination experienced in health care influence transgender people's health care access and utilization. Thus, understanding how stigma and discrimination manifest and function in health care encounters is critical to addressing health disparities for transgender people. A qualitative, grounded theory approach was taken to this study of stigma in health care interactions. Between January and July 2011, fifty-five transgender people and twelve medical providers participated in one-time in-depth interviews about stigma, discrimination, and health care interactions between providers and transgender patients. Due to the social and institutional stigma against transgender people, their care is excluded from medical training. Therefore, providers approach medical encounters with transgender patients with ambivalence and uncertainty. Transgender people anticipate that providers will not know how to meet their needs. This uncertainty and ambivalence in the medical encounter upsets the normal balance of power in provider-patient relationships. Interpersonal stigma functions to reinforce the power and authority of the medical provider during these interactions. Functional theories of stigma posit that we hold stigmatizing attitudes because they serve specific psychological functions. However, these theories ignore how hierarchies of power in social relationships serve to maintain and reinforce inequalities. The findings of this study suggest that interpersonal stigma also functions to reinforce medical power and authority in the face of provider uncertainty. Within functional theories of stigma, it is important to acknowledge the role of power and to understand how stigmatizing attitudes function to maintain systems of inequality that contribute to health disparities. Published by Elsevier Ltd.

"It's not if I get cancer, it's when I get cancer": BRCA-positive patients' (un)certain health experiences regarding hereditary breast and ovarian cancer risk.

PubMed

Dean, Marleah

2016-08-01

Women with a harmful mutation in the BReast CAncer (BRCA) gene are at significantly increased risk of developing hereditary breast and ovarian cancer (HBOC) during their lifetime, compared to those without. Such patients-with a genetic predisposition to develop cancer but who have not yet been diagnosed with cancer-live in a constant state of uncertainty and wonder not if they might get cancer but when. Framed by uncertainty management theory, the purpose of this study was to explore BRCA-positive patients' health experiences after testing positive for the BRCA genetic mutation, specifically identifying their sources of uncertainty. Thirty-four, qualitative interviews were conducted with female patients. Participants responded to online research postings on the non-profit organization Facing Our Risk of Cancer Empowered's (FORCE) message board and social media pages as well as HBOC-specific Facebook groups. The interview data were coded using the constant comparison method. Two major themes representing BRCA-positive patients' sources of uncertainty regarding their genetic predisposition and health experiences emerged from the data. Medical uncertainty included the following three subthemes: the unknown future, medical appointments, and personal cancer scares. Familial uncertainty encompassed the subthemes traumatic family cancer memories and motherhood. Overall, the study supports and extends existing research on uncertainty-revealing uncertainty is inherent in BRCA-positive patients' health experiences-and offers new insight regarding uncertainty management and HBOC risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

Info-Gap Decision Theory for Assessing the Management of Catchments for Timber Production and Urban Water Supply

NASA Astrophysics Data System (ADS)

McCarthy, Michael A.; Lindenmayer, David B.

2007-04-01

While previous studies have examined how forest management is influenced by the risk of fire, they rely on probabilistic estimates of the occurrence and impacts of fire. However, nonprobabilistic approaches are required for assessing the importance of fire risk when data are poor but risks are appreciable. We explore impacts of fire risk on forest management using as a case study a water catchment in the Australian Capital Territory (ACT) (southeastern Australia). In this forested area, urban water supply and timber yields from exotic plantations are potential joint but also competing land uses. Our analyses were stimulated by extensive wildfires in early 2003 that burned much of the existing exotic pine plantation estate in the water catchment and the resulting need to explore the relative economic benefits of revegetating the catchment with exotic plantations or native vegetation. The current mean fire interval in the ACT is approximately 40 years, making the establishment of a pine plantation economically marginal at a 4% discount rate. However, the relative impact on water yield of revegetation with native species and pines is very uncertain, as is the risk of fire under climate change. We use info-gap decision theory to account for these nonprobabilistic sources of uncertainty, demonstrating that the decision that is most robust to uncertainty is highly sensitive to the cost of native revegetation. If costs of native revegetation are sufficiently small, this option is more robust to uncertainty than revegetation with a commercial pine plantation.

Info-gap decision theory for assessing the management of catchments for timber production and urban water supply.

PubMed

McCarthy, Michael A; Lindenmayer, David B

2007-04-01

While previous studies have examined how forest management is influenced by the risk of fire, they rely on probabilistic estimates of the occurrence and impacts of fire. However, nonprobabilistic approaches are required for assessing the importance of fire risk when data are poor but risks are appreciable. We explore impacts of fire risk on forest management using as a case study a water catchment in the Australian Capital Territory (ACT) (southeastern Australia). In this forested area, urban water supply and timber yields from exotic plantations are potential joint but also competing land uses. Our analyses were stimulated by extensive wildfires in early 2003 that burned much of the existing exotic pine plantation estate in the water catchment and the resulting need to explore the relative economic benefits of revegetating the catchment with exotic plantations or native vegetation. The current mean fire interval in the ACT is approximately 40 years, making the establishment of a pine plantation economically marginal at a 4% discount rate. However, the relative impact on water yield of revegetation with native species and pines is very uncertain, as is the risk of fire under climate change. We use info-gap decision theory to account for these nonprobabilistic sources of uncertainty, demonstrating that the decision that is most robust to uncertainty is highly sensitive to the cost of native revegetation. If costs of native revegetation are sufficiently small, this option is more robust to uncertainty than revegetation with a commercial pine plantation.

Crisis Management for Biobanks.

PubMed

Parry-Jones, Alison; Hansen, Jarle; Simeon-Dubach, Daniel; Bjugn, Roger

2017-06-01

All organizations are subject to risk and uncertainty. Adverse events may disrupt normal organizational activity and may even cause complete failure of business operations. Biorepositories are also at risk and there have been instances where multiple samples or entire collections have been destroyed. Biobank guidelines accordingly recommend the establishment of contingency plans to reduce risk to an acceptable level. In this review article, we will use general theory on risk management and illustrate how such principles can be used to establish a practical crisis management plan for any biobank organization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faizal, Mir, E-mail: f2mir@uwaterloo.ca; Majumder, Barun, E-mail: barunbasanta@iitgn.ac.in

In this paper, we will incorporate the generalized uncertainty principle into field theories with Lifshitz scaling. We will first construct both bosonic and fermionic theories with Lifshitz scaling based on generalized uncertainty principle. After that we will incorporate the generalized uncertainty principle into a non-abelian gauge theory with Lifshitz scaling. We will observe that even though the action for this theory is non-local, it is invariant under local gauge transformations. We will also perform the stochastic quantization of this Lifshitz fermionic theory based generalized uncertainty principle.

Application of plausible reasoning to AI-based control systems

NASA Technical Reports Server (NTRS)

Berenji, Hamid; Lum, Henry, Jr.

1987-01-01

Some current approaches to plausible reasoning in artificial intelligence are reviewed and discussed. Some of the most significant recent advances in plausible and approximate reasoning are examined. A synergism among the techniques of uncertainty management is advocated, and brief discussions on the certainty factor approach, probabilistic approach, Dempster-Shafer theory of evidence, possibility theory, linguistic variables, and fuzzy control are presented. Some extensions to these methods are described, and the applications of the methods are considered.

Inexact Socio-Dynamic Modeling of Groundwater Contamination Management

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Zhang, X.

2015-12-01

Groundwater contamination may alter the behaviors of the public such as adaptation to such a contamination event. On the other hand, social behaviors may affect groundwater contamination and associated risk levels such as through changing ingestion amount of groundwater due to the contamination. Decisions should consider not only the contamination itself, but also social attitudes on such contamination events. Such decisions are inherently associated with uncertainty, such as subjective judgement from decision makers and their implicit knowledge on selection of whether to supply water or reduce the amount of supplied water under the scenario of the contamination. A socio-dynamic model based on the theories of information-gap and fuzzy sets is being developed to address the social behaviors facing the groundwater contamination and applied to a synthetic problem designed based on typical groundwater remediation sites where the effects of social behaviors on decisions are investigated and analyzed. Different uncertainties including deep uncertainty and vague/ambiguous uncertainty are effectively and integrally addressed. The results can provide scientifically-defensible decision supports for groundwater management in face of the contamination.

Bayesian Decision Support

NASA Astrophysics Data System (ADS)

Berliner, M.

2017-12-01

Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.

Adolescent Females and Their Mothers

PubMed Central

Volkman, Julie E.; Silk, Kami J.

2010-01-01

Recent research indicates environmental factors and personal behaviors are related to breast cancer risk, but adopting a healthy lifestyle as early as adolescence can serve a protective function. To investigate perceptions of breast cancer risk and the environment, 10 focus groups (N = 91) were conducted with adolescent females (n = 55) and mothers (n = 36) across four counties in the Midwest, USA. The Uncertainty Management Theory provides a framework for discussing statements, and results suggest that uncertainty is maintained through ambiguity about environmental risk factors and breast cancer. Recommendations for prevention messages are presented. PMID:18987091

Positioning Model-Supported, Participatory, Water Management Decision Making under Uncertainty within the Western Philosphical Discourse on Knowledge and Governance

NASA Astrophysics Data System (ADS)

Purkey, D. R.; Escobar, M.; Mehta, V. K.; Forni, L.

2016-12-01

Two important trends currently shape the manner in which water resources planning and decision making occurs. The first relates to the increasing reliance on participatory stakeholder processes as a forum for evaluating water management options and selecting the appropriate course of action. The second relates to the growing recognition that earlier deterministic approaches to this evaluation of options may no longer be appropriate, nor required. The convergence of these two trends poses questions as to the proper role of data, information, analysis and expertise in the inherently social and political process of negotiating water resources management agreements and implementing water resources management interventions. The question of how to discover the best or optimal option in the face of deep uncertainty related to climate change, demography, economic development, and regulatory reform is compelling. More fundamentally the question of whether the "perfect" option even exits to be discovered is perhaps more critical. While this existential question may be new to the water resource management community, it is not new to western political theory. This paper explores early classical philosophical writing related to issues of knowledge and governance as captured in the work of Plato and Aristotle; and then attempts to place a new approach to analysis-supported, stakeholder-driven water resources planning and decision making within this philosophical discourse. Using examples from river systems in California and the Andes, where the theory of Robust Decision Making has been used as an organizing construct for stakeholder processes, it is argued that the expectation that analysis will lead to the discovery of the perfect option is not warranted when stakeholders are engaged in the process of discovering a consensus option. This argument will touch upon issue of the diversity of values, model uncertainty and creditability, and the visualization of model output required to explore the implications of various management options across a range of inherently unknowable future conditions.

Management of uncertainties on parameters elicited by experts - Applications to sea-level rise and to CO2 storage operations risk assessment

NASA Astrophysics Data System (ADS)

Manceau, Jean-Charles; Loschetter, Annick; Rohmer, Jérémy; Le Cozannet, Gonéri; Lary Louis, de; Guénan Thomas, Le; Ken, Hnottavange-Telleen

2017-04-01

In a context of high degree of uncertainty, when very few data are available, experts are commonly requested to provide their opinions on input parameters of risk assessment models. Not only might each expert express a certain degree of uncertainty on his/her own statements, but the set of information collected from the pool of experts introduces an additional level of uncertainty. It is indeed very unlikely that all experts agree on exactly the same data, especially regarding parameters needed for natural risk assessments. In some cases, their opinions may differ only slightly (e.g. the most plausible value for a parameter is similar for different experts, and they only disagree on the level of uncertainties that taint the said value) while on other cases they may express incompatible opinions for a same parameter. Dealing with these different kinds of uncertainties remains a challenge for assessing geological hazards or/and risks. Extra-probabilistic approaches (such as the Dempster-Shafer theory or the possibility theory) have shown to offer promising solutions for representing parameters on which the knowledge is limited. It is the case for instance when the available information prevents an expert from identifying a unique probability law to picture the total uncertainty. Moreover, such approaches are known to be particularly flexible when it comes to aggregating several and potentially conflicting opinions. We therefore propose to discuss the opportunity of applying these new theories for managing the uncertainties on parameters elicited by experts, by a comparison with the application of more classical probability approaches. The discussion is based on two different examples. The first example deals with the estimation of the injected CO2 plume extent in a reservoir in the context of CO2 geological storage. This estimation requires information on the effective porosity of the reservoir, which has been estimated by 14 different experts. The Dempster-Shafer theory has been used to represent and aggregate these pieces of information. The results of different aggregation rules as well as those of a classical probabilistic approach are compared with the purpose of highlighting the elements each of them could provide to the decision-maker (Manceau et al., 2016). The second example focuses on projections of future sea-level rise. Based on IPCC's constraints on the projection quantiles, and on the scientific community consensus level on the physical limits to future sea-level rise, a possibility distribution of the projections by 2100 under the RCP 8.5 scenario has been established. This possibility distribution has been confronted with a set of previously published probabilistic sea-level projections, with a focus on their ability to explore high ranges of sea-level rise (Le Cozannet et al., 2016). These two examples are complementary in the sense that they allow to address various aspects of the problem (e.g. representation of different types of information, conflict among experts, sources dependence). Moreover, we believe that the issues faced during these two experiences can be generalized to many risks/hazards assessment situations. References Manceau, JC., Loschetter, A., Rohmer, J., de Lary, L., Le Guénan, T., Hnottavange-Telleen, K. (2016). Dealing with uncertainty on parameters elicited from a pool of experts for CCS risk assessment. Congrès λμ 20 (St-Malo, France). Le Cozannet G., Manceau JC., Rohmer, J. (2016). Bounding probabilistic sea-level rise projections within the framework of the possibility theory. Accepted in Environmental Research Letters.

Use of Uncertainty Reduction and Narrative Paradigm Theories in Management Consulting and Teaching: Lessons Learned

ERIC Educational Resources Information Center

Barker, Randolph T.; Gower, Kim

2009-01-01

Teaching business communication while performing professional business consulting is the perfect learning match. The bizarre but true stories from the consulting world provide excellent analogies for classroom learning, and feedback from students about the consulting experiences reaffirms the power of using stories for teaching. When discussing…

Examining Pseudotsuga menziesii biomass change dynamics through succession using a regional forest inventory system

Treesearch

David M. Bell; Andrew N. Gray

2015-01-01

Models of forest succession provide an appealing conceptual framework for understanding forest dynamics, but uncertainty in the degree to which patterns are regionally consistent might limit the application of successional theory in forest management. Remeasurements of forest inventory networks provide an opportunity to assess this consistency, improving our...

From clinical uncertainties to precision medicine: the emerging role of the gut barrier and microbiome in small bowel functional diseases.

PubMed

Marlicz, Wojciech; Yung, Diana E; Skonieczna-Żydecka, Karolina; Loniewski, Igor; van Hemert, Saskia; Loniewska, Beata; Koulaouzidis, Anastasios

2017-10-01

Over the last decade, remarkable progress has been made in the understanding of disease pathophysiology. Many new theories expound on the importance of emerging factors such as microbiome influences, genomics/omics, stem cells, innate intestinal immunity or mucosal barrier complexities. This has introduced a further dimension of uncertainty into clinical decision-making, but equally, may shed some light on less well-understood and difficult to manage conditions. Areas covered: Comprehensive review of the literature on gut barrier and microbiome relevant to small bowel pathology. A PubMed/Medline search from 1990 to April 2017 was undertaken and papers from this range were included. Expert commentary: The scenario of clinical uncertainty is well-illustrated by functional gastrointestinal disorders (FGIDs). The movement towards achieving a better understanding of FGIDs is expressed in the Rome IV guidelines. Novel diagnostic and therapeutic protocols focused on the GB and SB microbiome can facilitate diagnosis, management and improve our understanding of the underlying pathological mechanisms in FGIDs.

Doing our best: optimization and the management of risk.

PubMed

Ben-Haim, Yakov

2012-08-01

Tools and concepts of optimization are widespread in decision-making, design, and planning. There is a moral imperative to "do our best." Optimization underlies theories in physics and biology, and economic theories often presume that economic agents are optimizers. We argue that in decisions under uncertainty, what should be optimized is robustness rather than performance. We discuss the equity premium puzzle from financial economics, and explain that the puzzle can be resolved by using the strategy of satisficing rather than optimizing. We discuss design of critical technological infrastructure, showing that satisficing of performance requirements--rather than optimizing them--is a preferable design concept. We explore the need for disaster recovery capability and its methodological dilemma. The disparate domains--economics and engineering--illuminate different aspects of the challenge of uncertainty and of the significance of robust-satisficing. © 2012 Society for Risk Analysis.

Application of Dempster-Shafer theory of evidence model to geoelectric and hydraulic parameters for groundwater potential zonation

NASA Astrophysics Data System (ADS)

Mogaji, Kehinde Anthony; Lim, Hwee San

2018-06-01

The application of a GIS - based Dempster - Shafer data driven model named as evidential belief function EBF- methodology to groundwater potential conditioning factors (GPCFs) derived from geophysical and hydrogeological data sets for assessing groundwater potentiality was presented in this study. The proposed method's efficacy in managing degree of uncertainty in spatial predictive models motivated this research. The method procedural approaches entail firstly, the database containing groundwater data records (bore wells location inventory, hydrogeological data record, etc.) and geophysical measurement data construction. From the database, different influencing groundwater occurrence factors, namely aquifer layer thickness, aquifer layer resistivity, overburden material resistivity, overburden material thickness, aquifer hydraulic conductivity and aquifer transmissivity were extracted and prepared. Further, the bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training and 30% (9 wells) for model testing. The synthesized of the GPCFs via applying the DS - EBF model algorithms produced the groundwater productivity potential index (GPPI) map which demarcated the area into low - medium, medium, medium - high and high potential zones. The analyzed percentage degree of uncertainty for the predicted lows potential zones classes and mediums/highs potential zones classes are >10% and <10%, respectively. The DS theory model-based GPPI map's validation through ROC approach established prediction rate accuracy of 88.8%. Successively, the determined transverse resistance (TR) values in the range of 1280 and 30,000 Ω my for the area geoelectrically delineated aquifer units of the predicted potential zones through Dar - Zarrouk Parameter analysis quantitatively confirm the DS theory modeling prediction results. This research results have expand the capability of DS - EBF model in predictive modeling by effective uncertainty management. Thus, the produced map could form part of decision support system reliable to be used by local authorities for groundwater exploitation and management in the area.

Methods Used to Support a Life Cycle of Complex Engineering Products

NASA Astrophysics Data System (ADS)

Zakharova, Alexandra A.; Kolegova, Olga A.; Nekrasova, Maria E.; Eremenko, Andrey O.

2016-08-01

Management of companies involved in the design, development and operation of complex engineering products recognize the relevance of creating systems for product lifecycle management. A system of methods is proposed to support life cycles of complex engineering products, based on fuzzy set theory and hierarchical analysis. The system of methods serves to demonstrate the grounds for making strategic decisions in an environment of uncertainty, allows the use of expert knowledge, and provides interconnection of decisions at all phases of strategic management and all stages of a complex engineering product lifecycle.

Designing management strategies for carbon dioxide storage and utilization under uncertainty using inexact modelling

NASA Astrophysics Data System (ADS)

Wang, Yu; Fan, Jie; Xu, Ye; Sun, Wei; Chen, Dong

2017-06-01

Effective application of carbon capture, utilization and storage (CCUS) systems could help to alleviate the influence of climate change by reducing carbon dioxide (CO2) emissions. The research objective of this study is to develop an equilibrium chance-constrained programming model with bi-random variables (ECCP model) for supporting the CCUS management system under random circumstances. The major advantage of the ECCP model is that it tackles random variables as bi-random variables with a normal distribution, where the mean values follow a normal distribution. This could avoid irrational assumptions and oversimplifications in the process of parameter design and enrich the theory of stochastic optimization. The ECCP model is solved by an equilibrium change-constrained programming algorithm, which provides convenience for decision makers to rank the solution set using the natural order of real numbers. The ECCP model is applied to a CCUS management problem, and the solutions could be useful in helping managers to design and generate rational CO2-allocation patterns under complexities and uncertainties.

Fuzzy geometry, entropy, and image information

NASA Technical Reports Server (NTRS)

Pal, Sankar K.

1991-01-01

Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described.

Integrating telecare for chronic disease management in the community: What needs to be done?

PubMed Central

2011-01-01

Background Telecare could greatly facilitate chronic disease management in the community, but despite government promotion and positive demonstrations its implementation has been limited. This study aimed to identify factors inhibiting the implementation and integration of telecare systems for chronic disease management in the community. Methods Large scale comparative study employing qualitative data collection techniques: semi-structured interviews with key informants, task-groups, and workshops; framework analysis of qualitative data informed by Normalization Process Theory. Drawn from telecare services in community and domestic settings in England and Scotland, 221 participants were included, consisting of health professionals and managers; patients and carers; social care professionals and managers; and service suppliers and manufacturers. Results Key barriers to telecare integration were uncertainties about coherent and sustainable service and business models; lack of coordination across social and primary care boundaries, lack of financial or other incentives to include telecare within primary care services; a lack of a sense of continuity with previous service provision and self-care work undertaken by patients; and general uncertainty about the adequacy of telecare systems. These problems led to poor integration of policy and practice. Conclusion Telecare services may offer a cost effective and safe form of care for some people living with chronic illness. Slow and uneven implementation and integration do not stem from problems of adoption. They result from incomplete understanding of the role of telecare systems and subsequent adaption and embeddedness to context, and uncertainties about the best way to develop, coordinate, and sustain services that assist with chronic disease management. Interventions are therefore needed that (i) reduce uncertainty about the ownership of implementation processes and that lock together health and social care agencies; and (ii) ensure user centred rather than biomedical/service-centred models of care. PMID:21619596

Integrating telecare for chronic disease management in the community: what needs to be done?

PubMed

May, Carl R; Finch, Tracy L; Cornford, James; Exley, Catherine; Gately, Claire; Kirk, Sue; Jenkings, K Neil; Osbourne, Janice; Robinson, A Louise; Rogers, Anne; Wilson, Robert; Mair, Frances S

2011-05-27

Telecare could greatly facilitate chronic disease management in the community, but despite government promotion and positive demonstrations its implementation has been limited. This study aimed to identify factors inhibiting the implementation and integration of telecare systems for chronic disease management in the community. Large scale comparative study employing qualitative data collection techniques: semi-structured interviews with key informants, task-groups, and workshops; framework analysis of qualitative data informed by Normalization Process Theory. Drawn from telecare services in community and domestic settings in England and Scotland, 221 participants were included, consisting of health professionals and managers; patients and carers; social care professionals and managers; and service suppliers and manufacturers. Key barriers to telecare integration were uncertainties about coherent and sustainable service and business models; lack of coordination across social and primary care boundaries, lack of financial or other incentives to include telecare within primary care services; a lack of a sense of continuity with previous service provision and self-care work undertaken by patients; and general uncertainty about the adequacy of telecare systems. These problems led to poor integration of policy and practice. Telecare services may offer a cost effective and safe form of care for some people living with chronic illness. Slow and uneven implementation and integration do not stem from problems of adoption. They result from incomplete understanding of the role of telecare systems and subsequent adaption and embeddedness to context, and uncertainties about the best way to develop, coordinate, and sustain services that assist with chronic disease management. Interventions are therefore needed that (i) reduce uncertainty about the ownership of implementation processes and that lock together health and social care agencies; and (ii) ensure user centred rather than biomedical/service-centred models of care.

Conflicting stories about public scientific controversies: Effects of news convergence and divergence on scientists' credibility.

PubMed

Jensen, Jakob D; Hurley, Ryan J

2012-08-01

Surveys suggest that approximately one third of news consumers have encountered conflicting reports of the same information. News coverage of science is especially prone to conflict, but how news consumers perceive this situation is currently unknown. College students (N = 242) participated in a lab experiment where they were exposed to news coverage about one of two scientific controversies in the United States: dioxin in sewage sludge or the reintroduction of gray wolves to populated areas. Participants received (a) one news article (control), (b) two news articles that were consistent (convergent), or (c) two news articles that conflicted (divergent). The effects of divergence induced uncertainty differed by news story. Greater uncertainty was associated with increased scientists' credibility ratings for those reading dioxin regulation articles and decreased scientists' credibility ratings for those reading wolf reintroduction articles. Unlike other manifestations of uncertainty in scientific discourse, conflicting stories seem to generate effects that vary significantly by topic. Consistent with uncertainty management theory, uncertainty is embraced or rejected by situation.

Site selection for managed aquifer recharge using fuzzy rules: integrating geographical information system (GIS) tools and multi-criteria decision making

NASA Astrophysics Data System (ADS)

Malekmohammadi, Bahram; Ramezani Mehrian, Majid; Jafari, Hamid Reza

2012-11-01

One of the most important water-resources management strategies for arid lands is managed aquifer recharge (MAR). In establishing a MAR scheme, site selection is the prime prerequisite that can be assisted by geographic information system (GIS) tools. One of the most important uncertainties in the site-selection process using GIS is finite ranges or intervals resulting from data classification. In order to reduce these uncertainties, a novel method has been developed involving the integration of multi-criteria decision making (MCDM), GIS, and a fuzzy inference system (FIS). The Shemil-Ashkara plain in the Hormozgan Province of Iran was selected as the case study; slope, geology, groundwater depth, potential for runoff, land use, and groundwater electrical conductivity have been considered as site-selection factors. By defining fuzzy membership functions for the input layers and the output layer, and by constructing fuzzy rules, a FIS has been developed. Comparison of the results produced by the proposed method and the traditional simple additive weighted (SAW) method shows that the proposed method yields more precise results. In conclusion, fuzzy-set theory can be an effective method to overcome associated uncertainties in classification of geographic information data.

Living with uncertainty and hope: A qualitative study exploring parents' experiences of living with childhood multiple sclerosis.

PubMed

Hinton, Denise; Kirk, Susan

2017-06-01

Background There is growing recognition that multiple sclerosis is a possible, albeit uncommon, diagnosis in childhood. However, very little is known about the experiences of families living with childhood multiple sclerosis and this is the first study to explore this in depth. Objective Our objective was to explore the experiences of parents of children with multiple sclerosis. Methods Qualitative in-depth interviews with 31 parents using a grounded theory approach were conducted. Parents were sampled and recruited via health service and voluntary sector organisations in the United Kingdom. Results Parents' accounts of life with childhood multiple sclerosis were dominated by feelings of uncertainty associated with four sources; diagnostic uncertainty, daily uncertainty, interaction uncertainty and future uncertainty. Parents attempted to manage these uncertainties using specific strategies, which could in turn create further uncertainties about their child's illness. However, over time, ongoing uncertainty appeared to give parents hope for their child's future with multiple sclerosis. Conclusion Illness-related uncertainties appear to play a role in generating hope among parents of a child with multiple sclerosis. However, this may lead parents to avoid sources of information and support that threatens their fragile optimism. Professionals need to be sensitive to the role hope plays in supporting parental coping with childhood multiple sclerosis.

Flood risk assessment and robust management under deep uncertainty: Application to Dhaka City

NASA Astrophysics Data System (ADS)

Mojtahed, Vahid; Gain, Animesh Kumar; Giupponi, Carlo

2014-05-01

The socio-economic changes as well as climatic changes have been the main drivers of uncertainty in environmental risk assessment and in particular flood. The level of future uncertainty that researchers face when dealing with problems in a future perspective with focus on climate change is known as Deep Uncertainty (also known as Knightian uncertainty), since nobody has already experienced and undergone those changes before and our knowledge is limited to the extent that we have no notion of probabilities, and therefore consolidated risk management approaches have limited potential.. Deep uncertainty is referred to circumstances that analysts and experts do not know or parties to decision making cannot agree on: i) the appropriate models describing the interaction among system variables, ii) probability distributions to represent uncertainty about key parameters in the model 3) how to value the desirability of alternative outcomes. The need thus emerges to assist policy-makers by providing them with not a single and optimal solution to the problem at hand, such as crisp estimates for the costs of damages of natural hazards considered, but instead ranges of possible future costs, based on the outcomes of ensembles of assessment models and sets of plausible scenarios. Accordingly, we need to substitute optimality as a decision criterion with robustness. Under conditions of deep uncertainty, the decision-makers do not have statistical and mathematical bases to identify optimal solutions, while instead they should prefer to implement "robust" decisions that perform relatively well over all conceivable outcomes out of all future unknown scenarios. Under deep uncertainty, analysts cannot employ probability theory or other statistics that usually can be derived from observed historical data and therefore, we turn to non-statistical measures such as scenario analysis. We construct several plausible scenarios with each scenario being a full description of what may happen in future and based on a meaningful synthesis of parameters' values with control of their correlations for maintaining internal consistencies. This paper aims at incorporating a set of data mining and sampling tools to assess uncertainty of model outputs under future climatic and socio-economic changes for Dhaka city and providing a decision support system for robust flood management and mitigation policies. After constructing an uncertainty matrix to identify the main sources of uncertainty for Dhaka City, we identify several hazard and vulnerability maps based on future climatic and socio-economic scenarios. The vulnerability of each flood management alternative under different set of scenarios is determined and finally the robustness of each plausible solution considered is defined based on the above assessment.

Social-ecological resilience and law

USGS Publications Warehouse

Garmestani, Ahjond S.; Allen, Craig R.

2014-01-01

Environmental law envisions ecological systems as existing in an equilibrium state, reinforcing a rigid legal framework unable to absorb rapid environmental changes and innovations in sustainability. For the past four decades, “resilience theory,” which embraces uncertainty and nonlinear dynamics in complex adaptive systems, has provided a robust, invaluable foundation for sound environmental management. Reforming American law to incorporate this knowledge is the key to sustainability. This volume features top legal and resilience scholars speaking on resilience theory and its legal applications to climate change, biodiversity, national parks, and water law.

A Comparative Study of Uncertainty Reduction Theory in High- and Low-Context Cultures.

ERIC Educational Resources Information Center

Kim, Myoung-Hye; Yoon, Tae-Jin

To test the cross-cultural validity of uncertainty reduction theory, a study was conducted using students from South Korea and the United States who were chosen to represent high- and low-context cultures respectively. Uncertainty reduction theory is based upon the assumption that the primary concern of strangers upon meeting is one of uncertainty…

On the Minimal Length Uncertainty Relation and the Foundations of String Theory

DOE PAGES

Chang, Lay Nam; Lewis, Zachary; Minic, Djordje; ...

2011-01-01

We review our work on the minimal length uncertainty relation as suggested by perturbative string theory. We discuss simple phenomenological implications of the minimal length uncertainty relation and then argue that the combination of the principles of quantum theory and general relativity allow for a dynamical energy-momentum space. We discuss the implication of this for the problem of vacuum energy and the foundations of nonperturbative string theory.

Data Sufficiency Assessment and Pumping Test Design for Groundwater Prediction Using Decision Theory and Genetic Algorithms

NASA Astrophysics Data System (ADS)

McPhee, J.; William, Y. W.

2005-12-01

This work presents a methodology for pumping test design based on the reliability requirements of a groundwater model. Reliability requirements take into consideration the application of the model results in groundwater management, expressed in this case as a multiobjective management model. The pumping test design is formulated as a mixed-integer nonlinear programming (MINLP) problem and solved using a combination of genetic algorithm (GA) and gradient-based optimization. Bayesian decision theory provides a formal framework for assessing the influence of parameter uncertainty over the reliability of the proposed pumping test. The proposed methodology is useful for selecting a robust design that will outperform all other candidate designs under most potential 'true' states of the system

Optimal control of native predators

USGS Publications Warehouse

Martin, Julien; O'Connell, Allan F.; Kendall, William L.; Runge, Michael C.; Simons, Theodore R.; Waldstein, Arielle H.; Schulte, Shiloh A.; Converse, Sarah J.; Smith, Graham W.; Pinion, Timothy; Rikard, Michael; Zipkin, Elise F.

2010-01-01

We apply decision theory in a structured decision-making framework to evaluate how control of raccoons (Procyon lotor), a native predator, can promote the conservation of a declining population of American Oystercatchers (Haematopus palliatus) on the Outer Banks of North Carolina. Our management objective was to maintain Oystercatcher productivity above a level deemed necessary for population recovery while minimizing raccoon removal. We evaluated several scenarios including no raccoon removal, and applied an adaptive optimization algorithm to account for parameter uncertainty. We show how adaptive optimization can be used to account for uncertainties about how raccoon control may affect Oystercatcher productivity. Adaptive management can reduce this type of uncertainty and is particularly well suited for addressing controversial management issues such as native predator control. The case study also offers several insights that may be relevant to the optimal control of other native predators. First, we found that stage-specific removal policies (e.g., yearling versus adult raccoon removals) were most efficient if the reproductive values among stage classes were very different. Second, we found that the optimal control of raccoons would result in higher Oystercatcher productivity than the minimum levels recommended for this species. Third, we found that removing more raccoons initially minimized the total number of removals necessary to meet long term management objectives. Finally, if for logistical reasons managers cannot sustain a removal program by removing a minimum number of raccoons annually, managers may run the risk of creating an ecological trap for Oystercatchers.

Extended Importance Sampling for Reliability Analysis under Evidence Theory

NASA Astrophysics Data System (ADS)

Yuan, X. K.; Chen, B.; Zhang, B. Q.

2018-05-01

In early engineering practice, the lack of data and information makes uncertainty difficult to deal with. However, evidence theory has been proposed to handle uncertainty with limited information as an alternative way to traditional probability theory. In this contribution, a simulation-based approach, called ‘Extended importance sampling’, is proposed based on evidence theory to handle problems with epistemic uncertainty. The proposed approach stems from the traditional importance sampling for reliability analysis under probability theory, and is developed to handle the problem with epistemic uncertainty. It first introduces a nominal instrumental probability density function (PDF) for every epistemic uncertainty variable, and thus an ‘equivalent’ reliability problem under probability theory is obtained. Then the samples of these variables are generated in a way of importance sampling. Based on these samples, the plausibility and belief (upper and lower bounds of probability) can be estimated. It is more efficient than direct Monte Carlo simulation. Numerical and engineering examples are given to illustrate the efficiency and feasible of the proposed approach.

Against conventional wisdom: when the public, the media, and medical practice collide.

PubMed

Jensen, Jakob D; Krakow, Melinda; John, Kevin K; Liu, Miao

2013-01-01

In 2009, the U.S. Preventive Services Task Force released new mammography screening guidelines that sparked a torrent of criticism. The subsequent conflict was significant and pitted the Task Force against other health organizations, advocacy groups, the media, and the public at large. We argue that this controversy was driven by the systematic removal of uncertainty from science communication. To increase comprehension and adherence, health information communicators remove caveats, limitations, and hedging so science appears simple and more certain. This streamlining process is, in many instances, initiated by researchers as they engage in dissemination of their findings, and it is facilitated by public relations professionals, journalists, public health practitioners, and others whose tasks involve using the results from research for specific purposes. Uncertainty is removed from public communication because many communicators believe that it is difficult for people to process and/or that it is something the audience wants to avoid. Uncertainty management theory posits that people can find meaning and value in uncertainty. We define key terms relevant to uncertainty management, describe research on the processing of uncertainty, identify directions for future research, and offer recommendations for scientists, practitioners, and media professionals confronted with uncertain findings. Science is routinely simplified as it is prepared for public consumption. In line with the model of information overload, this practice may increase short-term adherence to recommendations at the expense of long-term message consistency and trust in science.

Against conventional wisdom: when the public, the media, and medical practice collide

PubMed Central

2013-01-01

Background In 2009, the U.S. Preventive Services Task Force released new mammography screening guidelines that sparked a torrent of criticism. The subsequent conflict was significant and pitted the Task Force against other health organizations, advocacy groups, the media, and the public at large. We argue that this controversy was driven by the systematic removal of uncertainty from science communication. To increase comprehension and adherence, health information communicators remove caveats, limitations, and hedging so science appears simple and more certain. This streamlining process is, in many instances, initiated by researchers as they engage in dissemination of their findings, and it is facilitated by public relations professionals, journalists, public health practitioners, and others whose tasks involve using the results from research for specific purposes. Analysis Uncertainty is removed from public communication because many communicators believe that it is difficult for people to process and/or that it is something the audience wants to avoid. Uncertainty management theory posits that people can find meaning and value in uncertainty. We define key terms relevant to uncertainty management, describe research on the processing of uncertainty, identify directions for future research, and offer recommendations for scientists, practitioners, and media professionals confronted with uncertain findings. Conclusions Science is routinely simplified as it is prepared for public consumption. In line with the model of information overload, this practice may increase short-term adherence to recommendations at the expense of long-term message consistency and trust in science. PMID:24565173

Symmetry, Contingency, Complexity: Accommodating Uncertainty in Public Relations Theory.

ERIC Educational Resources Information Center

Murphy, Priscilla

2000-01-01

Explores the potential of complexity theory as a unifying theory in public relations, where scholars have recently raised problems involving flux, uncertainty, adaptiveness, and loss of control. Describes specific complexity-based methodologies and their potential for public relations studies. Offers an account of complexity theory, its…

Measuring uncertainty by extracting fuzzy rules using rough sets and extracting fuzzy rules under uncertainty and measuring definability using rough sets

NASA Technical Reports Server (NTRS)

Worm, Jeffrey A.; Culas, Donald E.

1991-01-01

Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. This paper examines the concepts of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to provide the possible optimal solution. By incorporating principles from these theories, a decision-making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much we believe these rules is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of its fuzzy attributes is studied.

A methodology for uncertainty quantification in quantitative technology valuation based on expert elicitation

NASA Astrophysics Data System (ADS)

Akram, Muhammad Farooq Bin

The management of technology portfolios is an important element of aerospace system design. New technologies are often applied to new product designs to ensure their competitiveness at the time they are introduced to market. The future performance of yet-to- be designed components is inherently uncertain, necessitating subject matter expert knowledge, statistical methods and financial forecasting. Estimates of the appropriate parameter settings often come from disciplinary experts, who may disagree with each other because of varying experience and background. Due to inherent uncertain nature of expert elicitation in technology valuation process, appropriate uncertainty quantification and propagation is very critical. The uncertainty in defining the impact of an input on performance parameters of a system makes it difficult to use traditional probability theory. Often the available information is not enough to assign the appropriate probability distributions to uncertain inputs. Another problem faced during technology elicitation pertains to technology interactions in a portfolio. When multiple technologies are applied simultaneously on a system, often their cumulative impact is non-linear. Current methods assume that technologies are either incompatible or linearly independent. It is observed that in case of lack of knowledge about the problem, epistemic uncertainty is the most suitable representation of the process. It reduces the number of assumptions during the elicitation process, when experts are forced to assign probability distributions to their opinions without sufficient knowledge. Epistemic uncertainty can be quantified by many techniques. In present research it is proposed that interval analysis and Dempster-Shafer theory of evidence are better suited for quantification of epistemic uncertainty in technology valuation process. Proposed technique seeks to offset some of the problems faced by using deterministic or traditional probabilistic approaches for uncertainty propagation. Non-linear behavior in technology interactions is captured through expert elicitation based technology synergy matrices (TSM). Proposed TSMs increase the fidelity of current technology forecasting methods by including higher order technology interactions. A test case for quantification of epistemic uncertainty on a large scale problem of combined cycle power generation system was selected. A detailed multidisciplinary modeling and simulation environment was adopted for this problem. Results have shown that evidence theory based technique provides more insight on the uncertainties arising from incomplete information or lack of knowledge as compared to deterministic or probability theory methods. Margin analysis was also carried out for both the techniques. A detailed description of TSMs and their usage in conjunction with technology impact matrices and technology compatibility matrices is discussed. Various combination methods are also proposed for higher order interactions, which can be applied according to the expert opinion or historical data. The introduction of technology synergy matrix enabled capturing the higher order technology interactions, and improvement in predicted system performance.

Using complexity science and negotiation theory to resolve boundary-crossing water issues

NASA Astrophysics Data System (ADS)

Islam, Shafiqul; Susskind, Lawrence

2018-07-01

Many water governance and management issues are complex. The complexity of these issues is related to crossing of multiple boundaries: political, social and jurisdictional, as well as physical, ecological and biogeochemical. Resolution of these issues usually requires interactions of many parties with conflicting values and interests operating across multiple boundaries and scales to make decisions. The interdependence and feedback among interacting variables, processes, actors and institutions are hard to model and difficult to forecast. Thus, decision-making related to complex water problems needs be contingent and adaptive. This paper draws on a number of ideas from complexity science and negotiation theory that may make it easier to cope with the complexities and difficulties of managing boundary crossing water disputes. It begins with the Water Diplomacy Framework that was developed and tested over the past several years. Then, it uses three key ideas from complexity science (interdependence and interconnectedness; uncertainty and feedback; emergence and adaptation) and three from negotiation theory (stakeholder identification and engagement; joint fact finding; and value creation through option generation) to show how application of these ideas can help enhance effectiveness of water management.

Quantifying human behavior uncertainties in a coupled agent-based model for water resources management

NASA Astrophysics Data System (ADS)

Hyun, J. Y.; Yang, Y. C. E.; Tidwell, V. C.; Macknick, J.

2017-12-01

Modeling human behaviors and decisions in water resources management is a challenging issue due to its complexity and uncertain characteristics that affected by both internal (such as stakeholder's beliefs on any external information) and external factors (such as future policies and weather/climate forecast). Stakeholders' decision regarding how much water they need is usually not entirely rational in the real-world cases, so it is not quite suitable to model their decisions with a centralized (top-down) approach that assume everyone in a watershed follow the same order or pursue the same objective. Agent-based modeling (ABM) uses a decentralized approach (bottom-up) that allow each stakeholder to make his/her own decision based on his/her own objective and the belief of information acquired. In this study, we develop an ABM which incorporates the psychological human decision process by the theory of risk perception. The theory of risk perception quantifies human behaviors and decisions uncertainties using two sequential methodologies: the Bayesian Inference and the Cost-Loss Problem. The developed ABM is coupled with a regulation-based water system model: Riverware (RW) to evaluate different human decision uncertainties in water resources management. The San Juan River Basin in New Mexico (Figure 1) is chosen as a case study area, while we define 19 major irrigation districts as water use agents and their primary decision is to decide the irrigated area on an annual basis. This decision will be affected by three external factors: 1) upstream precipitation forecast (potential amount of water availability), 2) violation of the downstream minimum flow (required to support ecosystems), and 3) enforcement of a shortage sharing plan (a policy that is currently undertaken in the region for drought years). Three beliefs (as internal factors) that correspond to these three external factors will also be considered in the modeling framework. The objective of this study is to use the two-way coupling between ABM and RW to mimic how stakeholders' uncertain decisions that have been made through the theory of risk perception will affect local and basin-wide water uses.

[Influence of Uncertainty and Uncertainty Appraisal on Self-management in Hemodialysis Patients].

PubMed

Jang, Hyung Suk; Lee, Chang Suk; Yang, Young Hee

2015-04-01

This study was done to examine the relation of uncertainty, uncertainty appraisal, and self-management in patients undergoing hemodialysis, and to identify factors influencing self-management. A convenience sample of 92 patients receiving hemodialysis was selected. Data were collected using a structured questionnaire and medical records. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations and multiple regression analysis with the SPSS/WIN 20.0 program. The participants showed a moderate level of uncertainty with the highest score being for ambiguity among the four uncertainty subdomains. Scores for uncertainty danger or opportunity appraisals were under the mid points. The participants were found to perform a high level of self-management such as diet control, management of arteriovenous fistula, exercise, medication, physical management, measurements of body weight and blood pressure, and social activity. The self-management of participants undergoing hemodialysis showed a significant relationship with uncertainty and uncertainty appraisal. The significant factors influencing self-management were uncertainty, uncertainty opportunity appraisal, hemodialysis duration, and having a spouse. These variables explained 32.8% of the variance in self-management. The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of uncertainty opportunity appraisal among patients would improve the self-management of hemodialysis patients.

Operational Implementation of a Pc Uncertainty Construct for Conjunction Assessment Risk Analysis

NASA Technical Reports Server (NTRS)

Newman, Lauri K.; Hejduk, Matthew D.; Johnson, Lauren C.

2016-01-01

Earlier this year the NASA Conjunction Assessment and Risk Analysis (CARA) project presented the theoretical and algorithmic aspects of a method to include the uncertainties in the calculation inputs when computing the probability of collision (Pc) between two space objects, principally uncertainties in the covariances and the hard-body radius. The output of this calculation approach is to produce rather than a single Pc value an entire probability density function that will represent the range of possible Pc values given the uncertainties in the inputs and bring CA risk analysis methodologies more in line with modern risk management theory. The present study provides results from the exercise of this method against an extended dataset of satellite conjunctions in order to determine the effect of its use on the evaluation of conjunction assessment (CA) event risk posture. The effects are found to be considerable: a good number of events are downgraded from or upgraded to a serious risk designation on the basis of consideration of the Pc uncertainty. The findings counsel the integration of the developed methods into NASA CA operations.

A python framework for environmental model uncertainty analysis

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Doherty, John E.

2016-01-01

We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.

Sense of control under uncertainty depends on people's childhood environment: a life history theory approach.

PubMed

Mittal, Chiraag; Griskevicius, Vladas

2014-10-01

Past research found that environmental uncertainty leads people to behave differently depending on their childhood environment. For example, economic uncertainty leads people from poor childhoods to become more impulsive while leading people from wealthy childhoods to become less impulsive. Drawing on life history theory, we examine the psychological mechanism driving such diverging responses to uncertainty. Five experiments show that uncertainty alters people's sense of control over the environment. Exposure to uncertainty led people from poorer childhoods to have a significantly lower sense of control than those from wealthier childhoods. In addition, perceptions of control statistically mediated the effect of uncertainty on impulsive behavior. These studies contribute by demonstrating that sense of control is a psychological driver of behaviors associated with fast and slow life history strategies. We discuss the implications of this for theory and future research, including that environmental uncertainty might lead people who grew up poor to quit challenging tasks sooner than people who grew up wealthy. 2014 APA, all rights reserved

Task uncertainty and communication during nursing shift handovers.

PubMed

Mayor, Eric; Bangerter, Adrian; Aribot, Myriam

2012-09-01

We explore variations in handover duration and communication in nursing units. We hypothesize that duration per patient is higher in units facing high task uncertainty. We expect both topics and functions of communication to vary depending on task uncertainty. Handovers are changing in modern healthcare organizations, where standardized procedures are increasingly advocated for efficiency and reliability reasons. However, redesign of handover should take environmental contingencies of different clinical unit types into account. An important contingency in institutions is task uncertainty, which may affect how communicative routines like handover are accomplished. Nurse unit managers of 80 care units in 18 hospitals were interviewed in 2008 about topics and functions of handover communication and duration in their unit. Interviews were content-analysed. Clinical units were classified into a theory-based typology (unit type) that gradually increases on task uncertainty. Quantitative analyses were performed. Unit type affected resource allocation. Unit types facing higher uncertainty had higher handover duration per patient. As expected, unit type also affected communication content. Clinical units facing higher uncertainty discussed fewer topics, discussing treatment and care and organization of work less frequently. Finally, unit type affected functions of handover: sharing emotions was less often mentioned in unit types facing higher uncertainty. Task uncertainty and its relationship with functions and topics of handover should be taken into account during the design of handover procedures. © 2011 Blackwell Publishing Ltd.

Reconciling uncertain costs and benefits in bayes nets for invasive species management

USGS Publications Warehouse

Burgman, M.A.; Wintle, B.A.; Thompson, C.A.; Moilanen, A.; Runge, M.C.; Ben-Haim, Y.

2010-01-01

Bayes nets are used increasingly to characterize environmental systems and formalize probabilistic reasoning to support decision making. These networks treat probabilities as exact quantities. Sensitivity analysis can be used to evaluate the importance of assumptions and parameter estimates. Here, we outline an application of info-gap theory to Bayes nets that evaluates the sensitivity of decisions to possibly large errors in the underlying probability estimates and utilities. We apply it to an example of management and eradication of Red Imported Fire Ants in Southern Queensland, Australia and show how changes in management decisions can be justified when uncertainty is considered. ?? 2009 Society for Risk Analysis.

Exploring Best Practice Skills to Predict Uncertainties in Venture Capital Investment Decision-Making

NASA Astrophysics Data System (ADS)

Blum, David Arthur

Algae biodiesel is the sole sustainable and abundant transportation fuel source that can replace petrol diesel use; however, high competition and economic uncertainties exist, influencing independent venture capital decision making. Technology, market, management, and government action uncertainties influence competition and economic uncertainties in the venture capital industry. The purpose of this qualitative case study was to identify the best practice skills at IVC firms to predict uncertainty between early and late funding stages. The basis of the study was real options theory, a framework used to evaluate and understand the economic and competition uncertainties inherent in natural resource investment and energy derived from plant-based oils. Data were collected from interviews of 24 venture capital partners based in the United States who invest in algae and other renewable energy solutions. Data were analyzed by coding and theme development interwoven with the conceptual framework. Eight themes emerged: (a) expected returns model, (b) due diligence, (c) invest in specific sectors, (d) reduced uncertainty-late stage, (e) coopetition, (f) portfolio firm relationships, (g) differentiation strategy, and (h) modeling uncertainty and best practice. The most noteworthy finding was that predicting uncertainty at the early stage was impractical; at the expansion and late funding stages, however, predicting uncertainty was possible. The implications of these findings will affect social change by providing independent venture capitalists with best practice skills to increase successful exits, lessen uncertainty, and encourage increased funding of renewable energy firms, contributing to cleaner and healthier communities throughout the United States..

The modification of generalized uncertainty principle applied in the detection technique of femtosecond laser

NASA Astrophysics Data System (ADS)

Li, Ziyi

2017-12-01

Generalized uncertainty principle (GUP), also known as the generalized uncertainty relationship, is the modified form of the classical Heisenberg’s Uncertainty Principle in special cases. When we apply quantum gravity theories such as the string theory, the theoretical results suggested that there should be a “minimum length of observation”, which is about the size of the Planck-scale (10-35m). Taking into account the basic scale of existence, we need to fix a new common form of Heisenberg’s uncertainty principle in the thermodynamic system and make effective corrections to statistical physical questions concerning about the quantum density of states. Especially for the condition at high temperature and high energy levels, generalized uncertainty calculations have a disruptive impact on classical statistical physical theories but the present theory of Femtosecond laser is still established on the classical Heisenberg’s Uncertainty Principle. In order to improve the detective accuracy and temporal resolution of the Femtosecond laser, we applied the modified form of generalized uncertainty principle to the wavelength, energy and pulse time of Femtosecond laser in our work. And we designed three typical systems from micro to macro size to estimate the feasibility of our theoretical model and method, respectively in the chemical solution condition, crystal lattice condition and nuclear fission reactor condition.

Application of fuzzy system theory in addressing the presence of uncertainties

NASA Astrophysics Data System (ADS)

Yusmye, A. Y. N.; Goh, B. Y.; Adnan, N. F.; Ariffin, A. K.

2015-02-01

In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.

Implementation of a real option in a sustainable supply chain: an empirical study of alkaline battery recycling

NASA Astrophysics Data System (ADS)

Cucchiella, Federica; D'Adamo, Idiano; Gastaldi, Massimo; Lenny Koh, S. C.

2014-06-01

Green supply chain management (GSCM) has emerged as a key approach for enterprises seeking to become environmentally sustainable. This paper aims to evaluate and describe the advantages of a GSCM approach by analysing practices and performance consequences in the battery recycling sector. It seeks to integrate works in supply chain management (SCM), environmental management, performance management and real option (RO) theory into one framework. In particular, life cycle assessment (LCA) is applied to evaluate the environmental impact of a battery recycling plant project, and life cycle costing (LCC) is applied to evaluate its economic impact. Firms, also understanding the relevance of GSCM, have often avoided applying the green principles because of the elevated costs that such management involved. Such costs could also seem superior to the potential advantages since standard performance measurement systems are internally and business focused; for these reasons, we consider all the possible value deriving also by uncertainty associated to a green project using the RO theory. This work is one of the few and pioneering efforts to investigate GSCM practices in the battery recycling sector.

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

PubMed

Oddo, Perry C; Lee, Ben S; Garner, Gregory G; Srikrishnan, Vivek; Reed, Patrick M; Forest, Chris E; Keller, Klaus

2017-09-05

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies. © 2017 Society for Risk Analysis.

How It's Done: Using "Hitch" as a Guide to Uncertainty Reduction Theory

ERIC Educational Resources Information Center

Dawkins, Marcia Alesan

2010-01-01

Popular films can be important pedagogical tools in today's communication courses. Constructing classroom experiences that use film can make theory come alive for students. At the same time, theory can be used to probe deeper into the complexities of human behavior via astute film analysis. In the case of Uncertainty Reduction Theory (URT), a…

Uncertainty management by relaxation of conflicting constraints in production process scheduling

NASA Technical Reports Server (NTRS)

Dorn, Juergen; Slany, Wolfgang; Stary, Christian

1992-01-01

Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.

Defending Against Advanced Persistent Threats Using Game-Theory.

PubMed

Rass, Stefan; König, Sandra; Schauer, Stefan

2017-01-01

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker's incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system's protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.

From axiomatics of quantum probability to modelling geological uncertainty and management of intelligent hydrocarbon reservoirs with the theory of open quantum systems.

PubMed

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia

2018-04-28

As was recently shown by the authors, quantum probability theory can be used for the modelling of the process of decision-making (e.g. probabilistic risk analysis) for macroscopic geophysical structures such as hydrocarbon reservoirs. This approach can be considered as a geophysical realization of Hilbert's programme on axiomatization of statistical models in physics (the famous sixth Hilbert problem). In this conceptual paper , we continue development of this approach to decision-making under uncertainty which is generated by complexity, variability, heterogeneity, anisotropy, as well as the restrictions to accessibility of subsurface structures. The belief state of a geological expert about the potential of exploring a hydrocarbon reservoir is continuously updated by outputs of measurements, and selection of mathematical models and scales of numerical simulation. These outputs can be treated as signals from the information environment E The dynamics of the belief state can be modelled with the aid of the theory of open quantum systems: a quantum state (representing uncertainty in beliefs) is dynamically modified through coupling with E ; stabilization to a steady state determines a decision strategy. In this paper, the process of decision-making about hydrocarbon reservoirs (e.g. 'explore or not?'; 'open new well or not?'; 'contaminated by water or not?'; 'double or triple porosity medium?') is modelled by using the Gorini-Kossakowski-Sudarshan-Lindblad equation. In our model, this equation describes the evolution of experts' predictions about a geophysical structure. We proceed with the information approach to quantum theory and the subjective interpretation of quantum probabilities (due to quantum Bayesianism).This article is part of the theme issue 'Hilbert's sixth problem'. © 2018 The Author(s).

From axiomatics of quantum probability to modelling geological uncertainty and management of intelligent hydrocarbon reservoirs with the theory of open quantum systems

NASA Astrophysics Data System (ADS)

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia

2018-04-01

As was recently shown by the authors, quantum probability theory can be used for the modelling of the process of decision-making (e.g. probabilistic risk analysis) for macroscopic geophysical structures such as hydrocarbon reservoirs. This approach can be considered as a geophysical realization of Hilbert's programme on axiomatization of statistical models in physics (the famous sixth Hilbert problem). In this conceptual paper, we continue development of this approach to decision-making under uncertainty which is generated by complexity, variability, heterogeneity, anisotropy, as well as the restrictions to accessibility of subsurface structures. The belief state of a geological expert about the potential of exploring a hydrocarbon reservoir is continuously updated by outputs of measurements, and selection of mathematical models and scales of numerical simulation. These outputs can be treated as signals from the information environment E. The dynamics of the belief state can be modelled with the aid of the theory of open quantum systems: a quantum state (representing uncertainty in beliefs) is dynamically modified through coupling with E; stabilization to a steady state determines a decision strategy. In this paper, the process of decision-making about hydrocarbon reservoirs (e.g. `explore or not?'; `open new well or not?'; `contaminated by water or not?'; `double or triple porosity medium?') is modelled by using the Gorini-Kossakowski-Sudarshan-Lindblad equation. In our model, this equation describes the evolution of experts' predictions about a geophysical structure. We proceed with the information approach to quantum theory and the subjective interpretation of quantum probabilities (due to quantum Bayesianism). This article is part of the theme issue `Hilbert's sixth problem'.

Confronting uncertainty in wildlife management: performance of grizzly bear management.

PubMed

Artelle, Kyle A; Anderson, Sean C; Cooper, Andrew B; Paquet, Paul C; Reynolds, John D; Darimont, Chris T

2013-01-01

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone--discrepancy between expected and realized mortality levels--led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.

Application of fuzzy system theory in addressing the presence of uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yusmye, A. Y. N.; Goh, B. Y.; Adnan, N. F.

In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statisticalmore » approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.« less

Air traffic management as principled negotiation between intelligent agents

NASA Technical Reports Server (NTRS)

Wangermann, J. P.

1994-01-01

The major challenge facing the world's aircraft/airspace system (AAS) today is the need to provide increased capacity, while reducing delays, increasing the efficiency of flight operations, and improving safety. Technologies are emerging that should improve the performance of the system, but which could also introduce uncertainty, disputes, and inefficiency if not properly implemented. The aim of our research is to apply techniques from intelligent control theory and decision-making theory to define an Intelligent Aircraft/Airspace System (IAAS) for the year 2025. The IAAS would make effective use of the technical capabilities of all parts of the system to meet the demand for increased capacity with improved performance.

Reducing uncertainty about objective functions in adaptive management

USGS Publications Warehouse

Williams, B.K.

2012-01-01

This paper extends the uncertainty framework of adaptive management to include uncertainty about the objectives to be used in guiding decisions. Adaptive decision making typically assumes explicit and agreed-upon objectives for management, but allows for uncertainty as to the structure of the decision process that generates change through time. Yet it is not unusual for there to be uncertainty (or disagreement) about objectives, with different stakeholders expressing different views not only about resource responses to management but also about the appropriate management objectives. In this paper I extend the treatment of uncertainty in adaptive management, and describe a stochastic structure for the joint occurrence of uncertainty about objectives as well as models, and show how adaptive decision making and the assessment of post-decision monitoring data can be used to reduce uncertainties of both kinds. Different degrees of association between model and objective uncertainty lead to different patterns of learning about objectives. ?? 2011.

Autonomous Space Object Catalogue Construction and Upkeep Using Sensor Control Theory

NASA Astrophysics Data System (ADS)

Moretti, N.; Rutten, M.; Bessell, T.; Morreale, B.

The capability to track objects in space is critical to safeguard domestic and international space assets. Infrequent measurement opportunities, complex dynamics and partial observability of orbital state makes the tracking of resident space objects nontrivial. It is not uncommon for human operators to intervene with space tracking systems, particularly in scheduling sensors. This paper details the development of a system that maintains a catalogue of geostationary objects through dynamically tasking sensors in real time by managing the uncertainty of object states. As the number of objects in space grows the potential for collision grows exponentially. Being able to provide accurate assessment to operators regarding costly collision avoidance manoeuvres is paramount; the accuracy of which is highly dependent on how object states are estimated. The system represents object state and uncertainty using particles and utilises a particle filter for state estimation. Particle filters capture the model and measurement uncertainty accurately, allowing for a more comprehensive representation of the state’s probability density function. Additionally, the number of objects in space is growing disproportionally to the number of sensors used to track them. Maintaining precise positions for all objects places large loads on sensors, limiting the time available to search for new objects or track high priority objects. Rather than precisely track all objects our system manages the uncertainty in orbital state for each object independently. The uncertainty is allowed to grow and sensor data is only requested when the uncertainty must be reduced. For example when object uncertainties overlap leading to data association issues or if the uncertainty grows to beyond a field of view. These control laws are formulated into a cost function, which is optimised in real time to task sensors. By controlling an optical telescope the system has been able to construct and maintain a catalogue of approximately 100 geostationary objects.

Nursing students' time management, reducing stress and gaining satisfaction: a grounded theory study.

PubMed

Mirzaei, Tayebeh; Oskouie, Fatemeh; Rafii, Forough

2012-03-01

In the course of their studies, nursing students must learn many skills and acquire the knowledge required for their future profession. This study investigates how Iranian nursing students manage their time according to the circumstances and obstacles of their academic field. Research was conducted using the grounded theory method. Twenty-one nursing students were purposefully chosen as participants. Data was collected through semi-structured interviews and analyzed using the method suggested by Corbin and Strauss. One of the three processes that the nursing students used was "unidirectional time management." This pattern consists of accepting the nursing field, overcoming uncertainty, assessing conditions, feeling stress, and trying to reduce stress and create satisfaction. It was found that students allotted most of their time to academic tasks in an attempt to overcome their stress. The findings of this study indicate the need for these students to have time for the extra-curricular activities and responsibilities that are appropriate to their age. © 2012 Blackwell Publishing Asia Pty Ltd.

Comparative Education Research Framed by Neo-Institutional Theory: A Review of Diverse Approaches and Conflicting Assumptions

ERIC Educational Resources Information Center

Wiseman, Alexander W.; Astiz, M. Fernanda; Baker, David P.

2014-01-01

The rise in globalisation studies in comparative education places neo-institutional theory at the centre of many debates among comparative education researchers. However, uncertainty about how to interpret neo-institutional theory still persists among educational comparativists. With this uncertainty comes misinterpretation of its principles,…

Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management

NASA Astrophysics Data System (ADS)

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia; de Jesús Correa, María

2017-10-01

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Växjö interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir. Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management. This article is part of the themed issue `Second quantum revolution: foundational questions'.

Management of the Israeli National Water System under Uncertainty

NASA Astrophysics Data System (ADS)

Shamir, U.; Housh, M.; Ostfeld, A.; Zaide, M.

2009-12-01

Uncertainty in our region is due to the natural variability of hydrological patterns, with recurring extended droughts, reduced average and broadening variability of recharge that seem to indicate the effect of climate change, as well as to deterioration of water quality in the natural sources, to population growth and distribution, to shifting demand patterns among consumer sectors, and to expected future regional water agreements. These factors combine to create a challenging environment in which highly stressed water resources and water systems have to be developed, operated and managed. The natural sources have been used to their sustainable capacity and often beyond. The main policy responses are a shift of fresh water from agriculture to the cities, replacing it with treated wastewater for irrigation, and a major program for construction of sea-water desalination plants and the associated infrastructure needed for its integration into the supply systems. Organizational reforms, regulation, and demand management options are also being developed, including full-cost pricing. Management of the water resources and systems under these conditions requires a long-term perspective. The methodologies for supporting management decisions that have been used to date by the Israeli Water Authority include evaluation by scenarios, simulation, and optimization with sensitivity analysis. We review existing approaches and models for management of the Israeli water system (Zaide 2006) and then present some new methodologies for addressing operational decisions under hydrological uncertainty, which include generation of tradeoffs between the expected value and variability of the outcomes, and an Info-Gap (Ben-Haim 2006) based approach. These methodologies are demonstrated on examples that emulate portions of a regional water system and are then applied to the Israeli National Water System. Ben-Haim, Y. (2006) Info-Gap Theory: Decisions under Severe Uncertainty, 2nd Ed., Academic Press, London. Zaide, M. (2006) A Model for Management of Water Quantity and Quality in the Israeli National System", MSc Thesis, Faculty of Civil Engineering, Technion. http://urishamir.wri.technion.ac.il/files/documents/Miki%20Zaide%20-%20Thesis%20Final%2015.03.06.pdf

Cloud Condensation Nuclei Prediction Error from Application of Kohler Theory: Importance for the Aerosol Indirect Effect

NASA Technical Reports Server (NTRS)

Sotiropoulou, Rafaella-Eleni P.; Nenes, Athanasios; Adams, Peter J.; Seinfeld, John H.

2007-01-01

In situ observations of aerosol and cloud condensation nuclei (CCN) and the GISS GCM Model II' with an online aerosol simulation and explicit aerosol-cloud interactions are used to quantify the uncertainty in radiative forcing and autoconversion rate from application of Kohler theory. Simulations suggest that application of Koehler theory introduces a 10-20% uncertainty in global average indirect forcing and 2-11% uncertainty in autoconversion. Regionally, the uncertainty in indirect forcing ranges between 10-20%, and 5-50% for autoconversion. These results are insensitive to the range of updraft velocity and water vapor uptake coefficient considered. This study suggests that Koehler theory (as implemented in climate models) is not a significant source of uncertainty for aerosol indirect forcing but can be substantial for assessments of aerosol effects on the hydrological cycle in climatically sensitive regions of the globe. This implies that improvements in the representation of GCM subgrid processes and aerosol size distribution will mostly benefit indirect forcing assessments. Predictions of autoconversion, by nature, will be subject to considerable uncertainty; its reduction may require explicit representation of size-resolved aerosol composition and mixing state.

[Uncertainty characterization approaches for ecological risk assessment of polycyclic aromatic hydrocarbon in Taihu Lake].

PubMed

Guo, Guang-Hui; Wu, Feng-Chang; He, Hong-Ping; Feng, Cheng-Lian; Zhang, Rui-Qing; Li, Hui-Xian

2012-04-01

Probabilistic approaches, such as Monte Carlo Sampling (MCS) and Latin Hypercube Sampling (LHS), and non-probabilistic approaches, such as interval analysis, fuzzy set theory and variance propagation, were used to characterize uncertainties associated with risk assessment of sigma PAH8 in surface water of Taihu Lake. The results from MCS and LHS were represented by probability distributions of hazard quotients of sigma PAH8 in surface waters of Taihu Lake. The probabilistic distribution of hazard quotient were obtained from the results of MCS and LHS based on probabilistic theory, which indicated that the confidence intervals of hazard quotient at 90% confidence level were in the range of 0.000 18-0.89 and 0.000 17-0.92, with the mean of 0.37 and 0.35, respectively. In addition, the probabilities that the hazard quotients from MCS and LHS exceed the threshold of 1 were 9.71% and 9.68%, respectively. The sensitivity analysis suggested the toxicity data contributed the most to the resulting distribution of quotients. The hazard quotient of sigma PAH8 to aquatic organisms ranged from 0.000 17 to 0.99 using interval analysis. The confidence interval was (0.001 5, 0.016 3) at the 90% confidence level calculated using fuzzy set theory, and the confidence interval was (0.000 16, 0.88) at the 90% confidence level based on the variance propagation. These results indicated that the ecological risk of sigma PAH8 to aquatic organisms were low. Each method has its own set of advantages and limitations, which was based on different theory; therefore, the appropriate method should be selected on a case-by-case to quantify the effects of uncertainties on the ecological risk assessment. Approach based on the probabilistic theory was selected as the most appropriate method to assess the risk of sigma PAH8 in surface water of Taihu Lake, which provided an important scientific foundation of risk management and control for organic pollutants in water.

Modeling treatment of ischemic heart disease with partially observable Markov decision processes.

PubMed

Hauskrecht, M; Fraser, H

1998-01-01

Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of Partially observable Markov decision processes (POMDPs) developed and used in operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In the paper, we show how the POMDP framework could be used to model and solve the problem of the management of patients with ischemic heart disease, and point out modeling advantages of the framework over standard decision formalisms.

The Fundamental Uncertainty of Business: Real Options

NASA Astrophysics Data System (ADS)

Dyer, James S.

The purpose of this paper is to discuss the manner in which uncertainty is currently evaluated in business, with an emphasis on economic measures. In recent years, the accepted approach for the valuation of capital investment decisions has become one based on the theory of real options. From the standpoint of this workshop, the interesting aspect of real options is its focus on the flexibility of management to respond to changes in the environment as a feature of an alternative that has unique value, known as "option value." While this may not be surprising to most participants in this workshop, it does represent a radical change in traditional thinking about risk in business, where efforts have primarily been focused on the elimination of risk when possible.

Ups and downs of the expatriate experience? Understanding work adjustment trajectories and career outcomes.

PubMed

Zhu, Jing; Wanberg, Connie R; Harrison, David A; Diehn, Erica W

2016-04-01

We examine changes in work adjustment among 179 expatriates from 3 multinational organizations from predeparture through the first 9 months of a new international assignment. Our 10-wave results challenge classic U-shaped theories of expatriate adjustment (e.g., Torbiorn, 1982). Consistent with uncertainty reduction theory, our results instead suggest that expatriates typically experience a gradual increase in work adjustment over time. Two resources that expatriates bring to their assignments (previous culture-specific work experience and core self-evaluations) moderate the trajectory of work adjustment. Trajectory of adjustment predicts Month 9 career instrumentality and turnover intention, as well as career advancement (job promotion) 1.5 years further. Implications for theory, as well as for changes in expatriate management practices, are discussed. (c) 2016 APA, all rights reserved).

Variable fidelity robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

NASA Astrophysics Data System (ADS)

Hou, Liqiang; Cai, Yuanli; Liu, Jin; Hou, Chongyuan

2016-04-01

A variable fidelity robust optimization method for pulsed laser orbital debris removal (LODR) under uncertainty is proposed. Dempster-shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used in the robust optimization. The robust optimization method optimizes the performance while at the same time maximizing its belief value. A population based multi-objective optimization (MOO) algorithm based on a steepest descent like strategy with proper orthogonal decomposition (POD) is used to search robust Pareto solutions. Analytical and numerical lifetime predictors are used to evaluate the debris lifetime after the laser pulses. Trust region based fidelity management is designed to reduce the computational cost caused by the expensive model. When the solutions fall into the trust region, the analytical model is used to reduce the computational cost. The proposed robust optimization method is first tested on a set of standard problems and then applied to the removal of Iridium 33 with pulsed lasers. It will be shown that the proposed approach can identify the most robust solutions with minimum lifetime under uncertainty.

Understanding differences in electronic health record (EHR) use: linking individual physicians' perceptions of uncertainty and EHR use patterns in ambulatory care.

PubMed

Lanham, Holly Jordan; Sittig, Dean F; Leykum, Luci K; Parchman, Michael L; Pugh, Jacqueline A; McDaniel, Reuben R

2014-01-01

Electronic health records (EHR) hold great promise for managing patient information in ways that improve healthcare delivery. Physicians differ, however, in their use of this health information technology (IT), and these differences are not well understood. The authors study the differences in individual physicians' EHR use patterns and identify perceptions of uncertainty as an important new variable in understanding EHR use. Qualitative study using semi-structured interviews and direct observation of physicians (n=28) working in a multispecialty outpatient care organization. We identified physicians' perceptions of uncertainty as an important variable in understanding differences in EHR use patterns. Drawing on theories from the medical and organizational literatures, we identified three categories of perceptions of uncertainty: reduction, absorption, and hybrid. We used an existing model of EHR use to categorize physician EHR use patterns as high, medium, and low based on degree of feature use, level of EHR-enabled communication, and frequency that EHR use patterns change. Physicians' perceptions of uncertainty were distinctly associated with their EHR use patterns. Uncertainty reductionists tended to exhibit high levels of EHR use, uncertainty absorbers tended to exhibit low levels of EHR use, and physicians demonstrating both perspectives of uncertainty (hybrids) tended to exhibit medium levels of EHR use. We find evidence linking physicians' perceptions of uncertainty with EHR use patterns. Study findings have implications for health IT research, practice, and policy, particularly in terms of impacting health IT design and implementation efforts in ways that consider differences in physicians' perceptions of uncertainty.

Role of information theoretic uncertainty relations in quantum theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jizba, Petr, E-mail: p.jizba@fjfi.cvut.cz; ITP, Freie Universität Berlin, Arnimallee 14, D-14195 Berlin; Dunningham, Jacob A., E-mail: J.Dunningham@sussex.ac.uk

2015-04-15

Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again,more » improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.« less

Quantum Uncertainty and Decision-Making in Game Theory

NASA Astrophysics Data System (ADS)

Asano, M.; Ohya, M.; Tanaka, Y.; Khrennikov, A.; Basieva, I.

2011-01-01

Recently a few authors pointed to a possibility to apply the mathematical formalism of quantum mechanics to cognitive psychology, in particular, to games of the Prisoners Dilemma (PD) type.6_18 In this paper, we discuss the problem of rationality in game theory and point out that the quantum uncertainty is similar to the uncertainty of knowledge, which a player feels subjectively in his decision-making.

Evaluating a multispecies adaptive management framework: Must uncertainty impede effective decision-making?

USGS Publications Warehouse

Smith, David R.; McGowan, Conor P.; Daily, Jonathan P.; Nichols, James D.; Sweka, John A.; Lyons, James E.

2013-01-01

Application of adaptive management to complex natural resource systems requires careful evaluation to ensure that the process leads to improved decision-making. As part of that evaluation, adaptive policies can be compared with alternative nonadaptive management scenarios. Also, the value of reducing structural (ecological) uncertainty to achieving management objectives can be quantified.A multispecies adaptive management framework was recently adopted by the Atlantic States Marine Fisheries Commission for sustainable harvest of Delaware Bay horseshoe crabs Limulus polyphemus, while maintaining adequate stopover habitat for migrating red knots Calidris canutus rufa, the focal shorebird species. The predictive model set encompassed the structural uncertainty in the relationships between horseshoe crab spawning, red knot weight gain and red knot vital rates. Stochastic dynamic programming was used to generate a state-dependent strategy for harvest decisions given that uncertainty. In this paper, we employed a management strategy evaluation approach to evaluate the performance of this adaptive management framework. Active adaptive management was used by including model weights as state variables in the optimization and reducing structural uncertainty by model weight updating.We found that the value of information for reducing structural uncertainty is expected to be low, because the uncertainty does not appear to impede effective management. Harvest policy responded to abundance levels of both species regardless of uncertainty in the specific relationship that generated those abundances. Thus, the expected horseshoe crab harvest and red knot abundance were similar when the population generating model was uncertain or known, and harvest policy was robust to structural uncertainty as specified.Synthesis and applications. The combination of management strategy evaluation with state-dependent strategies from stochastic dynamic programming was an informative approach to evaluate adaptive management performance and value of learning. Although natural resource decisions are characterized by uncertainty, not all uncertainty will cause decisions to be altered substantially, as we found in this case. It is important to incorporate uncertainty into the decision framing and evaluate the effect of reducing that uncertainty on achieving the desired outcomes

Resilience canvas: a heuristic tool for socio-hydrological management under change

NASA Astrophysics Data System (ADS)

Mao, F.; Clark, J.; Buytaert, W.; Karpouzoglou, T.; Dewulf, A.; Hannah, D. M.

2016-12-01

Although resilience thinking has been gaining interest in managing socio-hydrological systems in a changing world, there are still gaps between the resilience theory and its applications in policy making and management. This research introduces the notion of the "resilience canvas" as a heuristic tool to support social-hydrological water management under change. We argue that resilience is a set of three systematic properties including absorptive, adaptive and transformative capacities. For socio-hydrological systems, each capacity type arises from different sources and can be managed in different ways. The "resilience canvas" can be constructed by combining absorptive and adaptive capacities as the x and y axes. At the corners of the two-dimensional space, four resulting quadrates are found, including most resilient, vulnerable, susceptible, and resistant system states. The resilience canvas can be used not only to understand the development trajectories of socio-hydrological systems at different scales from single river basin to global level, but also to design bespoke interventions and strategies to maintain or enhance resilience. To address projected change-induced uncertainties, this research recommends that future efforts should be focused on shifting socio-hydrological systems from resistant towards resilient status. This implies that interventions including ecosystem restoration, technological innovations and developments in institutional arrangements and management practices, such as polycentric governance and public participation, may play important roles to address future uncertainties and enhance resilience.

Diversity in Current Ecological Thinking: Implications for Environmental Management

NASA Astrophysics Data System (ADS)

Moore, Susan A.; Wallington, Tabatha J.; Hobbs, Richard J.; Ehrlich, Paul R.; Holling, C. S.; Levin, Simon; Lindenmayer, David; Pahl-Wostl, Claudia; Possingham, Hugh; Turner, Monica G.; Westoby, Mark

2009-01-01

Current ecological thinking emphasizes that systems are complex, dynamic, and unpredictable across space and time. What is the diversity in interpretation of these ideas among today’s ecologists, and what does this mean for environmental management? This study used a Policy Delphi survey of ecologists to explore their perspectives on a number of current topics in ecology. The results showed general concurrence with nonequilibrium views. There was agreement that disturbance is a widespread, normal feature of ecosystems with historically contingent responses. The importance of recognizing multiple levels of organization and the role of functional diversity in environmental change were also widely acknowledged. Views differed regarding the predictability of successional development, whether “patchiness” is a useful concept, and the benefits of shifting the focus from species to ecosystem processes. Because of their centrality to environmental management, these different views warrant special attention from both managers and ecologists. Such divergence is particularly problematic given widespread concerns regarding the poor linkages between science (here, ecology) and environmental policy and management, which have been attributed to scientific uncertainty and a lack of consensus among scientists, both jeopardizing the transfer of science into management. Several suggestions to help managers deal with these differences are provided, especially the need to interpret broader theory in the context of place-based assessments. The uncertainty created by these differences requires a proactive approach to environmental management, including clearly identifying environmental objectives, careful experimental design, and effective monitoring.

Operational Risk Management is Ineffective at Addressing Nonlinear Problems

DTIC Science & Technology

2009-02-20

brains are not linear: even though the sound of an oboe and the sound of a string section may be independent when they enter your ear, the emotional...impact of both sounds together may be very much greater than either one alone. (This is what keeps symphony orchestras in business .) Nor is the...involving people. “In nonlinear systems... chaos theory tells you that the slightest uncertainty in your knowledge of the initial conditions will often

Entropy bound of local quantum field theory with generalized uncertainty principle

NASA Astrophysics Data System (ADS)

Kim, Yong-Wan; Lee, Hyung Won; Myung, Yun Soo

2009-03-01

We study the entropy bound for local quantum field theory (LQFT) with generalized uncertainty principle. The generalized uncertainty principle provides naturally a UV cutoff to the LQFT as gravity effects. Imposing the non-gravitational collapse condition as the UV-IR relation, we find that the maximal entropy of a bosonic field is limited by the entropy bound A 3 / 4 rather than A with A the boundary area.

Defending Against Advanced Persistent Threats Using Game-Theory

PubMed Central

König, Sandra; Schauer, Stefan

2017-01-01

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker’s incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system’s protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest. PMID:28045922

Measuring uncertainty by extracting fuzzy rules using rough sets

NASA Technical Reports Server (NTRS)

Worm, Jeffrey A.

1991-01-01

Despite the advancements in the computer industry in the past 30 years, there is still one major deficiency. Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. The methods are examined of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to possibly provide the optimal solution. By incorporating principles from these theories, a decision making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much these rules is believed is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of a set of fuzzy attributes is studied.

Multiattribute risk analysis in nuclear emergency management.

PubMed

Hämäläinen, R P; Lindstedt, M R; Sinkko, K

2000-08-01

Radiation protection authorities have seen a potential for applying multiattribute risk analysis in nuclear emergency management and planning to deal with conflicting objectives, different parties involved, and uncertainties. This type of approach is expected to help in the following areas: to ensure that all relevant attributes are considered in decision making; to enhance communication between the concerned parties, including the public; and to provide a method for explicitly including risk analysis in the process. A multiattribute utility theory analysis was used to select a strategy for protecting the population after a simulated nuclear accident. The value-focused approach and the use of a neutral facilitator were identified as being useful.

Fishing decisions under uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harrison, C.G.

1982-02-01

The drilling manager often is forced by an extended fishing operation to choose between the known costs incurred with abandonment of retrieval attempts and the unknown costs of continuing fishing operations. The successful manager makes the decision that costs the company the least money. Continuing fishing operations beyond some economic limit is failure, even if the fish is retrieved and that portion of the hole saved, because more money has been spent in the fishing attempt than would have been spent by not fishing. The strategy is to minimize losses. This analysis closely follows the theory of utility developed bymore » J. von Neuman and O. Morgenstern. 1 ref.« less

Variability in perceived satisfaction of reservoir management objectives

USGS Publications Warehouse

Owen, W.J.; Gates, T.K.; Flug, M.

1997-01-01

Fuzzy set theory provides a useful model to address imprecision in interpreting linguistically described objectives for reservoir management. Fuzzy membership functions can be used to represent degrees of objective satisfaction for different values of management variables. However, lack of background information, differing experiences and qualifications, and complex interactions of influencing factors can contribute to significant variability among membership functions derived from surveys of multiple experts. In the present study, probabilistic membership functions are used to model variability in experts' perceptions of satisfaction of objectives for hydropower generation, fish habitat, kayaking, rafting, and scenery preservation on the Green River through operations of Flaming Gorge Dam. Degree of variability in experts' perceptions differed among objectives but resulted in substantial uncertainty in estimation of optimal reservoir releases.

Managing Uncertainty in Water Infrastructure Design Using Info-gap Robustness

NASA Astrophysics Data System (ADS)

Irias, X.; Cicala, D.

2013-12-01

Info-gap theory, a tool for managing deep uncertainty, can be of tremendous value for design of water systems in areas of high seismic risk. Maintaining reliable water service in those areas is subject to significant uncertainties including uncertainty of seismic loading, unknown seismic performance of infrastructure, uncertain costs of innovative seismic-resistant construction, unknown costs to repair seismic damage, unknown societal impacts from downtime, and more. Practically every major earthquake that strikes a population center reveals additional knowledge gaps. In situations of such deep uncertainty, info-gap can offer advantages over traditional approaches, whether deterministic approaches that use empirical safety factors to address the uncertainties involved, or probabilistic methods that attempt to characterize various stochastic properties and target a compromise between cost and reliability. The reason is that in situations of deep uncertainty, it may not be clear what safety factor would be reasonable, or even if any safety factor is sufficient to address the uncertainties, and we may lack data to characterize the situation probabilistically. Info-gap is a tool that recognizes up front that our best projection of the future may be wrong. Thus, rather than seeking a solution that is optimal for that projection, info-gap seeks a solution that works reasonably well for all plausible conditions. In other words, info-gap seeks solutions that are robust in the face of uncertainty. Info-gap has been used successfully across a wide range of disciplines including climate change science, project management, and structural design. EBMUD is currently using info-gap to help it gain insight into possible solutions for providing reliable water service to an island community within its service area. The island, containing about 75,000 customers, is particularly vulnerable to water supply disruption from earthquakes, since it has negligible water storage and is entirely dependent on four potentially fragile water transmission mains for its day-to-day water supply. Using info-gap analysis, EBMUD is evaluating competing strategies for providing water supply to the island, for example submarine pipelines versus tunnels. The analysis considers not only the likely or 'average' results for each strategy, but also the worst-case performance of each strategy under varying levels of uncertainty. This analysis is improving the quality of the planning process, since it can identify strategies that ensure minimal disruption of water supply following a major earthquake, even if the earthquake and resulting damage fail to conform to our expectations. Results to date are presented, including a discussion of how info-gap analysis complements existing tools for comparing alternative strategies, and how info-gap improves our ability to quantify our tolerance for uncertainty.

Methodology to explore interactions between the water system and society in order to identify adaptation strategies

NASA Astrophysics Data System (ADS)

Offermans, A. G. E.; Haasnoot, M.

2009-04-01

Development of sustainable water management strategies involves analysing current and future vulnerability, identification of adaptation possibilities, effect analysis and evaluation of the strategies under different possible futures. Recent studies on water management often followed the pressure-effect chain and compared the state of social, economic and ecological functions of the water systems in one or two future situations with the current situation. The future is, however, more complex and dynamic. Water management faces major challenges to cope with future uncertainties in both the water system as well as the social system. Uncertainties in our water system relate to (changes in) drivers and pressures and their effects on the state, like the effects of climate change on discharges. Uncertainties in the social world relate to changing of perceptions, objectives and demands concerning water (management), which are often related with the aforementioned changes in the physical environment. The methodology presented here comprises the 'Perspectives method', derived from the Cultural Theory, a method on analyzing and classifying social response to social and natural states and pressures. The method will be used for scenario analysis and to identify social responses including changes in perspectives and management strategies. The scenarios and responses will be integrated within a rapid assessment tool. The purpose of the tool is to provide users with insight about the interaction of the social and physical system and to identify robust water management strategies by analysing the effectiveness under different possible futures on the physical, social and socio-economic system. This method allows for a mutual interaction between the physical and social system. We will present the theoretical background of the perspectives method as well as a historical overview of perspective changes in the Dutch Meuse area to show how social and physical systems interrelate. We will also show how the integration of both can contribute to the identification of robust water management strategies.

Application of rrm as behavior mode choice on modelling transportation

NASA Astrophysics Data System (ADS)

Surbakti, M. S.; Sadullah, A. F.

2018-03-01

Transportation mode selection, the first step in transportation planning process, is probably one of the most important planning elements. The development of models that can explain the preference of passengers regarding their chosen mode of public transport option will contribute to the improvement and development of existing public transport. Logit models have been widely used to determine the mode choice models in which the alternative are different transport modes. Random Regret Minimization (RRM) theory is a theory developed from the behavior to choose (choice behavior) in a state of uncertainty. During its development, the theory was used in various disciplines, such as marketing, micro economy, psychology, management, and transportation. This article aims to show the use of RRM in various modes of selection, from the results of various studies that have been conducted both in north sumatera and western Java.

Planning treatment of ischemic heart disease with partially observable Markov decision processes.

PubMed

Hauskrecht, M; Fraser, H

2000-03-01

Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead, they are very often dependent and interleaved over time. This is mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of partially observable Markov decision processes (POMDPs) developed and used in the operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In this paper, we show how the POMDP framework can be used to model and solve the problem of the management of patients with ischemic heart disease (IHD), and demonstrate the modeling advantages of the framework over standard decision formalisms.

Building clinical and organizational resilience to reconcile safety threats, tensions and trade-offs: insights from theory and evidence.

PubMed

Jeffs, Lianne; Tregunno, Deborah; MacMillan, Kathleen; Espin, Sherry

2009-01-01

Healthcare delivery settings are complex adaptive and tightly coupled, interrelated systems. Within the larger healthcare system, a key subsystem is the "clinical microsystem" level. It is at this level that clinicians are faced with high levels of uncertainty in their daily work - uncertainty that impacts the quality and safety of care that patients receive. The first aim of this paper is to enhance healthcare leaders' understanding of what is currently known about safety threats and strategies to manage the inherent tensions and trade-offs that occur in everyday practice. The second aim is to inform strategies that build clinical and organizational resilience through a multi-level framework derived from the collective theoretical and empirical work. Together, this information can strengthen safety practices throughout healthcare organizations.

Challenges in Incorporating Climate Change Adaptation into Integrated Water Resources Management

NASA Astrophysics Data System (ADS)

Kirshen, P. H.; Cardwell, H.; Kartez, J.; Merrill, S.

2011-12-01

Over the last few decades, integrated water resources management (IWRM), under various names, has become the accepted philosophy for water management in the USA. While much is still to be learned about how to actually carry it out, implementation is slowly moving forward - spurred by both legislation and the demands of stakeholders. New challenges to IWRM have arisen because of climate change. Climate change has placed increased demands on the creativities of planners and engineers because they now must design systems that will function over decades of hydrologic uncertainties that dwarf any previous hydrologic or other uncertainties. Climate and socio-economic monitoring systems must also now be established to determine when the future climate has changed sufficiently to warrant undertaking adaptation. The requirements for taking some actions now and preserving options for future actions as well as the increased risk of social inequities in climate change impacts and adaptation are challenging experts in stakeholder participation. To meet these challenges, an integrated methodology is essential that builds upon scenario analysis, risk assessment, statistical decision theory, participatory planning, and consensus building. This integration will create cross-disciplinary boundaries for these disciplines to overcome.

When Advisors' True Intentions Are in Question. How Do Bank Customers Cope with Uncertainty in Financial Consultancies?

PubMed

Mackinger, Barbara; Jonas, Eva; Mühlberger, Christina

2017-01-01

When making financial decisions bank customers are confronted with two types of uncertainty: first, return on investments is uncertain and there is a risk of losing money. Second, customers cannot be certain about their financial advisor's true intentions. This might decrease customers' willingness to cooperate with advisors. However, the uncertainty management model and fairness heuristic theory predict that in uncertain situations customers are willing to cooperate with financial advisors when they perceive fairness. In the current study, we investigated how perceived fairness in the twofold uncertain situations increased people's intended future cooperation with an advisor. We asked customers of financial consultancies about their experienced uncertainty regarding both the investment decision and the advisor's intentions. Moreover, we asked them about their perceived fairness, as well as their intention to cooperate with the advisor in the future. A three-way moderation analysis showed that customers who faced high uncertainty regarding the investment decision and high uncertainty regarding the advisor's true intentions indicated the lowest intended cooperation with the advisor but high fairness increased their cooperation. Interestingly, when people were only uncertain about the advisor's intentions (but certain about the decision) they indicated less cooperation than when they were only uncertain about the decision (but certain about the advisor's intentions). A mediated moderation analysis revealed that this relationship was explained by customers' lower trust in their advisors.

Integrated and adaptive management for sustainable water use along ephemeral rivers under severe uncertainty of future flood regimes

NASA Astrophysics Data System (ADS)

Arnold, Sven; Attinger, Sabine; Frank, Karin; Baxter, Peter; Hildebrandt, Anke

2010-05-01

Ephemeral rivers are located throughout the world's arid regions. They are characterised by temporary surface flow that strongly varies between seasons and years. Along the river course often a coupled eco-hydrological vegetation-groundwater system has established, which is referred to as linear oasis, reflecting the ecological and socio-economic importance of ephemeral rivers in otherwise dry areas. The Kuiseb River denotes such a linear oasis and is one of the most diversely used environments among the ephemeral rivers in Namibia. Along the entire river course surface runoff and ground water are exploited for drinking, farming, and mining. The middle section of the Kuiseb River is characterised by strong eco-hydrological feedbacks between the vegetation and the ground water resource. Temporary floods infiltrate into sediments, which are accumulated in geological pools of impermeable bedrocks. This enables the formation of shallow ground water. The low depth to ground water allows root water uptake by plants and the establishment of a thriving ecosystem. The sustainable use of ecological and hydrological resources along ephemeral rivers is crucial to preserve the natural ecosystem. However, the investigation of management strategies that consider both the regulation of water extraction and vegetation structure requires models that explicitly consider the feedbacks between the water resource and the ecosystem structure. Further, uncertainties arise from stochastic hydrologic drivers such as flash flood events. Particularly in the face of climate change, the management strategies have to be applicable to a wide range of possible flood regimes, i.e. they have to be robust to the uncertainty of future flood regimes. In this study we assess a variety of management strategies regarding their robustness under different theoretical ecosystems and under uncertainty in the future stochastic flood regimes along the Kuiseb River. We consider the trade-off between ecological and human requirements by investigating the management strategies in terms of their ability to sustainably exploit the ground water resource while preserving the natural vegetation structure (here: coexistence of three tree species). We apply a conceptual ecohydrological model and use the information gap decision theory to estimate the robustness of strategies to failure due to flood parameter uncertainty. The performance of every strategy decreased as flood parameter uncertainty increased. However, ecological performance was more vulnerable with increasing uncertainty than the water supply performance, suggesting that the vegetation structure can be used as sensitive indicator and pre-warning system for changing environmental conditions. With the integrated and adaptive strategy it was most likely to sustainably use the ground water while preserving the natural vegetation structure, however, with the effect of reducing the probability of a large total system biomass.

Understanding medical decision making in hand surgery.

PubMed

Myers, John; McCabe, Steven J

2005-10-01

The practice of medicine takes place in an environment of uncertainty. Expected value decision making, prospect theory, and regret theory are three theories of decision making under uncertainty that may be used to help us learn how patients and physicians make decisions. These theories form the underpinnings of decision analysis and provide the opportunity to introduce the broad discipline of decision science. Because decision analysis and economic analysis are underrepresented in upper extremity surgery, the authors believe these are important areas for future research.

Opening new institutional spaces for grappling with uncertainty: A constructivist perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duncan, Ronlyn, E-mail: Ronlyn.Duncan@lincoln.ac.nz

In the context of an increasing reliance on predictive computer simulation models to calculate potential project impacts, it has become common practice in impact assessment (IA) to call on proponents to disclose uncertainties in assumptions and conclusions assembled in support of a development project. Understandably, it is assumed that such disclosures lead to greater scrutiny and better policy decisions. This paper questions this assumption. Drawing on constructivist theories of knowledge and an analysis of the role of narratives in managing uncertainty, I argue that the disclosure of uncertainty can obscure as much as it reveals about the impacts of amore » development project. It is proposed that the opening up of institutional spaces that can facilitate the negotiation and deliberation of foundational assumptions and parameters that feed into predictive models could engender greater legitimacy and credibility for IA outcomes. - Highlights: Black-Right-Pointing-Pointer A reliance on supposedly objective disclosure is unreliable in the predictive model context in which IA is now embedded. Black-Right-Pointing-Pointer A reliance on disclosure runs the risk of reductionism and leaves unexamined the social-interactive aspects of uncertainty. Black-Right-Pointing-Pointer Opening new institutional spaces could facilitate deliberation on foundational predictive model assumptions.« less

Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

USGS Publications Warehouse

Fienen, Michael N.; Doherty, John E.; Hunt, Randall J.; Reeves, Howard W.

2010-01-01

The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

Quantum Bayesian perspective for intelligence reservoir characterization, monitoring and management.

PubMed

Lozada Aguilar, Miguel Ángel; Khrennikov, Andrei; Oleschko, Klaudia; de Jesús Correa, María

2017-11-13

The paper starts with a brief review of the literature about uncertainty in geological, geophysical and petrophysical data. In particular, we present the viewpoints of experts in geophysics on the application of Bayesian inference and subjective probability. Then we present arguments that the use of classical probability theory (CP) does not match completely the structure of geophysical data. We emphasize that such data are characterized by contextuality and non-Kolmogorovness (the impossibility to use the CP model), incompleteness as well as incompatibility of some geophysical measurements. These characteristics of geophysical data are similar to the characteristics of quantum physical data. Notwithstanding all this, contextuality can be seen as a major deviation of quantum theory from classical physics. In particular, the contextual probability viewpoint is the essence of the Växjö interpretation of quantum mechanics. We propose to use quantum probability (QP) for decision-making during the characterization, modelling, exploring and management of the intelligent hydrocarbon reservoir Quantum Bayesianism (QBism), one of the recently developed information interpretations of quantum theory, can be used as the interpretational basis for such QP decision-making in geology, geophysics and petroleum projects design and management.This article is part of the themed issue 'Second quantum revolution: foundational questions'. © 2017 The Author(s).

Adaptive approaches to biosecurity governance.

PubMed

Cook, David C; Liu, Shuang; Murphy, Brendan; Lonsdale, W Mark

2010-09-01

This article discusses institutional changes that may facilitate an adaptive approach to biosecurity risk management where governance is viewed as a multidisciplinary, interactive experiment acknowledging uncertainty. Using the principles of adaptive governance, evolved from institutional theory, we explore how the concepts of lateral information flows, incentive alignment, and policy experimentation might shape Australia's invasive species defense mechanisms. We suggest design principles for biosecurity policies emphasizing overlapping complementary response capabilities and the sharing of invasive species risks via a polycentric system of governance. © 2010 Society for Risk Analysis

A Theory of Perceptual Learning: Uncertainty Reduction and Reading.

ERIC Educational Resources Information Center

Henk, William A.

Behaviorism cannot adequately explain language processing. A synthesis of the psycholinguistic and information processing approaches of cognitive psychology, however, can provide the basis for a speculative analysis of reading, if this synthesis is tempered by a perceptual learning theory of uncertainty reduction. Theorists of information…

DOE Office of Scientific and Technical Information (OSTI.GOV)

United States. Bonneville Power Administration.

This progress report broadly defines the scope of supplementation plans and activities in the Columbia Basin. It provides the foundation for more detailed analysis of supplementation in subsequent reports in this series. Topics included in this report are: definition of supplementation, project diversity, objectives and performance standards, uncertainties and theory. Since this is a progress report, the content is subject to modification with new information. The supplementation theory will continue to evolve throughout the duration of RASP and beyond. The other topics in this report are essentially complete and are not expected to change significantly. This is the first ofmore » a series of four reports which will summarize information contained in the larger, RASP progress and completion reports. Our goal is to make the findings of RASP more accessible by grouping related topics into smaller but complete narratives on important aspects of supplementation. We are planning to publish the following reports under the general title Supplementation in the Columbia River Basin: Part 1, Background, Description, Performance Measures, Uncertainty and Theory; Part 2, Theoretical Framework and Models; Part 3, Planning Guidelines; and Part 4, Regional Coordination of Research and Monitoring. Supplementation is expected to be a major contributor to the planned increase in salmon and steelhead production in the Columbia Basin. The Fish and Wildlife Program of the Northwest Power Planning Council (NPPC) uses three approaches to protect and enhance salmon and steelhead in the Columbia Basin: (1) enhance fish production; (2) improve passage in the mainstem rivers; and (3) revise harvest management to support the rebuilding of fish runs (NPPC 1987). The fish production segment calls for a three-part approach focused on natural production, hatchery production, and supplementation. Supplementation is planned to provide over half of the total production increases. The Regional Assessment of Supplementation Project (RASP) was initiated as a result of a request by NPPC to address long-standing concerns about the need to coordinate supplementation research, monitoring and evaluation. Such coordination was also recommended by the Supplementation Technical Work Group. In August 1990, the NPPC gave conditional approval to proceed with the final design of the Yakima Production Project. The Council called on the Bonneville Power Administration (BPA) to fund immediately a supplementation assessment to reevaluate, prioritize and coordinate all existing and planned supplementation monitoring and evaluation activities in the basin. Providing for the participation of the fishery agencies and tribes and others having expertise in this area. RASP addresses four principal objectives: (1) provide an overview of ongoing and planned supplementation activities and identify critical uncertainties associated with supplementation, (2) construct a conceptual framework and model which estimates the potential benefits and risks of supplementation and prioritizes uncertainties, (3) provide guidelines for the development of supplementation projects, (4) develop a plan for regional coordination of research and monitoring. These objectives, once attained, will provide the technical tools fishery managers need to carry out the Council's direction to protect and enhance salmon and steelhead. RASP has further divided the four broad objectives into 12 technical topics: (1) definition of supplementation; (2) description of the diversity of supplementation projects; (3) objectives and performance standards; (4) identification of uncertainties; (5) supplementation theory; (6) development of a conceptual model of supplemented populations; (7) development of spreadsheet model of risks and benefits of supplementation; (8) classification of stocks, streams, and supplementation strategies; (9) regional design of supplementation evaluation and monitoring; (10) guidelines for planning supplementation projects (11) application of the spreadsheet model to supplementation planning; and (12) experimental design and decision making with uncertainty.« less

The uncertainty room: strategies for managing uncertainty in a surgical waiting room.

PubMed

Stone, Anne M; Lammers, John C

2012-01-01

To describe experiences of uncertainty and management strategies for staff working with families in a hospital waiting room. A 288-bed, nonprofit community hospital in a Midwestern city. Data were collected during individual, semistructured interviews with 3 volunteers, 3 technical staff members, and 1 circulating nurse (n = 7), and during 40 hours of observation in a surgical waiting room. Interview transcripts were analyzed using constant comparative techniques. The surgical waiting room represents the intersection of several sources of uncertainty that families experience. Findings also illustrate the ways in which staff manage the uncertainty of families in the waiting room by communicating support. Staff in surgical waiting rooms are responsible for managing family members' uncertainty related to insufficient information. Practically, this study provided some evidence that staff are expected to help manage the uncertainty that is typical in a surgical waiting room, further highlighting the important role of communication in improving family members' experiences.

[Conflict as a reality and a cultural challenge in the practice of nurses' management].

PubMed

Prochnow, Adelina Giacomelli; Leite, Joséte Luzia; Erdmann, Alacoque Lorenzini; Trevizan, Maria Auxiliadora

2007-12-01

The practice of nurses' management is permeated by conflicts that can be interpreted through culture references. The objective of this study is to denote cultural specificities, analyzed according to Geertz's Cultural Interpretative Theory, that are expressed as conflicts in the scope of nurses' management at a University Hospital. The results denoted the incorporation of ideological elements and mechanisms of control and power, whose origin can be seen in the way in which the work is organized. The application of policies based on the profession's very values was observed. Practices highlight a cultural construction that elucidates some understandings regarding cognitive, social and behavioral processes, because they organize the interpretations and the answers to the events of nurses' practices in management. The results of this study point out the importance of organizational culture in the practice of Nursing management in the face of labor uncertainties in the complexity of a hospital environment.

On-Line Model-Based System For Nuclear Plant Monitoring

NASA Astrophysics Data System (ADS)

Tsoukalas, Lefteri H.; Lee, G. W.; Ragheb, Magdi; McDonough, T.; Niziolek, F.; Parker, M.

1989-03-01

A prototypical on-line model-based system, LASALLE1, developed at the University of Illinois in collaboration with the Illinois Department of Nuclear Safety (IDNS) is described. Its main purpose is to interpret about 300 signals, updated every two minutes at IDNS from the LaSalle Nuclear Power Plant, and to diagnose possible abnormal conditions. It is written in VAX/VMS OPS5 and operates on both the on-line and testing modes. In its knowledge base, operator and plant actions pertaining to the Emergency Operating Procedure(EOP) A-01, are encoded. This is a procedure driven by a reactor's coolant level and pressure signals; with the purpose of shutting down the reactor, maintaining adequate core cooling and reducing the reactor pressure and temperature to cold shutdown conditions ( about 90 to 200 °F). The monitoring of the procedure is performed from the perspective of Emergency Preparedness. Two major issues are addressed in this system. First, the management of the short-term or working memory of the system. LASALLE1 must reach its inferences, display its conclusion and update a message file every two minutes before a new set of data arrives from the plant. This was achieved by superimposing additional layers of control over the inferencing strategies inherent in OPS5, and developing special rules for the management of the used or outdated information. The second issue is the representation of information and its uncertainty. The concepts of information granularity and performance-level, which are based on a coupling of Probability Theory and the theory of Fuzzy Sets, are used for this purpose. The estimation of the performance-level incorporates a mathematical methodology which accounts for two types of uncertainty encountered in monitoring physical systems: Random uncertainty, in the form of of probability density functions generated by observations, measurements and sensors data and fuzzy uncertainty represented by membership functions based on symbolic , stochastic or numerical models estimating the "plausible", "possible" or "expected" values of the system parameters. Examples from both the on-line mode and the testing mode of the system will be discussed to illustrate the present methodology.

Uncertainty and equipoise: at interplay between epistemology, decision making and ethics.

PubMed

Djulbegovic, Benjamin

2011-10-01

In recent years, various authors have proposed that the concept of equipoise be abandoned because it conflates the practice of clinical care with clinical research. At the same time, the equipoise opponents acknowledge the necessity of clinical research if there are unresolved uncertainties about the effects of proposed healthcare interventions. As equipoise represents just 1 measure of uncertainty, proposals to abandon equipoise while maintaining a requirement for addressing uncertainties are contradictory and ultimately not valid. As acknowledgment and articulation of uncertainties represent key scientific and moral requirements for human experimentation, the concept of equipoise remains the most useful framework to link the theory of human experimentation with the theory of rational choice. In this article, I show how uncertainty (equipoise) is at the intersection between epistemology, decision making and ethics of clinical research. In particular, I show how our formulation of responses to uncertainties of hoped-for benefits and unknown harms of testing is a function of the way humans cognitively process information. This approach is based on the view that considerations of ethics and rationality cannot be separated. I analyze the response to uncertainties as it relates to the dual-processing theory, which postulates that rational approach to (clinical research) decision making depends both on analytical, deliberative processes embodied in scientific method (system II), and good human intuition (system I). Ultimately, our choices can only become wiser if we understand a close and intertwined relationship between irreducible uncertainty, inevitable errors and unavoidable injustice.

Uncertainty and Equipoise: At Interplay Between Epistemology, Decision-Making and Ethics

PubMed Central

Djulbegovic, Benjamin

2011-01-01

In recent years, various authors have proposed that the concept of equipoise be abandoned since it conflates the practice of clinical care with clinical research. At the same time, the equipoise opponents acknowledge the necessity of clinical research if there are unresolved uncertainties about the effects of proposed healthcare interventions. Since equipoise represents just one measure of uncertainty, proposals to abandon equipoise while maintaining a requirement for addressing uncertainties are contradictory and ultimately not valid. As acknowledgment and articulation of uncertainties represent key scientific and moral requirements for human experimentation, the concept of equipoise remains the most useful framework to link the theory of human experimentation with the theory of rational choice. In this paper, I show how uncertainty (equipoise) is at the intersection between epistemology, decision-making and ethics of clinical research. In particular, I show how our formulation of responses to uncertainties of hoped-for benefits and unknown harms of testing is a function of the way humans cognitively process information. This approach is based on the view that considerations of ethics and rationality cannot be separated. I analyze the response to uncertainties as it relates to the dual-processing theory, which postulates that rational approach to (clinical research) decision-making depends both on analytical, deliberative processes embodied in scientific method (system II) and “good” human intuition (system I). Ultimately, our choices can only become wiser if we understand a close and intertwined relationship between irreducible uncertainty, inevitable errors, and unavoidable injustice. PMID:21817885

Healthcare managers in negative media focus: a qualitative study of personification processes and their personal consequences

PubMed Central

2014-01-01

Background Over the last decade healthcare management and managers have increasingly been in focus in public debate. The purpose of the present study was to gain a deeper understanding of how prolonged, unfavorable media focus can influence both the individual as a person and his or her managerial practice in the healthcare organization. Methods In-depth interviews (n = 49) with 24 managers and their superiors, or subordinate human resources/information professionals, and partners were analyzed using a grounded theory approach. Results The conceptual model explains how perceived uncertainties related to the managerial role influence personification and its negative consequences. The role ambiguities comprised challenges regarding the separation of individual identity from the professional function, the interaction with intra-organizational support and political play, and the understanding and acceptance of roles in society. A higher degree of uncertainty in role ambiguity increased both personification and the personal reaction to intense media pressure. Three types of reactions were related to the feeling of being infringed: avoidance and narrow-mindedness; being hard on self, on subordinates, and/or family members; and resignation and dejection. The results are discussed so as to elucidate the importance of support from others within the organization when under media scrutiny. Conclusions The degree of personification seems to determine the personal consequences as well as the consequences for their managerial practice. Organizational support for managers appearing in the media would probably be beneficial for both the manager and the organization. PMID:24397306

Fear of cancer recurrence: a theoretical review and its relevance for clinical presentation and management.

PubMed

Simonelli, Laura E; Siegel, Scott D; Duffy, Nicole M

2017-10-01

There is increasing recognition of the unique physical and psychosocial concerns of the growing population of cancer survivors. An emerging literature demonstrates that fear of cancer recurrence (FCR) is a problematic long-term and late effect for cancer survivors. In fact, FCR is a top concern, and this article provides a necessary synthesis of the extant research evidence and theory. Literature searches were conducted using databases including MEDLINE and PsychINFO using specified search terms including 'fear of recurrence' and 'worry about recurrence'. A comprehensive narrative review summarizes early empirical findings on FCR including current definitions, assessment tools, clinical presentations, quality of life impact, prevalence, trajectory and risk factors. This paper also critically reviews the relevant theoretical frameworks to best understand these findings and considers multiple psychosocial treatment models that may have relevance for addressing FCR in the clinical setting. There is evidence of substantial prevalence and quality of life impact of FCR. Several theories (e.g. self-regulation model of illness, a family-based model, uncertainty in illness theory, social-cognitive processing theory, terror management theory) directly or indirectly help conceptualize FCR and inform potential treatment options for those with clinically significant distress or impairment resulting from FCR. Further investigation into FCR is warranted to promote evidence-based care for this significant cancer survivorship concern. Copyright © 2016 John Wiley & Sons, Ltd.

On uncertainty in information and ignorance in knowledge

NASA Astrophysics Data System (ADS)

Ayyub, Bilal M.

2010-05-01

This paper provides an overview of working definitions of knowledge, ignorance, information and uncertainty and summarises formalised philosophical and mathematical framework for their analyses. It provides a comparative examination of the generalised information theory and the generalised theory of uncertainty. It summarises foundational bases for assessing the reliability of knowledge constructed as a collective set of justified true beliefs. It discusses system complexity for ancestor simulation potentials. It offers value-driven communication means of knowledge and contrarian knowledge using memes and memetics.

50 CFR 648.231 - Closures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Dogfish Monitoring Committee shall identify and review the relevant sources of management uncertainty to... management uncertainty that were considered, technical approaches to mitigating these sources of uncertainty..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Spiny Dogfish...

Managing uncertainty in advanced liver disease: a qualitative, multiperspective, serial interview study.

PubMed

Kimbell, Barbara; Boyd, Kirsty; Kendall, Marilyn; Iredale, John; Murray, Scott A

2015-11-19

To understand the experiences and support needs of people with advanced liver disease and those of their lay and professional carers to inform improvements in the supportive and palliative care of this rapidly growing but currently neglected patient group. Multiperspective, serial interviews. We conducted up to three qualitative in-depth interviews with each patient and lay carer over 12 months and single interviews with case-linked healthcare professionals. Data were analysed using grounded theory techniques. Patients with advanced liver disease of diverse aetiologies recruited from an inpatient hepatology ward, and their lay carers and case-linked healthcare professionals nominated by the patients. Primary and secondary care in South-East Scotland. 37 participants (15 patients, 11 lay and 11 professional carers) completed 51 individual and 13 joint patient-carer interviews. Nine patients died during the study. Uncertainty dominated experiences throughout the course of the illness, across patients' considerable physical, psychological, social and existential needs and affected patients, lay carers and professionals. This related to the nature of the condition, the unpredictability of physical deterioration and prognosis, poor communication and information-sharing, and complexities of care. The pervasive uncertainty also shaped patients' and lay carers' strategies for coping and impeded care planning. While patients' acute medical care was usually well coordinated, their ongoing care lacked structure and focus. Living, dying and caring in advanced liver disease is dominated by pervasive, enduring and universally shared uncertainty. In the face of high levels of multidimensional patient distress, professionals must acknowledge this uncertainty in constructive ways that value its contribution to the person's coping approach. Pervasive uncertainty makes anticipatory care planning in advanced liver disease challenging, but planning 'just in case' is vital to ensure that patients receive timely and appropriate supportive and palliative care alongside effective management of this unpredictable illness. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Combining statistical inference and decisions in ecology

USGS Publications Warehouse

Williams, Perry J.; Hooten, Mevin B.

2016-01-01

Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation, and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem.

An aggregate method to calibrate the reference point of cumulative prospect theory-based route choice model for urban transit network

NASA Astrophysics Data System (ADS)

Zhang, Yufeng; Long, Man; Luo, Sida; Bao, Yu; Shen, Hanxia

2015-12-01

Transit route choice model is the key technology of public transit systems planning and management. Traditional route choice models are mostly based on expected utility theory which has an evident shortcoming that it cannot accurately portray travelers' subjective route choice behavior for their risk preferences are not taken into consideration. Cumulative prospect theory (CPT), a brand new theory, can be used to describe travelers' decision-making process under the condition of uncertainty of transit supply and risk preferences of multi-type travelers. The method to calibrate the reference point, a key parameter to CPT-based transit route choice model, determines the precision of the model to a great extent. In this paper, a new method is put forward to obtain the value of reference point which combines theoretical calculation and field investigation results. Comparing the proposed method with traditional method, it shows that the new method can promote the quality of CPT-based model by improving the accuracy in simulating travelers' route choice behaviors based on transit trip investigation from Nanjing City, China. The proposed method is of great significance to logical transit planning and management, and to some extent makes up the defect that obtaining the reference point is solely based on qualitative analysis.

Uncertainty in quantum mechanics: faith or fantasy?

PubMed

Penrose, Roger

2011-12-13

The word 'uncertainty', in the context of quantum mechanics, usually evokes an impression of an essential unknowability of what might actually be going on at the quantum level of activity, as is made explicit in Heisenberg's uncertainty principle, and in the fact that the theory normally provides only probabilities for the results of quantum measurement. These issues limit our ultimate understanding of the behaviour of things, if we take quantum mechanics to represent an absolute truth. But they do not cause us to put that very 'truth' into question. This article addresses the issue of quantum 'uncertainty' from a different perspective, raising the question of whether this term might be applied to the theory itself, despite its unrefuted huge success over an enormously diverse range of observed phenomena. There are, indeed, seeming internal contradictions in the theory that lead us to infer that a total faith in it at all levels of scale leads us to almost fantastical implications.

The Contemporary Setting for Water Management in the West: An Overview

NASA Astrophysics Data System (ADS)

Cummings, Ronald G.

1985-11-01

The socioeconomic, legal, and institutional settings for water resources management and use in the United States have undergone dramatic changes over recent years. The resulting confusion and uncertainties in terms of the structure of water rights, the prerogatives of sovereign states, and the limitations of state-federal interfaces provide the raison d'être for the collection of papers in this special section of Water Resources Research. This special issue is intended to serve several purposes: to inform, to provoke, and to invite. Thus many readers may find new and informative Tarlock's [this issue] overview of the evolution of groundwater law from English Common Law to laws based on notions of "reasonable use" and "sharing"; of particular importance is the readers appreciation of the changes and uncertainties in water law associated with the 1982 decision in Sporhase and in the later El Paso case. These cases, marking an end of the "immunity theory" wherein states were immune, in cases involving resources use, from challenges under the Commerce Clause, give rise to Tarlock's concern with the question, Might current conditions give rise to federal control of groundwater?

The Role of Nuclear Power in Reducing Risk of the Fossil Fuel Prices and Diversity of Electricity Generation in Tunisia: A Portfolio Approach

NASA Astrophysics Data System (ADS)

Abdelhamid, Mohamed Ben; Aloui, Chaker; Chaton, Corinne; Souissi, Jomâa

2010-04-01

This paper applies real options and mean-variance portfolio theories to analyze the electricity generation planning into presence of nuclear power plant for the Tunisian case. First, we analyze the choice between fossil fuel and nuclear production. A dynamic model is presented to illustrate the impact of fossil fuel cost uncertainty on the optimal timing to switch from gas to nuclear. Next, we use the portfolio theory to manage risk of the electricity generation portfolio and to determine the optimal fuel mix with the nuclear alternative. Based on portfolio theory, the results show that there is other optimal mix than the mix fixed for the Tunisian mix for the horizon 2010-2020, with lower cost for the same risk degree. In the presence of nuclear technology, we found that the optimal generating portfolio must include 13% of nuclear power technology share.

Dealing with Uncertainty in Water Management: Finding the Right Balance Between Risk and Opportunity to Build Trust and Create Value

NASA Astrophysics Data System (ADS)

Islam, S.; Susskind, L.

2012-12-01

Most difficulties in water management are the product of rigid assumptions about how water ought to be allocated in the face of ever-increasing demand and growing uncertainty. When stakeholders face contending water claims, one of the biggest obstacles to reaching agreement is uncertainty. Specifically, there are three types of uncertainty that need to be addressed: uncertainty of information, uncertainty of action and uncertainty of perception. All three shape water management decisions. Contrary to traditional approaches, we argue that management of uncertainty needs to include both risks and opportunities. When parties treat water as a flexible rather than a fixed resource, opportunities to create value can be invented. When they use the right processes and mechanisms to enhance trust, even parties in conflict can reach agreements that satisfy their competing water needs and interests simultaneously. Using examples from several boundary crossing water cases we will show how this balance between risks and opportunities can be found to manage water resources for an uncertain future.

'The thing is not knowing': patients' perspectives on surveillance of an indeterminate pulmonary nodule.

PubMed

Wiener, Renda Soylemez; Gould, Michael K; Woloshin, Steven; Schwartz, Lisa M; Clark, Jack A

2015-06-01

The hundreds of thousands of patients found to have a potentially malignant pulmonary nodule each year are faced with tremendous uncertainty regarding what the nodule is and how it should be evaluated. To explore patients' responses to the detection and evaluation of a pulmonary nodule. Qualitative study based on four focus-group discussions. We performed inductive analysis using principles of grounded theory to identify themes relating to responses to the nodule and strategies to manage uncertainty. Twenty-two patients from two medical centres who were undergoing surveillance for an indeterminate pulmonary nodule. Patient responses to an indeterminate pulmonary nodule were varied and evolved over time. Although almost all patients reported an initial fear about cancer, subsequent depictions of the nodule diverged into four types defined on two dimensions: cognitive ('it's cancer' vs. 'I don't know what it is' vs. 'it's nothing serious') and emotional (anxiety vs. equanimity). Most eventually accepted that the nodule was unlikely to be malignant; however, some remained anxious, convinced the nodule could turn into cancer at any time and should be aggressively monitored for life. Patients used results of surveillance tests as well as their own strategies (e.g. vigilance for symptoms, information-seeking, contemplating and controlling modifiable risk factors, avoidance, faith) to manage uncertainty. Surveillance for a pulmonary nodule can weigh heavily on some patients for months or years. Our findings may help clinicians prepare patients with a newly detected pulmonary nodule for the burden of the prolonged uncertainty of surveillance. © 2012 Blackwell Publishing Ltd.

Operationalising uncertainty in data and models for integrated water resources management.

PubMed

Blind, M W; Refsgaard, J C

2007-01-01

Key sources of uncertainty of importance for water resources management are (1) uncertainty in data; (2) uncertainty related to hydrological models (parameter values, model technique, model structure); and (3) uncertainty related to the context and the framing of the decision-making process. The European funded project 'Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)' has resulted in a range of tools and methods to assess such uncertainties, focusing on items (1) and (2). The project also engaged in a number of discussions surrounding uncertainty and risk assessment in support of decision-making in water management. Based on the project's results and experiences, and on the subsequent discussions a number of conclusions can be drawn on the future needs for successful adoption of uncertainty analysis in decision support. These conclusions range from additional scientific research on specific uncertainties, dedicated guidelines for operational use to capacity building at all levels. The purpose of this paper is to elaborate on these conclusions and anchoring them in the broad objective of making uncertainty and risk assessment an essential and natural part in future decision-making processes.

Ensembles vs. information theory: supporting science under uncertainty

NASA Astrophysics Data System (ADS)

Nearing, Grey S.; Gupta, Hoshin V.

2018-05-01

Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.

Religion in the face of uncertainty: an uncertainty-identity theory account of religiousness.

PubMed

Hogg, Michael A; Adelman, Janice R; Blagg, Robert D

2010-02-01

The authors characterize religions as social groups and religiosity as the extent to which a person identifies with a religion, subscribes to its ideology or worldview, and conforms to its normative practices. They argue that religions have attributes that make them well suited to reduce feelings of self-uncertainty. According to uncertainty-identity theory, people are motivated to reduce feelings of uncertainty about or reflecting on self; and identification with groups, particularly highly entitative groups, is a very effective way to reduce uncertainty. All groups provide belief systems and normative prescriptions related to everyday life. However, religions also address the nature of existence, invoking sacred entities and associated rituals and ceremonies. They are entitative groups that provide a moral compass and rules for living that pervade a person's life, making them particularly attractive in times of uncertainty. The authors document data supporting their analysis and discuss conditions that transform religiosity into religious zealotry and extremism.

Hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis method for mid-frequency analysis of built-up systems with epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yin, Shengwen; Yu, Dejie; Yin, Hui; Lü, Hui; Xia, Baizhan

2017-09-01

Considering the epistemic uncertainties within the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model when it is used for the response analysis of built-up systems in the mid-frequency range, the hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis (ETFE/SEA) model is established by introducing the evidence theory. Based on the hybrid ETFE/SEA model and the sub-interval perturbation technique, the hybrid Sub-interval Perturbation and Evidence Theory-based Finite Element/Statistical Energy Analysis (SIP-ETFE/SEA) approach is proposed. In the hybrid ETFE/SEA model, the uncertainty in the SEA subsystem is modeled by a non-parametric ensemble, while the uncertainty in the FE subsystem is described by the focal element and basic probability assignment (BPA), and dealt with evidence theory. Within the hybrid SIP-ETFE/SEA approach, the mid-frequency response of interest, such as the ensemble average of the energy response and the cross-spectrum response, is calculated analytically by using the conventional hybrid FE/SEA method. Inspired by the probability theory, the intervals of the mean value, variance and cumulative distribution are used to describe the distribution characteristics of mid-frequency responses of built-up systems with epistemic uncertainties. In order to alleviate the computational burdens for the extreme value analysis, the sub-interval perturbation technique based on the first-order Taylor series expansion is used in ETFE/SEA model to acquire the lower and upper bounds of the mid-frequency responses over each focal element. Three numerical examples are given to illustrate the feasibility and effectiveness of the proposed method.

An exploratory study of the role of trust in medication management within mental health services.

PubMed

Maidment, Ian D; Brown, Patrick; Calnan, Michael

2011-08-01

To develop understandings of the nature and influence of trust in the safe management of medication within mental health services. Mental health services in the UK. Qualitative methods were applied through focus groups across three different categories of service user--older adult, adults living in the community and forensic services. An inductive thematic analysis was carried out, using the method of constant comparison derived from grounded theory. Participants' views on the key factors influencing trust and the role of trust in safe medication management. The salient factors impacting trust were: the therapeutic relationship; uncertainty and vulnerability; and social control. Users of mental health services may be particularly vulnerable to adverse events and these can damage trust. Safe management of medication is facilitated by trust. However, this trust may be difficult to develop and maintain, exposing service users to adverse events and worsening adherence. Practice and policy should be oriented towards developing trust.

Characterizing Sources of Uncertainty in Item Response Theory Scale Scores

ERIC Educational Resources Information Center

Yang, Ji Seung; Hansen, Mark; Cai, Li

2012-01-01

Traditional estimators of item response theory scale scores ignore uncertainty carried over from the item calibration process, which can lead to incorrect estimates of the standard errors of measurement (SEMs). Here, the authors review a variety of approaches that have been applied to this problem and compare them on the basis of their statistical…

Cracks in the New Jar: The Limits of Tailored Deterrence

DTIC Science & Technology

2011-03-17

motivated biases, which result from subconscious psychological pressure that distorts perception. Motivated biases differ from cognitive biases...deterrence theory, including the uncertainty and cognitive biases inherent to both intelligence assessments and international relations. While...neglects some of the most important elements of contemporary deterrence theory, including the uncertainty and cognitive biases inherent to both

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

Uncertainty quantification and propagation in nuclear density functional theory

DOE PAGES

Schunck, N.; McDonnell, J. D.; Higdon, D.; ...

2015-12-23

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going eff orts seek to better root nuclear DFT in the theory of nuclear forces, energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in fi nite nuclei. In this study, we review recent eff orts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statisticalmore » analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.« less

Error and Uncertainty Quantification in the Numerical Simulation of Complex Fluid Flows

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2010-01-01

The failure of numerical simulation to predict physical reality is often a direct consequence of the compounding effects of numerical error arising from finite-dimensional approximation and physical model uncertainty resulting from inexact knowledge and/or statistical representation. In this topical lecture, we briefly review systematic theories for quantifying numerical errors and restricted forms of model uncertainty occurring in simulations of fluid flow. A goal of this lecture is to elucidate both positive and negative aspects of applying these theories to practical fluid flow problems. Finite-element and finite-volume calculations of subsonic and hypersonic fluid flow are presented to contrast the differing roles of numerical error and model uncertainty. for these problems.

The deuteron-radius puzzle is alive: A new analysis of nuclear structure uncertainties

NASA Astrophysics Data System (ADS)

Hernandez, O. J.; Ekström, A.; Nevo Dinur, N.; Ji, C.; Bacca, S.; Barnea, N.

2018-03-01

To shed light on the deuteron radius puzzle we analyze the theoretical uncertainties of the nuclear structure corrections to the Lamb shift in muonic deuterium. We find that the discrepancy between the calculated two-photon exchange correction and the corresponding experimentally inferred value by Pohl et al. [1] remain. The present result is consistent with our previous estimate, although the discrepancy is reduced from 2.6 σ to about 2 σ. The error analysis includes statistic as well as systematic uncertainties stemming from the use of nucleon-nucleon interactions derived from chiral effective field theory at various orders. We therefore conclude that nuclear theory uncertainty is more likely not the source of the discrepancy.

Generalized uncertainty principles and quantum field theory

NASA Astrophysics Data System (ADS)

Husain, Viqar; Kothawala, Dawood; Seahra, Sanjeev S.

2013-01-01

Quantum mechanics with a generalized uncertainty principle arises through a representation of the commutator [x^,p^]=if(p^). We apply this deformed quantization to free scalar field theory for f±=1±βp2. The resulting quantum field theories have a rich fine scale structure. For small wavelength modes, the Green’s function for f+ exhibits a remarkable transition from Lorentz to Galilean invariance, whereas for f- such modes effectively do not propagate. For both cases Lorentz invariance is recovered at long wavelengths.

Wildfire Decision Making Under Uncertainty

NASA Astrophysics Data System (ADS)

Thompson, M.

2013-12-01

Decisions relating to wildfire management are subject to multiple sources of uncertainty, and are made by a broad range of individuals, across a multitude of environmental and socioeconomic contexts. In this presentation I will review progress towards identification and characterization of uncertainties and how this information can support wildfire decision-making. First, I will review a typology of uncertainties common to wildfire management, highlighting some of the more salient sources of uncertainty and how they present challenges to assessing wildfire risk. This discussion will cover the expanding role of burn probability modeling, approaches for characterizing fire effects, and the role of multi-criteria decision analysis, and will provide illustrative examples of integrated wildfire risk assessment across a variety of planning scales. Second, I will describe a related uncertainty typology that focuses on the human dimensions of wildfire management, specifically addressing how social, psychological, and institutional factors may impair cost-effective risk mitigation. This discussion will encompass decision processes before, during, and after fire events, with a specific focus on active management of complex wildfire incidents. An improved ability to characterize uncertainties faced in wildfire management could lead to improved delivery of decision support, targeted communication strategies, and ultimately to improved wildfire management outcomes.

Effective theory for the nonrigid rotor in an electromagnetic field: Toward accurate and precise calculations of E2 transitions in deformed nuclei

DOE PAGES

Coello Pérez, Eduardo A.; Papenbrock, Thomas F.

2015-07-27

In this paper, we present a model-independent approach to electric quadrupole transitions of deformed nuclei. Based on an effective theory for axially symmetric systems, the leading interactions with electromagnetic fields enter as minimal couplings to gauge potentials, while subleading corrections employ gauge-invariant nonminimal couplings. This approach yields transition operators that are consistent with the Hamiltonian, and the power counting of the effective theory provides us with theoretical uncertainty estimates. We successfully test the effective theory in homonuclear molecules that exhibit a large separation of scales. For ground-state band transitions of rotational nuclei, the effective theory describes data well within theoreticalmore » uncertainties at leading order. To probe the theory at subleading order, data with higher precision would be valuable. For transitional nuclei, next-to-leading-order calculations and the high-precision data are consistent within the theoretical uncertainty estimates. In addition, we study the faint interband transitions within the effective theory and focus on the E2 transitions from the 0 2 + band (the “β band”) to the ground-state band. Here the predictions from the effective theory are consistent with data for several nuclei, thereby proposing a solution to a long-standing challenge.« less

A contribution to the calculation of measurement uncertainty and optimization of measuring strategies in coordinate measurement

NASA Astrophysics Data System (ADS)

Waeldele, F.

1983-01-01

The influence of sample shape deviations on the measurement uncertainties and the optimization of computer aided coordinate measurement were investigated for a circle and a cylinder. Using the complete error propagation law in matrix form the parameter uncertainties are calculated, taking the correlation between the measurement points into account. Theoretical investigations show that the measuring points have to be equidistantly distributed and that for a cylindrical body a measuring point distribution along a cross section is better than along a helical line. The theoretically obtained expressions to calculate the uncertainties prove to be a good estimation basis. The simple error theory is not satisfactory for estimation. The complete statistical data analysis theory helps to avoid aggravating measurement errors and to adjust the number of measuring points to the required measuring uncertainty.

Assessment of Uncertainties Related to Seismic Hazard Using Fuzzy Analysis

NASA Astrophysics Data System (ADS)

Jorjiashvili, N.; Yokoi, T.; Javakhishvili, Z.

2013-05-01

Seismic hazard analysis in last few decades has been become very important issue. Recently, new technologies and available data have been improved that helped many scientists to understand where and why earthquakes happen, physics of earthquakes, etc. They have begun to understand the role of uncertainty in Seismic hazard analysis. However, there is still significant problem how to handle existing uncertainty. The same lack of information causes difficulties to quantify uncertainty accurately. Usually attenuation curves are obtained in statistical way: regression analysis. Statistical and probabilistic analysis show overlapped results for the site coefficients. This overlapping takes place not only at the border between two neighboring classes, but also among more than three classes. Although the analysis starts from classifying sites using the geological terms, these site coefficients are not classified at all. In the present study, this problem is solved using Fuzzy set theory. Using membership functions the ambiguities at the border between neighboring classes can be avoided. Fuzzy set theory is performed for southern California by conventional way. In this study standard deviations that show variations between each site class obtained by Fuzzy set theory and classical way are compared. Results on this analysis show that when we have insufficient data for hazard assessment site classification based on Fuzzy set theory shows values of standard deviations less than obtained by classical way which is direct proof of less uncertainty.

Clinicians' Perspectives on Managing Symptom Clusters in Advanced Cancer: A Semistructured Interview Study.

PubMed

Dong, Skye T; Butow, Phyllis N; Agar, Meera; Lovell, Melanie R; Boyle, Frances; Stockler, Martin; Forster, Benjamin C; Tong, Allison

2016-04-01

Managing symptom clusters or multiple concurrent symptoms in patients with advanced cancer remains a clinical challenge. The optimal processes constituting effective management of symptom clusters remain uncertain. To describe the attitudes and strategies of clinicians in managing multiple co-occurring symptoms in patients with advanced cancer. Semistructured interviews were conducted with 48 clinicians (palliative care physicians [n = 10], oncologists [n = 6], general practitioners [n = 6], nurses [n = 12], and allied health providers [n = 14]), purposively recruited from two acute hospitals, two palliative care centers, and four community general practices in Sydney, Australia. Transcripts were analyzed using thematic analysis and adapted grounded theory. Six themes were identified: uncertainty in decision making (inadequacy of scientific evidence, relying on experiential knowledge, and pressure to optimize care); attunement to patient and family (sensitivity to multiple cues, prioritizing individual preferences, addressing psychosocial and physical interactions, and opening Pandora's box); deciphering cause to guide intervention (disaggregating symptoms and interactions, flexibility in assessment, and curtailing investigative intrusiveness); balancing complexities in medical management (trading off side effects, minimizing mismatched goals, and urgency in resolving severe symptoms); fostering hope and empowerment (allaying fear of the unknown, encouraging meaning making, championing patient empowerment, and truth telling); and depending on multidisciplinary expertise (maximizing knowledge exchange, sharing management responsibility, contending with hierarchical tensions, and isolation and discontinuity of care). Management of symptom clusters, as both an art and a science, is currently fraught with uncertainty in decision making. Strengthening multidisciplinary collaboration, continuity of care, more pragmatic planning of clinical trials to address more than one symptom, and training in symptom cluster management are required. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Bayesian Methods for Effective Field Theories

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah

Microscopic predictions of the properties of atomic nuclei have reached a high level of precision in the past decade. This progress mandates improved uncertainty quantification (UQ) for a robust comparison of experiment with theory. With the uncertainty from many-body methods under control, calculations are now sensitive to the input inter-nucleon interactions. These interactions include parameters that must be fit to experiment, inducing both uncertainty from the fit and from missing physics in the operator structure of the Hamiltonian. Furthermore, the implementation of the inter-nucleon interactions is not unique, which presents the additional problem of assessing results using different interactions. Effective field theories (EFTs) take advantage of a separation of high- and low-energy scales in the problem to form a power-counting scheme that allows the organization of terms in the Hamiltonian based on their expected contribution to observable predictions. This scheme gives a natural framework for quantification of uncertainty due to missing physics. The free parameters of the EFT, called the low-energy constants (LECs), must be fit to data, but in a properly constructed EFT these constants will be natural-sized, i.e., of order unity. The constraints provided by the EFT, namely the size of the systematic uncertainty from truncation of the theory and the natural size of the LECs, are assumed information even before a calculation is performed or a fit is done. Bayesian statistical methods provide a framework for treating uncertainties that naturally incorporates prior information as well as putting stochastic and systematic uncertainties on an equal footing. For EFT UQ Bayesian methods allow the relevant EFT properties to be incorporated quantitatively as prior probability distribution functions (pdfs). Following the logic of probability theory, observable quantities and underlying physical parameters such as the EFT breakdown scale may be expressed as pdfs that incorporate the prior pdfs. Problems of model selection, such as distinguishing between competing EFT implementations, are also natural in a Bayesian framework. In this thesis we focus on two complementary topics for EFT UQ using Bayesian methods--quantifying EFT truncation uncertainty and parameter estimation for LECs. Using the order-by-order calculations and underlying EFT constraints as prior information, we show how to estimate EFT truncation uncertainties. We then apply the result to calculating truncation uncertainties on predictions of nucleon-nucleon scattering in chiral effective field theory. We apply model-checking diagnostics to our calculations to ensure that the statistical model of truncation uncertainty produces consistent results. A framework for EFT parameter estimation based on EFT convergence properties and naturalness is developed which includes a series of diagnostics to ensure the extraction of the maximum amount of available information from data to estimate LECs with minimal bias. We develop this framework using model EFTs and apply it to the problem of extrapolating lattice quantum chromodynamics results for the nucleon mass. We then apply aspects of the parameter estimation framework to perform case studies in chiral EFT parameter estimation, investigating a possible operator redundancy at fourth order in the chiral expansion and the appropriate inclusion of truncation uncertainty in estimating LECs.

Learning to manage complexity through simulation: students' challenges and possible strategies.

PubMed

Gormley, Gerard J; Fenwick, Tara

2016-06-01

Many have called for medical students to learn how to manage complexity in healthcare. This study examines the nuances of students' challenges in coping with a complex simulation learning activity, using concepts from complexity theory, and suggests strategies to help them better understand and manage complexity.Wearing video glasses, participants took part in a simulation ward-based exercise that incorporated characteristics of complexity. Video footage was used to elicit interviews, which were transcribed. Using complexity theory as a theoretical lens, an iterative approach was taken to identify the challenges that participants faced and possible coping strategies using both interview transcripts and video footage.Students' challenges in coping with clinical complexity included being: a) unprepared for 'diving in', b) caught in an escalating system, c) captured by the patient, and d) unable to assert boundaries of acceptable practice.Many characteristics of complexity can be recreated in a ward-based simulation learning activity, affording learners an embodied and immersive experience of these complexity challenges. Possible strategies for managing complexity themes include: a) taking time to size up the system, b) attuning to what emerges, c) reducing complexity, d) boundary practices, and e) working with uncertainty. This study signals pedagogical opportunities for recognizing and dealing with complexity.

Uncertainty, robustness, and the value of information in managing a population of northern bobwhites

USGS Publications Warehouse

Johnson, Fred A.; Hagan, Greg; Palmer, William E.; Kemmerer, Michael

2014-01-01

The abundance of northern bobwhites (Colinus virginianus) has decreased throughout their range. Managers often respond by considering improvements in harvest and habitat management practices, but this can be challenging if substantial uncertainty exists concerning the cause(s) of the decline. We were interested in how application of decision science could be used to help managers on a large, public management area in southwestern Florida where the bobwhite is a featured species and where abundance has severely declined. We conducted a workshop with managers and scientists to elicit management objectives, alternative hypotheses concerning population limitation in bobwhites, potential management actions, and predicted management outcomes. Using standard and robust approaches to decision making, we determined that improved water management and perhaps some changes in hunting practices would be expected to produce the best management outcomes in the face of uncertainty about what is limiting bobwhite abundance. We used a criterion called the expected value of perfect information to determine that a robust management strategy may perform nearly as well as an optimal management strategy (i.e., a strategy that is expected to perform best, given the relative importance of different management objectives) with all uncertainty resolved. We used the expected value of partial information to determine that management performance could be increased most by eliminating uncertainty over excessive-harvest and human-disturbance hypotheses. Beyond learning about the factors limiting bobwhites, adoption of a dynamic management strategy, which recognizes temporal changes in resource and environmental conditions, might produce the greatest management benefit. Our research demonstrates that robust approaches to decision making, combined with estimates of the value of information, can offer considerable insight into preferred management approaches when great uncertainty exists about system dynamics and the effects of management.

Change detection of bitemporal multispectral images based on FCM and D-S theory

NASA Astrophysics Data System (ADS)

Shi, Aiye; Gao, Guirong; Shen, Shaohong

2016-12-01

In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.

Saccadic Suppression of Flash Detection: the Uncertainty Theory VS. Alternative Theories.

NASA Astrophysics Data System (ADS)

Greenhouse, Daniel Stephen

Helmholtz('1) and others have proposed that when a saccadic eye movement occurs, stability of the visual world is maintained by a process that utilizes a corollary to the efferent motor signal for the eye movement, allowing the visual frame of reference to translate equal in magnitude, but opposite in sign, to the movement itself. This process is now known to be synchronous neither with the saccadic trajectory('2,3) nor in all parts of the visual field.('4) In addition, this process has been shown to have variability('2) whereby the perceived visual direction of a flash presented to a fixed retinal locus during a saccade may change from trial to trial. Hence, uncertainty with respect to visual location of a stimulus may exist during and just before a saccade. It has been established for normal vision that uncertainty produces a decline in detectability of a weak stimulus.('5,6,7) The research reported in this dissertation was performed to test the notion, first suggested by L. Matin,('8) that uncertainty is responsible for saccadic suppression, the decline in detectability that has been reported('9,10,11) for a brief flash presented during a saccade. After having established the existence of suppression under the conditions we employed (1(DEGREES) foveal flash occurring 2 1/2(DEGREES) into a 10(DEGREES) voluntary saccade, presented against an illuminated background) we conducted an initial test of the uncertainty theory. We employed a pedestal (flash at the spatial, temporal, and chromatic locus of the stimulus, occurring on all trials, and sufficiently intense as to be visible during saccades) in an attempt to reduce uncertainty. Suppression was nearly eliminated for all subjects. We interpreted this result in terms of the uncertainty theory, but were unable to reject alternative theories of suppression, which include forms of neural inhibition,('10,11) increaed noise level in the retina during saccades,('12) and metacontrast masking.('13). The next experiment involved the generation of receiver operating characteristic (ROC) curves. The results, interpreted within the framework of the Theory of Signal Detectability, served to establish the presence of uncertainty for two of four subjects. The magnitude of uncertainty, estimated from the ROC curves, was comparable with that which could account for the decline in detectability observed in earlier experiments, and we concluded that uncertainty could account entirely for suppression in these subjects. In the final experiment, we employed spatially separate marker flashes as cues in an attempt to reduce uncertainty. For one of two subjects, detectability of a stimulus presented during a saccade improved substantially when the markers were employed. This result was interpreted in terms of the uncertainty theory. The evidence, in total, leads us to conclude that, with respect to other theories which have appeared in the literature, the uncertainty theory of saccadic suppression is a viable alternative. ('1)Helmholtz, H. (1866) A Treatise on Physiological Optics, Vol. 3, Dover Publications, New York (1963). ('2)Matin, L., Matin, E., and Pearce, D. (1969) Perception and Psychophysics 5, 65-80. ('3)Matin, L., Matin, E., and Pola, J. (1970) Perception and Psychophysics 8, 9-14. ('4)Matin, L. and Matin, E. (1972) Bibliotheca Ophtalmologica 82, 358-368. ('5)Cohn, T. C. and Lasley, D. J. (1974) J. Opt. Soc. Am. 64, 1715-1719. ('6)Lasley, D. J., Greenhouse, D. S., and Cohn, T. C. (1976), J. Opt. Soc. Am. 66, 1079 (abstract). ('7)Greenhouse, D. S. and Cohn, T. C. (1978) J. Opt. Soc. Am. 68, 266-267. ('8)Matin, L. (1965) Personal communication to E. Matin reported in Matin, E. (1974), Psychological Bulletin 81, 899-917. ('9)Latour, P. (1962), Vision Research 2, 261-262. ('10)Volkmann, F. (1962), J. Opt. Soc. Am. 52, 571-578. ('11)Zuber, B. and Stark, L. (1966) Experimental Neurology 16, 65-79. ('12)Richards, W. (1969), J. Opt. Soc. Am. 59, 617-623. ('13)Matin, E., Clymer, A., and Matin, L. (1972) Science 178, 179-182.

Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program

USGS Publications Warehouse

Runge, Michael C.; Converse, Sarah J.; Lyons, James E.

2011-01-01

Natural resource management is plagued with uncertainty of many kinds, but not all uncertainties are equally important to resolve. The promise of adaptive management is that learning in the short-term will improve management in the long-term; that promise is best kept if the focus of learning is on those uncertainties that most impede achievement of management objectives. In this context, an existing tool of decision analysis, the expected value of perfect information (EVPI), is particularly valuable in identifying the most important uncertainties. Expert elicitation can be used to develop preliminary predictions of management response under a series of hypotheses, as well as prior weights for those hypotheses, and the EVPI can be used to determine how much management could improve if uncertainty was resolved. These methods were applied to management of whooping cranes (Grus americana), an endangered migratory bird that is being reintroduced in several places in North America. The Eastern Migratory Population of whooping cranes had exhibited almost no successful reproduction through 2009. Several dozen hypotheses can be advanced to explain this failure, and many of them lead to very different management responses. An expert panel articulated the hypotheses, provided prior weights for them, developed potential management strategies, and made predictions about the response of the population to each strategy under each hypothesis. Multi-criteria decision analysis identified a preferred strategy in the face of uncertainty, and analysis of the expected value of information identified how informative each strategy could be. These results provide the foundation for design of an adaptive management program.

Insights into water managers' perception and handling of uncertainties - a study of the role of uncertainty in practitioners' planning and decision-making

NASA Astrophysics Data System (ADS)

Höllermann, Britta; Evers, Mariele

2017-04-01

Planning and decision-making under uncertainty is common in water management due to climate variability, simplified models, societal developments, planning restrictions just to name a few. Dealing with uncertainty can be approached from two sites, hereby affecting the process and form of communication: Either improve the knowledge base by reducing uncertainties or apply risk-based approaches to acknowledge uncertainties throughout the management process. Current understanding is that science more strongly focusses on the former approach, while policy and practice are more actively applying a risk-based approach to handle incomplete and/or ambiguous information. The focus of this study is on how water managers perceive and handle uncertainties at the knowledge/decision interface in their daily planning and decision-making routines. How they evaluate the role of uncertainties for their decisions and how they integrate this information into the decision-making process. Expert interviews and questionnaires among practitioners and scientists provided an insight into their perspectives on uncertainty handling allowing a comparison of diverse strategies between science and practice as well as between different types of practitioners. Our results confirmed the practitioners' bottom up approach from potential measures upwards instead of impact assessment downwards common in science-based approaches. This science-practice gap may hinder effective uncertainty integration and acknowledgement in final decisions. Additionally, the implementation of an adaptive and flexible management approach acknowledging uncertainties is often stalled by rigid regulations favouring a predict-and-control attitude. However, the study showed that practitioners' level of uncertainty recognition varies with respect to his or her affiliation to type of employer and business unit, hence, affecting the degree of the science-practice-gap with respect to uncertainty recognition. The level of working experience was examined as a cross-cutting property of science and practice with increasing levels of uncertainty awareness and integration among more experienced researchers and practitioners. In conclusion, our study of water managers' perception and handling of uncertainties provides valuable insights for finding routines for uncertainty communication and integration into planning and decision-making processes by acknowledging the divers perceptions among producers, users and receivers of uncertainty information. These results can contribute to more effective integration of hydrological forecast and improved decisions.

Design of crusher liner based on time - varying uncertainty theory

NASA Astrophysics Data System (ADS)

Tang, J. C.; Shi, B. Q.; Yu, H. J.; Wang, R. J.; Zhang, W. Y.

2017-09-01

This article puts forward the time-dependent design method considering the load fluctuation factors for the liner based on the time-varying uncertainty theory. In this method, the time-varying uncertainty design model of liner is constructed by introducing the parameters that affect the wear rate, the volatility and the drift rate. Based on the design example, the timevarying design outline of the moving cone liner is obtained. Based on the theory of minimum wear, the gap curve of wear resistant cavity is designed, and the optimized cavity is obtained by the combination of the thickness of the cone and the cavity gap. Taking the PYGB1821 multi cylinder hydraulic cone crusher as an example, it is proved that the service life of the new liner is improved by more than 14.3%.

Controlling uncertainty: a review of human behavior in complex dynamic environments.

PubMed

Osman, Magda

2010-01-01

Complex dynamic control (CDC) tasks are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (economics, engineering, ergonomics, human-computer interaction, management, psychology), but they remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (monitoring and control framework) as a tool for integrating theory and findings. The main thesis of the monitoring and control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks.

Making framing of uncertainty in water management practice explicit by using a participant-structured approach.

PubMed

Isendahl, Nicola; Dewulf, Art; Pahl-Wostl, Claudia

2010-01-01

By now, the need for addressing uncertainty in the management of water resources is widely recognized, yet there is little expertise and experience how to effectively deal with uncertainty in practice. Uncertainties in water management practice so far are mostly dealt with intuitively or based on experience. That way decisions can be quickly taken but analytic processes of deliberate reasoning are bypassed. To meet the desire of practitioners for better guidance and tools how to deal with uncertainty more practice-oriented systematic approaches are needed. For that purpose we consider it important to understand how practitioners frame uncertainties. In this paper we present an approach where water managers developed criteria of relevance to understand and address uncertainties. The empirical research took place in the Doñana region of the Guadalquivir estuary in southern Spain making use of the method of card sorting. Through the card sorting exercise a broad range of criteria to make sense of and describe uncertainties was produced by different subgroups, which were then merged into a shared list of criteria. That way framing differences were made explicit and communication on uncertainty and on framing differences was enhanced. In that, the present approach constitutes a first step to enabling reframing and overcoming framing differences, which are important features on the way to robust decision-making. Moreover, the elaborated criteria build a basis for the development of more structured approaches to deal with uncertainties in water management practice. Copyright 2009 Elsevier Ltd. All rights reserved.

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

Facing uncertainty in ecosystem services-based resource management.

PubMed

Grêt-Regamey, Adrienne; Brunner, Sibyl H; Altwegg, Jürg; Bebi, Peter

2013-09-01

The concept of ecosystem services is increasingly used as a support for natural resource management decisions. While the science for assessing ecosystem services is improving, appropriate methods to address uncertainties in a quantitative manner are missing. Ignoring parameter uncertainties, modeling uncertainties and uncertainties related to human-environment interactions can modify decisions and lead to overlooking important management possibilities. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks to a Geographic Information System for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We demonstrate that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services in a case study in the Swiss Alps. The results suggest that not only the total value of the bundle of ecosystem services is highly dependent on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain forest ecosystems, which have long rotation stands and are highly sensitive to pressing climate and socio-economic changes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Resolving structural uncertainty in natural resources management using POMDP approaches

USGS Publications Warehouse

Williams, B.K.

2011-01-01

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems. ?? 2011.

Combining statistical inference and decisions in ecology.

PubMed

Williams, Perry J; Hooten, Mevin B

2016-09-01

Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods, including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem. © 2016 by the Ecological Society of America.

Prospect Theory and Interval-Valued Hesitant Set for Safety Evacuation Model

NASA Astrophysics Data System (ADS)

Kou, Meng; Lu, Na

2018-01-01

The study applies the research results of prospect theory and multi attribute decision making theory, combined with the complexity, uncertainty and multifactor influence of the underground mine fire system and takes the decision makers’ psychological behavior of emotion and intuition into full account to establish the intuitionistic fuzzy multiple attribute decision making method that is based on the prospect theory. The model established by this method can explain the decision maker’s safety evacuation decision behavior in the complex system of underground mine fire due to the uncertainty of the environment, imperfection of the information and human psychological behavior and other factors.

Are needs to manage uncertainty and threat associated with political conservatism or ideological extremity?

PubMed

Jost, John T; Napier, Jaime L; Thorisdottir, Hulda; Gosling, Samuel D; Palfai, Tibor P; Ostafin, Brian

2007-07-01

Three studies are conducted to assess the uncertainty- threat model of political conservatism, which posits that psychological needs to manage uncertainty and threat are associated with political orientation. Results from structural equation models provide consistent support for the hypothesis that uncertainty avoidance (e.g., need for order, intolerance of ambiguity, and lack of openness to experience) and threat management (e.g., death anxiety, system threat, and perceptions of a dangerous world) each contributes independently to conservatism (vs. liberalism). No support is obtained for alternative models, which predict that uncertainty and threat management are associated with ideological extremism or extreme forms of conservatism only. Study 3 also reveals that resistance to change fully mediates the association between uncertainty avoidance and conservatism, whereas opposition to equality partially mediates the association between threat and conservatism. Implications for understanding the epistemic and existential bases of political orientation are discussed.

Evaluating data worth for ground-water management under uncertainty

USGS Publications Warehouse

Wagner, B.J.

1999-01-01

A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models-a chance-constrained ground-water management model and an integer-programing sampling network design model-to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information-i.e., the projected reduction in management costs-with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models - a chance-constrained ground-water management model and an integer-programming sampling network design model - to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information - i.e., the projected reduction in management costs - with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.

A methodology for computing uncertainty bounds of multivariable systems based on sector stability theory concepts

NASA Technical Reports Server (NTRS)

Waszak, Martin R.

1992-01-01

The application of a sector-based stability theory approach to the formulation of useful uncertainty descriptions for linear, time-invariant, multivariable systems is explored. A review of basic sector properties and sector-based approach are presented first. The sector-based approach is then applied to several general forms of parameter uncertainty to investigate its advantages and limitations. The results indicate that the sector uncertainty bound can be used effectively to evaluate the impact of parameter uncertainties on the frequency response of the design model. Inherent conservatism is a potential limitation of the sector-based approach, especially for highly dependent uncertain parameters. In addition, the representation of the system dynamics can affect the amount of conservatism reflected in the sector bound. Careful application of the model can help to reduce this conservatism, however, and the solution approach has some degrees of freedom that may be further exploited to reduce the conservatism.

Robust root clustering for linear uncertain systems using generalized Lyapunov theory

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1993-01-01

Consideration is given to the problem of matrix root clustering in subregions of a complex plane for linear state space models with real parameter uncertainty. The nominal matrix root clustering theory of Gutman & Jury (1981) using the generalized Liapunov equation is extended to the perturbed matrix case, and bounds are derived on the perturbation to maintain root clustering inside a given region. The theory makes it possible to obtain an explicit relationship between the parameters of the root clustering region and the uncertainty range of the parameter space.

Situational Favorability and Perceived Environmental Uncertainty: An Integrative Approach

ERIC Educational Resources Information Center

Nebeker, Delbert M.

1975-01-01

Presents the conceptual and empirical basis for a possible combining of Fiedler's contingency model of leadership effectiveness and Lawrence and Lorsch's contingency organization theory. Using perceived environmental uncertainty as the integrating concept, a measure of decision uncertainty was found to be significantly related to Fiedler's…

Management of California Oak Woodlands: Uncertainties and Modeling

Treesearch

Jay E. Noel; Richard P. Thompson

1995-01-01

A mathematical policy model of oak woodlands is presented. The model illustrates the policy uncertainties that exist in the management of oak woodlands. These uncertainties include: (1) selection of a policy criterion function, (2) woodland dynamics, (3) initial and final state of the woodland stock. The paper provides a review of each of the uncertainty issues. The...

A Conceptual Methodology for Assessing Acquisition Requirements Robustness against Technology Uncertainties

NASA Astrophysics Data System (ADS)

Chou, Shuo-Ju

2011-12-01

In recent years the United States has shifted from a threat-based acquisition policy that developed systems for countering specific threats to a capabilities-based strategy that emphasizes the acquisition of systems that provide critical national defense capabilities. This shift in policy, in theory, allows for the creation of an "optimal force" that is robust against current and future threats regardless of the tactics and scenario involved. In broad terms, robustness can be defined as the insensitivity of an outcome to "noise" or non-controlled variables. Within this context, the outcome is the successful achievement of defense strategies and the noise variables are tactics and scenarios that will be associated with current and future enemies. Unfortunately, a lack of system capability, budget, and schedule robustness against technology performance and development uncertainties has led to major setbacks in recent acquisition programs. This lack of robustness stems from the fact that immature technologies have uncertainties in their expected performance, development cost, and schedule that cause to variations in system effectiveness and program development budget and schedule requirements. Unfortunately, the Technology Readiness Assessment process currently used by acquisition program managers and decision-makers to measure technology uncertainty during critical program decision junctions does not adequately capture the impact of technology performance and development uncertainty on program capability and development metrics. The Technology Readiness Level metric employed by the TRA to describe program technology elements uncertainties can only provide a qualitative and non-descript estimation of the technology uncertainties. In order to assess program robustness, specifically requirements robustness, against technology performance and development uncertainties, a new process is needed. This process should provide acquisition program managers and decision-makers with the ability to assess or measure the robustness of program requirements against such uncertainties. A literature review of techniques for forecasting technology performance and development uncertainties and subsequent impacts on capability, budget, and schedule requirements resulted in the conclusion that an analysis process that coupled a probabilistic analysis technique such as Monte Carlo Simulations with quantitative and parametric models of technology performance impact and technology development time and cost requirements would allow the probabilities of meeting specific constraints of these requirements to be established. These probabilities of requirements success metrics can then be used as a quantitative and probabilistic measure of program requirements robustness against technology uncertainties. Combined with a Multi-Objective Genetic Algorithm optimization process and computer-based Decision Support System, critical information regarding requirements robustness against technology uncertainties can be captured and quantified for acquisition decision-makers. This results in a more informed and justifiable selection of program technologies during initial program definition as well as formulation of program development and risk management strategies. To meet the stated research objective, the ENhanced TEchnology Robustness Prediction and RISk Evaluation (ENTERPRISE) methodology was formulated to provide a structured and transparent process for integrating these enabling techniques to provide a probabilistic and quantitative assessment of acquisition program requirements robustness against technology performance and development uncertainties. In order to demonstrate the capabilities of the ENTERPRISE method and test the research Hypotheses, an demonstration application of this method was performed on a notional program for acquiring the Carrier-based Suppression of Enemy Air Defenses (SEAD) using Unmanned Combat Aircraft Systems (UCAS) and their enabling technologies. The results of this implementation provided valuable insights regarding the benefits and inner workings of this methodology as well as its limitations that should be addressed in the future to narrow the gap between current state and the desired state.

Information theoretic quantification of diagnostic uncertainty.

PubMed

Westover, M Brandon; Eiseman, Nathaniel A; Cash, Sydney S; Bianchi, Matt T

2012-01-01

Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes' rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians' deficiencies in probabilistic reasoning, especially with unexpected test results. Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability estimation by placing this type of uncertainty within a principled information theoretic framework. We address several obstacles hindering physicians' application of information theoretic concepts to diagnostic test interpretation. These include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point estimates of pre-test probability.

Aircraft ride quality controller design using new robust root clustering theory for linear uncertain systems

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1992-01-01

The aspect of controller design for improving the ride quality of aircraft in terms of damping ratio and natural frequency specifications on the short period dynamics is addressed. The controller is designed to be robust with respect to uncertainties in the real parameters of the control design model such as uncertainties in the dimensional stability derivatives, imperfections in actuator/sensor locations and possibly variations in flight conditions, etc. The design is based on a new robust root clustering theory developed by the author by extending the nominal root clustering theory of Gutman and Jury to perturbed matrices. The proposed methodology allows to get an explicit relationship between the parameters of the root clustering region and the uncertainty radius of the parameter space. The current literature available for robust stability becomes a special case of this unified theory. The bounds derived on the parameter perturbation for robust root clustering are then used in selecting the robust controller.

On the role of model-based monitoring for adaptive planning under uncertainty

NASA Astrophysics Data System (ADS)

Raso, Luciano; Kwakkel, Jan; Timmermans, Jos; Haasnoot, Mariolijn

2016-04-01

Adaptive plans, designed to anticipate and respond to an unfolding uncertain future, have found a fertile application domain in the planning of deltas that are exposed to rapid socioeconomic development and climate change. Adaptive planning, under the moniker of adaptive delta management, is used in the Dutch Delta Program for developing a nation-wide plan to prepare for uncertain climate change and socio-economic developments. Scientifically, adaptive delta management relies heavily on Dynamic Adaptive Policy Pathways. Currently, in the Netherlands the focus is shifting towards implementing the adaptive delta plan. This shift is especially relevant because the efficacy of adaptive plans hinges on monitoring on-going developments and ensuring that actions are indeed taken if and when necessary. In the design of an effective monitoring system for an adaptive plan, three challenges have to be confronted: • Shadow of the past: The development of adaptive plans and the design of their monitoring system relies heavily on current knowledge of the system, and current beliefs about plausible future developments. A static monitoring system is therefore exposed to the exact same uncertainties one tries to address through adaptive planning. • Inhibition of learning: Recent applications of adaptive planning tend to overlook the importance of learning and new information, and fail to account for this explicitly in the design of adaptive plans. • Challenge of surprise: Adaptive policies are designed in light of the current foreseen uncertainties. However, developments that are not considered during the design phase as being plausible could still substantially affect the performance of adaptive policies. The shadow of the past, the inhibition of learning, and the challenge of surprise taken together suggest that there is a need for redesigning the concepts of monitoring and evaluation to support the implementation of adaptive plans. Innovations from control theory, triggered by the challenge of uncertainty in operational control, may offer solutions from which monitoring for adaptive planning can benefit. Specifically: (i) in control, observations are incorporated into the model through data assimilation, updating the present state, boundary conditions, and parameters based on new observations, diminishing the shadow of the past; (ii) adaptive control is a way to modify the characteristics of the internal model, incorporating new knowledge on the system, countervailing the inhibition of learning; and (iii) in closed-loop control, a continuous system update equips the controller with "inherent robustness", i.e. to capacity to adapts to new conditions even when these were not initially considered. We aim to explore how inherent robustness addresses the challenge of surprise. Innovations in model-based control might help to improve and adapt the models used to support adaptive delta management to new information (reducing uncertainty). Moreover, this would offer a starting point for using these models not only in the design of adaptive plans, but also as part of the monitoring. The proposed research requires multidisciplinary cooperation between control theory, the policy sciences, and integrated assessment modeling.

Different methodologies to quantify uncertainties of air emissions.

PubMed

Romano, Daniela; Bernetti, Antonella; De Lauretis, Riccardo

2004-10-01

Characterization of the uncertainty associated with air emission estimates is of critical importance especially in the compilation of air emission inventories. In this paper, two different theories are discussed and applied to evaluate air emissions uncertainty. In addition to numerical analysis, which is also recommended in the framework of the United Nation Convention on Climate Change guidelines with reference to Monte Carlo and Bootstrap simulation models, fuzzy analysis is also proposed. The methodologies are discussed and applied to an Italian example case study. Air concentration values are measured from two electric power plants: a coal plant, consisting of two boilers and a fuel oil plant, of four boilers; the pollutants considered are sulphur dioxide (SO(2)), nitrogen oxides (NO(X)), carbon monoxide (CO) and particulate matter (PM). Monte Carlo, Bootstrap and fuzzy methods have been applied to estimate uncertainty of these data. Regarding Monte Carlo, the most accurate results apply to Gaussian distributions; a good approximation is also observed for other distributions with almost regular features either positive asymmetrical or negative asymmetrical. Bootstrap, on the other hand, gives a good uncertainty estimation for irregular and asymmetrical distributions. The logic of fuzzy analysis, where data are represented as vague and indefinite in opposition to the traditional conception of neatness, certain classification and exactness of the data, follows a different description. In addition to randomness (stochastic variability) only, fuzzy theory deals with imprecision (vagueness) of data. Fuzzy variance of the data set was calculated; the results cannot be directly compared with empirical data but the overall performance of the theory is analysed. Fuzzy theory may appear more suitable for qualitative reasoning than for a quantitative estimation of uncertainty, but it suits well when little information and few measurements are available and when distributions of data are not properly known.

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling.

PubMed

Ganju, Neil K; Brush, Mark J; Rashleigh, Brenda; Aretxabaleta, Alfredo L; Del Barrio, Pilar; Grear, Jason S; Harris, Lora A; Lake, Samuel J; McCardell, Grant; O'Donnell, James; Ralston, David K; Signell, Richard P; Testa, Jeremy M; Vaudrey, Jamie M P

2016-03-01

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear, because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a "theory of everything" for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling

USGS Publications Warehouse

Ganju, Neil K.; Brush, Mark J.; Rashleigh, Brenda; Aretxabaleta, Alfredo L.; del Barrio, Pilar; Grear, Jason S.; Harris, Lora A.; Lake, Samuel J.; McCardell, Grant; O'Donnell, James; Ralston, David K.; Signell, Richard P.; Testa, Jeremy; Vaudrey, Jamie M. P.

2016-01-01

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review, we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling

PubMed Central

Ganju, Neil K.; Brush, Mark J.; Rashleigh, Brenda; Aretxabaleta, Alfredo L.; del Barrio, Pilar; Grear, Jason S.; Harris, Lora A.; Lake, Samuel J.; McCardell, Grant; O’Donnell, James; Ralston, David K.; Signell, Richard P.; Testa, Jeremy M.; Vaudrey, Jamie M.P.

2016-01-01

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear, because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy. PMID:27721675

Science 101: What, Exactly, Is the Heisenberg Uncertainty Principle?

ERIC Educational Resources Information Center

Robertson, Bill

2016-01-01

Bill Robertson is the author of the NSTA Press book series, "Stop Faking It! Finally Understanding Science So You Can Teach It." In this month's issue, Robertson describes and explains the Heisenberg Uncertainty Principle. The Heisenberg Uncertainty Principle was discussed on "The Big Bang Theory," the lead character in…

Rotorcraft control system design for uncertain vehicle dynamics using quantitative feedback theory

NASA Technical Reports Server (NTRS)

Hess, R. A.

1994-01-01

Quantitative Feedback Theory describes a frequency-domain technique for the design of multi-input, multi-output control systems which must meet time or frequency domain performance criteria when specified uncertainty exists in the linear description of the vehicle dynamics. This theory is applied to the design of the longitudinal flight control system for a linear model of the BO-105C rotorcraft. Uncertainty in the vehicle model is due to the variation in the vehicle dynamics over a range of airspeeds from 0-100 kts. For purposes of exposition, the vehicle description contains no rotor or actuator dynamics. The design example indicates the manner in which significant uncertainty exists in the vehicle model. The advantage of using a sequential loop closure technique to reduce the cost of feedback is demonstrated by example.

Dissociated neural processing for decisions in managers and non-managers.

PubMed

Caspers, Svenja; Heim, Stefan; Lucas, Marc G; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl

2012-01-01

Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.

Dissociated Neural Processing for Decisions in Managers and Non-Managers

PubMed Central

Caspers, Svenja; Heim, Stefan; Lucas, Marc G.; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl

2012-01-01

Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing. PMID:22927984

Holistic uncertainty analysis in river basin modeling for climate vulnerability assessment

NASA Astrophysics Data System (ADS)

Taner, M. U.; Wi, S.; Brown, C.

2017-12-01

The challenges posed by uncertain future climate are a prominent concern for water resources managers. A number of frameworks exist for assessing the impacts of climate-related uncertainty, including internal climate variability and anthropogenic climate change, such as scenario-based approaches and vulnerability-based approaches. While in many cases climate uncertainty may be dominant, other factors such as future evolution of the river basin, hydrologic response and reservoir operations are potentially significant sources of uncertainty. While uncertainty associated with modeling hydrologic response has received attention, very little attention has focused on the range of uncertainty and possible effects of the water resources infrastructure and management. This work presents a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. The uncertainties explored include those associated with climate variability and change, hydrologic model parameters, and water system operation rules. A Bayesian framework is used to quantify and model the uncertainties at each modeling steps in integrated fashion, including prior and the likelihood information about model parameters. The framework is demonstrated in a case study for the St. Croix Basin located at border of United States and Canada.

Managing uncertainty about food risks - Consumer use of food labelling.

PubMed

Tonkin, Emma; Coveney, John; Meyer, Samantha B; Wilson, Annabelle M; Webb, Trevor

2016-12-01

General consumer knowledge of and engagement with the production of food has declined resulting in increasing consumer uncertainty about, and sensitivity to, food risks. Emphasis is therefore placed on providing information for consumers to reduce information asymmetry regarding food risks, particularly through food labelling. This study examines the role of food labelling in influencing consumer perceptions of food risks. In-depth, 1-h interviews were conducted with 24 Australian consumers. Participants were recruited based on an a priori defined food safety risk scale, and to achieve a diversity of demographic characteristics. The methodological approach used, adaptive theory, was chosen to enable a constant interweaving of theoretical understandings and empirical data throughout the study. Participants discussed perceiving both traditional (food spoilage/microbial contamination) and modern (social issues, pesticide and 'chemical' contamination) risks as present in the food system. Food labelling was a symbol of the food system having managed traditional risks, and a tool for consumers to personally manage perceived modern risks. However, labelling also raised awareness of modern risks not previously considered. The consumer framing of risk presented demonstrates the need for more meaningful consumer engagement in policy decision making to ensure risk communication and management meet public expectations. This research innovatively identifies food labelling as both a symbol of, and a tool for, the management of perceived risks for consumers. Therefore it is imperative that food system actors ensure the authenticity and trustworthiness of all aspects of food labelling, not only those related to food safety. Copyright © 2016 Elsevier Ltd. All rights reserved.

A tool for efficient, model-independent management optimization under uncertainty

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

2018-01-01

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

Forest Management Under Uncertainty for Multiple Bird Population Objectives

Treesearch

Clinton T. Moore; W. Todd Plummer; Michael J. Conroy

2005-01-01

We advocate adaptive programs of decision making and monitoring for the management of forest birds when responses by populations to management, and particularly management trade-offs among populations, are uncertain. Models are necessary components of adaptive management. Under this approach, uncertainty about the behavior of a managed system is explicitly captured in...

The role of price and enforcement in water allocation: insights from Game Theory

NASA Astrophysics Data System (ADS)

Souza Filho, F.; Lall, U.; Porto, R.

2007-12-01

As many countries are moving towards water sector reforms, practical issues of how water management institutions can better effect allocation, regulation and enforcement of water rights have emerged. The uncertainty associated with water that is available at a particular diversion point becomes a parameter that is likely to influence the behavior of water users as to their application for water licenses, as well as their willingness to pay for licensed use. The ability of a water agency to reduce this uncertainty through effective water rights enforcement is related to the fiscal ability of the agency to sustain the enforcement effort. In this paper, this interplay across the users and the agency is explored, considering the hydraulic structure or sequence of water use, and parameters that define the users and the agency's economics. The potential for free rider behavior by the users, as well as their proposals for licensed use are derived conditional on this setting. The analyses presented are developed in the framework of the theory of "Law and Economics", with user interactions modeled as a game theoretic enterprise. The state of Ceara, Brazil is used loosely as an example setting, with parameter values for the experiments indexed to be approximately those relevant for current decisions. The potential for using the ideas in participatory decision making is discussed.

Manager-physician relationships: an organizational theory perspective.

PubMed

Kaissi, Amer

2005-01-01

Manager-physician relationships are a critical determinant of the success of health care organizations. As the health care industry is moving toward a situation characterized by higher scarcity of resources, fiercer competition, more corporitization, and strict cost-containment approaches, managers and physicians should, more than ever, work together under conjoint or shared authority. Thus, their relationship can be described as one of high rewards, but also of high risk because of the wide range of differences that exist between them: different socializations and trainings resulting in different worldviews, value orientation and expectations and different cultures. In brief, managers and physicians represent different "tribes," each with its language, values, culture, thought patterns, and rules of the game. This article's main objective is to determine the underlying factors in the manager-physician relationship and to suggest ways that make this relationship more effective. Four different organizational perspectives will be used. The occupational perspective will give insights on the internal characteristics of the occupational communities of managers and physicians. The theory of deprofessionalization of physicians will also be discussed. The structuring perspective will look at the manager-physician relationship as a structure in the organization and will determine the effects of contextual factors (size, task uncertainty, strategy, and environment) on this relationship and the resulting effect on performance and effectiveness of the organization. The culture and control perspective will help detect the cultural differences between managers and physicians and how these interact to affect control over the decision-making areas in the hospital. The power, conflict, and dialectics perspective will shed the light on the conflicting interests of managers and physicians and how these shape the "power game" in the organization. Consequently, a theoretical model of manager-physician relationships that encompasses all these perspectives is developed.

Accounting for uncertainty in marine reserve design.

PubMed

Halpern, Benjamin S; Regan, Helen M; Possingham, Hugh P; McCarthy, Michael A

2006-01-01

Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

Ecology and space: A case study in mapping harmful invasive species

USGS Publications Warehouse

David T. Barnett,; Jarnevich, Catherine S.; Chong, Geneva W.; Stohlgren, Thomas J.; Sunil Kumar,; Holcombe, Tracy R.; Brunn, Stanley D.; Dodge, Martin

2017-01-01

The establishment and invasion of non-native plant species have the ability to alter the composition of native species and functioning of ecological systems with financial costs resulting from mitigation and loss of ecological services. Spatially documenting invasions has applications for management and theory, but the utility of maps is challenged by availability and uncertainty of data, and the reliability of extrapolating mapped data in time and space. The extent and resolution of projections also impact the ability to inform invasive species science and management. Early invasive species maps were coarse-grained representations that underscored the phenomena, but had limited capacity to direct management aside from development of watch lists for priorities for prevention and containment. Integrating mapped data sets with fine-resolution environmental variables in the context of species-distribution models allows a description of species-environment relationships and an understanding of how, why, and where invasions may occur. As with maps, the extent and resolution of models impact the resulting insight. Models of cheatgrass (Bromus tectorum) across a variety of spatial scales and grain result in divergent species-environment relationships. New data can improve models and efficiently direct further inventories. Mapping can target areas of greater model uncertainty or the bounds of modeled distribution to efficiently refine models and maps. This iterative process results in dynamic, living maps capable of describing the ongoing process of species invasions.

An adaptive approach to invasive plant management on U.S. Fish and Wildlife Service-owned native prairies in the Prairie Pothole Region: decision support under uncertainity

USGS Publications Warehouse

Gannon, Jill J.; Moore, Clinton T.; Shaffer, Terry L.; Flanders-Wanner, Bridgette

2011-01-01

Much of the native prairie managed by the U.S. Fish and Wildlife Service (Service) in the Prairie Pothole Region (PPR) is extensively invaded by the introduced cool-season grasses smooth brome (Bromus inermis) and Kentucky bluegrass (Poa pratensis). The central challenge to managers is selecting appropriate management actions in the face of biological and environmental uncertainties. We describe the technical components of a USGS management project, and explain how the components integrate and inform each other, how data feedback from individual cooperators serves to reduce uncertainty across the whole region, and how a successful adaptive management project is coordinated and maintained on a large scale. In partnership with the Service, the U.S. Geological Survey is developing an adaptive decision support framework to assist managers in selecting management actions under uncertainty and maximizing learning from management outcomes. The framework is built around practical constraints faced by refuge managers and includes identification of the management objective and strategies, analysis of uncertainty and construction of competing decision models, monitoring, and mechanisms for model feedback and decision selection. Nineteen Service field stations, spanning four states of the PPR, are participating in the project. They share a common management objective, available management strategies, and biological uncertainties. While the scope is broad, the project interfaces with individual land managers who provide refuge-specific information and receive updated decision guidance that incorporates understanding gained from the collective experience of all cooperators.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cardenas, Ibsen C., E-mail: c.cardenas@utwente.nl; Halman, Johannes I.M., E-mail: J.I.M.Halman@utwente.nl

Uncertainty is virtually unavoidable in environmental impact assessments (EIAs). From the literature related to treating and managing uncertainty, we have identified specific techniques for coping with uncertainty in EIAs. Here, we have focused on basic steps in the decision-making process that take place within an EIA setting. More specifically, we have identified uncertainties involved in each decision-making step and discussed the extent to which these can be treated and managed in the context of an activity or project that may have environmental impacts. To further demonstrate the relevance of the techniques identified, we have examined the extent to which themore » EIA guidelines currently used in Colombia consider and provide guidance on managing the uncertainty involved in these assessments. Some points that should be considered in order to provide greater robustness in impact assessments in Colombia have been identified. These include the management of stakeholder values, the systematic generation of project options, and their associated impacts as well as the associated management actions, and the evaluation of uncertainties and assumptions. We believe that the relevant and specific techniques reported here can be a reference for future evaluations of other EIA guidelines in different countries. - Highlights: • uncertainty is unavoidable in environmental impact assessments, EIAs; • we have identified some open techniques to EIAs for treating and managing uncertainty in these assessments; • points for improvement that should be considered in order to provide greater robustness in EIAs in Colombia have been identified; • the paper provides substantiated a reference for possible examinations of EIAs guidelines in other countries.« less

The Relationship between Intolerance of Uncertainty, Sensory Sensitivities, and Anxiety in Autistic and Typically Developing Children

ERIC Educational Resources Information Center

Neil, Louise; Olsson, Nora Choque; Pellicano, Elizabeth

2016-01-01

Guided by a recent theory that proposes fundamental differences in how autistic individuals deal with uncertainty, we investigated the extent to which the cognitive construct "intolerance of uncertainty" and anxiety were related to parental reports of sensory sensitivities in 64 autistic and 85 typically developing children aged…

The value of information for woodland management: Updating a state–transition model

USGS Publications Warehouse

Morris, William K.; Runge, Michael C.; Vesk, Peter A.

2017-01-01

Value of information (VOI) analyses reveal the expected benefit of reducing uncertainty to a decision maker. Most ecological VOI analyses have focused on population models rarely addressing more complex community models. We performed a VOI analysis for a complex state–transition model of Box-Ironbark Forest and Woodland management. With three management alternatives (limited harvest/firewood removal (HF), ecological thinning (ET), and no management), managing the system optimally (for 150 yr) with the original information would, on average, increase the amount of forest in a desirable state from 19% to 35% (a 16-percentage point increase). Resolving all uncertainty would, on average, increase the final percentage to 42% (a 19-percentage point increase). However, only resolving the uncertainty for a single parameter was worth almost two-thirds the value of resolving all uncertainty. We found the VOI to depend on the number of management options, increasing as the management flexibility increased. Our analyses show it is more cost-effective to monitor low-density regrowth forest than other states and more cost-effective to experiment with the no-management alternative than the other management alternatives. Importantly, the most cost-effective strategies did not include either the most desired forest states or the least understood management strategy, ET. This implies that managers cannot just rely on intuition to tell them where the most VOI will lie, as critical uncertainties in a complex system are sometimes cryptic.

Uncertainty quantification of effective nuclear interactions

DOE PAGES

Pérez, R. Navarro; Amaro, J. E.; Arriola, E. Ruiz

2016-03-02

We give a brief review on the development of phenomenological NN interactions and the corresponding quanti cation of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean eld calculations through the Skyrme parameters and effective eld theory counter-terms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different tting strategies on the light of recent developments.

Uncertainty quantification of effective nuclear interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pérez, R. Navarro; Amaro, J. E.; Arriola, E. Ruiz

We give a brief review on the development of phenomenological NN interactions and the corresponding quanti cation of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean eld calculations through the Skyrme parameters and effective eld theory counter-terms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different tting strategies on the light of recent developments.

Risk evaluation of highway engineering project based on the fuzzy-AHP

NASA Astrophysics Data System (ADS)

Yang, Qian; Wei, Yajun

2011-10-01

Engineering projects are social activities, which integrate with technology, economy, management and organization. There are uncertainties in each respect of engineering projects, and it needs to strengthen risk management urgently. Based on the analysis of the characteristics of highway engineering, and the study of the basic theory on risk evaluation, the paper built an index system of highway project risk evaluation. Besides based on fuzzy mathematics principle, analytical hierarchy process was used and as a result, the model of the comprehensive appraisal method of fuzzy and AHP was set up for the risk evaluation of express way concessionary project. The validity and the practicability of the risk evaluation of expressway concessionary project were verified after the model was applied to the practice of a project.

How much is new information worth? Evaluating the financial benefit of resolving management uncertainty

USGS Publications Warehouse

Maxwell, Sean L.; Rhodes, Jonathan R.; Runge, Michael C.; Possingham, Hugh P.; Ng, Chooi Fei; McDonald Madden, Eve

2015-01-01

Conservation decision-makers face a trade-off between spending limited funds on direct management action, or gaining new information in an attempt to improve management performance in the future. Value-of-information analysis can help to resolve this trade-off by evaluating how much management performance could improve if new information was gained. Value-of-information analysis has been used extensively in other disciplines, but there are only a few examples where it has informed conservation planning, none of which have used it to evaluate the financial value of gaining new information. We address this gap by applying value-of-information analysis to the management of a declining koala Phascolarctos cinereuspopulation. Decision-makers responsible for managing this population face uncertainty about survival and fecundity rates, and how habitat cover affects mortality threats. The value of gaining new information about these uncertainties was calculated using a deterministic matrix model of the koala population to find the expected population growth rate if koala mortality threats were optimally managed under alternative model hypotheses, which represented the uncertainties faced by koala managers. Gaining new information about survival and fecundity rates and the effect of habitat cover on mortality threats will do little to improve koala management. Across a range of management budgets, no more than 1·7% of the budget should be spent on resolving these uncertainties. The value of information was low because optimal management decisions were not sensitive to the uncertainties we considered. Decisions were instead driven by a substantial difference in the cost efficiency of management actions. The value of information was up to forty times higher when the cost efficiencies of different koala management actions were similar. Synthesis and applications. This study evaluates the ecological and financial benefits of gaining new information to inform a conservation problem. We also theoretically demonstrate that the value of reducing uncertainty is highest when it is not clear which management action is the most cost efficient. This study will help expand the use of value-of-information analyses in conservation by providing a cost efficiency metric by which to evaluate research or monitoring.

Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective

PubMed Central

Qian, Xiaoning; Dougherty, Edward R.

2017-01-01

The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models. PMID:28824268

Barriers to regaining control within a constructivist grounded theory of family resilience in ICU: Living with uncertainty.

PubMed

Wong, Pauline; Liamputtong, Pranee; Koch, Susan; Rawson, Helen

2017-12-01

To discuss families' experiences of their interactions when a relative is admitted unexpectedly to an Australian intensive care unit. The overwhelming emotions associated with the unexpected admission of a relative to an intensive care unit are often due to the uncertainty surrounding the condition of their critically ill relative. There is limited in-depth understanding of the nature of uncertainty experienced by families in intensive care, and interventions perceived by families to minimise their uncertainty are not well documented. Furthermore, the interrelationships between factors, such as staff-family interactions and the intensive care unit environment, and its influence on families' uncertainty particularly in the context of the Australian healthcare system, are not well delineated. A grounded theory methodology was adopted for the study. Data were collected between 2009-2013, using in-depth interviews with 25 family members of 21 critically ill patients admitted to a metropolitan, tertiary-level intensive care unit in Australia. This paper describes the families experiences of heightened emotional vulnerability and uncertainty when a relative is admitted unexpectedly to the intensive care unit. Families uncertainty is directly influenced by their emotional state, the foreign environment and perceptions of being 'kept in the dark', as well as the interrelationships between these factors. Staff are offered an improved understanding of the barriers to families' ability to regain control, guided by a grounded theory of family resilience in the intensive care unit. The findings reveal in-depth understanding of families' uncertainty in intensive care. It suggests that intensive care unit staff need to focus clinical interventions on reducing factors that heighten their uncertainty, while optimising strategies that help alleviate it. Families are facilitated to move beyond feelings of helplessness and loss of control, and cope better with their situation. © 2017 John Wiley & Sons Ltd.

Parton shower and NLO-matching uncertainties in Higgs boson pair production

NASA Astrophysics Data System (ADS)

Jones, Stephen; Kuttimalai, Silvan

2018-02-01

We perform a detailed study of NLO parton shower matching uncertainties in Higgs boson pair production through gluon fusion at the LHC based on a generic and process independent implementation of NLO subtraction and parton shower matching schemes for loop-induced processes in the Sherpa event generator. We take into account the full top-quark mass dependence in the two-loop virtual corrections and compare the results to an effective theory approximation. In the full calculation, our findings suggest large parton shower matching uncertainties that are absent in the effective theory approximation. We observe large uncertainties even in regions of phase space where fixed-order calculations are theoretically well motivated and parton shower effects expected to be small. We compare our results to NLO matched parton shower simulations and analytic resummation results that are available in the literature.

A new look at the theory uncertainty of ϵ K

DOE PAGES

Ligeti, Z.; Sala, F.

2016-09-01

The observable ϵ K is sensitive to flavor violation at some of the highest scales. While its experimental uncertainty is at the half percent level, the theoretical one is in the ballpark of 15%. We explore the nontrivial dependence of the theory prediction and uncertainty on various conventions, like the phase of the kaon fields. In particular, we show how such a rephasing allows to make the short-distance contribution of the box diagram with two charm quarks, η cc , purely real. Our results allow to slightly reduce the total theoretical uncertainty of ϵ K , while increasing themore » relative impact of the imaginary part of the long distance contribution, underlining the need to compute it reliably. We also give updated bounds on the new physics operators that contribute to ϵ K .« less

A new look at the theory uncertainty of ϵ K

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ligeti, Z.; Sala, F.

The observable ϵ K is sensitive to flavor violation at some of the highest scales. While its experimental uncertainty is at the half percent level, the theoretical one is in the ballpark of 15%. We explore the nontrivial dependence of the theory prediction and uncertainty on various conventions, like the phase of the kaon fields. In particular, we show how such a rephasing allows to make the short-distance contribution of the box diagram with two charm quarks, η cc , purely real. Our results allow to slightly reduce the total theoretical uncertainty of ϵ K , while increasing themore » relative impact of the imaginary part of the long distance contribution, underlining the need to compute it reliably. We also give updated bounds on the new physics operators that contribute to ϵ K .« less

Uncertainty

USGS Publications Warehouse

Hunt, Randall J.

2012-01-01

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Increased scientific rigor will improve reliability of research and effectiveness of management

USGS Publications Warehouse

Sells, Sarah N.; Bassing, Sarah B.; Barker, Kristin J.; Forshee, Shannon C.; Keever, Allison; Goerz, James W.; Mitchell, Michael S.

2018-01-01

Rigorous science that produces reliable knowledge is critical to wildlife management because it increases accurate understanding of the natural world and informs management decisions effectively. Application of a rigorous scientific method based on hypothesis testing minimizes unreliable knowledge produced by research. To evaluate the prevalence of scientific rigor in wildlife research, we examined 24 issues of the Journal of Wildlife Management from August 2013 through July 2016. We found 43.9% of studies did not state or imply a priori hypotheses, which are necessary to produce reliable knowledge. We posit that this is due, at least in part, to a lack of common understanding of what rigorous science entails, how it produces more reliable knowledge than other forms of interpreting observations, and how research should be designed to maximize inferential strength and usefulness of application. Current primary literature does not provide succinct explanations of the logic behind a rigorous scientific method or readily applicable guidance for employing it, particularly in wildlife biology; we therefore synthesized an overview of the history, philosophy, and logic that define scientific rigor for biological studies. A rigorous scientific method includes 1) generating a research question from theory and prior observations, 2) developing hypotheses (i.e., plausible biological answers to the question), 3) formulating predictions (i.e., facts that must be true if the hypothesis is true), 4) designing and implementing research to collect data potentially consistent with predictions, 5) evaluating whether predictions are consistent with collected data, and 6) drawing inferences based on the evaluation. Explicitly testing a priori hypotheses reduces overall uncertainty by reducing the number of plausible biological explanations to only those that are logically well supported. Such research also draws inferences that are robust to idiosyncratic observations and unavoidable human biases. Offering only post hoc interpretations of statistical patterns (i.e., a posteriorihypotheses) adds to uncertainty because it increases the number of plausible biological explanations without determining which have the greatest support. Further, post hocinterpretations are strongly subject to human biases. Testing hypotheses maximizes the credibility of research findings, makes the strongest contributions to theory and management, and improves reproducibility of research. Management decisions based on rigorous research are most likely to result in effective conservation of wildlife resources. 

Uncertainty and risk in wildland fire management: A review

Treesearch

Matthew P. Thompson; Dave E. Calkin

2011-01-01

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to...

Social, institutional, and psychological factors affecting wildfire incident decision making

Treesearch

Matthew P. Thompson

2014-01-01

Managing wildland fire incidents can be fraught with complexity and uncertainty. Myriad human factors can exert significant influence on incident decision making, and can contribute additional uncertainty regarding programmatic evaluations of wildfire management and attainment of policy goals. This article develops a framework within which human sources of uncertainty...

The ends of uncertainty: Air quality science and planning in Central California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fine, James

Air quality planning in Central California is complicated and controversial despite millions of dollars invested to improve scientific understanding. This research describes and critiques the use of photochemical air quality simulation modeling studies in planning to attain standards for ground-level ozone in the San Francisco Bay Area and the San Joaquin Valley during the 1990's. Data are gathered through documents and interviews with planners, modelers, and policy-makers at public agencies and with representatives from the regulated and environmental communities. Interactions amongst organizations are diagramed to identify significant nodes of interaction. Dominant policy coalitions are described through narratives distinguished by theirmore » uses of and responses to uncertainty, their exposures to risks, and their responses to the principles of conservatism, civil duty, and caution. Policy narratives are delineated using aggregated respondent statements to describe and understand advocacy coalitions. I found that models impacted the planning process significantly, but were used not purely for their scientific capabilities. Modeling results provided justification for decisions based on other constraints and political considerations. Uncertainties were utilized opportunistically by stakeholders instead of managed explicitly. Ultimately, the process supported the partisan views of those in control of the modeling. Based on these findings, as well as a review of model uncertainty analysis capabilities, I recommend modifying the planning process to allow for the development and incorporation of uncertainty information, while addressing the need for inclusive and meaningful public participation. By documenting an actual air quality planning process these findings provide insights about the potential for using new scientific information and understanding to achieve environmental goals, most notably the analysis of uncertainties in modeling applications. Concurrently, needed uncertainty information is identified and capabilities to produce it are assessed. Practices to facilitate incorporation of uncertainty information are suggested based on research findings, as well as theory from the literatures of the policy sciences, decision sciences, science and technology studies, consensus-based and communicative planning, and modeling.« less

Adjoint-Based Uncertainty Quantification with MCNP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seifried, Jeffrey E.

2011-09-01

This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence inmore » the simulation is acquired.« less

Strong Unitary and Overlap Uncertainty Relations: Theory and Experiment

NASA Astrophysics Data System (ADS)

Bong, Kok-Wei; Tischler, Nora; Patel, Raj B.; Wollmann, Sabine; Pryde, Geoff J.; Hall, Michael J. W.

2018-06-01

We derive and experimentally investigate a strong uncertainty relation valid for any n unitary operators, which implies the standard uncertainty relation and others as special cases, and which can be written in terms of geometric phases. It is saturated by every pure state of any n -dimensional quantum system, generates a tight overlap uncertainty relation for the transition probabilities of any n +1 pure states, and gives an upper bound for the out-of-time-order correlation function. We test these uncertainty relations experimentally for photonic polarization qubits, including the minimum uncertainty states of the overlap uncertainty relation, via interferometric measurements of generalized geometric phases.

Some applications of uncertainty relations in quantum information

NASA Astrophysics Data System (ADS)

Majumdar, A. S.; Pramanik, T.

2016-08-01

We discuss some applications of various versions of uncertainty relations for both discrete and continuous variables in the context of quantum information theory. The Heisenberg uncertainty relation enables demonstration of the Einstein, Podolsky and Rosen (EPR) paradox. Entropic uncertainty relations (EURs) are used to reveal quantum steering for non-Gaussian continuous variable states. EURs for discrete variables are studied in the context of quantum memory where fine-graining yields the optimum lower bound of uncertainty. The fine-grained uncertainty relation is used to obtain connections between uncertainty and the nonlocality of retrieval games for bipartite and tripartite systems. The Robertson-Schrödinger (RS) uncertainty relation is applied for distinguishing pure and mixed states of discrete variables.

Adaptive management for ecosystem services (j/a)

EPA Science Inventory

Management of natural resources for the production of ecosystem services, which are vital for human well-being, is necessary even when there is uncertainty regarding system response to management action. This uncertainty is the result of incomplete controllability, complex intern...

A generalized Lyapunov theory for robust root clustering of linear state space models with real parameter uncertainty

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1992-01-01

The problem of analyzing and designing controllers for linear systems subject to real parameter uncertainty is considered. An elegant, unified theory for robust eigenvalue placement is presented for a class of D-regions defined by algebraic inequalities by extending the nominal matrix root clustering theory of Gutman and Jury (1981) to linear uncertain time systems. The author presents explicit conditions for matrix root clustering for different D-regions and establishes the relationship between the eigenvalue migration range and the parameter range. The bounds are all obtained by one-shot computation in the matrix domain and do not need any frequency sweeping or parameter gridding. The method uses the generalized Lyapunov theory for getting the bounds.

Study of Electron G-2 From 1947 To Present

NASA Astrophysics Data System (ADS)

Kinoshita, Toichiro

2014-03-01

In 1947 Kusch and Foley discovered in the study of Zeeman splitting of Ga atom that the electron g-factor was about 0.2% larger than the value 2 predicted by the Dirac equation. Soon afterwards Schwinger showed that it can be explained as the effect of radiative correction. His calculation, in the second order perturbation theory of the Lorentz invariant formulation of renormalized quantum electrodynamics, showed that the electron has an excess magnetic moment ae ≡ (g - 2) / 2 = α / (2 π) , where α is the fine structure constant, in agreement with the measurement within 3%. Thus began a long series of friendly competition between experimentalists and theorists to improve the precision of ae. Over the period of more than 60 years measurement precision of ae was improved by more than 104 by the spin precession technique, and further 103 by the Penning trap experiments. In step with the progress of measurement, the theory of ae, expressed as a power series in α, has been pushed to the fifth power of α. Including small contributions from hadronic effects and weak interaction effect and using the best non-QED value of α: α-1 = 137 . 035999049 (90) , one finds ae (theory) = 1159652181 . 72 (77) ×10-12 . The uncertainty is about 0 . 66 ppb , where 1 ppb =10-9 . The intrinsic uncertainty of theory itself is less than 0 . 1 ppb . The over all uncertainty comes mostly from the uncertainty of non-QED α mentioned above, which is about 0 . 66 ppb . This is in good agreement with the latest measurement: ae (experiment) = 1159652180 . 73 (28) ×10-12 . The uncertainty of measurement is 0 . 24 ppb . An alternate approach to test QED is to assume the validity of QED (and the Standard Model of particle physics) and obtain α by solving the equation ae (experiment) =ae (theory) . This yields α-1 (ae) = 137 . 0359991727 (342) , whose uncertainty is 0 . 25 ppb , better than α obtained by any other means. Although comparison of theory and experiment of ae began historically as a test of the validity of QED, it has now evolved into a precision test of fine structure constant at the level exceeding 1 ppb , which may be regarded as a test of internal consistency of quantum mechanics as a whole. Supported in part by the U. S. National Science Foundation under Grant No. NSF-PHY-0757868.

Uncertainty Analysis of Consequence Management (CM) Data Products.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, Brian D.; Eckert-Gallup, Aubrey Celia; Cochran, Lainy Dromgoole

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

Uncertainty in BMP evaluation and optimization for watershed management

NASA Astrophysics Data System (ADS)

Chaubey, I.; Cibin, R.; Sudheer, K.; Her, Y.

2012-12-01

Use of computer simulation models have increased substantially to make watershed management decisions and to develop strategies for water quality improvements. These models are often used to evaluate potential benefits of various best management practices (BMPs) for reducing losses of pollutants from sources areas into receiving waterbodies. Similarly, use of simulation models in optimizing selection and placement of best management practices under single (maximization of crop production or minimization of pollutant transport) and multiple objective functions has increased recently. One of the limitations of the currently available assessment and optimization approaches is that the BMP strategies are considered deterministic. Uncertainties in input data (e.g. precipitation, streamflow, sediment, nutrient and pesticide losses measured, land use) and model parameters may result in considerable uncertainty in watershed response under various BMP options. We have developed and evaluated options to include uncertainty in BMP evaluation and optimization for watershed management. We have also applied these methods to evaluate uncertainty in ecosystem services from mixed land use watersheds. In this presentation, we will discuss methods to to quantify uncertainties in BMP assessment and optimization solutions due to uncertainties in model inputs and parameters. We have used a watershed model (Soil and Water Assessment Tool or SWAT) to simulate the hydrology and water quality in mixed land use watershed located in Midwest USA. The SWAT model was also used to represent various BMPs in the watershed needed to improve water quality. SWAT model parameters, land use change parameters, and climate change parameters were considered uncertain. It was observed that model parameters, land use and climate changes resulted in considerable uncertainties in BMP performance in reducing P, N, and sediment loads. In addition, climate change scenarios also affected uncertainties in SWAT simulated crop yields. Considerable uncertainties in the net cost and the water quality improvements resulted due to uncertainties in land use, climate change, and model parameter values.

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

Treatment of uncertainties in the IPCC: a philosophical analysis

NASA Astrophysics Data System (ADS)

Jebeile, J.; Drouet, I.

2014-12-01

The IPCC produces scientific reports out of findings on climate and climate change. Because the findings are uncertain in many respects, the production of reports requires aggregating assessments of uncertainties of different kinds. This difficult task is currently regulated by the Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. The note recommends that two metrics—i.e. confidence and likelihood— be used for communicating the degree of certainty in findings. Confidence is expressed qualitatively "based on the type, amount, quality, and consistency of evidence […] and the degree of agreement", while likelihood is expressed probabilistically "based on statistical analysis of observations or model results, or expert judgment". Therefore, depending on the evidence evaluated, authors have the choice to present either an assigned level of confidence or a quantified measure of likelihood. But aggregating assessments of uncertainties of these two different kinds express distinct and conflicting methodologies. So the question arises whether the treatment of uncertainties in the IPCC is rationally justified. In order to answer the question, it is worth comparing the IPCC procedures with the formal normative theories of epistemic rationality which have been developed by philosophers. These theories—which include contributions to the philosophy of probability and to bayesian probabilistic confirmation theory—are relevant for our purpose because they are commonly used to assess the rationality of common collective jugement formation based on uncertain knowledge. In this paper we make the comparison and pursue the following objectives: i/we determine whether the IPCC confidence and likelihood can be compared with the notions of uncertainty targeted by or underlying the formal normative theories of epistemic rationality; ii/we investigate whether the formal normative theories of epistemic rationality justify treating uncertainty along those two dimensions, and indicate how this can be avoided.

An Analytical Framework for Soft and Hard Data Fusion: A Dempster-Shafer Belief Theoretic Approach

DTIC Science & Technology

2012-08-01

fusion. Therefore, we provide a detailed discussion on uncertain data types, their origins and three uncertainty pro- cessing formalisms that are popular...suitable membership functions corresponding to the fuzzy sets. 3.2.3 DS Theory The DS belief theory, originally proposed by Dempster, can be thought of as... originated and various imperfections of the source. Uncertainty handling formalisms provide techniques for modeling and working with these uncertain data types

Leadership of risk decision making in a complex, technology organization: The deliberative decision making model

NASA Astrophysics Data System (ADS)

Flaming, Susan C.

2007-12-01

The continuing saga of satellite technology development is as much a story of successful risk management as of innovative engineering. How do program leaders on complex, technology projects manage high stakes risks that threaten business success and satellite performance? This grounded theory study of risk decision making portrays decision leadership practices at one communication satellite company. Integrated product team (IPT) leaders of multi-million dollar programs were interviewed and observed to develop an extensive description of the leadership skills required to navigate organizational influences and drive challenging risk decisions to closure. Based on the study's findings the researcher proposes a new decision making model, Deliberative Decision Making, to describe the program leaders' cognitive and organizational leadership practices. This Deliberative Model extends the insights of prominent decision making models including the rational (or classical) and the naturalistic and qualifies claims made by bounded rationality theory. The Deliberative Model describes how leaders proactively engage resources to play a variety of decision leadership roles. The Model incorporates six distinct types of leadership decision activities, undertaken in varying sequence based on the challenges posed by specific risks. Novel features of the Deliberative Decision Model include: an inventory of leadership methods for managing task challenges, potential stakeholder bias and debates; four types of leadership meta-decisions that guide decision processes, and aligned organizational culture. Both supporting and constraining organizational influences were observed as leaders managed major risks, requiring active leadership on the most difficult decisions. Although the company's engineering culture emphasized the importance of data-based decisions, the uncertainties intrinsic to satellite risks required expert engineering judgment to be exercised throughout. An investigation into the co-variation of decision methods with uncertainty suggests that perceived risk severity may serve as a robust indicator for choices about decision practices. The Deliberative Decision processes incorporate multiple organizational and cultural controls as cross-checks to mitigate potential parochial bias of individuals, stakeholder groups, or leaders. Overall the Deliberative Decision framework describes how expert leadership practices, supportive organizational systems along with aligned cultural values and behavioral norms help leaders drive high stakes risk decisions to closure in this complex, advanced-technology setting.

Knowledge-Based Systems Approach to Wilderness Fire Management.

NASA Astrophysics Data System (ADS)

Saveland, James M.

The 1988 and 1989 forest fire seasons in the Intermountain West highlight the shortcomings of current fire policy. To fully implement an optimization policy that minimizes the costs and net value change of resources affected by fire, long-range fire severity information is essential, yet lacking. This information is necessary for total mobility of suppression forces, implementing contain and confine suppression strategies, effectively dealing with multiple fire situations, scheduling summer prescribed burning, and wilderness fire management. A knowledge-based system, Delphi, was developed to help provide long-range information. Delphi provides: (1) a narrative of advice on where a fire might spread, if allowed to burn, (2) a summary of recent weather and fire danger information, and (3) a Bayesian analysis of long-range fire danger potential. Uncertainty is inherent in long-range information. Decision theory and judgment research can be used to help understand the heuristics experts use to make decisions under uncertainty, heuristics responsible both for expert performance and bias. Judgment heuristics and resulting bias are examined from a fire management perspective. Signal detection theory and receiver operating curve (ROC) analysis can be used to develop a long-range forecast to improve decisions. ROC analysis mimics some of the heuristics and compensates for some of the bias. Most importantly, ROC analysis displays a continuum of bias from which an optimum operating point can be selected. ROC analysis is especially appropriate for long-range forecasting since (1) the occurrence of possible future events is stated in terms of probability, (2) skill prediction is displayed, (3) inherent trade-offs are displayed, and (4) fire danger is explicitly defined. Statements on the probability of the energy release component of the National Fire Danger Rating System exceeding a critical value later in the fire season can be made early July in the Intermountain West. Delphi was evaluated formally and informally. Continual evaluation and feedback to update knowledge-based systems results in a repository for current knowledge, and a means to devise policy that will augment existing knowledge. Thus, knowledge-based systems can help implement adaptive resource management.

50 CFR 648.141 - Closure.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Bass Monitoring Committee shall identify and review the relevant sources of management uncertainty to... sources of management uncertainty that were considered, technical approaches to mitigating these sources..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Black Sea Bass...

50 CFR 648.121 - Closures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... management uncertainty to recommend ACTs for the commercial and recreational fishing sectors as part of the... sources of management uncertainty that were considered, technical approaches to mitigating these sources..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Scup Fishery...

Life-cycle assessment of municipal solid waste management alternatives with consideration of uncertainty: SIWMS development and application.

PubMed

Hanandeh, Ali El; El-Zein, Abbas

2010-05-01

This paper describes the development and application of the Stochastic Integrated Waste Management Simulator (SIWMS) model. SIWMS provides a detailed view of the environmental impacts and associated costs of municipal solid waste (MSW) management alternatives under conditions of uncertainty. The model follows a life-cycle inventory approach extended with compensatory systems to provide more equitable bases for comparing different alternatives. Economic performance is measured by the net present value. The model is verified against four publicly available models under deterministic conditions and then used to study the impact of uncertainty on Sydney's MSW management 'best practices'. Uncertainty has a significant effect on all impact categories. The greatest effect is observed in the global warming category where a reversal of impact direction is predicted. The reliability of the system is most sensitive to uncertainties in the waste processing and disposal. The results highlight the importance of incorporating uncertainty at all stages to better understand the behaviour of the MSW system. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Assessing and reporting uncertainties in dietary exposure analysis - Part II: Application of the uncertainty template to a practical example of exposure assessment.

PubMed

Tennant, David; Bánáti, Diána; Kennedy, Marc; König, Jürgen; O'Mahony, Cian; Kettler, Susanne

2017-11-01

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Matthew Reynolds | NREL

Science.gov Websites

food science. Matthew's research at NREL is focused on applying uncertainty quantification techniques . Research Interests Uncertainty quantification Computational multilinear algebra Approximation theory of and the Canonical Tensor Decomposition, Journal of Computational Physics (2017) Randomized Alternating

Parton shower and NLO-matching uncertainties in Higgs boson pair production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Stephen; Kuttimalai, Silvan

We perform a detailed study of NLO parton shower matching uncertainties in Higgs boson pair production through gluon fusion at the LHC based on a generic and process independent implementation of NLO subtraction and parton shower matching schemes for loop-induced processes in the Sherpa event generator. We take into account the full top-quark mass dependence in the two-loop virtual corrections and compare the results to an effective theory approximation. In the full calculation, our findings suggest large parton shower matching uncertainties that are absent in the effective theory approximation. Here, we observe large uncertainties even in regions of phase spacemore » where fixed-order calculations are theoretically well motivated and parton shower effects expected to be small. We compare our results to NLO matched parton shower simulations and analytic resummation results that are available in the literature.« less

Parton shower and NLO-matching uncertainties in Higgs boson pair production

DOE PAGES

Jones, Stephen; Kuttimalai, Silvan

2018-02-28

We perform a detailed study of NLO parton shower matching uncertainties in Higgs boson pair production through gluon fusion at the LHC based on a generic and process independent implementation of NLO subtraction and parton shower matching schemes for loop-induced processes in the Sherpa event generator. We take into account the full top-quark mass dependence in the two-loop virtual corrections and compare the results to an effective theory approximation. In the full calculation, our findings suggest large parton shower matching uncertainties that are absent in the effective theory approximation. Here, we observe large uncertainties even in regions of phase spacemore » where fixed-order calculations are theoretically well motivated and parton shower effects expected to be small. We compare our results to NLO matched parton shower simulations and analytic resummation results that are available in the literature.« less

Research on uncertainty evaluation measure and method of voltage sag severity

NASA Astrophysics Data System (ADS)

Liu, X. N.; Wei, J.; Ye, S. Y.; Chen, B.; Long, C.

2018-01-01

Voltage sag is an inevitable serious problem of power quality in power system. This paper focuses on a general summarization and reviews on the concepts, indices and evaluation methods about voltage sag severity. Considering the complexity and uncertainty of influencing factors, damage degree, the characteristics and requirements of voltage sag severity in the power source-network-load sides, the measure concepts and their existing conditions, evaluation indices and methods of voltage sag severity have been analyzed. Current evaluation techniques, such as stochastic theory, fuzzy logic, as well as their fusion, are reviewed in detail. An index system about voltage sag severity is provided for comprehensive study. The main aim of this paper is to propose thought and method of severity research based on advanced uncertainty theory and uncertainty measure. This study may be considered as a valuable guide for researchers who are interested in the domain of voltage sag severity.

Management Choices in an Uncertain Future: Navigating Snow, Precipitation, and Temperature Projections in the Pacific Northwest U.S. to Assess Water Management Alternatives

NASA Astrophysics Data System (ADS)

Luce, C.

2014-12-01

Climate and hydrology models are regularly applied to assess potential changes in water resources and to inform adaptation decisions. An increasingly common question is, "What if we are wrong?" While climate models show substantial agreement on metrics such as pressure, temperature, and wind, they are notoriously uncertain in projecting precipitation change. The response to that uncertainty varies depending on the water management context and the nature of the uncertainty. In the southwestern U.S., large storage reservoirs (relative to annual supply) and general expectations of decreasing precipitation have guided extensive discussion on water management towards uncertainties in annual-scale water balances, precipitation, and evapotranspiration. In contrast, smaller reservoirs and little expectation for change in annual precipitation have focused discussions of Pacific Northwest water management toward shifts in runoff seasonality. The relative certainty of temperature impacts on snowpacks compared to the substantial uncertainty in precipitation has yielded a consistent narrative on earlier snowmelt. This narrative has been reinforced by a perception of essentially the same behavior in the historical record. This perception has led to calls in the political arena for more reservoir storage to replace snowpack storage for water supplies. Recent findings on differences in trends in precipitation at high versus low elevations, however, has recalled the uncertainty in precipitation futures and generated questions about alternative water management strategies. An important question with respect to snowpacks is whether the precipitation changes matter in the context of such substantial projections for temperature change. Here we apply an empirical snowpack model to analyze spatial differences in the uncertainty of snowpack responses to temperature and precipitation forcing across the Pacific Northwest U.S. The analysis reveals a strong geographic gradient in uncertainty of snowpack response to future climate, from the coastal regions, where precipitation uncertainty is relatively inconsequential for snowpack changes, to interior mountains where minor uncertainties in precipitation are on par with expected changes relative to temperature.

On the Monte Carlo simulation of electron transport in the sub-1 keV energy range.

PubMed

Thomson, Rowan M; Kawrakow, Iwan

2011-08-01

The validity of "classic" Monte Carlo (MC) simulations of electron and positron transport at sub-1 keV energies is investigated in the context of quantum theory. Quantum theory dictates that uncertainties on the position and energy-momentum four-vectors of radiation quanta obey Heisenberg's uncertainty relation; however, these uncertainties are neglected in "classical" MC simulations of radiation transport in which position and momentum are known precisely. Using the quantum uncertainty relation and electron mean free path, the magnitudes of uncertainties on electron position and momentum are calculated for different kinetic energies; a validity bound on the classical simulation of electron transport is derived. In order to satisfy the Heisenberg uncertainty principle, uncertainties of 5% must be assigned to position and momentum for 1 keV electrons in water; at 100 eV, these uncertainties are 17 to 20% and are even larger at lower energies. In gaseous media such as air, these uncertainties are much smaller (less than 1% for electrons with energy 20 eV or greater). The classical Monte Carlo transport treatment is questionable for sub-1 keV electrons in condensed water as uncertainties on position and momentum must be large (relative to electron momentum and mean free path) to satisfy the quantum uncertainty principle. Simulations which do not account for these uncertainties are not faithful representations of the physical processes, calling into question the results of MC track structure codes simulating sub-1 keV electron transport. Further, the large difference in the scale at which quantum effects are important in gaseous and condensed media suggests that track structure measurements in gases are not necessarily representative of track structure in condensed materials on a micrometer or a nanometer scale.

Development of perspective-based water management strategies for the Rhine and Meuse basins.

PubMed

van Deursen, W P A; Middelkoop, H

2005-01-01

Water management is surrounded by uncertainties. Water management thus has to answer the question: given the uncertainties, what is the best management strategy? This paper describes the application of the perspectives method on water management in the Rhine and Meuse basins. In the perspectives method, a structured framework to analyse water management strategies under uncertainty is provided. Various strategies are clustered in perspectives according to their underlying assumptions. This framework allows for an analysis of current water management strategies, but also allows for evaluation of the robustness of proposed future water strategies. It becomes clear that no water management strategy is superior to the others, but that inherent choices on risk acceptance and costs make a real political dilemma which will not be solved by further optimisation.

Quantum Probability -- A New Direction for Modeling in Cognitive Science

NASA Astrophysics Data System (ADS)

Roy, Sisir

2014-07-01

Human cognition is still a puzzling issue in research and its appropriate modeling. It depends on how the brain behaves at that particular instance and identifies and responds to a signal among myriads of noises that are present in the surroundings (called external noise) as well as in the neurons themselves (called internal noise). Thus it is not surprising to assume that the functionality consists of various uncertainties, possibly a mixture of aleatory and epistemic uncertainties. It is also possible that a complicated pathway consisting of both types of uncertainties in continuum play a major role in human cognition. For more than 200 years mathematicians and philosophers have been using probability theory to describe human cognition. Recently in several experiments with human subjects, violation of traditional probability theory has been clearly revealed in plenty of cases. Literature survey clearly suggests that classical probability theory fails to model human cognition beyond a certain limit. While the Bayesian approach may seem to be a promising candidate to this problem, the complete success story of Bayesian methodology is yet to be written. The major problem seems to be the presence of epistemic uncertainty and its effect on cognition at any given time. Moreover the stochasticity in the model arises due to the unknown path or trajectory (definite state of mind at each time point), a person is following. To this end a generalized version of probability theory borrowing ideas from quantum mechanics may be a plausible approach. A superposition state in quantum theory permits a person to be in an indefinite state at each point of time. Such an indefinite state allows all the states to have the potential to be expressed at each moment. Thus a superposition state appears to be able to represent better, the uncertainty, ambiguity or conflict experienced by a person at any moment demonstrating that mental states follow quantum mechanics during perception and cognition of ambiguous figures.

Robust Control Design for Uncertain Nonlinear Dynamic Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

NASA Astrophysics Data System (ADS)

Raje, Deepashree; Mujumdar, P. P.

2010-09-01

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.

Forest management under uncertainty for multiple bird population objectives

USGS Publications Warehouse

Moore, C.T.; Plummer, W.T.; Conroy, M.J.; Ralph, C. John; Rich, Terrell D.

2005-01-01

We advocate adaptive programs of decision making and monitoring for the management of forest birds when responses by populations to management, and particularly management trade-offs among populations, are uncertain. Models are necessary components of adaptive management. Under this approach, uncertainty about the behavior of a managed system is explicitly captured in a set of alternative models. The models generate testable predictions about the response of populations to management, and monitoring data provide the basis for assessing these predictions and informing future management decisions. To illustrate these principles, we examine forest management at the Piedmont National Wildlife Refuge, where management attention is focused on the recovery of the Red-cockaded Woodpecker (Picoides borealis) population. However, managers are also sensitive to the habitat needs of many non-target organisms, including Wood Thrushes (Hylocichla mustelina) and other forest interior Neotropical migratory birds. By simulating several management policies on a set of-alternative forest and bird models, we found a decision policy that maximized a composite response by woodpeckers and Wood Thrushes despite our complete uncertainty regarding system behavior. Furthermore, we used monitoring data to update our measure of belief in each alternative model following one cycle of forest management. This reduction of uncertainty translates into a reallocation of model influence on the choice of optimal decision action at the next decision opportunity.

Temporal Vulnerability and the Post-Disaster 'Window of Opportunity to Woo:' a Case Study of an African-American Floodplain Neighborhood after Hurricane Floyd in North Carolina.

PubMed

de Vries, Daniel H

2017-01-01

After major flooding associated with Hurricane Floyd (1999) in North Carolina, mitigation managers seized upon the "window of opportunity" to woo residents to accept residential buyout offers despite sizable community resistance. I present a theoretical explanation of how post-crisis periods turn into "opportunities" based on a temporal referential theory that complements alternative explanations based on temporal coincidence, panarchy, and shock-doctrine theories. Results from fieldwork conducted from 2002 to 2004 illustrate how several temporal influences compromised collective calibration of "normalcy" in local cultural models, leading to an especially heightened vulnerability to collective surprise. Four factors particularly influenced this temporal vulnerability: 1) epistemological uncertainty of floodplain dynamics due to colonization; 2) cultural practices that maintained a casual amnesia; 3) meaning attributed to stochastic timing of floods; and 4) competitive impact of referential flood baseline attractors.

An optimization design for evacuation planning based on fuzzy credibility theory and genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, D.; Zhang, W. Y.

2017-08-01

Evacuation planning is an important activity in disaster management. It has to be planned in advance due to the unpredictable occurrence of disasters. It is necessary that the evacuation plans are as close as possible to the real evacuation work. However, the evacuation plan is extremely challenging because of the inherent uncertainty of the required information. There is a kind of vehicle routing problem based on the public traffic evacuation. In this paper, the demand for each evacuation set point is a fuzzy number, and each routing selection of the point is based on the fuzzy credibility preference index. This paper proposes an approximate optimal solution for this problem by the genetic algorithm based on the fuzzy reliability theory. Finally, the algorithm is applied to an optimization model, and the experiment result shows that the algorithm is effective.

The theory of agency and breastfeeding.

PubMed

Ryan, Kath; Team, Victoria; Alexander, Jo

2017-03-01

In this paper, we apply psychological agency theory to women's interviews of their breastfeeding experiences to understand the role of agency in relation to breastfeeding initiation, maintenance and duration. Qualitative, video interviews were collected from 49 women in the UK from a wide range of ethnic, religious, educational and employment backgrounds about their breastfeeding experiences. We undertook secondary analysis of the data focusing on their accounts of vulnerability and agency. Women's agency was impacted by a variety of factors including their own vulnerability, knowledge, expectations and experience, the feeding environment and the support of health professionals in sharing decision-making and dealing with uncertainty. Health professionals as co-agents with women are well positioned to maintain, enhance or restore women's sense of agency. Breastfeeding goals should be included in women's birth plans. Training related to agency, continuity of care, and staffing and workload management supported by national breastfeeding policies could improve breastfeeding rates and experiences.

Hypertension and Cerebral Hemorrhage: A Malpractice Controversy

PubMed Central

Franklin, Stanley S.; Hunt, Marshall T.; Vogt, Thomas; Walsh, Gregory; Paglia, Donald E.

1980-01-01

The plaintiff alleged that failure of the attending physician to manage her husband's hypertension properly resulted in his death from intracerebral hemorrhage. Four lines of evidence supported the defendant: (1) In 1970 to 1971 there was uncertainty in the medical community whether mild hypertension should be treated with drugs; this uncertainty still existed at the time of the trial. (2) Severe hypertension and advanced age are the two most important predisposing factors leading to intracerebral hemorrhage; the deceased patient had neither. (3) Hemorrhage into the cerebral cortex and underlying white matter is not typical of hypertensive intracerebral bleeding; more likely, rupture of an arteriovenous malformation occurred. (4) A diagnosis of hypertensive intracerebral hemorrhage is not one of exclusion but requires objective evidence of vascular change in the brain, heart and kidney; these changes were not found in the deceased patient. In conclusion, an expert witness should testify objectively rather than be the advocate of a lawyer's theory of liability. ImagesFig. 6.Fig. 7.Fig. 9.Fig. 10. PMID:7233893

Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory.

PubMed

Chen, Qiuwen; Rui, Han; Li, Weifeng; Zhang, Yanhui

2014-06-01

Algal blooms are a serious problem in waters, which damage aquatic ecosystems and threaten drinking water safety. However, the outbreak mechanism of algal blooms is very complex with great uncertainty, especially for large water bodies where environmental conditions have obvious variation in both space and time. This study developed an innovative method which integrated a self-organizing map (SOM) and fuzzy information diffusion theory to comprehensively analyze algal bloom risks with uncertainties. The Lake Taihu was taken as study case and the long-term (2004-2010) on-site monitoring data were used. The results showed that algal blooms in Taihu Lake were classified into four categories and exhibited obvious spatial-temporal patterns. The lake was mainly characterized by moderate bloom but had high uncertainty, whereas severe blooms with low uncertainty were observed in the northwest part of the lake. The study gives insight on the spatial-temporal dynamics of algal blooms, and should help government and decision-makers outline policies and practices on bloom monitoring and prevention. The developed method provides a promising approach to estimate algal bloom risks under uncertainties. Copyright © 2014 Elsevier B.V. All rights reserved.

The uncertainty principle and quantum chaos

NASA Technical Reports Server (NTRS)

Chirikov, Boris V.

1993-01-01

The conception of quantum chaos is described in some detail. The most striking feature of this novel phenomenon is that all the properties of classical dynamical chaos persist here but, typically, on the finite and different time scales only. The ultimate origin of such a universal quantum stability is in the fundamental uncertainty principle which makes discrete the phase space and, hence, the spectrum of bounded quantum motion. Reformulation of the ergodic theory, as a part of the general theory of dynamical systems, is briefly discussed.

The application of Signalling Theory to health-related trust problems: The example of herbal clinics in Ghana and Tanzania.

PubMed

Hampshire, Kate; Hamill, Heather; Mariwah, Simon; Mwanga, Joseph; Amoako-Sakyi, Daniel

2017-09-01

In contexts where healthcare regulation is weak and levels of uncertainty high, how do patients decide whom and what to trust? In this paper, we explore the potential for using Signalling Theory (ST, a form of Behavioural Game Theory) to investigate health-related trust problems under conditions of uncertainty, using the empirical example of 'herbal clinics' in Ghana and Tanzania. Qualitative, ethnographic fieldwork was conducted over an eight-month period (2015-2016) in eight herbal clinics in Ghana and ten in Tanzania, including semi-structured interviews with herbalists (N = 18) and patients (N = 68), plus detailed ethnographic observations and twenty additional key informant interviews. The data were used to explore four ST-derived predictions, relating to herbalists' strategic communication ('signalling') of their trustworthiness to patients, and patients' interpretation of those signals. Signalling Theory is shown to provide a useful analytical framework, allowing us to go beyond the primary trust problem addressed by other researchers - cataloguing observable indicators of trustworthiness - and providing tools for tackling the trickier secondary trust problem, where the trustworthiness of those indicators must be ascertained. Signalling Theory also enables a basis for comparative work between different empirical contexts that share the underlying condition of uncertainty. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling and Performing Relational Theories in the Classroom

ERIC Educational Resources Information Center

Suter, Elizabeth A.; West, Carrie L.

2011-01-01

Although directly related to students' everyday lives, the abstract and even intimidating nature of relational theories often bars students from recognizing the immediate relevance to their relationships. The theories of symbolic interactionism, social exchange, relational dialectics, social penetration, and uncertainty reduction offer students…

Robustness for slope stability modelling under deep uncertainty

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

2015-04-01

Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.

The Value of Failing in Career Development: A Chaos Theory Perspective

ERIC Educational Resources Information Center

Pryor, Robert G. L.; Bright, James E. H.

2012-01-01

Failing is a neglected topic in career development theory and counselling practice. Most theories see failing as simply the opposite of success and something to be avoided. It is contended that the Chaos Theory of Careers with its emphasis on complexity, uncertainty and consequent human imitations, provides a conceptually coherent account of…

Managing Transition with Support: Experiences of Transition from Child and Adolescent Psychiatry to General Adult Psychiatry Narrated by Young Adults and Relatives

PubMed Central

Söderberg, Siv; Skär, Lisa

2014-01-01

Young adults with mental illness who need continuing care when they turn 18 are referred from child and adolescent psychiatry to general adult psychiatry. During this process, young adults are undergoing multiple transitions as they come of age while they transfer to another unit in healthcare. The aim of this study was to explore expectations and experiences of transition from child and adolescent psychiatry to general adult psychiatry as narrated by young adults and relatives. Individual interviews were conducted with three young adults and six relatives and analysed according to grounded theory. The analysis resulted in a core category: managing transition with support, and three categories: being of age but not mature, walking out of security and into uncertainty, and feeling omitted and handling concerns. The young adults' and relatives' main concerns were that they might be left out and feel uncertainty about the new situation during the transition process. To facilitate the transition process, individual care planning is needed. It is essential that young adults and relatives are participating in the process to be prepared for the changes and achieve a successful transition. Knowledge about the simultaneous processes seems to be an important issue for facilitating transition. PMID:24829900

"Utilizing" signal detection theory.

PubMed

Lynn, Spencer K; Barrett, Lisa Feldman

2014-09-01

What do inferring what a person is thinking or feeling, judging a defendant's guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows signal detection theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to signal detection theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A "utilized" signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. © The Author(s) 2014.

“UTILIZING” SIGNAL DETECTION THEORY

PubMed Central

Lynn, Spencer K.; Barrett, Lisa Feldman

2014-01-01

What do inferring what a person is thinking or feeling, deciding to report a symptom to your doctor, judging a defendant’s guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, which engender different appropriate responses), and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial we show how, by incorporating the economic concept of utility, signal detection theory serves as a model of optimal decision making, beyond its common use as an analytic method. This utility approach to signal detection theory highlights potentially enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (a functional relationship between bias and sensitivity). A “utilized” signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. PMID:25097061

Implications of theories of asteroid and comet impact for policy options for management of spent nuclear fuel and high-level radioactive wastes

USGS Publications Warehouse

Trask, Newell J.

1994-01-01

Concern with the threat posed by terrestrial asteroid and comet impacts has heightened as the catastrophic consequences of such events have become better appreciated. Although the probabilities of such impacts are very small, a reasonable question for debate is whether such phenomena should be taken into account in deciding policy for the management of spent fuel and high-level radioactive waste. The rate at which asteroid or comet impacts would affect areas of surface storage of radioactive waste is about the same as the estimated rate at which volcanic activity would affect the Yucca Mountain area. The Underground Retrievable Storage (URS) concept could satisfactorily reduce the risk from cosmic impact with its associated uncertainties in addition to providing other benefits described by previous authors.

Uncertainty and risk in wildland fire management: a review.

PubMed

Thompson, Matthew P; Calkin, Dave E

2011-08-01

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making. Published by Elsevier Ltd.

Handling Uncertain Gross Margin and Water Demand in Agricultural Water Resources Management using Robust Optimization

NASA Astrophysics Data System (ADS)

Chaerani, D.; Lesmana, E.; Tressiana, N.

2018-03-01

In this paper, an application of Robust Optimization in agricultural water resource management problem under gross margin and water demand uncertainty is presented. Water resource management is a series of activities that includes planning, developing, distributing and managing the use of water resource optimally. Water resource management for agriculture can be one of the efforts to optimize the benefits of agricultural output. The objective function of agricultural water resource management problem is to maximizing total benefits by water allocation to agricultural areas covered by the irrigation network in planning horizon. Due to gross margin and water demand uncertainty, we assume that the uncertain data lies within ellipsoidal uncertainty set. We employ robust counterpart methodology to get the robust optimal solution.

How accurate are lexile text measures?

PubMed

Stenner, A Jackson; Burdick, Hal; Sanford, Eleanor E; Burdick, Donald S

2006-01-01

The Lexile Framework for Reading models comprehension as the difference between a reader measure and a text measure. Uncertainty in comprehension rates results from unreliability in reader measures and inaccuracy in text readability measures. Whole-text processing eliminates sampling error in text measures. However, Lexile text measures are imperfect due to misspecification of the Lexile theory. The standard deviation component associated with theory misspecification is estimated at 64L for a standard-length passage (approximately 125 words). A consequence is that standard errors for longer texts (2,500 to 150,000 words) are measured on the Lexile scale with uncertainties in the single digits. Uncertainties in expected comprehension rates are largely due to imprecision in reader ability and not inaccuracies in text readabilities.

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

MANAGING UNCERTAINTIES ASSOCIATED WITH RADIOACTIVE WASTE DISPOSAL: TASK GROUP 4 OF THE IAEA PRISM PROJECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seitz, R.

2011-03-02

It is widely recognized that the results of safety assessment calculations provide an important contribution to the safety arguments for a disposal facility, but cannot in themselves adequately demonstrate the safety of the disposal system. The safety assessment and a broader range of arguments and activities need to be considered holistically to justify radioactive waste disposal at any particular site. Many programs are therefore moving towards the production of what has become known as a Safety Case, which includes all of the different activities that are conducted to demonstrate the safety of a disposal concept. Recognizing the growing interest inmore » the concept of a Safety Case, the International Atomic Energy Agency (IAEA) is undertaking an intercomparison and harmonization project called PRISM (Practical Illustration and use of the Safety Case Concept in the Management of Near-surface Disposal). The PRISM project is organized into four Task Groups that address key aspects of the Safety Case concept: Task Group 1 - Understanding the Safety Case; Task Group 2 - Disposal facility design; Task Group 3 - Managing waste acceptance; and Task Group 4 - Managing uncertainty. This paper addresses the work of Task Group 4, which is investigating approaches for managing the uncertainties associated with near-surface disposal of radioactive waste and their consideration in the context of the Safety Case. Emphasis is placed on identifying a wide variety of approaches that can and have been used to manage different types of uncertainties, especially non-quantitative approaches that have not received as much attention in previous IAEA projects. This paper includes discussions of the current results of work on the task on managing uncertainty, including: the different circumstances being considered, the sources/types of uncertainties being addressed and some initial proposals for approaches that can be used to manage different types of uncertainties.« less

Qualitative process study to explore the perceived burdens and benefits of a digital intervention for self-managing high blood pressure in Primary Care in the UK.

PubMed

Morton, Katherine; Dennison, Laura; Bradbury, Katherine; Band, Rebecca Jane; May, Carl; Raftery, James; Little, Paul; McManus, Richard J; Yardley, Lucy

2018-05-08

Digital interventions can change patients' experiences of managing their health, either creating additional burden or improving their experience of healthcare. This qualitative study aimed to explore perceived burdens and benefits for patients using a digital self-management intervention for reducing high blood pressure. A secondary aim was to further our understanding of how best to capture burdens and benefits when evaluating health interventions. Inductive qualitative process study nested in a randomised controlled trial. Primary Care in the UK. 35 participants taking antihypertensive medication and with uncontrolled blood pressure at baseline participated in semistructured telephone interviews. Digital self-management intervention to support blood pressure self-monitoring and medication change when recommended by the healthcare professional. Data were analysed using inductive thematic analysis with techniques from grounded theory. Seven themes were developed which reflected perceived burdens and benefits of using the intervention, including worry about health, uncertainty about self-monitoring and reassurance. The analysis showed how beliefs about their condition and treatment appeared to influence participants' appraisal of the value of the intervention. This suggested that considering illness and treatment perceptions in Burden of Treatment theory could further our understanding of how individuals appraise the personal costs and benefits of self-managing their health. Patients' appraisal of the burden or benefit of using a complex self-management intervention seemed to be influenced by experiences within the intervention (such as perceived availability of support) and beliefs about their condition and treatment (such as perceived control and risk of side effects). Developing our ability to adequately capture these salient burdens and benefits for patients could help enhance evaluation of self-management interventions in the future. Many participants perceived important benefits from using the intervention, highlighting the need for theory to recognise that engaging in self-management can include positive as well as negative aspects. ISRCTN13790648; Pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Methods for exploring uncertainty in groundwater management predictions

USGS Publications Warehouse

Guillaume, Joseph H. A.; Hunt, Randall J.; Comunian, Alessandro; Fu, Baihua; Blakers, Rachel S; Jakeman, Anthony J.; Barreteau, Olivier; Hunt, Randall J.; Rinaudo, Jean-Daniel; Ross, Andrew

2016-01-01

Models of groundwater systems help to integrate knowledge about the natural and human system covering different spatial and temporal scales, often from multiple disciplines, in order to address a range of issues of concern to various stakeholders. A model is simply a tool to express what we think we know. Uncertainty, due to lack of knowledge or natural variability, means that there are always alternative models that may need to be considered. This chapter provides an overview of uncertainty in models and in the definition of a problem to model, highlights approaches to communicating and using predictions of uncertain outcomes and summarises commonly used methods to explore uncertainty in groundwater management predictions. It is intended to raise awareness of how alternative models and hence uncertainty can be explored in order to facilitate the integration of these techniques with groundwater management.

Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Weaver, Jesse R.

In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexitymore » and human cognition. Here we build on J{\\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the 2012 Billion Triple dataset from the Semantic Web Challenge.« less

On the relativity and uncertainty of distance, time, and energy measurements by man. (1) Derivation of the Weber psychophysical law from the Heisenberg uncertainty principle applied to a superconductive biological detector. (2) The reverse derivation. (3) A human theory of relativity.

PubMed

Cope, F W

1981-01-01

The Weber psychophysical law, which describes much experimental data on perception by man, is derived from the Heisenberg uncertainty principle on the assumption that human perception occurs by energy detection by superconductive microregions within man . This suggests that psychophysical perception by man might be considered merely a special case of physical measurement in general. The reverse derivation-i.e., derivation of the Heisenberg principle from the Weber law-may be of even greater interest. It suggest that physical measurements could be regarded as relative to the perceptions by the detectors within man. Thus one may develop a "human" theory of relativity that could have the advantage of eliminating hidden assumptions by forcing physical theories to conform more completely to the measurements made by man rather than to concepts that might not accurately describe nature.

Adaptive management: Chapter 1

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.; Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Adaptive management

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Advanced Computational Framework for Environmental Management ZEM, Version 1.x

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vesselinov, Velimir V.; O'Malley, Daniel; Pandey, Sachin

2016-11-04

Typically environmental management problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly integration into a series of models with different complexity for various types of model analyses where the data are applied as soft and hard model constraints. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions).more » To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian- Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org). The ZEM framework is open-source and can be applied to any environmental management site. The framework will be open-source and released under GPL V3 license.« less

An adaptive decision framework for the conservation of a threatened plant

USGS Publications Warehouse

Moore, Clinton T.; Fonnesbeck, Christopher J.; Shea, Katriona; Lah, Kristopher J.; McKenzie, Paul M.; Ball, Lianne C.; Runge, Michael C.; Alexander, Helen M.

2011-01-01

Mead's milkweed Asclepias meadii, a long-lived perennial herb of tallgrass prairie and glade communities of the central United States, is a species designated as threatened under the U.S. Endangered Species Act. Challenges to its successful management include the facts that much about its life history is unknown, its age at reproductive maturity is very advanced, certain life stages are practically unobservable, its productivity is responsive to unpredictable environmental events, and most of the known populations occur on private lands unprotected by any legal conservation instrument. One critical source of biological uncertainty is the degree to which fire promotes growth and reproductive response in the plant. To aid in its management, we developed a prototype population-level state-dependent decision-making framework that explicitly accounts for this uncertainty and for uncertainties related to stochastic environmental effects and vital rates. To parameterize the decision model, we used estimates found in the literature, and we analyzed data from a long-term monitoring program where fates of individual plants were observed through time. We demonstrate that different optimal courses of action are followed according to how one believes that fire influences reproductive response, and we show that the action taken for certain population states is informative for resolving uncertainty about competing beliefs regarding the effect of fire. We advocate the use of a model-predictive approach for the management of rare populations, particularly when management uncertainty is profound. Over time, an adaptive management approach should reduce uncertainty and improve management performance as predictions of management outcome generated under competing models are continually informed and updated by monitoring data.

Optimal control, investment and utilization schemes for energy storage under uncertainty

NASA Astrophysics Data System (ADS)

Mirhosseini, Niloufar Sadat

Energy storage has the potential to offer new means for added flexibility on the electricity systems. This flexibility can be used in a number of ways, including adding value towards asset management, power quality and reliability, integration of renewable resources and energy bill savings for the end users. However, uncertainty about system states and volatility in system dynamics can complicate the question of when to invest in energy storage and how best to manage and utilize it. This work proposes models to address different problems associated with energy storage within a microgrid, including optimal control, investment, and utilization. Electric load, renewable resources output, storage technology cost and electricity day-ahead and spot prices are the factors that bring uncertainty to the problem. A number of analytical methodologies have been adopted to develop the aforementioned models. Model Predictive Control and discretized dynamic programming, along with a new decomposition algorithm are used to develop optimal control schemes for energy storage for two different levels of renewable penetration. Real option theory and Monte Carlo simulation, coupled with an optimal control approach, are used to obtain optimal incremental investment decisions, considering multiple sources of uncertainty. Two stage stochastic programming is used to develop a novel and holistic methodology, including utilization of energy storage within a microgrid, in order to optimally interact with energy market. Energy storage can contribute in terms of value generation and risk reduction for the microgrid. The integration of the models developed here are the basis for a framework which extends from long term investments in storage capacity to short term operational control (charge/discharge) of storage within a microgrid. In particular, the following practical goals are achieved: (i) optimal investment on storage capacity over time to maximize savings during normal and emergency operations; (ii) optimal market strategy of buy and sell over 24-hour periods; (iii) optimal storage charge and discharge in much shorter time intervals.

Water, Resilience and the Law: From General Concepts and Governance Design Principles to Actionable Mechanisms

NASA Astrophysics Data System (ADS)

Hill Clarvis, M.; Allan, A.; Hannah, D. M.

2013-12-01

Climate change has significant ramifications for water law and governance, yet, there is strong evidence that legal regulations have often failed to protect environments or promote sustainable development. Scholars have increasingly suggested that the preservation and restoration paradigms of legislation and regulation are no longer adequate for climate change related challenges in complex and cross-scale social-ecological systems. This is namely due to past assumptions of stationarity, uniformitarianism and the perception of ecosystem change as predictable and reversible. This paper reviews the literature on law and resilience and then presents and discusses a set of practical examples of legal mechanisms from the water resources management sector, identified according to a set of guiding principles from the literature on adaptive capacity, adaptive governance as well as adaptive and integrated water resources management. It then assesses the aptness of these different measures according to scientific evidence of increased uncertainty and changing ecological baselines. A review of the best practice examples demonstrates that there are a number of best practice examples attempting to integrate adaptive elements of flexibility, iterativity, connectivity and subsidiarity into a variety of legislative mechanisms, suggesting that there is not as significant a tension between resilience and the law as many scholars have suggested. However, while many of the mechanisms may indeed be suitable for addressing challenges relating to current levels of change and uncertainty, analysis across a broader range of uncertainty highlights challenges relating to more irreversible changes associated with greater levels of warming. Furthermore the paper identifies a set of pre-requisites that are fundamental to the successful implementation of such mechanisms, namely monitoring and data sharing, financial and technical capacity, particularly in nations that are most at risk with the least data infrastructure. The article aims to contribute to both theory and practice, enabling policy makers to translate resilience based terminology and adaptive governance principles into clear instructions for incorporating uncertainty into legislation and policy design.

Recognizing Uncertainty in the Q-Matrix via a Bayesian Extension of the DINA Model

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2012-01-01

In the typical application of a cognitive diagnosis model, the Q-matrix, which reflects the theory with respect to the skills indicated by the items, is assumed to be known. However, the Q-matrix is usually determined by expert judgment, and so there can be uncertainty about some of its elements. Here it is shown that this uncertainty can be…

Statistical uncertainties of a chiral interaction at next-to-next-to leading order

DOE PAGES

Ekström, A.; Carlsson, B. D.; Wendt, K. A.; ...

2015-02-05

In this paper, we have quantified the statistical uncertainties of the low-energy coupling-constants (LECs) of an optimized nucleon–nucleon interaction from chiral effective field theory at next-to-next-to-leading order. Finally, in addition, we have propagated the impact of the uncertainties of the LECs to two-nucleon scattering phase shifts, effective range parameters, and deuteron observables.

Workshop on Squeezed States and Uncertainty Relations

NASA Technical Reports Server (NTRS)

Han, Daesoo (Editor); Kim, Y. S. (Editor); Zachary, W. W. (Editor)

1992-01-01

The proceedings from the workshop are presented, and the focus was on the application of squeezed states. There are many who say that the potential for industrial applications is enormous, as the history of the conventional laser suggests. All those who worked so hard to produce squeezed states of light are continuing their efforts to construct more efficient squeezed-state lasers. Quite naturally, they are looking for new experiments using these lasers. The physical basis of squeezed states is the uncertainty relation in Fock space, which is also the basis for the creation and annihilation of particles in quantum field theory. Indeed, squeezed states provide a unique opportunity for field theoreticians to develop a measurement theory for quantum field theory.

Probabilistic Flood Maps to support decision-making: Mapping the Value of Information

NASA Astrophysics Data System (ADS)

Alfonso, L.; Mukolwe, M. M.; Di Baldassarre, G.

2016-02-01

Floods are one of the most frequent and disruptive natural hazards that affect man. Annually, significant flood damage is documented worldwide. Flood mapping is a common preimpact flood hazard mitigation measure, for which advanced methods and tools (such as flood inundation models) are used to estimate potential flood extent maps that are used in spatial planning. However, these tools are affected, largely to an unknown degree, by both epistemic and aleatory uncertainty. Over the past few years, advances in uncertainty analysis with respect to flood inundation modeling show that it is appropriate to adopt Probabilistic Flood Maps (PFM) to account for uncertainty. However, the following question arises; how can probabilistic flood hazard information be incorporated into spatial planning? Thus, a consistent framework to incorporate PFMs into the decision-making is required. In this paper, a novel methodology based on Decision-Making under Uncertainty theories, in particular Value of Information (VOI) is proposed. Specifically, the methodology entails the use of a PFM to generate a VOI map, which highlights floodplain locations where additional information is valuable with respect to available floodplain management actions and their potential consequences. The methodology is illustrated with a simplified example and also applied to a real case study in the South of France, where a VOI map is analyzed on the basis of historical land use change decisions over a period of 26 years. Results show that uncertain flood hazard information encapsulated in PFMs can aid decision-making in floodplain planning.

Evaluation of Neutron-induced Cross Sections and their Related Covariances with Physical Constraints

NASA Astrophysics Data System (ADS)

De Saint Jean, C.; Archier, P.; Privas, E.; Noguère, G.; Habert, B.; Tamagno, P.

2018-02-01

Nuclear data, along with numerical methods and the associated calculation schemes, continue to play a key role in reactor design, reactor core operating parameters calculations, fuel cycle management and criticality safety calculations. Due to the intensive use of Monte-Carlo calculations reducing numerical biases, the final accuracy of neutronic calculations increasingly depends on the quality of nuclear data used. This paper gives a broad picture of all ingredients treated by nuclear data evaluators during their analyses. After giving an introduction to nuclear data evaluation, we present implications of using the Bayesian inference to obtain evaluated cross sections and related uncertainties. In particular, a focus is made on systematic uncertainties appearing in the analysis of differential measurements as well as advantages and drawbacks one may encounter by analyzing integral experiments. The evaluation work is in general done independently in the resonance and in the continuum energy ranges giving rise to inconsistencies in evaluated files. For future evaluations on the whole energy range, we call attention to two innovative methods used to analyze several nuclear reaction models and impose constraints. Finally, we discuss suggestions for possible improvements in the evaluation process to master the quantification of uncertainties. These are associated with experiments (microscopic and integral), nuclear reaction theories and the Bayesian inference.

Analysis of air quality management with emphasis on transportation sources

NASA Technical Reports Server (NTRS)

English, T. D.; Divita, E.; Lees, L.

1980-01-01

The current environment and practices of air quality management were examined for three regions: Denver, Phoenix, and the South Coast Air Basin of California. These regions were chosen because the majority of their air pollution emissions are related to mobile sources. The impact of auto exhaust on the air quality management process is characterized and assessed. An examination of the uncertainties in air pollutant measurements, emission inventories, meteorological parameters, atmospheric chemistry, and air quality simulation models is performed. The implications of these uncertainties to current air quality management practices is discussed. A set of corrective actions are recommended to reduce these uncertainties.

Understanding Grief and Loss

MedlinePlus

... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ...

NNLO QCD corrections to Higgs boson production at large transverse momentum

NASA Astrophysics Data System (ADS)

Chen, X.; Cruz-Martinez, J.; Gehrmann, T.; Glover, E. W. N.; Jaquier, M.

2016-10-01

We derive the second-order QCD corrections to the production of a Higgs boson recoiling against a parton with finite transverse momentum, working in the effective field theory in which the top quark contributions are integrated out. To account for quark mass effects, we supplement the effective field theory result by the full quark mass dependence at leading order. Our calculation is fully differential in the final state kinematics and includes the decay of the Higgs boson to a photon pair. It allows one to make next-to-next-to-leading order (NNLO)-accurate theory predictions for Higgs-plus-jet final states and for the transverse momentum distribution of the Higgs boson, accounting for the experimental definition of the fiducial cross sections. The NNLO QCD corrections are found to be moderate and positive, they lead to a substantial reduction of the theory uncertainty on the predictions. We compare our results to 8 TeV LHC data from ATLAS and CMS. While the shape of the data is well-described for both experiments, we agree on the normalization only for CMS. By normalizing data and theory to the inclusive fiducial cross section for Higgs production, good agreement is found for both experiments, however at the expense of an increased theory uncertainty. We make predictions for Higgs production observables at the 13 TeV LHC, which are in good agreement with recent ATLAS data. At this energy, the leading order mass corrections to the effective field theory prediction become significant at large transverse momenta, and we discuss the resulting uncertainties on the predictions.

Influences of system uncertainties on the numerical transfer path analysis of engine systems

NASA Astrophysics Data System (ADS)

Acri, A.; Nijman, E.; Acri, A.; Offner, G.

2017-10-01

Practical mechanical systems operate with some degree of uncertainty. In numerical models uncertainties can result from poorly known or variable parameters, from geometrical approximation, from discretization or numerical errors, from uncertain inputs or from rapidly changing forcing that can be best described in a stochastic framework. Recently, random matrix theory was introduced to take parameter uncertainties into account in numerical modeling problems. In particular in this paper, Wishart random matrix theory is applied on a multi-body dynamic system to generate random variations of the properties of system components. Multi-body dynamics is a powerful numerical tool largely implemented during the design of new engines. In this paper the influence of model parameter variability on the results obtained from the multi-body simulation of engine dynamics is investigated. The aim is to define a methodology to properly assess and rank system sources when dealing with uncertainties. Particular attention is paid to the influence of these uncertainties on the analysis and the assessment of the different engine vibration sources. Examples of the effects of different levels of uncertainties are illustrated by means of examples using a representative numerical powertrain model. A numerical transfer path analysis, based on system dynamic substructuring, is used to derive and assess the internal engine vibration sources. The results obtained from this analysis are used to derive correlations between parameter uncertainties and statistical distribution of results. The derived statistical information can be used to advance the knowledge of the multi-body analysis and the assessment of system sources when uncertainties in model parameters are considered.

Comparison of specificity and information for fuzzy domains

NASA Technical Reports Server (NTRS)

Ramer, Arthur

1992-01-01

This paper demonstrates how an integrated theory can be built on the foundation of possibility theory. Information and uncertainty were considered in 'fuzzy' literature since 1982. Our departing point is the model proposed by Klir for the discrete case. It was elaborated axiomatically by Ramer, who also introduced the continuous model. Specificity as a numerical function was considered mostly within Dempster-Shafer evidence theory. An explicity definition was given first by Yager, who has also introduced it in the context of possibility theory. Axiomatic approach and the continuous model have been developed very recently by Ramer and Yager. They also establish a close analytical correspondence between specificity and information. In literature to date, specificity and uncertainty are defined only for the discrete finite domains, with a sole exception. Our presentation removes these limitations. We define specificity measures for arbitrary measurable domains.

Modification of Schrödinger-Newton equation due to braneworld models with minimal length

NASA Astrophysics Data System (ADS)

Bhat, Anha; Dey, Sanjib; Faizal, Mir; Hou, Chenguang; Zhao, Qin

2017-07-01

We study the correction of the energy spectrum of a gravitational quantum well due to the combined effect of the braneworld model with infinite extra dimensions and generalized uncertainty principle. The correction terms arise from a natural deformation of a semiclassical theory of quantum gravity governed by the Schrödinger-Newton equation based on a minimal length framework. The two fold correction in the energy yields new values of the spectrum, which are closer to the values obtained in the GRANIT experiment. This raises the possibility that the combined theory of the semiclassical quantum gravity and the generalized uncertainty principle may provide an intermediate theory between the semiclassical and the full theory of quantum gravity. We also prepare a schematic experimental set-up which may guide to the understanding of the phenomena in the laboratory.

The effects of harvest on waterfowl populations

USGS Publications Warehouse

Cooch, Evan G.; Guillemain, Matthieu; Boomer, G Scott; Lebreton, Jean-Dominique; Nichols, James D.

2014-01-01

Overall, there is substantial uncertainty about system dynamics, about the impacts of potential management and conservation decisions on those dynamics, and how to optimise management decisions in the presence of such uncertainties. Such relationships are unlikely to be stationary over space or time, and selective harvest of some individuals can potentially alter life history allocation of resources over time – both of which will potentially influence optimal harvest strategies. These sources of variation and uncertainty argue for the use of adaptive approaches to waterfowl harvest management.

A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region.

PubMed

Hu, X H; Li, Y P; Huang, G H; Zhuang, X W; Ding, X W

2016-05-01

In this study, a Bayesian-based two-stage inexact optimization (BTIO) method is developed for supporting water quality management through coupling Bayesian analysis with interval two-stage stochastic programming (ITSP). The BTIO method is capable of addressing uncertainties caused by insufficient inputs in water quality model as well as uncertainties expressed as probabilistic distributions and interval numbers. The BTIO method is applied to a real case of water quality management for the Xiangxi River basin in the Three Gorges Reservoir region to seek optimal water quality management schemes under various uncertainties. Interval solutions for production patterns under a range of probabilistic water quality constraints have been generated. Results obtained demonstrate compromises between the system benefit and the system failure risk due to inherent uncertainties that exist in various system components. Moreover, information about pollutant emission is accomplished, which would help managers to adjust production patterns of regional industry and local policies considering interactions of water quality requirement, economic benefit, and industry structure.

A Monte-Carlo game theoretic approach for Multi-Criteria Decision Making under uncertainty

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Lund, Jay R.

2011-05-01

Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California's Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes.

Assessing climate change and socio-economic uncertainties in long term management of water resources

NASA Astrophysics Data System (ADS)

Jahanshahi, Golnaz; Dawson, Richard; Walsh, Claire; Birkinshaw, Stephen; Glenis, Vassilis

2015-04-01

Long term management of water resources is challenging for decision makers given the range of uncertainties that exist. Such uncertainties are a function of long term drivers of change, such as climate, environmental loadings, demography, land use and other socio economic drivers. Impacts of climate change on frequency of extreme events such as drought make it a serious threat to water resources and water security. The release of probabilistic climate information, such as the UKCP09 scenarios, provides improved understanding of some uncertainties in climate models. This has motivated a more rigorous approach to dealing with other uncertainties in order to understand the sensitivity of investment decisions to future uncertainty and identify adaptation options that are as far as possible robust. We have developed and coupled a system of models that includes a weather generator, simulations of catchment hydrology, demand for water and the water resource system. This integrated model has been applied in the Thames catchment which supplies the city of London, UK. This region is one of the driest in the UK and hence sensitive to water availability. In addition, it is one of the fastest growing parts of the UK and plays an important economic role. Key uncertainties in long term water resources in the Thames catchment, many of which result from earth system processes, are identified and quantified. The implications of these uncertainties are explored using a combination of uncertainty analysis and sensitivity testing. The analysis shows considerable uncertainty in future rainfall, river flow and consequently water resource. For example, results indicate that by the 2050s, low flow (Q95) in the Thames catchment will range from -44 to +9% compared with the control scenario (1970s). Consequently, by the 2050s the average number of drought days are expected to increase 4-6 times relative to the 1970s. Uncertainties associated with urban growth increase these risks further. Adaptation measures, such as new reservoirs can manage these risks to a certain extent, but our sensitivity testing demonstrates that they are less robust to future uncertainties than measures taken to reduce water demand. Keywords: Climate change, Uncertainty, Decision making, Drought, Risk, Water resources management.

Testing gravity with EG: mapping theory onto observations

NASA Astrophysics Data System (ADS)

Leonard, C. Danielle; Ferreira, Pedro G.; Heymans, Catherine

2015-12-01

We present a complete derivation of the observationally motivated definition of the modified gravity statistic EG. Using this expression, we investigate how variations to theory and survey parameters may introduce uncertainty in the general relativistic prediction of EG. We forecast errors on EG for measurements using two combinations of upcoming surveys, and find that theoretical uncertainties may dominate for a futuristic measurement. Finally, we compute predictions of EG under modifications to general relativity in the quasistatic regime, and comment on the pros and cons of using EG to test gravity with future surveys.

The actual content of quantum theoretical kinematics and mechanics

NASA Technical Reports Server (NTRS)

Heisenberg, W.

1983-01-01

First, exact definitions are supplied for the terms: position, velocity, energy, etc. (of the electron, for instance), such that they are valid also in quantum mechanics. Canonically conjugated variables are determined simultaneously only with a characteristic uncertainty. This uncertainty is the intrinsic reason for the occurrence of statistical relations in quantum mechanics. Mathematical formulation is made possible by the Dirac-Jordan theory. Beginning from the basic principles thus obtained, macroscopic processes are understood from the viewpoint of quantum mechanics. Several imaginary experiments are discussed to elucidate the theory.

Making Decisions about an Educational Game, Simulation or Workshop: A 'Game Theory' Perspective.

ERIC Educational Resources Information Center

Cryer, Patricia

1988-01-01

Uses game theory to help practitioners make decisions about educational games, simulations, or workshops whose outcomes depend to some extent on chance. Highlights include principles for making decisions involving risk; elementary laws of probability; utility theory; and principles for making decisions involving uncertainty. (eight references)…

Grieving the Loss of a Sibling

MedlinePlus

... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ...

Quantifying uncertainty in partially specified biological models: how can optimal control theory help us?

PubMed

Adamson, M W; Morozov, A Y; Kuzenkov, O A

2016-09-01

Mathematical models in biology are highly simplified representations of a complex underlying reality and there is always a high degree of uncertainty with regards to model function specification. This uncertainty becomes critical for models in which the use of different functions fitting the same dataset can yield substantially different predictions-a property known as structural sensitivity. Thus, even if the model is purely deterministic, then the uncertainty in the model functions carries through into uncertainty in model predictions, and new frameworks are required to tackle this fundamental problem. Here, we consider a framework that uses partially specified models in which some functions are not represented by a specific form. The main idea is to project infinite dimensional function space into a low-dimensional space taking into account biological constraints. The key question of how to carry out this projection has so far remained a serious mathematical challenge and hindered the use of partially specified models. Here, we propose and demonstrate a potentially powerful technique to perform such a projection by using optimal control theory to construct functions with the specified global properties. This approach opens up the prospect of a flexible and easy to use method to fulfil uncertainty analysis of biological models.

Multi-criteria dynamic decision under uncertainty: a stochastic viability analysis and an application to sustainable fishery management.

PubMed

De Lara, M; Martinet, V

2009-02-01

Managing natural resources in a sustainable way is a hard task, due to uncertainties, dynamics and conflicting objectives (ecological, social, and economical). We propose a stochastic viability approach to address such problems. We consider a discrete-time control dynamical model with uncertainties, representing a bioeconomic system. The sustainability of this system is described by a set of constraints, defined in practice by indicators - namely, state, control and uncertainty functions - together with thresholds. This approach aims at identifying decision rules such that a set of constraints, representing various objectives, is respected with maximal probability. Under appropriate monotonicity properties of dynamics and constraints, having economic and biological content, we characterize an optimal feedback. The connection is made between this approach and the so-called Management Strategy Evaluation for fisheries. A numerical application to sustainable management of Bay of Biscay nephrops-hakes mixed fishery is given.

Optimization and resilience in natural resources management

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2015-01-01

We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources of uncertainty: partial observability of system state and uncertainty as to system dynamics. Optimal management strategies will vary considerably depending on the timeframe being considered and the amount and quality of information that is available to characterize system features and project the consequences of potential decisions. But in all cases an optimal decision making framework, if properly identified and focused, can be useful in recognizing sound decisions. We argue that under the conditions of deep uncertainty that characterize many resource systems, an optimal decision process that focuses on robustness does not automatically induce a loss of resilience.

Evidence Theory Based Uncertainty Quantification in Radiological Risk due to Accidental Release of Radioactivity from a Nuclear Power Plant

NASA Astrophysics Data System (ADS)

Ingale, S. V.; Datta, D.

2010-10-01

Consequence of the accidental release of radioactivity from a nuclear power plant is assessed in terms of exposure or dose to the members of the public. Assessment of risk is routed through this dose computation. Dose computation basically depends on the basic dose assessment model and exposure pathways. One of the exposure pathways is the ingestion of contaminated food. The aim of the present paper is to compute the uncertainty associated with the risk to the members of the public due to the ingestion of contaminated food. The governing parameters of the ingestion dose assessment model being imprecise, we have approached evidence theory to compute the bound of the risk. The uncertainty is addressed by the belief and plausibility fuzzy measures.

Knowledge management system for risk mitigation in supply chain uncertainty: case from automotive battery supply chain

NASA Astrophysics Data System (ADS)

Marie, I. A.; Sugiarto, D.; Surjasa, D.; Witonohadi, A.

2018-01-01

Automotive battery supply chain include battery manufacturer, sulphuric acid suppliers, polypropylene suppliers, lead suppliers, transportation service providers, warehouses, retailers and even customers. Due to the increasingly dynamic condition of the environment, supply chain actors were required to improve their ability to overcome various uncertainty issues in the environment. This paper aims to describe the process of designing a knowledge management system for risk mitigation in supply chain uncertainty. The design methodology began with the identification of the knowledge needed to solve the problems associated with uncertainty and analysis of system requirements. The design of the knowledge management system was described in the form of a data flow diagram. The results of the study indicated that key knowledge area that needs to be managed were the knowledge to maintain the stability of process in sulphuric acid process and knowledge to overcome the wastes in battery manufacturing process. The system was expected to be a media acquisition, dissemination and storage of knowledge associated with the uncertainty in the battery supply chain and increase the supply chain performance.

Adaptive management of social-ecological systems: the path forward

USGS Publications Warehouse

Allen, Craig R.

2015-01-01

Adaptive management remains at the forefront of environmental management nearly 40 years after its original conception, largely because we have yet to develop other methodologies that offer the same promise. Despite the criticisms of adaptive management and the numerous failed attempts to implement it, adaptive management has yet to be replaced with a better alternative. The concept persists because it is simple, allows action despite uncertainty, and fosters learning. Moving forward, adaptive management of social-ecological systems provides policymakers, managers and scientists a powerful tool for managing for resilience in the face of uncertainty.

Spatial planning using probabilistic flood maps

NASA Astrophysics Data System (ADS)

Alfonso, Leonardo; Mukolwe, Micah; Di Baldassarre, Giuliano

2015-04-01

Probabilistic flood maps account for uncertainty in flood inundation modelling and convey a degree of certainty in the outputs. Major sources of uncertainty include input data, topographic data, model structure, observation data and parametric uncertainty. Decision makers prefer less ambiguous information from modellers; this implies that uncertainty is suppressed to yield binary flood maps. Though, suppressing information may potentially lead to either surprise or misleading decisions. Inclusion of uncertain information in the decision making process is therefore desirable and transparent. To this end, we utilise the Prospect theory and information from a probabilistic flood map to evaluate potential decisions. Consequences related to the decisions were evaluated using flood risk analysis. Prospect theory explains how choices are made given options for which probabilities of occurrence are known and accounts for decision makers' characteristics such as loss aversion and risk seeking. Our results show that decision making is pronounced when there are high gains and loss, implying higher payoffs and penalties, therefore a higher gamble. Thus the methodology may be appropriately considered when making decisions based on uncertain information.

Practical differences among probabilities, possibilities, and credibilities

NASA Astrophysics Data System (ADS)

Grandin, Jean-Francois; Moulin, Caroline

2002-03-01

This paper presents some important differences that exist between theories, which allow the uncertainty management in data fusion. The main comparative results illustrated in this paper are the followings: Incompatibility between decisions got from probabilities and credibilities is highlighted. In the dynamic frame, as remarked in [19] or [17], belief and plausibility of Dempster-Shafer model do not frame the Bayesian probability. This framing can however be obtained by the Modified Dempster-Shafer approach. It also can be obtained in the Bayesian framework either by simulation techniques, or with a studentization. The uncommitted in the Dempster-Shafer way, e.g. the mass accorded to the ignorance, gives a mechanism similar to the reliability in the Bayesian model. Uncommitted mass in Dempster-Shafer theory or reliability in Bayes theory act like a filter that weakens extracted information, and improves robustness to outliners. So, it is logical to observe on examples like the one presented particularly by D.M. Buede, a faster convergence of a Bayesian method that doesn't take into account the reliability, in front of Dempster-Shafer method which uses uncommitted mass. But, on Bayesian masses, if reliability is taken into account, at the same level that the uncommited, e.g. F=1-m, we observe an equivalent rate for convergence. When Dempster-Shafer and Bayes operator are informed by uncertainty, faster or lower convergence can be exhibited on non Bayesian masses. This is due to positive or negative synergy between information delivered by sensors. This effect is a direct consequence of non additivity when considering non Bayesian masses. Unknowledge of the prior in bayesian techniques can be quickly compensated by information accumulated as time goes on by a set of sensors. All these results are presented on simple examples, and developed when necessary.

Evaluating the combined effects of source zone mass release rates and aquifer heterogeneity on solute discharge uncertainty

NASA Astrophysics Data System (ADS)

de Barros, Felipe P. J.

2018-07-01

Quantifying the uncertainty in solute mass discharge at an environmentally sensitive location is key to assess the risks due to groundwater contamination. Solute mass fluxes are strongly affected by the spatial variability of hydrogeological properties as well as release conditions at the source zone. This paper provides a methodological framework to investigate the interaction between the ubiquitous heterogeneity of the hydraulic conductivity and the mass release rate at the source zone on the uncertainty of mass discharge. Through the use of perturbation theory, we derive analytical and semi-analytical expressions for the statistics of the solute mass discharge at a control plane in a three-dimensional aquifer while accounting for the solute mass release rates at the source. The derived solutions are limited to aquifers displaying low-to-mild heterogeneity. Results illustrate the significance of the source zone mass release rate in controlling the mass discharge uncertainty. The relative importance of the mass release rate on the mean solute discharge depends on the distance between the source and the control plane. On the other hand, we find that the solute release rate at the source zone has a strong impact on the variance of the mass discharge. Within a risk context, we also compute the peak mean discharge as a function of the parameters governing the spatial heterogeneity of the hydraulic conductivity field and mass release rates at the source zone. The proposed physically-based framework is application-oriented, computationally efficient and capable of propagating uncertainty from different parameters onto risk metrics. Furthermore, it can be used for preliminary screening purposes to guide site managers to perform system-level sensitivity analysis and better allocate resources.

Uncertainty: the Curate's egg in financial economics.

PubMed

Pixley, Jocelyn

2014-06-01

Economic theories of uncertainty are unpopular with financial experts. As sociologists, we rightly refuse predictions, but the uncertainties of money are constantly sifted and turned into semi-denial by a financial economics set on somehow beating the future. Picking out 'bits' of the future as 'risk' and 'parts' as 'information' is attractive but socially dangerous, I argue, because money's promises are always uncertain. New studies of uncertainty are reversing sociology's neglect of the unavoidable inability to know the forces that will shape the financial future. © London School of Economics and Political Science 2014.

Uncertainty Budget Analysis for Dimensional Inspection Processes (U)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valdez, Lucas M.

2012-07-26

This paper is intended to provide guidance and describe how to prepare an uncertainty analysis of a dimensional inspection process through the utilization of an uncertainty budget analysis. The uncertainty analysis is stated in the same methodology as that of the ISO GUM standard for calibration and testing. There is a specific distinction between how Type A and Type B uncertainty analysis is used in a general and specific process. All theory and applications are utilized to represent both a generalized approach to estimating measurement uncertainty and how to report and present these estimations for dimensional measurements in a dimensionalmore » inspection process. The analysis of this uncertainty budget shows that a well-controlled dimensional inspection process produces a conservative process uncertainty, which can be attributed to the necessary assumptions in place for best possible results.« less

Uncertainty-accounting environmental policy and management of water systems.

PubMed

Baresel, Christian; Destouni, Georgia

2007-05-15

Environmental policies for water quality and ecosystem management do not commonly require explicit stochastic accounts of uncertainty and risk associated with the quantification and prediction of waterborne pollutant loads and abatement effects. In this study, we formulate and investigate a possible environmental policy that does require an explicit stochastic uncertainty account. We compare both the environmental and economic resource allocation performance of such an uncertainty-accounting environmental policy with that of deterministic, risk-prone and risk-averse environmental policies under a range of different hypothetical, yet still possible, scenarios. The comparison indicates that a stochastic uncertainty-accounting policy may perform better than deterministic policies over a range of different scenarios. Even in the absence of reliable site-specific data, reported literature values appear to be useful for such a stochastic account of uncertainty.

The Social Construction of Uncertainty in Healthcare Delivery

NASA Astrophysics Data System (ADS)

Begun, James W.; Kaissi, Amer A.

We explore the following question: How would healthcare delivery be different if uncertainty were widely recognized, accurately diagnosed, and appropriately managed? Unlike most studies of uncertainty, we examine uncertainty at more than one level of analysis, considering uncertainty that arises at the patient-clinician interaction level and at the organizational level of healthcare delivery. We consider the effects of history, as the forces and systems that currently shape and manage uncertainty have emerged over a long time period. The purpose of this broad and speculative "thought exercise" is to generate greater sensemaking of the current state of healthcare delivery, particularly in the realm of organizational and public policy, and to generate new research questions about healthcare delivery. The discussion is largely based on experience in the United States, which may limit its generalizability.

Fundamental Flaws In The Derivation Of Stevens' Law For Taste Within Norwich's Entropy Theory of Perception

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nizami, Lance

2010-03-01

Norwich's Entropy Theory of Perception (1975-present) is a general theory of perception, based on Shannon's Information Theory. Among many bold claims, the Entropy Theory presents a truly astounding result: that Stevens' Law with an Index of 1, an empirical power relation of direct proportionality between perceived taste intensity and stimulus concentration, arises from theory alone. Norwich's theorizing starts with several extraordinary hypotheses. First, 'multiple, parallel receptor-neuron units' without collaterals 'carry essentially the same message to the brain', i.e. the rate-level curves are identical. Second, sensation is proportional to firing rate. Third, firing rate is proportional to the taste receptor's 'resolvablemore » uncertainty'. Fourth, the 'resolvable uncertainty' is obtained from Shannon's Information Theory. Finally, 'resolvable uncertainty' also depends upon the microscopic thermodynamic density fluctuation of the tasted solute. Norwich proves that density fluctuation is density variance, which is proportional to solute concentration, all based on the theory of fluctuations in fluid composition from Tolman's classic physics text, 'The Principles of Statistical Mechanics'. Altogether, according to Norwich, perceived taste intensity is theoretically proportional to solute concentration. Such a universal rule for taste, one that is independent of solute identity, personal physiological differences, and psychophysical task, is truly remarkable and is well-deserving of scrutiny. Norwich's crucial step was the derivation of density variance. That step was meticulously reconstructed here. It transpires that the appropriate fluctuation is Tolman's mean-square fractional density fluctuation, not density variance as used by Norwich. Tolman's algebra yields a 'Stevens Index' of -1 rather than 1. As 'Stevens Index' empirically always exceeds zero, the Index of -1 suggests that it is risky to infer psychophysical laws of sensory response from information theory and stimulus physics while ignoring empirical biological transformations, such as sensory transduction. Indeed, it raises doubts as to whether the Entropy Theory actually describes psychophysical laws at all.« less

Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

NASA Astrophysics Data System (ADS)

Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

2016-12-01

Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate decision making under uncertainty methods from the state of the art. We will compare the efficiency of alternative approaches to the two case studies. Finally, we will present a hybrid decision analytic tool to address the synthesis of uncertainties.

Sustainable yield in theory and practice: bridging scientific and mainstream vernacular.

PubMed

Rudestam, Kirsten; Langridge, Ruth

2014-09-01

Groundwater is a vital resource in California, and the concept of "sustainable yield" is an attempt to determine a metric that can ensure the long-term resilience of groundwater systems. However, its meaning is ambiguous and quantification is challenging. To provide insight into developing a working definition that encompasses the inherent uncertainty and complexity of the term, this paper examines how sustainable yield in groundwater is interpreted by (1) scientists, (2) the courts in groundwater adjudications, (3) state agencies, and (4) local water practitioners. Through qualitative interviews, this paper identifies problems that local water agencies in the state encounter in engaging with sustainable yield as they incorporate the term in groundwater management practices. The authors recommend that any definitions make explicit the human dimensions of, and assumptions embedded in, the use of these terms in groundwater management practices, and they point to the value of participation in this process. © 2014, National Ground Water Association.

Evaluative Research in Corrections: The Uncertain Road.

ERIC Educational Resources Information Center

Adams, Stuart

Martinson's provocative article in Public Interest (Spring, 1974), denying efficacy in prisoner reform, singled out one of the uncertainties in correctional research. In their totality, these uncertainties embrace not only rehabilitative programs but also the method, theory, and organization of correctional research. To comprehend the status and…

Can agent based models effectively reduce fisheries management implementation uncertainty?

NASA Astrophysics Data System (ADS)

Drexler, M.

2016-02-01

Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

Decision-making under surprise and uncertainty: Arsenic contamination of water supplies

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Mozumder, Pallab; Halim, Nafisa

2018-05-01

With ignorance and potential surprise dominating decision making in water resources, a framework for dealing with such uncertainty is a critical need in hydrology. We operationalize the 'potential surprise' criterion proposed by Shackle, Vickers, and Katzner (SVK) to derive decision rules to manage water resources under uncertainty and ignorance. We apply this framework to managing water supply systems in Bangladesh that face severe, naturally occurring arsenic contamination. The uncertainty involved with arsenic in water supplies makes the application of conventional analysis of decision-making ineffective. Given the uncertainty and surprise involved in such cases, we find that optimal decisions tend to favor actions that avoid irreversible outcomes instead of conventional cost-effective actions. We observe that a diversification of the water supply system also emerges as a robust strategy to avert unintended outcomes of water contamination. Shallow wells had a slight higher optimal level (36%) compare to deep wells and surface treatment which had allocation levels of roughly 32% under each. The approach can be applied in a variety of other cases that involve decision making under uncertainty and surprise, a frequent situation in natural resources management.

The importance of parameterization when simulating the hydrologic response of vegetative land-cover change

NASA Astrophysics Data System (ADS)

White, Jeremy; Stengel, Victoria; Rendon, Samuel; Banta, John

2017-08-01

Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash-Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.

The importance of parameterization when simulating the hydrologic response of vegetative land-cover change

USGS Publications Warehouse

White, Jeremy; Stengel, Victoria G.; Rendon, Samuel H.; Banta, John

2017-01-01

Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.

Forest processes from stands to landscapes: exploring model forecast uncertainties using cross-scale model comparison

Treesearch

Michael J. Papaik; Andrew Fall; Brian Sturtevant; Daniel Kneeshaw; Christian Messier; Marie-Josee Fortin; Neal Simon

2010-01-01

Forest management practices conducted primarily at the stand scale result in simplified forests with regeneration problems and low structural and biological diversity. Landscape models have been used to help design management strategies to address these problems. However, there remains a great deal of uncertainty that the actual management practices result in the...

Induction of models under uncertainty

NASA Technical Reports Server (NTRS)

Cheeseman, Peter

1986-01-01

This paper outlines a procedure for performing induction under uncertainty. This procedure uses a probabilistic representation and uses Bayes' theorem to decide between alternative hypotheses (theories). This procedure is illustrated by a robot with no prior world experience performing induction on data it has gathered about the world. The particular inductive problem is the formation of class descriptions both for the tutored and untutored cases. The resulting class definitions are inherently probabilistic and so do not have any sharply defined membership criterion. This robot example raises some fundamental problems about induction; particularly, it is shown that inductively formed theories are not the best way to make predictions. Another difficulty is the need to provide prior probabilities for the set of possible theories. The main criterion for such priors is a pragmatic one aimed at keeping the theory structure as simple as possible, while still reflecting any structure discovered in the data.

The economics of motion perception and invariants of visual sensitivity.

PubMed

Gepshtein, Sergei; Tyukin, Ivan; Kubovy, Michael

2007-06-21

Neural systems face the challenge of optimizing their performance with limited resources, just as economic systems do. Here, we use tools of neoclassical economic theory to explore how a frugal visual system should use a limited number of neurons to optimize perception of motion. The theory prescribes that vision should allocate its resources to different conditions of stimulation according to the degree of balance between measurement uncertainties and stimulus uncertainties. We find that human vision approximately follows the optimal prescription. The equilibrium theory explains why human visual sensitivity is distributed the way it is and why qualitatively different regimes of apparent motion are observed at different speeds. The theory offers a new normative framework for understanding the mechanisms of visual sensitivity at the threshold of visibility and above the threshold and predicts large-scale changes in visual sensitivity in response to changes in the statistics of stimulation and system goals.

Optimal Groundwater Extraction under Uncertainty and a Spatial Stock Externality

EPA Science Inventory

We introduce a model that incorporates two important elements to estimating welfare gains from groundwater management: stochasticity and a spatial stock externality. We estimate welfare gains resulting from optimal management under uncertainty as well as a gradual stock externali...

Applying Chaos Theory to Lesson Planning and Delivery

ERIC Educational Resources Information Center

Cvetek, Slavko

2008-01-01

In this article, some of the ways in which thinking about chaos theory can help teachers and student-teachers to accept uncertainty and randomness as natural conditions in the classroom are considered. Building on some key features of complex systems commonly attributed to chaos theory (e.g. complexity, nonlinearity, sensitivity to initial…

The conceptual foundation of environmental decision support.

PubMed

Reichert, Peter; Langhans, Simone D; Lienert, Judit; Schuwirth, Nele

2015-05-01

Environmental decision support intends to use the best available scientific knowledge to help decision makers find and evaluate management alternatives. The goal of this process is to achieve the best fulfillment of societal objectives. This requires a careful analysis of (i) how scientific knowledge can be represented and quantified, (ii) how societal preferences can be described and elicited, and (iii) how these concepts can best be used to support communication with authorities, politicians, and the public in environmental management. The goal of this paper is to discuss key requirements for a conceptual framework to address these issues and to suggest how these can best be met. We argue that a combination of probability theory and scenario planning with multi-attribute utility theory fulfills these requirements, and discuss adaptations and extensions of these theories to improve their application for supporting environmental decision making. With respect to (i) we suggest the use of intersubjective probabilities, if required extended to imprecise probabilities, to describe the current state of scientific knowledge. To address (ii), we emphasize the importance of value functions, in addition to utilities, to support decisions under risk. We discuss the need for testing "non-standard" value aggregation techniques, the usefulness of flexibility of value functions regarding attribute data availability, the elicitation of value functions for sub-objectives from experts, and the consideration of uncertainty in value and utility elicitation. With respect to (iii), we outline a well-structured procedure for transparent environmental decision support that is based on a clear separation of scientific prediction and societal valuation. We illustrate aspects of the suggested methodology by its application to river management in general and with a small, didactical case study on spatial river rehabilitation prioritization. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Therapeutic jurisprudence and sex offenders: a psycho-legal approach to protection.

PubMed

Birgden, Astrid

2004-10-01

Societal response to sex offenders is marked by uncertainty about whether punishment or treatment should occur. The distinction between punishment, prevention, and protection is useful to determine how best to assess, treat, and manage sex offenders within the criminal justice system. Once convicted, both law and psychology are concerned with sex offenders changing their behavior in order to protect the community. Therapeutic jurisprudence is a legal theory that aims to maximize therapeutic effects of the law and minimize anti-therapeutic consequences of the law. Therapeutic jurisprudence provides a framework to combine legal and psychological processes to balance prevention and protection. Legal and correctional practitioners can work together to address both community protection and offender protection concerns.

Clinical leadership in mental health nursing: the importance of a calm and confident approach.

PubMed

Ennis, Gary; Happell, Brenda; Reid-Searl, Kerry

2015-01-01

Explore the perceptions of nurses working in mental health of effective clinical leadership. In-depth interviews were conducted with registered nurses employed in a mental health setting. Qualitative research using grounded theory. Remaining calm and confident in times of crisis and uncertainty was identified as one attribute of clinical leadership. Participants noted clinical leaders' demeanor during stressful or crisis situations, and their ability to manage unpredictable or unexpected clinical situations as contributing positively to clinical practice. Understanding these characteristics and how they can influence positive outcomes for clients is crucial in addressing the recruitment and retention challenges for the nursing workforce. © 2014 Wiley Periodicals, Inc.

Confirmation of general relativity on large scales from weak lensing and galaxy velocities.

PubMed

Reyes, Reinabelle; Mandelbaum, Rachel; Seljak, Uros; Baldauf, Tobias; Gunn, James E; Lombriser, Lucas; Smith, Robert E

2010-03-11

Although general relativity underlies modern cosmology, its applicability on cosmological length scales has yet to be stringently tested. Such a test has recently been proposed, using a quantity, E(G), that combines measures of large-scale gravitational lensing, galaxy clustering and structure growth rate. The combination is insensitive to 'galaxy bias' (the difference between the clustering of visible galaxies and invisible dark matter) and is thus robust to the uncertainty in this parameter. Modified theories of gravity generally predict values of E(G) different from the general relativistic prediction because, in these theories, the 'gravitational slip' (the difference between the two potentials that describe perturbations in the gravitational metric) is non-zero, which leads to changes in the growth of structure and the strength of the gravitational lensing effect. Here we report that E(G) = 0.39 +/- 0.06 on length scales of tens of megaparsecs, in agreement with the general relativistic prediction of E(G) approximately 0.4. The measured value excludes a model within the tensor-vector-scalar gravity theory, which modifies both Newtonian and Einstein gravity. However, the relatively large uncertainty still permits models within f(R) theory, which is an extension of general relativity. A fivefold decrease in uncertainty is needed to rule out these models.

Confirmation of general relativity on large scales from weak lensing and galaxy velocities

NASA Astrophysics Data System (ADS)

Reyes, Reinabelle; Mandelbaum, Rachel; Seljak, Uros; Baldauf, Tobias; Gunn, James E.; Lombriser, Lucas; Smith, Robert E.

2010-03-01

Although general relativity underlies modern cosmology, its applicability on cosmological length scales has yet to be stringently tested. Such a test has recently been proposed, using a quantity, EG, that combines measures of large-scale gravitational lensing, galaxy clustering and structure growth rate. The combination is insensitive to `galaxy bias' (the difference between the clustering of visible galaxies and invisible dark matter) and is thus robust to the uncertainty in this parameter. Modified theories of gravity generally predict values of EG different from the general relativistic prediction because, in these theories, the `gravitational slip' (the difference between the two potentials that describe perturbations in the gravitational metric) is non-zero, which leads to changes in the growth of structure and the strength of the gravitational lensing effect. Here we report that EG = 0.39+/-0.06 on length scales of tens of megaparsecs, in agreement with the general relativistic prediction of EG~0.4. The measured value excludes a model within the tensor-vector-scalar gravity theory, which modifies both Newtonian and Einstein gravity. However, the relatively large uncertainty still permits models within f() theory, which is an extension of general relativity. A fivefold decrease in uncertainty is needed to rule out these models.

Adaptability: How Students' Responses to Uncertainty and Novelty Predict Their Academic and Non-Academic Outcomes

ERIC Educational Resources Information Center

Martin, Andrew J.; Nejad, Harry G.; Colmar, Susan; Liem, Gregory Arief D.

2013-01-01

Adaptability is defined as appropriate cognitive, behavioral, and/or affective adjustment in the face of uncertainty and novelty. Building on prior measurement work demonstrating the psychometric properties of an adaptability construct, the present study investigates dispositional predictors (personality, implicit theories) of adaptability, and…

The Relationship of Cultural Similarity, Communication Effectiveness and Uncertainty Reduction.

ERIC Educational Resources Information Center

Koester, Jolene; Olebe, Margaret

To investigate the relationship of cultural similarity/dissimilarity, communication effectiveness, and communication variables associated with uncertainty reduction theory, a study examined two groups of students--a multinational group living on an "international floor" in a dormitory at a state university and an unrelated group of U.S.…

Spin and Uncertainty in the Interpretation of Quantum Mechanics.

ERIC Educational Resources Information Center

Hestenes, David

1979-01-01

Points out that quantum mechanics interpretations, using Heisenberg's Uncertainty Relations for the position and momentum of an electron, have their drawbacks. The interpretations are limited to the Schrodinger theory and fail to take into account either spin or relativity. Shows why spin cannot be ignored. (Author/GA)

Generalized uncertainty principle and quantum gravity phenomenology

NASA Astrophysics Data System (ADS)

Bosso, Pasquale

The fundamental physical description of Nature is based on two mutually incompatible theories: Quantum Mechanics and General Relativity. Their unification in a theory of Quantum Gravity (QG) remains one of the main challenges of theoretical physics. Quantum Gravity Phenomenology (QGP) studies QG effects in low-energy systems. The basis of one such phenomenological model is the Generalized Uncertainty Principle (GUP), which is a modified Heisenberg uncertainty relation and predicts a deformed canonical commutator. In this thesis, we compute Planck-scale corrections to angular momentum eigenvalues, the hydrogen atom spectrum, the Stern-Gerlach experiment, and the Clebsch-Gordan coefficients. We then rigorously analyze the GUP-perturbed harmonic oscillator and study new coherent and squeezed states. Furthermore, we introduce a scheme for increasing the sensitivity of optomechanical experiments for testing QG effects. Finally, we suggest future projects that may potentially test QG effects in the laboratory.

Essential information: Uncertainty and optimal control of Ebola outbreaks

USGS Publications Warehouse

Li, Shou-Li; Bjornstad, Ottar; Ferrari, Matthew J.; Mummah, Riley; Runge, Michael C.; Fonnesbeck, Christopher J.; Tildesley, Michael J.; Probert, William J. M.; Shea, Katriona

2017-01-01

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Essential information: Uncertainty and optimal control of Ebola outbreaks.

PubMed

Li, Shou-Li; Bjørnstad, Ottar N; Ferrari, Matthew J; Mummah, Riley; Runge, Michael C; Fonnesbeck, Christopher J; Tildesley, Michael J; Probert, William J M; Shea, Katriona

2017-05-30

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Adaptive management for improving species conservation across the captive-wild spectrum

USGS Publications Warehouse

Canessa, Stefano; Guillera-Arroita, Gurutzeta; Lahoz-Monfort, José J.; Southwell, Darren M; Armstrong, Doug P.; Chadès, Iadine; Lacy, Robert C; Converse, Sarah J.

2016-01-01

Conservation of endangered species increasingly envisages complex strategies that integrate captive and wild management actions. Management decisions in this context must be made in the face of uncertainty, often with limited capacity to collect information. Adaptive management (AM) combines management and monitoring, with the aim of updating knowledge and improving decision-making over time. We provide a guide for managers who may realize the potential of AM, but are unsure where to start. The urgent need for iterative management decisions, the existence of uncertainty, and the opportunity for learning offered by often highly-controlled captive environments create favorable conditions for AM. However, experiments and monitoring may be complicated by small sample sizes, and the ability to control the system, including stochasticity and observability, may be limited toward the wild end of the spectrum. We illustrate the key steps to implementing AM in threatened species management using four case studies, including the management of captive programs for cheetah (Acinonyx jubatus) and whooping cranes (Grus americana), of a translocation protocol for Arizona cliffroses Purshia subintegra and of ongoing supplementary feeding of reintroduced hihi (Notiomystis cincta) populations. For each case study, we explain (1) how to clarify whether the decision can be improved by learning (i.e. it is iterative and complicated by uncertainty) and what the management objectives are; (2) how to articulate uncertainty via alternative, testable hypotheses such as competing models or parameter distributions; (3) how to formally define how additional information can be collected and incorporated in future management decisions.

A duality theorem-based algorithm for inexact quadratic programming problems: Application to waste management under uncertainty

NASA Astrophysics Data System (ADS)

Kong, X. M.; Huang, G. H.; Fan, Y. R.; Li, Y. P.

2016-04-01

In this study, a duality theorem-based algorithm (DTA) for inexact quadratic programming (IQP) is developed for municipal solid waste (MSW) management under uncertainty. It improves upon the existing numerical solution method for IQP problems. The comparison between DTA and derivative algorithm (DAM) shows that the DTA method provides better solutions than DAM with lower computational complexity. It is not necessary to identify the uncertain relationship between the objective function and decision variables, which is required for the solution process of DAM. The developed method is applied to a case study of MSW management and planning. The results indicate that reasonable solutions have been generated for supporting long-term MSW management and planning. They could provide more information as well as enable managers to make better decisions to identify desired MSW management policies in association with minimized cost under uncertainty.

Using global sensitivity analysis of demographic models for ecological impact assessment.

PubMed

Aiello-Lammens, Matthew E; Akçakaya, H Resit

2017-02-01

Population viability analysis (PVA) is widely used to assess population-level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input-parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input-parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea-level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions. © 2016 Society for Conservation Biology.

Regional Sea Level Scenarios for Coastal Risk Management: Managing the Uncertainty of Future Sea Level Change and Extreme Water Levels for Department of Defense Coastal Sites Worldwide

DTIC Science & Technology

2016-04-01

SERDP NOAA USACE Ocean MANAGING THE UNCERTAINTY OF FUTURE SEA LEVEL CHANGE AND EXTREME WATER LEVELS FOR DEPARTMENT OF DEFENSE COASTAL SITES...WORLDWIDE APRIL 2016 REGIONAL SEA LEVEL SCENARIOS FOR COASTAL RISK MANAGEMENT: COVER PHOTOS, FROM LEFT TO RIGHT: - Overwash of the island of Roi-Namur on...J.A., S. Gill, J. Obeysekera, W. Sweet, K. Knuuti, and J. Marburger. 2016. Regional Sea Level Scenarios for Coastal Risk Management: Managing the

Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?

NASA Astrophysics Data System (ADS)

Honti, Mark; Schuwirth, Nele; Rieckermann, Jörg; Stamm, Christian

2017-03-01

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual iWaQa model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

Comparing administered and market-based water allocation systems using an agent-based modeling approach

NASA Astrophysics Data System (ADS)

Zhao, J.; Cai, X.; Wang, Z.

2009-12-01

It also has been well recognized that market-based systems can have significant advantages over administered systems for water allocation. However there are not many successful water markets around the world yet and administered systems exist commonly in water allocation management practice. This paradox has been under discussion for decades and still calls for attention for both research and practice. This paper explores some insights for the paradox and tries to address why market systems have not been widely implemented for water allocation. Adopting the theory of agent-based system we develop a consistent analytical model to interpret both systems. First we derive some theorems based on the analytical model, with respect to the necessary conditions for economic efficiency of water allocation. Following that the agent-based model is used to illustrate the coherence and difference between administered and market-based systems. The two systems are compared from three aspects: 1) the driving forces acting on the system state, 2) system efficiency, and 3) equity. Regarding economic efficiency, penalty on the violation of water use permits (or rights) under an administered system can lead to system-wide economic efficiency, as well as being acceptable by some agents, which follows the theory of the so-call rational violation. Ideal equity will be realized if penalty equals incentive with an administered system and if transaction costs are zero with a market system. The performances of both agents and the over system are explained with an administered system and market system, respectively. The performances of agents are subject to different mechanisms of interactions between agents under the two systems. The system emergency (i.e., system benefit, equilibrium market price, etc), resulting from the performance at the agent level, reflects the different mechanism of the two systems, the “invisible hand” with the market system and administrative measures (penalty and subsidy) with the administered system. Furthermore, the impact of hydrological uncertainty on the performance of water users under the two systems is analyzed by extending the deterministic model to a stochastic one subject to the uncertainty of water availability. It is found that the system response to hydrologic uncertainty depends on risk management mechanics - sharing risk equally among the agents or by prescribed priorities on some agents. Figure1. Agent formulation and its implications in administered system and market-based system

From conditional oughts to qualitative decision theory

NASA Technical Reports Server (NTRS)

Pearl, Judea

1994-01-01

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., 'You ought to do A, if C') that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound basis for qualitative decision theory, thus providing a framework for qualitative planning under uncertainty. In particular, we show that adding causal relationships (in the form of a single graph) as part of an epistemic state is sufficient to facilitate the analysis of action sequences, their consequences, their interaction with observations, their expected utilities, and the synthesis of plans and strategies under uncertainty.

Mass discharge estimation from contaminated sites: Multi-model solutions for assessment of conceptual uncertainty

NASA Astrophysics Data System (ADS)

Thomsen, N. I.; Troldborg, M.; McKnight, U. S.; Binning, P. J.; Bjerg, P. L.

2012-04-01

Mass discharge estimates are increasingly being used in the management of contaminated sites. Such estimates have proven useful for supporting decisions related to the prioritization of contaminated sites in a groundwater catchment. Potential management options can be categorised as follows: (1) leave as is, (2) clean up, or (3) further investigation needed. However, mass discharge estimates are often very uncertain, which may hamper the management decisions. If option 1 is incorrectly chosen soil and water quality will decrease, threatening or destroying drinking water resources. The risk of choosing option 2 is to spend money on remediating a site that does not pose a problem. Choosing option 3 will often be safest, but may not be the optimal economic solution. Quantification of the uncertainty in mass discharge estimates can therefore greatly improve the foundation for selecting the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level of information. Parameter uncertainty is quantified using Monte Carlo simulations. For each conceptual model we calculate a transient mass discharge estimate with uncertainty bounds resulting from the parametric uncertainty. To quantify the conceptual uncertainty from a given site, we combine the outputs from the different conceptual models using Bayesian model averaging. The weight for each model is obtained by integrating available data and expert knowledge using Bayesian belief networks. The multi-model approach is applied to a contaminated site. At the site a DNAPL (dense non aqueous phase liquid) spill consisting of PCE (perchloroethylene) has contaminated a fractured clay till aquitard overlaying a limestone aquifer. The exact shape and nature of the source is unknown and so is the importance of transport in the fractures. The result of the multi-model approach is a visual representation of the uncertainty of the mass discharge estimates for the site which can be used to support the management options.

A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty

NASA Astrophysics Data System (ADS)

Llopis-Albert, Carlos; Palacios-Marqués, Daniel; Merigó, José M.

2014-04-01

In this paper a methodology for the stochastic management of groundwater quality problems is presented, which can be used to provide agricultural advisory services. A stochastic algorithm to solve the coupled flow and mass transport inverse problem is combined with a stochastic management approach to develop methods for integrating uncertainty; thus obtaining more reliable policies on groundwater nitrate pollution control from agriculture. The stochastic inverse model allows identifying non-Gaussian parameters and reducing uncertainty in heterogeneous aquifers by constraining stochastic simulations to data. The management model determines the spatial and temporal distribution of fertilizer application rates that maximizes net benefits in agriculture constrained by quality requirements in groundwater at various control sites. The quality constraints can be taken, for instance, by those given by water laws such as the EU Water Framework Directive (WFD). Furthermore, the methodology allows providing the trade-off between higher economic returns and reliability in meeting the environmental standards. Therefore, this new technology can help stakeholders in the decision-making process under an uncertainty environment. The methodology has been successfully applied to a 2D synthetic aquifer, where an uncertainty assessment has been carried out by means of Monte Carlo simulation techniques.

Randomized trial of an uncertainty self-management telephone intervention for patients awaiting liver transplant.

PubMed

Bailey, Donald E; Hendrix, Cristina C; Steinhauser, Karen E; Stechuchak, Karen M; Porter, Laura S; Hudson, Julie; Olsen, Maren K; Muir, Andrew; Lowman, Sarah; DiMartini, Andrea; Salonen, Laurel Williams; Tulsky, James A

2017-03-01

We tested an uncertainty self-management telephone intervention (SMI) with patients awaiting liver transplant and their caregivers. Participants were recruited from four transplant centers and completed questionnaires at baseline, 10, and 12 weeks from baseline (generally two and four weeks after intervention delivery, respectively). Dyads were randomized to either SMI (n=56) or liver disease education (LDE; n=59), both of which involved six weekly telephone sessions. SMI participants were taught coping skills and uncertainty management strategies while LDE participants learned about liver function and how to stay healthy. Outcomes included illness uncertainty, uncertainty management, depression, anxiety, self-efficacy, and quality of life. General linear models were used to test for group differences. No differences were found between the SMI and LDE groups for study outcomes. This trial offers insight regarding design for future interventions that may allow greater flexibility in length of delivery beyond our study's 12-week timeframe. Our study was designed for the time constraints of today's clinical practice setting. This trial is a beginning point to address the unmet needs of these patients and their caregivers as they wait for transplants that could save their lives. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Uncertainty-based simulation-optimization using Gaussian process emulation: Application to coastal groundwater management

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ketabchi, Hamed

2017-12-01

Combined simulation-optimization (S/O) schemes have long been recognized as a valuable tool in coastal groundwater management (CGM). However, previous applications have mostly relied on deterministic seawater intrusion (SWI) simulations. This is a questionable simplification, knowing that SWI models are inevitably prone to epistemic and aleatory uncertainty, and hence a management strategy obtained through S/O without consideration of uncertainty may result in significantly different real-world outcomes than expected. However, two key issues have hindered the use of uncertainty-based S/O schemes in CGM, which are addressed in this paper. The first issue is how to solve the computational challenges resulting from the need to perform massive numbers of simulations. The second issue is how the management problem is formulated in presence of uncertainty. We propose the use of Gaussian process (GP) emulation as a valuable tool in solving the computational challenges of uncertainty-based S/O in CGM. We apply GP emulation to the case study of Kish Island (located in the Persian Gulf) using an uncertainty-based S/O algorithm which relies on continuous ant colony optimization and Monte Carlo simulation. In doing so, we show that GP emulation can provide an acceptable level of accuracy, with no bias and low statistical dispersion, while tremendously reducing the computational time. Moreover, five new formulations for uncertainty-based S/O are presented based on concepts such as energy distances, prediction intervals and probabilities of SWI occurrence. We analyze the proposed formulations with respect to their resulting optimized solutions, the sensitivity of the solutions to the intended reliability levels, and the variations resulting from repeated optimization runs.

Latent dimensions of social anxiety disorder: A re-evaluation of the Social Phobia Inventory (SPIN).

PubMed

Campbell-Sills, Laura; Espejo, Emmanuel; Ayers, Catherine R; Roy-Byrne, Peter; Stein, Murray B

2015-12-01

The Social Phobia Inventory (SPIN; Connor et al., 2000) is a well-validated instrument for assessing severity of social anxiety disorder (SAD). However, evaluations of its factor structure have produced inconsistent results and this aspect of the scale requires further study. Primary care patients with SAD (N=397) completed the SPIN as part of baseline assessment for the Coordinated Anxiety Learning and Management study (Roy-Byrne et al., 2010). These data were used for exploratory and confirmatory factor analysis of the SPIN. A 3-factor model provided the best fit for the data and factors were interpreted as Fear of Negative Evaluation, Fear of Physical Symptoms, and Fear of Uncertainty in Social Situations. Tests of a second-order model showed that the three factors loaded strongly on a single higher-order factor that was labeled Social Anxiety. Findings are consistent with theories identifying Fear of Negative Evaluation as the core feature of SAD, and with evidence that anxiety sensitivity and intolerance of uncertainty further contribute to SAD severity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Uncertainty quantification for nuclear density functional theory and information content of new measurements.

PubMed

McDonnell, J D; Schunck, N; Higdon, D; Sarich, J; Wild, S M; Nazarewicz, W

2015-03-27

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.

Removal of Asperger's syndrome from the DSM V: community response to uncertainty.

PubMed

Parsloe, Sarah M; Babrow, Austin S

2016-01-01

The May 2013 release of the new version of the Diagnostic and Statistical Manual of Mental Disorders (DSM V) subsumed Asperger's syndrome under the wider diagnostic label of autism spectrum disorder (ASD). The revision has created much uncertainty in the community affected by this condition. This study uses problematic integration theory and thematic analysis to investigate how participants in Wrong Planet, a large online community associated with autism and Asperger's syndrome, have constructed these uncertainties. The analysis illuminates uncertainties concerning both the likelihood of diagnosis and value of diagnosis, and it details specific issues within these two general areas of uncertainty. The article concludes with both conceptual and practical implications.

Quantum corrections to newtonian potential and generalized uncertainty principle

NASA Astrophysics Data System (ADS)

Scardigli, Fabio; Lambiase, Gaetano; Vagenas, Elias

2017-08-01

We use the leading quantum corrections to the newtonian potential to compute the deformation parameter of the generalized uncertainty principle. By assuming just only General Relativity as theory of Gravitation, and the thermal nature of the GUP corrections to the Hawking spectrum, our calculation gives, to first order, a specific numerical result. We briefly discuss the physical meaning of this value, and compare it with the previously obtained bounds on the generalized uncertainty principle deformation parameter.

A strategy for monitoring and managing declines in an amphibian community.

PubMed

Grant, Evan H Campbell; Zipkin, Elise F; Nichols, James D; Campbell, J Patrick

2013-12-01

Although many taxa have declined globally, conservation actions are inherently local. Ecosystems degrade even in protected areas, and maintaining natural systems in a desired condition may require active management. Implementing management decisions under uncertainty requires a logical and transparent process to identify objectives, develop management actions, formulate system models to link actions with objectives, monitor to reduce uncertainty and identify system state (i.e., resource condition), and determine an optimal management strategy. We applied one such structured decision-making approach that incorporates these critical elements to inform management of amphibian populations in a protected area managed by the U.S. National Park Service. Climate change is expected to affect amphibian occupancy of wetlands and to increase uncertainty in management decision making. We used the tools of structured decision making to identify short-term management solutions that incorporate our current understanding of the effect of climate change on amphibians, emphasizing how management can be undertaken even with incomplete information. Estrategia para Monitorear y Manejar Disminuciones en una Comunidad de Anfibios. © 2013 Society for Conservation Biology.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

USGS Publications Warehouse

Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay; Betancourt, Julio L.; Hooten, Mevin B.; Jarnevich, Catherine S.; Keitt, Timothy H.; Kenney, Melissa A.; Laney, Christine M.; Larsen, Laurel G.; Loescher, Henry W.; Lunch, Claire K.; Pijanowski, Bryan; Randerson, James T.; Read, Emily; Tredennick, Andrew T.; Vargas, Rodrigo; Weathers, Kathleen C.; White, Ethan P.

2018-01-01

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

PubMed

Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P

2018-02-13

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Robust control design with real parameter uncertainty using absolute stability theory. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

How, Jonathan P.; Hall, Steven R.

1993-01-01

The purpose of this thesis is to investigate an extension of mu theory for robust control design by considering systems with linear and nonlinear real parameter uncertainties. In the process, explicit connections are made between mixed mu and absolute stability theory. In particular, it is shown that the upper bounds for mixed mu are a generalization of results from absolute stability theory. Both state space and frequency domain criteria are developed for several nonlinearities and stability multipliers using the wealth of literature on absolute stability theory and the concepts of supply rates and storage functions. The state space conditions are expressed in terms of Riccati equations and parameter-dependent Lyapunov functions. For controller synthesis, these stability conditions are used to form an overbound of the H2 performance objective. A geometric interpretation of the equivalent frequency domain criteria in terms of off-axis circles clarifies the important role of the multiplier and shows that both the magnitude and phase of the uncertainty are considered. A numerical algorithm is developed to design robust controllers that minimize the bound on an H2 cost functional and satisfy an analysis test based on the Popov stability multiplier. The controller and multiplier coefficients are optimized simultaneously, which avoids the iteration and curve-fitting procedures required by the D-K procedure of mu synthesis. Several benchmark problems and experiments on the Middeck Active Control Experiment at M.I.T. demonstrate that these controllers achieve good robust performance and guaranteed stability bounds.

An interval-based possibilistic programming method for waste management with cost minimization and environmental-impact abatement under uncertainty.

PubMed

Li, Y P; Huang, G H

2010-09-15

Considerable public concerns have been raised in the past decades since a large amount of pollutant emissions from municipal solid waste (MSW) disposal of processes pose risks on surrounding environment and human health. Moreover, in MSW management, various uncertainties exist in the related costs, impact factors and objectives, which can affect the optimization processes and the decision schemes generated. In this study, an interval-based possibilistic programming (IBPP) method is developed for planning the MSW management with minimized system cost and environmental impact under uncertainty. The developed method can deal with uncertainties expressed as interval values and fuzzy sets in the left- and right-hand sides of constraints and objective function. An interactive algorithm is provided for solving the IBPP problem, which does not lead to more complicated intermediate submodels and has a relatively low computational requirement. The developed model is applied to a case study of planning a MSW management system, where mixed integer linear programming (MILP) technique is introduced into the IBPP framework to facilitate dynamic analysis for decisions of timing, sizing and siting in terms of capacity expansion for waste-management facilities. Three cases based on different waste-management policies are examined. The results obtained indicate that inclusion of environmental impacts in the optimization model can change the traditional waste-allocation pattern merely based on the economic-oriented planning approach. The results obtained can help identify desired alternatives for managing MSW, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty. Copyright 2010 Elsevier B.V. All rights reserved.

[Nurses' perceptions of the vulnerabilities to STD/AIDS in light of the process of adolescence].

PubMed

Silva, Ítalo Rodolfo; Gomes, Antonio Marcos Tosoli; Valadares, Glaucia Valente; dos Santos, Nereida Lúcia Palko; da Silva, Thiago Privado; Leite, Joséte Luzia

2015-09-01

to understand the perception of nurses on the vulnerabilities to STD/AIDS in light of the process of adolescence. qualitative research conducted with 15 nurses in a centre for the studies of adolescent healthcare of a university hospital in Rio de Janeiro/Brazil. The adopted theoretical and methodological frameworks were the Complexity Theory and the Grounded Theory, respectively. The semi-structured interview was used to collect data from January to August 2012. this research presents the category: Nurses' perceptions of the vulnerabilities to STD/AIDS in light of the process of adolescence, and the subcategories: Risks and uncertainties of the process of adolescence: paths to STD/AIDS; Age-adolescence complex: expanding knowledge from the perception of nurses. once the nurses understand the complexity of adolescence, they create strategies to reduce the vulnerability of adolescents to STD/AIDS. This signals the need to invest in education, assistance and the management of nursing care for adolescents.

Subjective indicators as a gauge for improving organizational well-being. An attempt to apply the cognitive activation theory to organizations.

PubMed

Arnetz, Bengt B

2005-11-01

Globally, organizations are undergoing substantial changes, commonly resulting in significant employee stress. However, facing similar stressors and challenges, departments within an organizations, as well as companies within the same area of business, vary in the way they cope with change. It was hypothesized that collective uncertainty about the future as well as unclear organizational goals contribute to chronic stress in organizations exposed to change. Applying the theoretical cognitive activation theory of stress--CATS--model by Ursin and Eriksen at an organizational level, support was found for the above hypothesis. Changes in chronic stress indicators between two assessments were related to clarity of organizational goals. It is suggested that the CATS model might be fruitful, not only in understanding variations in individual stress responses and experiences, but also to interpret and manage organizational stress. By doing so, both organizational health and well-being will improve, creating enterprises with healthy employees and healthy productivity and economic results.

Contribution of the Multi Attribute Value Theory to conflict resolution in groundwater management. Application to the Mancha Oriental groundwater system, Spain

NASA Astrophysics Data System (ADS)

Apperl, B.; Andreu, J.; Karjalainen, T. P.; Pulido-Velazquez, M.

2014-09-01

The implementation of the EU Water Framework Directive demands participatory water resource management approaches. Decision making in groundwater quantity and quality management is complex because of the existence of many independent actors, heterogeneous stakeholder interests, multiple objectives, different potential policies, and uncertain outcomes. Conflicting stakeholder interests have been often identified as an impediment to the realization and success of water regulations and policies. The management of complex groundwater systems requires clarifying stakeholders' positions (identifying stakeholders preferences and values), improving transparency with respect to outcomes of alternatives, and moving the discussion from the selection of alternatives towards definition of fundamental objectives (value-thinking approach), what facilitates negotiation. The aims of the study are to analyse the potential of the multi attribute value theory for conflict resolution in groundwater management and to evaluate the benefit of stakeholder incorporation in the different stages of the planning process to find an overall satisfying solution for groundwater management. The research was conducted in the Mancha Oriental groundwater system (Spain), subject to an intensive use of groundwater for irrigation. A complex set of objectives and attributes were defined, and the management alternatives were created by a combination of different fundamental actions, considering different implementation stages and future changes in water resources availability. Interviews were conducted with representative stakeholder groups using an interactive platform, showing simultaneously the consequences of changes of preferences to the alternative ranking. Results show that the acceptation of alternatives depends strongly on the combination of measures and the implementation stages. Uncertainties of the results were notable but did not influence heavily on the alternative ranking. The expected reduction of future groundwater resources by climate change increases the conflict potential. The implementation of the method to a very complex case study, with many conflicting objectives and alternatives and uncertain outcomes, including future scenarios under water limiting conditions, illustrate the potential of the method for supporting management decisions.

Contribution of the multi-attribute value theory to conflict resolution in groundwater management - application to the Mancha Oriental groundwater system, Spain

NASA Astrophysics Data System (ADS)

Apperl, B.; Pulido-Velazquez, M.; Andreu, J.; Karjalainen, T. P.

2015-03-01

The implementation of the EU Water Framework Directive demands participatory water resource management approaches. Decision making in groundwater quantity and quality management is complex because of the existence of many independent actors, heterogeneous stakeholder interests, multiple objectives, different potential policies, and uncertain outcomes. Conflicting stakeholder interests have often been identified as an impediment to the realisation and success of water regulations and policies. The management of complex groundwater systems requires the clarification of stakeholders' positions (identifying stakeholder preferences and values), improving transparency with respect to outcomes of alternatives, and moving the discussion from the selection of alternatives towards the definition of fundamental objectives (value-thinking approach), which facilitates negotiation. The aims of the study are to analyse the potential of the multi-attribute value theory for conflict resolution in groundwater management and to evaluate the benefit of stakeholder incorporation into the different stages of the planning process, to find an overall satisfying solution for groundwater management. The research was conducted in the Mancha Oriental groundwater system (Spain), subject to intensive use of groundwater for irrigation. A complex set of objectives and attributes was defined, and the management alternatives were created by a combination of different fundamental actions, considering different implementation stages and future changes in water resource availability. Interviews were conducted with representative stakeholder groups using an interactive platform, showing simultaneously the consequences of changes in preferences to the alternative ranking. Results show that the approval of alternatives depends strongly on the combination of measures and the implementation stages. Uncertainties in the results were notable, but did not influence the alternative ranking heavily. The expected reduction in future groundwater resources by climate change increases the conflict potential. The implementation of the method in a very complex case study, with many conflicting objectives and alternatives and uncertain outcomes, including future scenarios under water limiting conditions, illustrates the potential of the method for supporting management decisions.

Valuing flexibilities in the design of urban water management systems.

PubMed

Deng, Yinghan; Cardin, Michel-Alexandre; Babovic, Vladan; Santhanakrishnan, Deepak; Schmitter, Petra; Meshgi, Ali

2013-12-15

Climate change and rapid urbanization requires decision-makers to develop a long-term forward assessment on sustainable urban water management projects. This is further complicated by the difficulties of assessing sustainable designs and various design scenarios from an economic standpoint. A conventional valuation approach for urban water management projects, like Discounted Cash Flow (DCF) analysis, fails to incorporate uncertainties, such as amount of rainfall, unit cost of water, and other uncertainties associated with future changes in technological domains. Such approach also fails to include the value of flexibility, which enables managers to adapt and reconfigure systems over time as uncertainty unfolds. This work describes an integrated framework to value investments in urban water management systems under uncertainty. It also extends the conventional DCF analysis through explicit considerations of flexibility in systems design and management. The approach incorporates flexibility as intelligent decision-making mechanisms that enable systems to avoid future downside risks and increase opportunities for upside gains over a range of possible futures. A water catchment area in Singapore was chosen to assess the value of a flexible extension of standard drainage canals and a flexible deployment of a novel water catchment technology based on green roofs and porous pavements. Results show that integrating uncertainty and flexibility explicitly into the decision-making process can reduce initial capital expenditure, improve value for investment, and enable decision-makers to learn more about system requirements during the lifetime of the project. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling for waste management associated with environmental-impact abatement under uncertainty.

PubMed

Li, P; Li, Y P; Huang, G H; Zhang, J L

2015-04-01

Municipal solid waste (MSW) treatment can generate significant amounts of pollutants, and thus pose a risk on human health. Besides, in MSW management, various uncertainties exist in the related costs, impact factors, and objectives, which can affect the optimization processes and the decision schemes generated. In this study, a life cycle assessment-based interval-parameter programming (LCA-IPP) method is developed for MSW management associated with environmental-impact abatement under uncertainty. The LCA-IPP can effectively examine the environmental consequences based on a number of environmental impact categories (i.e., greenhouse gas equivalent, acid gas emissions, and respiratory inorganics), through analyzing each life cycle stage and/or major contributing process related to various MSW management activities. It can also tackle uncertainties existed in the related costs, impact factors, and objectives and expressed as interval numbers. Then, the LCA-IPP method is applied to MSW management for the City of Beijing, the capital of China, where energy consumptions and six environmental parameters [i.e., CO2, CO, CH4, NOX, SO2, inhalable particle (PM10)] are used as systematic tool to quantify environmental releases in entire life cycle stage of waste collection, transportation, treatment, and disposal of. Results associated with system cost, environmental impact, and the related policy implication are generated and analyzed. Results can help identify desired alternatives for managing MSW flows, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty.

Managing the uncertainties of the streamflow data produced by the French national hydrological services

NASA Astrophysics Data System (ADS)

Puechberty, Rachel; Bechon, Pierre-Marie; Le Coz, Jérôme; Renard, Benjamin

2015-04-01

The French national hydrological services (NHS) manage the production of streamflow time series throughout the national territory. The hydrological data are made available to end-users through different web applications and the national hydrological archive (Banque Hydro). Providing end-users with qualitative and quantitative information on the uncertainty of the hydrological data is key to allow them drawing relevant conclusions and making appropriate decisions. Due to technical and organisational issues that are specific to the field of hydrometry, quantifying the uncertainty of hydrological measurements is still challenging and not yet standardized. The French NHS have made progress on building a consistent strategy to assess the uncertainty of their streamflow data. The strategy consists of addressing the uncertainties produced and propagated at each step of the data production with uncertainty analysis tools that are compatible with each other and compliant with international uncertainty guidance and standards. Beyond the necessary research and methodological developments, operational software tools and procedures are absolutely necessary to the data management and uncertainty analysis by field hydrologists. A first challenge is to assess, and if possible reduce, the uncertainty of streamgauging data, i.e. direct stage-discharge measurements. Interlaboratory experiments proved to be a very efficient way to empirically measure the uncertainty of a given streamgauging technique in given measurement conditions. The Q+ method (Le Coz et al., 2012) was developed to improve the uncertainty propagation method proposed in the ISO748 standard for velocity-area gaugings. Both empirical or computed (with Q+) uncertainty values can now be assigned in BAREME, which is the software used by the French NHS for managing streamgauging measurements. A second pivotal step is to quantify the uncertainty related to stage-discharge rating curves and their application to water level records to produce continuous discharge time series. The management of rating curves is also done using BAREME. The BaRatin method (Le Coz et al., 2014) was developed as a Bayesian approach of rating curve development and uncertainty analysis. Since BaRatin accounts for the individual uncertainties of gauging data used to build the rating curve, it was coupled with BAREME. The BaRatin method is still undergoing development and research, in particular to address non univocal or time-varying stage-discharge relations, due to hysteresis, variable backwater, rating shifts, etc. A new interface including new options is under development. The next steps are now to propagate the uncertainties of water level records, through uncertain rating curves, up to discharge time series and derived variables (e.g. annual mean flow) and statistics (e.g. flood quantiles). Bayesian tools are already available for both tasks but further validation and development is necessary for their integration in the operational data workflow of the French NHS. References Le Coz, J., Camenen, B., Peyrard, X., Dramais, G., 2012. Uncertainty in open-channel discharges measured with the velocity-area method. Flow Measurement and Instrumentation 26, 18-29. Le Coz, J., Renard, B., Bonnifait, L., Branger, F., Le Boursicaud, R., 2014. Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: a Bayesian approach, Journal of Hydrology, 509, 573-587.

Analyzing the management and disturbance in European forest based on self-thinning theory

NASA Astrophysics Data System (ADS)

Yan, Y.; Gielen, B.; Schelhaas, M.; Mohren, F.; Luyssaert, S.; Janssens, I. A.

2012-04-01

There is increasing awareness that natural and anthropogenic disturbance in forests affects exchange of CO2, H2O and energy between the ecosystem and the atmosphere. Consequently quantification of land use and disturbance intensity is one of the next steps needed to improve our understanding of the carbon cycle, its interactions with the atmosphere and its main drivers at local as well as at global level. The conventional NPP-based approaches to quantify the intensity of land management are limited because they lack a sound ecological basis. Here we apply a new way of characterising the degree of management and disturbance in forests using the self- thinning theory and observations of diameter at breast height and stand density. We used plot level information on dominant tree species, diameter at breast height, stand density and soil type from the French national forest inventory from 2005 to 2010. Stand density and diameter at breast height were used to parameterize the intercept of the self-thinning relationship and combined with theoretical slope to obtain an upper boundary for stand productivity given its density. Subsequently, we tested the sensitivity of the self-thinning relationship for tree species, soil type, climate and other environmental characteristics. We could find statistical differences in the self-thinning relationship between species and soil types, mainly due to the large uncertainty of the parameter estimates. Deviation from the theoretical self-thinning line defined as DBH=αN-3/4, was used as a proxy for disturbances, allowing to make spatially explicit maps of forest disturbance over France. The same framework was used to quantify the density-DBH trajectory of even-aged stand management of beech and oak over France. These trajectories will be used as a driver of forest management in the land surface model ORCHIDEE.

The Nature of Natural Hazards Communication (Invited)

NASA Astrophysics Data System (ADS)

Kontar, Y. Y.

2013-12-01

Some of the many issues of interest to natural hazards professionals include the analysis of proactive approaches to the governance of risk from natural hazards and approaches to broaden the scope of public policies related to the management of risks from natural hazards, as well as including emergency and environmental management, community development and spatial planning related to natural hazards. During the talk we will present results of scientific review, analysis and synthesis, which emphasize same new trends in communication of the natural hazards theories and practices within an up-to-the-minute context of new environmental and climate change issues, new technologies, and a new focus on resiliency. The presentation is divided into five sections that focus on natural hazards communication in terms of education, risk management, public discourse, engaging the public, theoretical perspectives, and new media. It includes results of case studies and best practices. It delves into natural hazards communication theories, including diffusion, argumentation, and constructivism, to name a few. The presentation will provide information about: (1) A manual of natural hazards communication for scientists, policymakers, and media; (2) An up-to-the-minute context of environmental hazards, new technologies & political landscape; (3) A work by natural hazards scientists for geoscientists working with social scientists and communication principles; (4) A work underpinned by key natural hazards communication theories and interspersed with pragmatic solutions; (5) A work that crosses traditional natural hazards boundaries: international, interdisciplinary, theoretical/applied. We will further explore how spatial planning can contribute to risk governance by influencing the occupation of natural hazard-prone areas, and review the central role of emergency management in risk policy. The goal of this presentation is to contribute to the augmentation of the conceptual framework of risk governance and increase the awareness of practitioners and decision-makers to the need to adopt proactive policies, leading to a more integrated, participative, and adaptive governance that can respond more efficiently to the increasing uncertainty resulting from escalating natural hazards risk exposure.

State-independent uncertainty relations and entanglement detection

NASA Astrophysics Data System (ADS)

Qian, Chen; Li, Jun-Li; Qiao, Cong-Feng

2018-04-01

The uncertainty relation is one of the key ingredients of quantum theory. Despite the great efforts devoted to this subject, most of the variance-based uncertainty relations are state-dependent and suffering from the triviality problem of zero lower bounds. Here we develop a method to get uncertainty relations with state-independent lower bounds. The method works by exploring the eigenvalues of a Hermitian matrix composed by Bloch vectors of incompatible observables and is applicable for both pure and mixed states and for arbitrary number of N-dimensional observables. The uncertainty relation for the incompatible observables can be explained by geometric relations related to the parallel postulate and the inequalities in Horn's conjecture on Hermitian matrix sum. Practical entanglement criteria are also presented based on the derived uncertainty relations.

Conservation of northern bobwhite on private lands in Georgia, USA under uncertainty about landscape-level habitat effects

USGS Publications Warehouse

Howell, J.E.; Moore, C.T.; Conroy, M.J.; Hamrick, R.G.; Cooper, R.J.; Thackston, R.E.; Carroll, J.P.

2009-01-01

Large-scale habitat enhancement programs for birds are becoming more widespread, however, most lack monitoring to resolve uncertainties and enhance program impact over time. Georgia?s Bobwhite Quail Initiative (BQI) is a competitive, proposal-based system that provides incentives to landowners to establish habitat for northern bobwhites (Colinus virginianus). Using data from monitoring conducted in the program?s first years (1999?2001), we developed alternative hierarchical models to predict bobwhite abundance in response to program habitat modifications on local and regional scales. Effects of habitat and habitat management on bobwhite population response varied among geographical scales, but high measurement variability rendered the specific nature of these scaled effects equivocal. Under some models, BQI had positive impact at both local farm scales (1, 9 km2), particularly when practice acres were clustered, whereas other credible models indicated that bird response did not depend on spatial arrangement of practices. Thus, uncertainty about landscape-level effects of management presents a challenge to program managers who must decide which proposals to accept. We demonstrate that optimal selection decisions can be made despite this uncertainty and that uncertainty can be reduced over time, with consequent improvement in management efficacy. However, such an adaptive approach to BQI program implementation would require the reestablishment of monitoring of bobwhite abundance, an effort for which funding was discontinued in 2002. For landscape-level conservation programs generally, our approach demonstrates the value in assessing multiple scales of impact of habitat modification programs, and it reveals the utility of addressing management uncertainty through multiple decision models and system monitoring.

Analogy as a strategy for supporting complex problem solving under uncertainty.

PubMed

Chan, Joel; Paletz, Susannah B F; Schunn, Christian D

2012-11-01

Complex problem solving in naturalistic environments is fraught with uncertainty, which has significant impacts on problem-solving behavior. Thus, theories of human problem solving should include accounts of the cognitive strategies people bring to bear to deal with uncertainty during problem solving. In this article, we present evidence that analogy is one such strategy. Using statistical analyses of the temporal dynamics between analogy and expressed uncertainty in the naturalistic problem-solving conversations among scientists on the Mars Rover Mission, we show that spikes in expressed uncertainty reliably predict analogy use (Study 1) and that expressed uncertainty reduces to baseline levels following analogy use (Study 2). In addition, in Study 3, we show with qualitative analyses that this relationship between uncertainty and analogy is not due to miscommunication-related uncertainty but, rather, is primarily concentrated on substantive problem-solving issues. Finally, we discuss a hypothesis about how analogy might serve as an uncertainty reduction strategy in naturalistic complex problem solving.

Intelligent Information Retrieval: Diagnosing Information Need. Part II. Uncertainty Expansion in a Prototype of a Diagnostic IR Tool.

ERIC Educational Resources Information Center

Cole, Charles; Cantero, Pablo; Sauve, Diane

1998-01-01

Outlines a prototype of an intelligent information-retrieval tool to facilitate information access for an undergraduate seeking information for a term paper. Topics include diagnosing the information need, Kuhlthau's information-search-process model, Shannon's mathematical theory of communication, and principles of uncertainty expansion and…

Assessing Uncertainty in Expert Judgments About Natural Resources

Treesearch

David A. Cleaves

1994-01-01

Judgments are necessary in natural resources management, but uncertainty about these judgments should be assessed. When all judgments are rejected in the absence of hard data, valuable professional experience and knowledge are not utilized fully. The objective of assessing uncertainty is to get the best representation of knowledge and its bounds. Uncertainty...

Beliefs about personal control and self-management in 30-40 year olds living with Inflammatory Bowel Disease: a qualitative study.

PubMed

Cooper, Joanne M; Collier, Jacqueline; James, Veronica; Hawkey, Christopher J

2010-12-01

Inflammatory Bowel Disease is a collective term for two distinct long term conditions: Ulcerative Colitis and Crohn's disease. There is increasing emphasis on patients taking greater personal control and self-management of this condition, reflecting earlier research into the management of chronic illness. Nurses play a pivotal role in this process, yet how optimal personal control is self-assessed and self-managed in Inflammatory Bowel Disease is poorly understood. This study set out to explore beliefs about personal control and self-management of Inflammatory Bowel Disease. It focused on the role of physical, psychological and socio-economic factors within the individual's life experience. A qualitative approach was used comprising 24, one-to-one, semi-structured interviews with participants aged 30-40 years. Participants with a histological diagnosis of Inflammatory Bowel Disease for at least 12 months were eligible and recruited by gastrointestinal specialist staff from outpatient clinics at a large National Health Service Trust in the United Kingdom. Interviews were transcribed verbatim. Data analysis was informed by existing theories of personal control and used the 'systematic framework analysis' approach. In addition to existing theories of personal control, self-discrepancy theory helped to explain how people viewed the control and self-management of Inflammatory Bowel Disease. One main theme emerged from the findings: 'Reconciliation of the self in IBD', this was supported by three sub-themes and eight basic themes. Some participants found that being unable to control and predict the course of their condition was distressing, however for others this limited control was not viewed as a negative outcome. Being able to share control of IBD with specialist health care staff was beneficial, and participants stated that other priorities in life were as equally important to manage and control. A key barrier to ensuring greater personal control and self-management was a lack of knowledge and awareness by non-specialist health care staff, employers and the wider society. Nurses involved in the care of individuals with Inflammatory Bowel Disease should support and prepare patients for the discrepancies and uncertainties of living with the condition. Greater training about Inflammatory Bowel Disease is recommended, specifically for non-specialist health care staff and employers. Copyright © 2010 Elsevier Ltd. All rights reserved.

Managing North American waterfowl in the face of uncertainty

USGS Publications Warehouse

Robbins, C.S.

1995-01-01

Informed management of waterfowl (or any animal population) requires management goals and objectives, the ability to implement management actions, periodic information about population and goal-related varlables, and knowledge of effects of management actions on population and goal-related variables. In North America, international treaties mandate a primary objective of protecting migratory bird populations, with a secondary objective of providing hunting opportunity in a manner compatible with such protection. Through the years, annual establishment of hunting regulations and acquisition and man-agement of habitat have been the primary management actions taken by federal agencies. Various information-gathering programs were established and, by the 1960s, had developed into arguably the best monitoring system in the world for continentally distributed animal populations. Retrospective analyses using estimates from this monitoring system have been used to investigate effects of management actions on waterfowl population and harvest dynamics, but key relationships arc still characterized by uncertainty. We recommend actively adaptive management as an approach that can meet short-term harvest objectives, while reducing uncertainty and ensuring sustainable populations over the long-term.

Optimizing Irrigation Water Allocation under Multiple Sources of Uncertainty in an Arid River Basin

NASA Astrophysics Data System (ADS)

Wei, Y.; Tang, D.; Gao, H.; Ding, Y.

2015-12-01

Population growth and climate change add additional pressures affecting water resources management strategies for meeting demands from different economic sectors. It is especially challenging in arid regions where fresh water is limited. For instance, in the Tailanhe River Basin (Xinjiang, China), a compromise must be made between water suppliers and users during drought years. This study presents a multi-objective irrigation water allocation model to cope with water scarcity in arid river basins. To deal with the uncertainties from multiple sources in the water allocation system (e.g., variations of available water amount, crop yield, crop prices, and water price), the model employs a interval linear programming approach. The multi-objective optimization model developed from this study is characterized by integrating eco-system service theory into water-saving measures. For evaluation purposes, the model is used to construct an optimal allocation system for irrigation areas fed by the Tailan River (Xinjiang Province, China). The objective functions to be optimized are formulated based on these irrigation areas' economic, social, and ecological benefits. The optimal irrigation water allocation plans are made under different hydroclimate conditions (wet year, normal year, and dry year), with multiple sources of uncertainty represented. The modeling tool and results are valuable for advising decision making by the local water authority—and the agricultural community—especially on measures for coping with water scarcity (by incorporating uncertain factors associated with crop production planning).

Uncertainty quantification metrics for whole product life cycle cost estimates in aerospace innovation

NASA Astrophysics Data System (ADS)

Schwabe, O.; Shehab, E.; Erkoyuncu, J.

2015-08-01

The lack of defensible methods for quantifying cost estimate uncertainty over the whole product life cycle of aerospace innovations such as propulsion systems or airframes poses a significant challenge to the creation of accurate and defensible cost estimates. Based on the axiomatic definition of uncertainty as the actual prediction error of the cost estimate, this paper provides a comprehensive overview of metrics used for the uncertainty quantification of cost estimates based on a literature review, an evaluation of publicly funded projects such as part of the CORDIS or Horizon 2020 programs, and an analysis of established approaches used by organizations such NASA, the U.S. Department of Defence, the ESA, and various commercial companies. The metrics are categorized based on their foundational character (foundations), their use in practice (state-of-practice), their availability for practice (state-of-art) and those suggested for future exploration (state-of-future). Insights gained were that a variety of uncertainty quantification metrics exist whose suitability depends on the volatility of available relevant information, as defined by technical and cost readiness level, and the number of whole product life cycle phases the estimate is intended to be valid for. Information volatility and number of whole product life cycle phases can hereby be considered as defining multi-dimensional probability fields admitting various uncertainty quantification metric families with identifiable thresholds for transitioning between them. The key research gaps identified were the lacking guidance grounded in theory for the selection of uncertainty quantification metrics and lacking practical alternatives to metrics based on the Central Limit Theorem. An innovative uncertainty quantification framework consisting of; a set-theory based typology, a data library, a classification system, and a corresponding input-output model are put forward to address this research gap as the basis for future work in this field.

An Intuitionistic Fuzzy Logic Models for Multicriteria Decision Making Under Uncertainty

NASA Astrophysics Data System (ADS)

Jana, Biswajit; Mohanty, Sachi Nandan

2017-04-01

The purpose of this paper is to enhance the applicability of the fuzzy sets for developing mathematical models for decision making under uncertainty, In general a decision making process consist of four stages, namely collection of information from various sources, compile the information, execute the information and finally take the decision/action. Only fuzzy sets theory is capable to quantifying the linguistic expression to mathematical form in complex situation. Intuitionistic fuzzy set (IFSs) which reflects the fact that the degree of non membership is not always equal to one minus degree of membership. There may be some degree of hesitation. Thus, there are some situations where IFS theory provides a more meaningful and applicable to cope with imprecise information present for solving multiple criteria decision making problem. This paper emphasis on IFSs, which is help for solving real world problem in uncertainty situation.

An information theory approach for evaluating earth radiation budget (ERB) measurements - Nonuniform sampling of diurnal longwave flux variations

NASA Technical Reports Server (NTRS)

Halyo, Nesim; Direskeneli, Haldun; Barkstrom, Bruce R.

1991-01-01

Satellite measurements are subject to a wide range of uncertainties due to their temporal, spatial, and directional sampling characteristics. An information-theory approach is suggested to examine the nonuniform temporal sampling of ERB measurements. The information (i.e., its entropy or uncertainty) before and after the measurements is determined, and information gain (IG) is defined as a reduction in the uncertainties involved. A stochastic model for the diurnal outgoing flux variations that affect the ERB is developed. Using Gaussian distributions for the a priori and measured radiant exitance fields, the IG is obtained by computing the a posteriori covariance. The IG for the monthly outgoing flux measurements is examined for different orbital parameters and orbital tracks, using the Earth Observing System orbital parameters as specific examples. Variations in IG due to changes in the orbit's inclination angle and the initial ascending node local time are investigated.

Incorporating parametric uncertainty into population viability analysis models

USGS Publications Warehouse

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

A taxonomy of medical uncertainties in clinical genome sequencing.

PubMed

Han, Paul K J; Umstead, Kendall L; Bernhardt, Barbara A; Green, Robert C; Joffe, Steven; Koenig, Barbara; Krantz, Ian; Waterston, Leo B; Biesecker, Leslie G; Biesecker, Barbara B

2017-08-01

Clinical next-generation sequencing (CNGS) is introducing new opportunities and challenges into the practice of medicine. Simultaneously, these technologies are generating uncertainties of an unprecedented scale that laboratories, clinicians, and patients are required to address and manage. We describe in this report the conceptual design of a new taxonomy of uncertainties around the use of CNGS in health care. Interviews to delineate the dimensions of uncertainty in CNGS were conducted with genomics experts and themes were extracted in order to expand on a previously published three-dimensional taxonomy of medical uncertainty. In parallel, we developed an interactive website to disseminate the CNGS taxonomy to researchers and engage them in its continued refinement. The proposed taxonomy divides uncertainty along three axes-source, issue, and locus-and further discriminates the uncertainties into five layers with multiple domains. Using a hypothetical clinical example, we illustrate how the taxonomy can be applied to findings from CNGS and used to guide stakeholders through interpretation and implementation of variant results. The utility of the proposed taxonomy lies in promoting consistency in describing dimensions of uncertainty in publications and presentations, to facilitate research design and management of the uncertainties inherent in the implementation of CNGS.Genet Med advance online publication 19 January 2017.

A Taxonomy of Medical Uncertainties in Clinical Genome Sequencing

PubMed Central

Han, Paul K. J.; Umstead, Kendall L.; Bernhardt, Barbara A.; Green, Robert C.; Joffe, Steven; Koenig, Barbara; Krantz, Ian; Waterston, Leo B.; Biesecker, Leslie G.; Biesecker, Barbara B.

2017-01-01

Purpose Clinical next generation sequencing (CNGS) is introducing new opportunities and challenges into the practice of medicine. Simultaneously, these technologies are generating uncertainties of unprecedented scale that laboratories, clinicians, and patients are required to address and manage. We describe in this report the conceptual design of a new taxonomy of uncertainties around the use of CNGS in health care. Methods Interviews to delineate the dimensions of uncertainty in CNGS were conducted with genomics experts, and themes were extracted in order to expand upon a previously published three-dimensional taxonomy of medical uncertainty. In parallel we developed an interactive website to disseminate the CNGS taxonomy to researchers and engage them in its continued refinement. Results The proposed taxonomy divides uncertainty along three axes: source, issue, and locus, and further discriminates the uncertainties into five layers with multiple domains. Using a hypothetical clinical example, we illustrate how the taxonomy can be applied to findings from CNGS and used to guide stakeholders through interpretation and implementation of variant results. Conclusion The utility of the proposed taxonomy lies in promoting consistency in describing dimensions of uncertainty in publications and presentations, to facilitate research design and management of the uncertainties inherent in the implementation of CNGS. PMID:28102863

DecisionMaker software and extracting fuzzy rules under uncertainty

NASA Technical Reports Server (NTRS)

Walker, Kevin B.

1992-01-01

Knowledge acquisition under uncertainty is examined. Theories proposed in deKorvin's paper 'Extracting Fuzzy Rules Under Uncertainty and Measuring Definability Using Rough Sets' are discussed as they relate to rule calculation algorithms. A data structure for holding an arbitrary number of data fields is described. Limitations of Pascal for loops in the generation of combinations are also discussed. Finally, recursive algorithms for generating all possible combination of attributes and for calculating the intersection of an arbitrary number of fuzzy sets are presented.

A PROBABILISTIC APPROACH FOR ANALYSIS OF UNCERTAINTY IN THE EVALUATION OF WATERSHED MANAGEMENT PRACTICES

EPA Science Inventory

A computational framework is presented for analyzing the uncertainty in model estimates of water quality benefits of best management practices (BMPs) in two small (<10 km²) watersheds in Indiana. The analysis specifically recognizes the significance of the difference b...

Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management: Application to Lake Oxygen Recovery Model

EPA Science Inventory

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...

Leadership Values, Trust and Negative Capability: Managing the Uncertainties of Future English Higher Education

ERIC Educational Resources Information Center

Jameson, Jill

2012-01-01

The complex leadership attribute of "negative capability" in managing uncertainty and engendering trust may be amongst the qualities enabling institutions to cope with multiple recent government policy challenges affecting English higher education, including significant increases in student fees. Research findings are reported on changes…

Patterns of zone management uncertainty in cotton using tarnished plant bug distributions, NDVI, soil EC, yield and thermal imagery

USDA-ARS?s Scientific Manuscript database

Management zones for various crops have been delineated using NDVI (Normalized Difference Vegetation Index), apparent bulk soil electrical conductivity (ECa - Veris), and yield data; however, estimations of uncertainty for these data layers are equally important considerations. The objective of this...

Rethinking Social Barriers to Effective Adaptive Management.

PubMed

West, Simon; Schultz, Lisen; Bekessy, Sarah

2016-09-01

Adaptive management is an approach to environmental management based on learning-by-doing, where complexity, uncertainty, and incomplete knowledge are acknowledged and management actions are treated as experiments. However, while adaptive management has received significant uptake in theory, it remains elusively difficult to enact in practice. Proponents have blamed social barriers and have called for social science contributions. We address this gap by adopting a qualitative approach to explore the development of an ecological monitoring program within an adaptive management framework in a public land management organization in Australia. We ask what practices are used to enact the monitoring program and how do they shape learning? We elicit a rich narrative through extensive interviews with a key individual, and analyze the narrative using thematic analysis. We discuss our results in relation to the concept of 'knowledge work' and Westley's (2002) framework for interpreting the strategies of adaptive managers-'managing through, in, out and up.' We find that enacting the program is conditioned by distinct and sometimes competing logics-scientific logics prioritizing experimentation and learning, public logics emphasizing accountability and legitimacy, and corporate logics demanding efficiency and effectiveness. In this context, implementing adaptive management entails practices of translation to negotiate tensions between objective and situated knowledge, external experts and organizational staff, and collegiate and hierarchical norms. Our contribution embraces the 'doing' of learning-by-doing and marks a shift from conceptualizing the social as an external barrier to adaptive management to be removed to an approach that situates adaptive management as social knowledge practice.

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems.

PubMed

Boukattaya, Mohamed; Mezghani, Neila; Damak, Tarak

2018-06-01

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Perceptions of risk, risk aversion, and barriers to adoption of decision support systems and integrated pest management: an introduction.

PubMed

Gent, David H; De Wolf, Erick; Pethybridge, Sarah J

2011-06-01

Rational management of plant diseases, both economically and environmentally, involves assessing risks and the costs associated with both correct and incorrect tactical management decisions to determine when control measures are warranted. Decision support systems can help to inform users of plant disease risk and thus assist in accurately targeting events critical for management. However, in many instances adoption of these systems for use in routine disease management has been perceived as slow. The under-utilization of some decision support systems is likely due to both technical and perception constraints that have not been addressed adequately during development and implementation phases. Growers' perceptions of risk and their aversion to these perceived risks can be reasons for the "slow" uptake of decision support systems and, more broadly, integrated pest management (IPM). Decision theory provides some tools that may assist in quantifying and incorporating subjective and/or measured probabilities of disease occurrence or crop loss into decision support systems. Incorporation of subjective probabilities into IPM recommendations may be one means to reduce grower uncertainty and improve trust of these systems because management recommendations could be explicitly informed by growers' perceptions of risk and economic utility. Ultimately though, we suggest that an appropriate measure of the value and impact of decision support systems is grower education that enables more skillful and informed management decisions independent of consultation of the support tool outputs.

Uncertainties in scaling factors for ab initio vibrational zero-point energies

NASA Astrophysics Data System (ADS)

Irikura, Karl K.; Johnson, Russell D.; Kacker, Raghu N.; Kessel, Rüdiger

2009-03-01

Vibrational zero-point energies (ZPEs) determined from ab initio calculations are often scaled by empirical factors. An empirical scaling factor partially compensates for the effects arising from vibrational anharmonicity and incomplete treatment of electron correlation. These effects are not random but are systematic. We report scaling factors for 32 combinations of theory and basis set, intended for predicting ZPEs from computed harmonic frequencies. An empirical scaling factor carries uncertainty. We quantify and report, for the first time, the uncertainties associated with scaling factors for ZPE. The uncertainties are larger than generally acknowledged; the scaling factors have only two significant digits. For example, the scaling factor for B3LYP/6-31G(d) is 0.9757±0.0224 (standard uncertainty). The uncertainties in the scaling factors lead to corresponding uncertainties in predicted ZPEs. The proposed method for quantifying the uncertainties associated with scaling factors is based upon the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization. We also present a new reference set of 60 diatomic and 15 polyatomic "experimental" ZPEs that includes estimated uncertainties.

Physics of risk and uncertainty in quantum decision making

NASA Astrophysics Data System (ADS)

Yukalov, V. I.; Sornette, D.

2009-10-01

The Quantum Decision Theory, developed recently by the authors, is applied to clarify the role of risk and uncertainty in decision making and in particular in relation to the phenomenon of dynamic inconsistency. By formulating this notion in precise mathematical terms, we distinguish three types of inconsistency: time inconsistency, planning paradox, and inconsistency occurring in some discounting effects. While time inconsistency is well accounted for in classical decision theory, the planning paradox is in contradiction with classical utility theory. It finds a natural explanation in the frame of the Quantum Decision Theory. Different types of discounting effects are analyzed and shown to enjoy a straightforward explanation within the suggested theory. We also introduce a general methodology based on self-similar approximation theory for deriving the evolution equations for the probabilities of future prospects. This provides a novel classification of possible discount factors, which include the previously known cases (exponential or hyperbolic discounting), but also predicts a novel class of discount factors that decay to a strictly positive constant for very large future time horizons. This class may be useful to deal with very long-term discounting situations associated with intergenerational public policy choices, encompassing issues such as global warming and nuclear waste disposal.

Conflict or Caveats? Effects of Media Portrayals of Scientific Uncertainty on Audience Perceptions of New Technologies.

PubMed

Binder, Andrew R; Hillback, Elliott D; Brossard, Dominique

2016-04-01

Research indicates that uncertainty in science news stories affects public assessment of risk and uncertainty. However, the form in which uncertainty is presented may also affect people's risk and uncertainty assessments. For example, a news story that features an expert discussing both what is known and what is unknown about a topic may convey a different form of scientific uncertainty than a story that features two experts who hold conflicting opinions about the status of scientific knowledge of the topic, even when both stories contain the same information about knowledge and its boundaries. This study focuses on audience uncertainty and risk perceptions regarding the emerging science of nanotechnology by manipulating whether uncertainty in a news story about potential risks is attributed to expert sources in the form of caveats (individual uncertainty) or conflicting viewpoints (collective uncertainty). Results suggest that the type of uncertainty portrayed does not impact audience feelings of uncertainty or risk perceptions directly. Rather, the presentation of the story influences risk perceptions only among those who are highly deferent to scientific authority. Implications for risk communication theory and practice are discussed. © 2015 Society for Risk Analysis.

Estimating the spatial distribution of wintering little brown bat populations in the eastern United States

USGS Publications Warehouse

Russell, Robin E.; Tinsley, Karl; Erickson, Richard A.; Thogmartin, Wayne E.; Jennifer A. Szymanski,

2014-01-01

Depicting the spatial distribution of wildlife species is an important first step in developing management and conservation programs for particular species. Accurate representation of a species distribution is important for predicting the effects of climate change, land-use change, management activities, disease, and other landscape-level processes on wildlife populations. We developed models to estimate the spatial distribution of little brown bat (Myotis lucifugus) wintering populations in the United States east of the 100th meridian, based on known hibernacula locations. From this data, we developed several scenarios of wintering population counts per county that incorporated uncertainty in the spatial distribution of the hibernacula as well as uncertainty in the size of the current little brown bat population. We assessed the variability in our results resulting from effects of uncertainty. Despite considerable uncertainty in the known locations of overwintering little brown bats in the eastern United States, we believe that models accurately depicting the effects of the uncertainty are useful for making management decisions as these models are a coherent organization of the best available information.

Decision Making Under Uncertainty and Complexity: A Model-Based Scenario Approach to Supporting Integrated Water Resources Management

NASA Astrophysics Data System (ADS)

Liu, Y.; Gupta, H.; Wagener, T.; Stewart, S.; Mahmoud, M.; Hartmann, H.; Springer, E.

2007-12-01

Some of the most challenging issues facing contemporary water resources management are those typified by complex coupled human-environmental systems with poorly characterized uncertainties. In other words, major decisions regarding water resources have to be made in the face of substantial uncertainty and complexity. It has been suggested that integrated models can be used to coherently assemble information from a broad set of domains, and can therefore serve as an effective means for tackling the complexity of environmental systems. Further, well-conceived scenarios can effectively inform decision making, particularly when high complexity and poorly characterized uncertainties make the problem intractable via traditional uncertainty analysis methods. This presentation discusses the integrated modeling framework adopted by SAHRA, an NSF Science & Technology Center, to investigate stakeholder-driven water sustainability issues within the semi-arid southwestern US. The multi-disciplinary, multi-resolution modeling framework incorporates a formal scenario approach to analyze the impacts of plausible (albeit uncertain) alternative futures to support adaptive management of water resources systems. Some of the major challenges involved in, and lessons learned from, this effort will be discussed.

Rethinking Social Barriers to Effective Adaptive Management

NASA Astrophysics Data System (ADS)

West, Simon; Schultz, Lisen; Bekessy, Sarah

2016-09-01

Adaptive management is an approach to environmental management based on learning-by-doing, where complexity, uncertainty, and incomplete knowledge are acknowledged and management actions are treated as experiments. However, while adaptive management has received significant uptake in theory, it remains elusively difficult to enact in practice. Proponents have blamed social barriers and have called for social science contributions. We address this gap by adopting a qualitative approach to explore the development of an ecological monitoring program within an adaptive management framework in a public land management organization in Australia. We ask what practices are used to enact the monitoring program and how do they shape learning? We elicit a rich narrative through extensive interviews with a key individual, and analyze the narrative using thematic analysis. We discuss our results in relation to the concept of `knowledge work' and Westley's 2002) framework for interpreting the strategies of adaptive managers—`managing through, in, out and up.' We find that enacting the program is conditioned by distinct and sometimes competing logics—scientific logics prioritizing experimentation and learning, public logics emphasizing accountability and legitimacy, and corporate logics demanding efficiency and effectiveness. In this context, implementing adaptive management entails practices of translation to negotiate tensions between objective and situated knowledge, external experts and organizational staff, and collegiate and hierarchical norms. Our contribution embraces the `doing' of learning-by-doing and marks a shift from conceptualizing the social as an external barrier to adaptive management to be removed to an approach that situates adaptive management as social knowledge practice.

Testing gravity with E{sub G}: mapping theory onto observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leonard, C. Danielle; Ferreira, Pedro G.; Heymans, Catherine, E-mail: danielle.leonard@physics.ox.ac.uk, E-mail: p.ferreira1@physics.ox.ac.uk, E-mail: heymans@roe.ac.uk

We present a complete derivation of the observationally motivated definition of the modified gravity statistic E{sub G}. Using this expression, we investigate how variations to theory and survey parameters may introduce uncertainty in the general relativistic prediction of E{sub G}. We forecast errors on E{sub G} for measurements using two combinations of upcoming surveys, and find that theoretical uncertainties may dominate for a futuristic measurement. Finally, we compute predictions of E{sub G} under modifications to general relativity in the quasistatic regime, and comment on the pros and cons of using E{sub G} to test gravity with future surveys.

The uncertainty processing theory of motivation.

PubMed

Anselme, Patrick

2010-04-02

Most theories describe motivation using basic terminology (drive, 'wanting', goal, pleasure, etc.) that fails to inform well about the psychological mechanisms controlling its expression. This leads to a conception of motivation as a mere psychological state 'emerging' from neurophysiological substrates. However, the involvement of motivation in a large number of behavioural parameters (triggering, intensity, duration, and directedness) and cognitive abilities (learning, memory, decision, etc.) suggest that it should be viewed as an information processing system. The uncertainty processing theory (UPT) presented here suggests that motivation is the set of cognitive processes allowing organisms to extract information from the environment by reducing uncertainty about the occurrence of psychologically significant events. This processing of information is shown to naturally result in the highlighting of specific stimuli. The UPT attempts to solve three major problems: (i) how motivations can affect behaviour and cognition so widely, (ii) how motivational specificity for objects and events can result from nonspecific neuropharmacological causal factors (such as mesolimbic dopamine), and (iii) how motivational interactions can be conceived in psychological terms, irrespective of their biological correlates. The UPT is in keeping with the conceptual tradition of the incentive salience hypothesis while trying to overcome the shortcomings inherent to this view. Copyright 2009 Elsevier B.V. All rights reserved.

Uncertainty in weather and climate prediction

PubMed Central

Slingo, Julia; Palmer, Tim

2011-01-01

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. PMID:22042896

The physical origins of the uncertainty theorem

NASA Astrophysics Data System (ADS)

Giese, Albrecht

2013-10-01

The uncertainty principle is an important element of quantum mechanics. It deals with certain pairs of physical parameters which cannot be determined to an arbitrary level of precision at the same time. According to the so-called Copenhagen interpretation of quantum mechanics, this uncertainty is an intrinsic property of the physical world. - This paper intends to show that there are good reasons for adopting a different view. According to the author, the uncertainty is not a property of the physical world but rather a limitation of our knowledge about the actual state of a physical process. This view conforms to the quantum theory of Louis de Broglie and to Albert Einstein's interpretation.

Using functional theory to promote HIV testing: the impact of value-expressive messages, uncertainty, and fear.

PubMed

Hullett, Craig R

2006-01-01

This study tests the utility of the functional theory of attitudes and arousal of fear in motivating college students to get tested for HIV. It is argued from the perspective of functional theory that value-expressive appeals to get tested for the purpose of taking care of one's own health could be effective if that goal is desired by message targets who are sexually active and unaware of their sexually transmitted disease status. As part of the process, the effectiveness of these appeals is increased by the arousal of uncertainty and fear. A model detailing the mediating processes is proposed and found to be consistent with the data. Overall, messages advocating testing for the self-interested reason of one's own health were more effective than messages advocating testing for the goal of protecting one's partners.

On Becoming a Language Teacher.

ERIC Educational Resources Information Center

Jakobovits, Leon A.

Underlying this essay on psycholinguistic theory is the belief that language teachers often suffer from neurotic symptoms of confusion, anxiety, and uncertainty in connection with their work. The author discusses his "BALT" theory (battered language teachers). Philosophically-oriented remarks are directed toward teachers wishing to redirect their…

Adaptive harvest management of North American waterfowl populations - recent successes and future prospects

USGS Publications Warehouse

Nichols, J.D.; Runge, M.C.; Johnson, F.A.; Williams, B.K.; Schodde, Richard; Hannon, Susan; Scheiffarth, Gregor; Bairlein, Franz

2006-01-01

The history of North American waterfowl harvest management has been characterized by attempts to use population monitoring data to make informed harvest management decisions. Early attempts can be characterized as intuitive decision processes, and later efforts were guided increasingly by population models and associated predictions. In 1995, a formal adaptive management process was implemented, and annual decisions about duck harvest regulations in the United States are still based on this process. This formal decision process is designed to deal appropriately with the various forms of uncertainty that characterize management decisions, environmental uncertainty, structural uncertainty, partial controllability and partial observability. The key components of the process are (1) objectives, (2) potential management actions, (3) model(s) of population response to management actions, (4) credibility measures for these models, and (5) a monitoring program. The operation of this iterative process is described, and a brief history of a decade of its use is presented. Future challenges range from social and political issues such as appropriate objectives and management actions, to technical issues such as multispecies management, geographic allocation of harvest, and incorporation of actions that include habitat acquisition and management.

A Note on the Treatment of Uncertainty in Economics and Finance

ERIC Educational Resources Information Center

Carilli, Anthony M.; Dempster, Gregory M.

2003-01-01

The treatment of uncertainty in the business classroom has been dominated by the application of risk theory to the utility-maximization framework. Nonetheless, the relevance of the standard risk model as a positive description of economic decision making often has been called into question in theoretical work. In this article, the authors offer an…

Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly

ERIC Educational Resources Information Center

Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.

2013-01-01

Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…

Analyzing Uncertainty and Risk in the Management of Water Resources in the State Of Texas

NASA Astrophysics Data System (ADS)

Singh, A.; Hauffpauir, R.; Mishra, S.; Lavenue, M.

2010-12-01

The State of Texas updates its state water plan every five years to determine the water demand required to meet its growing population. The plan compiles forecasts of water deficits from state-wide regional water planning groups as well as the water supply strategies to address these deficits. To date, the plan has adopted a deterministic framework, where reference values (e.g., best estimates, worst-case scenario) are used for key factors such as population growth, demand for water, severity of drought, water availability, etc. These key factors can, however, be affected by multiple sources of uncertainties such as - the impact of climate on surface water and groundwater availability, uncertainty in population projections, changes in sectoral composition of the economy, variability in water usage, feasibility of the permitting process, cost of implementation, etc. The objective of this study was to develop a generalized and scalable methodology for addressing uncertainty and risk in water resources management both at the regional and the local water planning level. The study proposes a framework defining the elements of an end-to-end system model that captures the key components of demand, supply and planning modules along with their associated uncertainties. The framework preserves the fundamental elements of the well-established planning process in the State of Texas, promoting an incremental and stakeholder-driven approach to adding different levels of uncertainty (and risk) into the decision-making environment. The uncertainty in the water planning process is broken down into two primary categories: demand uncertainty and supply uncertainty. Uncertainty in Demand is related to the uncertainty in population projections and the per-capita usage rates. Uncertainty in Supply, in turn, is dominated by the uncertainty in future climate conditions. Climate is represented in terms of time series of precipitation, temperature and/or surface evaporation flux for some future time period of interest, which can be obtained as outputs of global climate models (GCMs). These are then linked with hydrologic and water-availability models (WAMs) to estimate water availability for the worst drought conditions under each future climate scenario. Combining the demand scenarios with the water availability scenarios yields multiple scenarios for water shortage (or surplus). Given multiple shortage/surplus scenarios, various water management strategies can be assessed to evaluate the reliability of meeting projected deficits. These reliabilities are then used within a multi-criteria decision-framework to assess trade-offs between various water management objectives, thus helping to make more robust decisions while planning for the water needs of the future.

Scale-invariant instantons and the complete lifetime of the standard model

NASA Astrophysics Data System (ADS)

Andreassen, Anders; Frost, William; Schwartz, Matthew D.

2018-03-01

In a classically scale-invariant quantum field theory, tunneling rates are infrared divergent due to the existence of instantons of any size. While one expects such divergences to be resolved by quantum effects, it has been unclear how higher-loop corrections can resolve a problem appearing already at one loop. With a careful power counting, we uncover a series of loop contributions that dominate over the one-loop result and sum all the necessary terms. We also clarify previously incomplete treatments of related issues pertaining to global symmetries, gauge fixing, and finite mass effects. In addition, we produce exact closed-form solutions for the functional determinants over scalars, fermions, and vector bosons around the scale-invariant bounce, demonstrating manifest gauge invariance in the vector case. With these problems solved, we produce the first complete calculation of the lifetime of our Universe: 1 0139 years . With 95% confidence, we expect our Universe to last more than 1 058 years . The uncertainty is part experimental uncertainty on the top quark mass and on αs and part theory uncertainty from electroweak threshold corrections. Using our complete result, we provide phase diagrams in the mt/mh and the mt/αs planes, with uncertainty bands. To rule out absolute stability to 3 σ confidence, the uncertainty on the top quark pole mass would have to be pushed below 250 MeV or the uncertainty on αs(mZ) pushed below 0.00025.

Uncertainty quantification for nuclear density functional theory and information content of new measurements

DOE PAGES

McDonnell, J. D.; Schunck, N.; Higdon, D.; ...

2015-03-24

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squaresmore » optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. In addition, the example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.« less

Uncertainty quantification for nuclear density functional theory and information content of new measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDonnell, J. D.; Schunck, N.; Higdon, D.

2015-03-24

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squaresmore » optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. As a result, the example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.« less

Foundations to the unified psycho-cognitive engine.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernard, Michael Lewis; Bier, Asmeret Brooke; Backus, George A.

This document outlines the key features of the SNL psychological engine. The engine is designed to be a generic presentation of cognitive entities interacting among themselves and with the external world. The engine combines the most accepted theories of behavioral psychology with those of behavioral economics to produce a unified simulation of human response from stimuli through executed behavior. The engine explicitly recognizes emotive and reasoned contributions to behavior and simulates the dynamics associated with cue processing, learning, and choice selection. Most importantly, the model parameterization can come from available media or survey information, as well subject-matter-expert information. The frameworkmore » design allows the use of uncertainty quantification and sensitivity analysis to manage confidence in using the analysis results for intervention decisions.« less

Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.

PubMed

Guo, P; Huang, G H

2010-03-01

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context. Copyright 2009 Elsevier Ltd. All rights reserved.

Climate change and European forests: what do we know, what are the uncertainties, and what are the implications for forest management?

PubMed

Lindner, Marcus; Fitzgerald, Joanne B; Zimmermann, Niklaus E; Reyer, Christopher; Delzon, Sylvain; van der Maaten, Ernst; Schelhaas, Mart-Jan; Lasch, Petra; Eggers, Jeannette; van der Maaten-Theunissen, Marieke; Suckow, Felicitas; Psomas, Achilleas; Poulter, Benjamin; Hanewinkel, Marc

2014-12-15

The knowledge about potential climate change impacts on forests is continuously expanding and some changes in growth, drought induced mortality and species distribution have been observed. However despite a significant body of research, a knowledge and communication gap exists between scientists and non-scientists as to how climate change impact scenarios can be interpreted and what they imply for European forests. It is still challenging to advise forest decision makers on how best to plan for climate change as many uncertainties and unknowns remain and it is difficult to communicate these to practitioners and other decision makers while retaining emphasis on the importance of planning for adaptation. In this paper, recent developments in climate change observations and projections, observed and projected impacts on European forests and the associated uncertainties are reviewed and synthesised with a view to understanding the implications for forest management. Current impact assessments with simulation models contain several simplifications, which explain the discrepancy between results of many simulation studies and the rapidly increasing body of evidence about already observed changes in forest productivity and species distribution. In simulation models uncertainties tend to cascade onto one another; from estimating what future societies will be like and general circulation models (GCMs) at the global level, down to forest models and forest management at the local level. Individual climate change impact studies should not be uncritically used for decision-making without reflection on possible shortcomings in system understanding, model accuracy and other assumptions made. It is important for decision makers in forest management to realise that they have to take long-lasting management decisions while uncertainty about climate change impacts are still large. We discuss how to communicate about uncertainty - which is imperative for decision making - without diluting the overall message. Considering the range of possible trends and uncertainties in adaptive forest management requires expert knowledge and enhanced efforts for providing science-based decision support. Copyright © 2014 Elsevier Ltd. All rights reserved.

Forest management under climatic and social uncertainty: trade-offs between reducing climate change impacts and fostering adaptive capacity.

PubMed

Seidl, Rupert; Lexer, Manfred J

2013-01-15

The unabated continuation of anthropogenic greenhouse gas emissions and the lack of an international consensus on a stringent climate change mitigation policy underscore the importance of adaptation for coping with the all but inevitable changes in the climate system. Adaptation measures in forestry have particularly long lead times. A timely implementation is thus crucial for reducing the considerable climate vulnerability of forest ecosystems. However, since future environmental conditions as well as future societal demands on forests are inherently uncertain, a core requirement for adaptation is robustness to a wide variety of possible futures. Here we explicitly address the roles of climatic and social uncertainty in forest management, and tackle the question of robustness of adaptation measures in the context of multi-objective sustainable forest management (SFM). We used the Austrian Federal Forests (AFF) as a case study, and employed a comprehensive vulnerability assessment framework based on ecosystem modeling, multi-criteria decision analysis, and practitioner participation. We explicitly considered climate uncertainty by means of three climate change scenarios, and accounted for uncertainty in future social demands by means of three societal preference scenarios regarding SFM indicators. We found that the effects of climatic and social uncertainty on the projected performance of management were in the same order of magnitude, underlining the notion that climate change adaptation requires an integrated social-ecological perspective. Furthermore, our analysis of adaptation measures revealed considerable trade-offs between reducing adverse impacts of climate change and facilitating adaptive capacity. This finding implies that prioritization between these two general aims of adaptation is necessary in management planning, which we suggest can draw on uncertainty analysis: Where the variation induced by social-ecological uncertainty renders measures aiming to reduce climate change impacts statistically insignificant (i.e., for approximately one third of the investigated management units of the AFF case study), fostering adaptive capacity is suggested as the preferred pathway for adaptation. We conclude that climate change adaptation needs to balance between anticipating expected future conditions and building the capacity to address unknowns and surprises. Copyright © 2012 Elsevier Ltd. All rights reserved.

Global sensitivity and uncertainty analysis of the nitrate leaching and crop yield simulation under different water and nitrogen management practices

USDA-ARS?s Scientific Manuscript database

Agricultural system models have become important tools in studying water and nitrogen (N) dynamics, as well as crop growth, under different management practices. Complexity in input parameters often leads to significant uncertainty when simulating dynamic processes such as nitrate leaching or crop y...

Uncertainty analysis of an irrigation scheduling model for water management in crop production

USDA-ARS?s Scientific Manuscript database

Irrigation scheduling tools are critical to allow producers to manage water resources for crop production in an accurate and timely manner. To be useful, these tools need to be accurate, complete, and relatively reliable. The current work presents the uncertainty analysis and its results for the Mis...

78 FR 25862 - Fisheries of the Northeastern United States; Final 2013-2015 Spiny Dogfish Fishery Specifications

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-03

... the status quo. The action is expected to maximize the profitability for the spiny dogfish fishery... possible commercial quotas by not making a deduction from the ACL accounting for management uncertainty...) in 2015; however, not accounting for management uncertainty would have increased the risk of...

Accepting uncertainty, assessing risk: decision quality in managing wildfire, forest resource values, and new technology

Treesearch

Jeffrey G. Borchers

2005-01-01

The risks, uncertainties, and social conflicts surrounding uncharacteristic wildfire and forest resource values have defied conventional approaches to planning and decision-making. Paradoxically, the adoption of technological innovations such as risk assessment, decision analysis, and landscape simulation models by land management organizations has been limited. The...

Predictive Uncertainty And Parameter Sensitivity Of A Sediment-Flux Model: Nitrogen Flux and Sediment Oxygen Demand

EPA Science Inventory

Estimating model predictive uncertainty is imperative to informed environmental decision making and management of water resources. This paper applies the Generalized Sensitivity Analysis (GSA) to examine parameter sensitivity and the Generalized Likelihood Uncertainty Estimation...

Making partnerships work: issues of risk, trust and control for managers and service providers.

PubMed

Walker, Rae; Smith, Penny; Adam, Jenny

2009-03-01

Trust is widely recognised is a core feature of partnership relationships and one that facilitates joint work. It is an issue that must be addressed if partnerships are to enhance service system integration. In recent literature trust has been linked to concepts of risk and control. In this study of trust within a Primary Care Partnership (PCP) in Australia the experiences of risk and uncertainty, and control, of participants in different structural positions, were explored in detail. The data used in this paper was qualitative, derived from 63 interviews with managers and service providers participating in committees of the PCP. This paper reports on the differences in the experience of risk and uncertainty, trust and control, of managers and service providers working as boundary spanners through the committees of a PCP. For managers there were significant risks and uncertainties, and trust and control were important. For service providers there were few risks and uncertainties, and trust and control were of much less importance. Some policy implications of the differences in perspective are discussed, as are important areas for further research.

Family support in the transition to adulthood in Portugal--its effects on identity capital development, uncertainty management and psychological well-being.

PubMed

Oliveira, José Egídio; Mendonça, Marina; Coimbra, Susana; Fontaine, Anne Marie

2014-12-01

In a familistic southern European society such as the Portuguese, the family has historically played a prominent role in supporting the negotiation of transition pathways into adulthood. The present study aimed at capturing (1) the relative weight of parental financial support and autonomy support in contributing to the youngsters' psychological well-being (PWB), and (2) the mediating role of identity capital and uncertainty management in this relationship. A total of 620 participants completed measures of parental support, identity capital, uncertainty management and PWB. Autonomy support was found to be the strongest predictor of PWB, both directly and indirectly through its effects on identity capital and the use of target focused uncertainty management strategies. Conversely, financial support evidenced only a minor indirect impact through the mediation of tangible identity capital. Autonomy stimulation may constitute one of the most developmentally determinant family challenges in assisting the process of coming of age in Portugal. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty.

PubMed

Li, W; Wang, B; Xie, Y L; Huang, G H; Liu, L

2015-02-01

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

USGS Publications Warehouse

Zajac, Zuzanna; Stith, Bradley M.; Bowling, Andrea C.; Langtimm, Catherine A.; Swain, Eric D.

2015-01-01

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

Smart Aquifer Characterisation validated using Information Theory and Cost benefit analysis

NASA Astrophysics Data System (ADS)

Moore, Catherine

2016-04-01

The field data acquisition required to characterise aquifer systems are time consuming and expensive. Decisions regarding field testing, the type of field measurements to make and the spatial and temporal resolution of measurements have significant cost repercussions and impact the accuracy of various predictive simulations. The Smart Aquifer Characterisation (SAC) research programme (New Zealand (NZ)) addresses this issue by assembling and validating a suite of innovative methods for characterising groundwater systems at the large, regional and national scales. The primary outcome is a suite of cost effective tools and procedures provided to resource managers to advance the understanding and management of groundwater systems and thereby assist decision makers and communities in the management of their groundwater resources, including the setting of land use limits that protect fresh water flows and quality and the ecosystems dependent on that fresh water. The programme has focused novel investigation approaches including the use of geophysics, satellite remote sensing, temperature sensing and age dating. The SMART (Save Money And Reduce Time) aspect of the programme emphasises techniques that use these passive cost effective data sources to characterise groundwater systems at both the aquifer and the national scale by: • Determination of aquifer hydraulic properties • Determination of aquifer dimensions • Quantification of fluxes between ground waters and surface water • Groundwater age dating These methods allow either a lower cost method for estimating these properties and fluxes, or a greater spatial and temporal coverage for the same cost. To demonstrate the cost effectiveness of the methods a 'data worth' analysis is undertaken. The data worth method involves quantification of the utility of observation data in terms of how much it reduces the uncertainty of model parameters and decision focussed predictions which depend on these parameters. Such decision focussed predictions can include many aspects of system behaviour which underpin management decisions e.g., drawdown of groundwater levels, salt water intrusion, stream depletion, or wetland water level. The value of a data type or an observation location (e.g. remote sensing data (Westerhoff 2015) or a distributed temperature sensing measurement) is greater the more it enhances the certainty with which the model is able to predict such environmental behaviour. By comparing the difference in predictive uncertainty with or without such data, the value of potential observations is assessed. This can easily be achieved using rapid linear predictive uncertainty analysis methods (Moore 2005, Moore and Doherty 2006). By assessing the tension between the cost of data acquisition and the predictive accuracy achieved by gathering these observations in a pareto analysis, the relative cost effectiveness of these novel methods can be compared with more traditional measurements (e.g. bore logs, aquifer pumping tests, and simultaneous stream loss gaugings) for a suite of pertinent groundwater management decisions (Wallis et al 2014). This comparison illuminates those field data acquisition methods which offer the best value for the specific issues managers face in any region, and also indicates the diminishing returns of increasingly large and expensive data sets. References: Wallis I, Moore C, Post V, Wolf L, Martens E, Prommer. Using predictive uncertainty analysis to optimise tracer test design and data acquisition. Journal of Hydrology 515 (2014) 191-204. Moore, C. (2005). The use of regularized inversion in groundwater model calibration and prediction uncertainty analysis. Thesis submitted for the degree of Doctor of Philosophy at The University of Queensland, Australia. Moore, C., and Doherty, D. (2005). Role of the calibration process in reducing model predictive error. Water Resources Research 41, no.5 W05050. Westerhoff RS. Using uncertainty of Penman and Penman-Monteith methods in combined satellite and ground-based evapotranspiration estimates. Remote Sensing of Environment 169, 102-112

A fuzzy stochastic framework for managing hydro-environmental and socio-economic interactions under uncertainty

NASA Astrophysics Data System (ADS)

Subagadis, Yohannes Hagos; Schütze, Niels; Grundmann, Jens

2014-05-01

An amplified interconnectedness between a hydro-environmental and socio-economic system brings about profound challenges of water management decision making. In this contribution, we present a fuzzy stochastic approach to solve a set of decision making problems, which involve hydrologically, environmentally, and socio-economically motivated criteria subjected to uncertainty and ambiguity. The proposed methodological framework combines objective and subjective criteria in a decision making procedure for obtaining an acceptable ranking in water resources management alternatives under different type of uncertainty (subjective/objective) and heterogeneous information (quantitative/qualitative) simultaneously. The first step of the proposed approach involves evaluating the performance of alternatives with respect to different types of criteria. The ratings of alternatives with respect to objective and subjective criteria are evaluated by simulation-based optimization and fuzzy linguistic quantifiers, respectively. Subjective and objective uncertainties related to the input information are handled through linking fuzziness and randomness together. Fuzzy decision making helps entail the linguistic uncertainty and a Monte Carlo simulation process is used to map stochastic uncertainty. With this framework, the overall performance of each alternative is calculated using an Order Weighted Averaging (OWA) aggregation operator accounting for decision makers' experience and opinions. Finally, ranking is achieved by conducting pair-wise comparison of management alternatives. This has been done on the basis of the risk defined by the probability of obtaining an acceptable ranking and mean difference in total performance for the pair of management alternatives. The proposed methodology is tested in a real-world hydrosystem, to find effective and robust intervention strategies for the management of a coastal aquifer system affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. The results show that the approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

On the Teaching of Portfolio Theory.

ERIC Educational Resources Information Center

Biederman, Daniel K.

1992-01-01

Demonstrates how a simple portfolio problem expressed explicitly as an expected utility maximization problem can be used to instruct students in portfolio theory. Discusses risk aversion, decision making under uncertainty, and the limitations of the traditional mean variance approach. Suggests students may develop a greater appreciation of general…

Management of groundwater in-situ bioremediation system using reactive transport modelling under parametric uncertainty: field scale application

NASA Astrophysics Data System (ADS)

Verardo, E.; Atteia, O.; Rouvreau, L.

2015-12-01

In-situ bioremediation is a commonly used remediation technology to clean up the subsurface of petroleum-contaminated sites. Forecasting remedial performance (in terms of flux and mass reduction) is a challenge due to uncertainties associated with source properties and the uncertainties associated with contribution and efficiency of concentration reducing mechanisms. In this study, predictive uncertainty analysis of bio-remediation system efficiency is carried out with the null-space Monte Carlo (NSMC) method which combines the calibration solution-space parameters with the ensemble of null-space parameters, creating sets of calibration-constrained parameters for input to follow-on remedial efficiency. The first step in the NSMC methodology for uncertainty analysis is model calibration. The model calibration was conducted by matching simulated BTEX concentration to a total of 48 observations from historical data before implementation of treatment. Two different bio-remediation designs were then implemented in the calibrated model. The first consists in pumping/injection wells and the second in permeable barrier coupled with infiltration across slotted piping. The NSMC method was used to calculate 1000 calibration-constrained parameter sets for the two different models. Several variants of the method were implemented to investigate their effect on the efficiency of the NSMC method. The first variant implementation of the NSMC is based on a single calibrated model. In the second variant, models were calibrated from different initial parameter sets. NSMC calibration-constrained parameter sets were sampled from these different calibrated models. We demonstrate that in context of nonlinear model, second variant avoids to underestimate parameter uncertainty which may lead to a poor quantification of predictive uncertainty. Application of the proposed approach to manage bioremediation of groundwater in a real site shows that it is effective to provide support in management of the in-situ bioremediation systems. Moreover, this study demonstrates that the NSMC method provides a computationally efficient and practical methodology of utilizing model predictive uncertainty methods in environmental management.

Adaptive resource management and the value of information

USGS Publications Warehouse

Williams, Byron K.; Eaton, Mitchell J.; Breininger, David R.

2011-01-01

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time.

Adaptive resource management and the value of information

USGS Publications Warehouse

Williams, B.K.; Eaton, M.J.; Breininger, D.R.

2011-01-01

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time. ?? 2011.

Feedback System Theory

DTIC Science & Technology

1978-11-01

R 2. GOVT A $ SION NO. 3 RIEqLPýIVT’S.;TALOG NUMBER r/ 4. TITLE (and wbiFflT, -L M4 1 , FEEDBACK SYSTEM THEORY ~r Inter in- 6. PERFORMING ORG. REPORT...ANNUAL REPORT FEEDBACK SYSTEM THEORY AFOSR GRANT NO. 76-2946B Air Force Office of Scientific Research for year ending October 31, 1978 79 02 08 L|I...re less stringent than in other synthesis techniques which cannot handle significant parameter uncertainty. _I FEEDBACK SYSTEM THEORY 1. Introduction

Flood resilience and uncertainty in flood risk assessment

NASA Astrophysics Data System (ADS)

Beven, K.; Leedal, D.; Neal, J.; Bates, P.; Hunter, N.; Lamb, R.; Keef, C.

2012-04-01

Flood risk assessments do not normally take account of the uncertainty in assessing flood risk. There is no requirement in the EU Floods Directive to do so. But given the generally short series (and potential non-stationarity) of flood discharges, the extrapolation to smaller exceedance potentials may be highly uncertain. This means that flood risk mapping may also be highly uncertainty, with additional uncertainties introduced by the representation of flood plain and channel geometry, conveyance and infrastructure. This suggests that decisions about flood plain management should be based on exceedance probability of risk rather than the deterministic hazard maps that are common in most EU countries. Some examples are given from 2 case studies in the UK where a framework for good practice in assessing uncertainty in flood risk mapping has been produced as part of the Flood Risk Management Research Consortium and Catchment Change Network Projects. This framework provides a structure for the communication and audit of assumptions about uncertainties.

Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.

Mathematical Fundamentals of Probabilistic Semantics for High-Level Fusion

DTIC Science & Technology

2013-12-02

understanding of the fundamental aspects of uncertainty representation and reasoning that a theory of hard and soft high-level fusion must encompass...representation and reasoning that a theory of hard and soft high-level fusion must encompass. Successful completion requires an unbiased, in-depth...and soft information is the lack of a fundamental HLIF theory , backed by a consistent mathematical framework and supporting algorithms. Although there

Isomorphic pressures, institutional strategies, and knowledge creation in the health care sector.

PubMed

Yang, Chen-Wei; Fang, Shih-Chieh; Huang, Wei-Min

2007-01-01

Health care organizations are facing surprisingly complex challenges, including new treatment and diagnostic technologies, ongoing pressures for health care institutional reform, the emergence of new organizational governance structures, and knowledge creation for the health care system. To maintain legitimacy in demanding environments, organizations tend to copy practices of similar organizations, which lead to isomorphism, and to use internal strategies to accommodate changes. A concern is that a poor fit between isomorphic pressures and internal strategies can interfere with developmental processes, such as knowledge creation. The purposes of this article are to, first, develop a set of propositions, based on institutional theory, as a theoretical framework that might explain the influence of isomorphic pressures on institutional processes through which knowledge is created within the health care sector and, second, propose that a good fit between isomorphic pressures factors and health care organizations' institutional strategic choices will enhance the health care organizations' ability to create knowledge. To develop a theoretical framework, we developed a set of propositions based on literature pertaining to the institutional theory perspective of isomorphic pressures and the response of health care organizations to isomorphic pressures. Institutional theory perspectives of isomorphic pressures and institutional strategies may provide a new understanding for health care organizations seeking effective knowledge creation strategies within institutional environment of health care sector. First, the ability to identify three forces for isomorphic change is critical for managers. Second, the importance of a contingency approach by health care managers can lead to strategies tailoring to cope with uncertainties facing their organizations.

Designing flexible engineering systems utilizing embedded architecture options

NASA Astrophysics Data System (ADS)

Pierce, Jeff G.

This dissertation develops and applies an integrated framework for embedding flexibility in an engineered system architecture. Systems are constantly faced with unpredictability in the operational environment, threats from competing systems, obsolescence of technology, and general uncertainty in future system demands. Current systems engineering and risk management practices have focused almost exclusively on mitigating or preventing the negative consequences of uncertainty. This research recognizes that high uncertainty also presents an opportunity to design systems that can flexibly respond to changing requirements and capture additional value throughout the design life. There does not exist however a formalized approach to designing appropriately flexible systems. This research develops a three stage integrated flexibility framework based on the concept of architecture options embedded in the system design. Stage One defines an eight step systems engineering process to identify candidate architecture options. This process encapsulates the operational uncertainty though scenario development, traces new functional requirements to the affected design variables, and clusters the variables most sensitive to change. The resulting clusters can generate insight into the most promising regions in the architecture to embed flexibility in the form of architecture options. Stage Two develops a quantitative option valuation technique, grounded in real options theory, which is able to value embedded architecture options that exhibit variable expiration behavior. Stage Three proposes a portfolio optimization algorithm, for both discrete and continuous options, to select the optimal subset of architecture options, subject to budget and risk constraints. Finally, the feasibility, extensibility and limitations of the framework are assessed by its application to a reconnaissance satellite system development problem. Detailed technical data, performance models, and cost estimates were compiled for the Tactical Imaging Constellation Architecture Study and leveraged to complete a realistic proof-of-concept.

Rapid research and implementation priority setting for wound care uncertainties.

PubMed

Gray, Trish A; Dumville, Jo C; Christie, Janice; Cullum, Nicky A

2017-01-01

People with complex wounds are more likely to be elderly, living with multimorbidity and wound related symptoms. A variety of products are available for managing complex wounds and a range of healthcare professionals are involved in wound care, yet there is a lack of good evidence to guide practice and services. These factors create uncertainty for those who deliver and those who manage wound care. Formal priority setting for research and implementation topics is needed to more accurately target the gaps in treatment and services. We solicited practitioner and manager uncertainties in wound care and held a priority setting workshop to facilitate a collaborative approach to prioritising wound care-related uncertainties. We recruited healthcare professionals who regularly cared for patients with complex wounds, were wound care specialists or managed wound care services. Participants submitted up to five wound care uncertainties in consultation with their colleagues, via an on-line survey and attended a priority setting workshop. Submitted uncertainties were collated, sorted and categorised according professional group. On the day of the workshop, participants were divided into four groups depending on their profession. Uncertainties submitted by their professional group were viewed, discussed and amended, prior to the first of three individual voting rounds. Participants cast up to ten votes for the uncertainties they judged as being high priority. Continuing in the professional groups, the top 10 uncertainties from each group were displayed, and the process was repeated. Groups were then brought together for a plenary session in which the final priorities were individually scored on a scale of 0-10 by participants. Priorities were ranked and results presented. Nominal group technique was used for generating the final uncertainties, voting and discussions. Thirty-three participants attended the workshop comprising; 10 specialist nurses, 10 district nurses, seven podiatrists and six managers. Participants had been qualified for a mean of 20.7 years with a mean of 16.8 years of wound care experience. One hundred and thirty-nine uncertainties were submitted electronically and a further 20 were identified on the day of the workshop following lively, interactive group discussions. Twenty-five uncertainties from the total of 159 generated made it to the final prioritised list. These included six of the 20 new uncertainties. The uncertainties varied in focus, but could be broadly categorised into three themes: service delivery and organisation, patient centred care and treatment options. Specialist nurses were more likely to vote for service delivery and organisation topics, podiatrists for patient centred topics, district nurses for treatment options and operational leads for a broad range. This collaborative priority setting project is the first to engage front-line clinicians in prioritising research and implementation topics in wound care. We have shown that it is feasible to conduct topic prioritisation in a short time frame. This project has demonstrated that with careful planning and rigor, important questions that are raised in the course of clinicians' daily decision making can be translated into meaningful research and implementation initiatives that could make a difference to service delivery and patient care.

Rapid research and implementation priority setting for wound care uncertainties

PubMed Central

Dumville, Jo C.; Christie, Janice; Cullum, Nicky A.

2017-01-01

Introduction People with complex wounds are more likely to be elderly, living with multimorbidity and wound related symptoms. A variety of products are available for managing complex wounds and a range of healthcare professionals are involved in wound care, yet there is a lack of good evidence to guide practice and services. These factors create uncertainty for those who deliver and those who manage wound care. Formal priority setting for research and implementation topics is needed to more accurately target the gaps in treatment and services. We solicited practitioner and manager uncertainties in wound care and held a priority setting workshop to facilitate a collaborative approach to prioritising wound care-related uncertainties. Methods We recruited healthcare professionals who regularly cared for patients with complex wounds, were wound care specialists or managed wound care services. Participants submitted up to five wound care uncertainties in consultation with their colleagues, via an on-line survey and attended a priority setting workshop. Submitted uncertainties were collated, sorted and categorised according professional group. On the day of the workshop, participants were divided into four groups depending on their profession. Uncertainties submitted by their professional group were viewed, discussed and amended, prior to the first of three individual voting rounds. Participants cast up to ten votes for the uncertainties they judged as being high priority. Continuing in the professional groups, the top 10 uncertainties from each group were displayed, and the process was repeated. Groups were then brought together for a plenary session in which the final priorities were individually scored on a scale of 0–10 by participants. Priorities were ranked and results presented. Nominal group technique was used for generating the final uncertainties, voting and discussions. Results Thirty-three participants attended the workshop comprising; 10 specialist nurses, 10 district nurses, seven podiatrists and six managers. Participants had been qualified for a mean of 20.7 years with a mean of 16.8 years of wound care experience. One hundred and thirty-nine uncertainties were submitted electronically and a further 20 were identified on the day of the workshop following lively, interactive group discussions. Twenty-five uncertainties from the total of 159 generated made it to the final prioritised list. These included six of the 20 new uncertainties. The uncertainties varied in focus, but could be broadly categorised into three themes: service delivery and organisation, patient centred care and treatment options. Specialist nurses were more likely to vote for service delivery and organisation topics, podiatrists for patient centred topics, district nurses for treatment options and operational leads for a broad range. Conclusions This collaborative priority setting project is the first to engage front-line clinicians in prioritising research and implementation topics in wound care. We have shown that it is feasible to conduct topic prioritisation in a short time frame. This project has demonstrated that with careful planning and rigor, important questions that are raised in the course of clinicians’ daily decision making can be translated into meaningful research and implementation initiatives that could make a difference to service delivery and patient care. PMID:29206884

Assessing the cumulative environmental effects of marine renewable energy developments: Establishing common ground.

PubMed

Willsteed, Edward; Gill, Andrew B; Birchenough, Silvana N R; Jude, Simon

2017-01-15

Assessing and managing the cumulative impacts of human activities on the environment remains a major challenge to sustainable development. This challenge is highlighted by the worldwide expansion of marine renewable energy developments (MREDs) in areas already subject to multiple activities and climate change. Cumulative effects assessments in theory provide decision makers with adequate information about how the environment will respond to the incremental effects of licensed activities and are a legal requirement in many nations. In practise, however, such assessments are beset by uncertainties resulting in substantial delays during the licensing process that reduce MRED investor confidence and limit progress towards meeting climate change targets. In light of these targets and ambitions to manage the marine environment sustainably, reducing the uncertainty surrounding MRED effects and cumulative effects assessment are timely and vital. This review investigates the origins and evolution of cumulative effects assessment to identify why the multitude of approaches and pertinent research have emerged, and discusses key considerations and challenges relevant to assessing the cumulative effects of MREDs and other activities on ecosystems. The review recommends a shift away from the current reliance on disparate environmental impact assessments and limited strategic environmental assessments, and a move towards establishing a common system of coordinated data and research relative to ecologically meaningful areas, focussed on the needs of decision makers tasked with protecting and conserving marine ecosystems and services. Copyright © 2016. Published by Elsevier B.V.

Spirituality, Illness Unpredictability, and Math Anxiety Effects on Negative Affect and Affect-Management Coping for Individuals Diagnosed with Alpha-1 Antitrypsin Deficiency.

PubMed

Worthington, Amber K; Parrott, Roxanne L; Smith, Rachel A

2018-04-01

A growing number of genetic tests are included in diagnostic protocols associated with many common conditions. A positive diagnosis associated with the presence of some gene versions in many instances predicts a range of possible outcomes, and the uncertainty linked to such results contributes to the need to understand varied responses and plan strategic communication. Uncertainty in illness theory (UIT; Mishel, 1988, 1990) guided the investigation of efforts to feel in control and hopeful regarding genetic testing and diagnosis for alpha-1 antitrypsin deficiency (AATD). Participants included 137 individuals with AATD recruited from the Alpha-1 Research Registry who were surveyed about their subjective numeracy, anxiety about math, spirituality, perceptions of illness unpredictability, negative affect regarding genetic testing, and coping strategies about a diagnosis. Results revealed that experiencing more fear and worry contributed both directly and indirectly to affect-management coping strategies, operating through individual perceptions of illness unpredictability. The inability to predict the symptoms and course of events related to a genetic illness and anxiety regarding math heightened fear and worry. Spirituality lessened both illness unpredictability and negative affective responses to a diagnosis. Results affirm the importance of clinician and counselor efforts to incorporate attention to patient spirituality. They also illustrate the complexity associated with strategic efforts to plan communication about the different versions of a gene's effects on well-being, when some versions align with mild health effects and others with severe effects.

Experiences of liver health related uncertainty and self-reported stress among people who inject drugs living with hepatitis C virus: a qualitative study.

PubMed

Goutzamanis, Stelliana; Doyle, Joseph S; Thompson, Alexander; Dietze, Paul; Hellard, Margaret; Higgs, Peter

2018-04-02

People who inject drugs (PWID) are most at risk of hepatitis C virus infection in Australia. The introduction of transient elastography (TE) (measuring hepatitis fibrosis) and direct acting antiviral medications will likely alter the experience of living with hepatitis C. We aimed to explore positive and negative influences on wellbeing and stress among PWID with hepatitis C. The Treatment and Prevention (TAP) study examines the feasibility of treating hepatitis C mono-infected PWID in community settings. Semi-structured interviews were conducted with 16 purposively recruited TAP participants. Participants were aware of their hepatitis C seropositive status and had received fibrosis assessment (measured by TE) prior to interview. Questions were open-ended, focusing on the impact of health status on wellbeing and self-reported stress. Interviews were voice recorded, transcribed verbatim and thematically analysed, guided by Mishel's (1988) theory of Uncertainty in Illness. In line with Mishel's theory of Uncertainty in Illness all participants reported hepatitis C-related uncertainty, particularly mis-information or a lack of knowledge surrounding liver health and the meaning of TE results. Those with greater fibrosis experienced an extra layer of prognostic uncertainty. Experiences of uncertainty were a key motivation to seek treatment, which was seen as a way to regain some stability in life. Treatment completion alleviated hepatitis C-related stress, and promoted feelings of empowerment and confidence in addressing other life challenges. TE scores seemingly provide some certainty. However, when paired with limited knowledge, particularly among people with severe fibrosis, TE may be a source of uncertainty and increased personal stress. This suggests the need for simple education programs and resources on liver health to minimise stress.

Black hole complementarity with the generalized uncertainty principle in Gravity's Rainbow

NASA Astrophysics Data System (ADS)

Gim, Yongwan; Um, Hwajin; Kim, Wontae

2018-02-01

When gravitation is combined with quantum theory, the Heisenberg uncertainty principle could be extended to the generalized uncertainty principle accompanying a minimal length. To see how the generalized uncertainty principle works in the context of black hole complementarity, we calculate the required energy to duplicate information for the Schwarzschild black hole. It shows that the duplication of information is not allowed and black hole complementarity is still valid even assuming the generalized uncertainty principle. On the other hand, the generalized uncertainty principle with the minimal length could lead to a modification of the conventional dispersion relation in light of Gravity's Rainbow, where the minimal length is also invariant as well as the speed of light. Revisiting the gedanken experiment, we show that the no-cloning theorem for black hole complementarity can be made valid in the regime of Gravity's Rainbow on a certain combination of parameters.

Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna

Treesearch

Andrea Havron; Chris Goldfinger; Sarah Henkel; Bruce G. Marcot; Chris Romsos; Lisa Gilbane

2017-01-01

Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data-poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor uncertainties from data collection and modeling processes; yet these limitations...

Decision making under uncertainty: Recommendations for the Wildland Fire Decision Support System (WFDSS)

Treesearch

Matthew P. Thompson

2015-01-01

The management of wildfire is a dynamic, complex, and fundamentally uncertain enterprise. Fire managers face uncertainties regarding fire weather and subsequent influence on fire behavior, the effects of fire on socioeconomic and ecological resources, and the efficacy of alternative suppression actions on fire outcomes. In these types of difficult decision environments...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kastenberg, W.E.; Apostolakis, G.; Dhir, V.K.

Severe accident management can be defined as the use of existing and/or altemative resources, systems and actors to prevent or mitigate a core-melt accident. For each accident sequence and each combination of severe accident management strategies, there may be several options available to the operator, and each involves phenomenological and operational considerations regarding uncertainty. Operational uncertainties include operator, system and instrumentation behavior during an accident. A framework based on decision trees and influence diagrams has been developed which incorporates such criteria as feasibility, effectiveness, and adverse effects, for evaluating potential severe accident management strategies. The framework is also capable ofmore » propagating both data and model uncertainty. It is applied to several potential strategies including PWR cavity flooding, BWR drywell flooding, PWR depressurization and PWR feed and bleed.« less

Markov random fields and graphs for uncertainty management and symbolic data fusion in an urban scene interpretation

NASA Astrophysics Data System (ADS)

Moissinac, Henri; Maitre, Henri; Bloch, Isabelle

1995-11-01

An image interpretation method is presented for the automatic processing of aerial pictures of a urban landscape. In order to improve the picture analysis, some a priori knowledge extracted from a geographic map is introduced. A coherent graph-based model of the city is built, starting with the road network. A global uncertainty management scheme has been designed in order to evaluate the final confidence we can have in the final results. This model and the uncertainty management tend to reflect the hierarchy of the available data and the interpretation levels. The symbolic relationships linking the different kinds of elements are taken into account while propagating and combining the confidence measures along the interpretation process.

Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations.

PubMed

Ferdous, Refaul; Khan, Faisal; Sadiq, Rehan; Amyotte, Paul; Veitch, Brian

2011-01-01

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed. © 2010 Society for Risk Analysis.

Uncertainty, imprecision, and the precautionary principle in climate change assessment.

PubMed

Borsuk, M E; Tomassini, L

2005-01-01

Statistical decision theory can provide useful support for climate change decisions made under conditions of uncertainty. However, the probability distributions used to calculate expected costs in decision theory are themselves subject to uncertainty, disagreement, or ambiguity in their specification. This imprecision can be described using sets of probability measures, from which upper and lower bounds on expectations can be calculated. However, many representations, or classes, of probability measures are possible. We describe six of the more useful classes and demonstrate how each may be used to represent climate change uncertainties. When expected costs are specified by bounds, rather than precise values, the conventional decision criterion of minimum expected cost is insufficient to reach a unique decision. Alternative criteria are required, and the criterion of minimum upper expected cost may be desirable because it is consistent with the precautionary principle. Using simple climate and economics models as an example, we determine the carbon dioxide emissions levels that have minimum upper expected cost for each of the selected classes. There can be wide differences in these emissions levels and their associated costs, emphasizing the need for care when selecting an appropriate class.

Barriers and Bridges to the Integration of Social-ecological Resilience and Law

EPA Science Inventory

There is a fundamental rift between how ecologists and lawyers view uncertainty. In the study of ecology, uncertainty is a catalyst for exploration. However, uncertainty is antithetical to the rule of law. This issue is particularly troubling in environmental management, where th...

Quantum Theory from Observer's Mathematics Point of View

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khots, Dmitriy; Khots, Boris

2010-05-04

This work considers the linear (time-dependent) Schrodinger equation, quantum theory of two-slit interference, wave-particle duality for single photons, and the uncertainty principle in a setting of arithmetic, algebra, and topology provided by Observer's Mathematics, see [1]. Certain theoretical results and communications pertaining to these theorems are also provided.

Estimating Solar Proton Flux at LEO From a Geomagnetic Cutoff Model

DTIC Science & Technology

2015-07-14

simple shadow cones (using nomenclature from Stormer theory of particle motion in a dipole magnetic field [6]), that result from particles trajectories...basic Stormer theory [7]. However, in LEO the changes would be small relative to uncertainties in the model and therefore unnecessary. If the model were

The Effects of Message Framing on College Students' Career Decision Making

ERIC Educational Resources Information Center

Tansley, Denny P.; Jome, LaRae M.; Haase, Richard F.; Martens, Matthew P.

2007-01-01

Social cognitive career theory posits that verbal persuasion can affect individuals' career self-efficacy, outcome expectations, goals and/or intentions, and behaviors. Prospect theory holds that negatively framed messages can have a powerful effect on people's cognitions related to adopting particular behaviors in situations of uncertainty.…

Combined effects of uncertainty and organizational justice on employee health: testing the uncertainty management model of fairness judgments among Finnish public sector employees.

PubMed

Elovainio, Marko; van den Bos, Kees; Linna, Anne; Kivimäki, Mika; Ala-Mursula, Leena; Pentti, Jaana; Vahtera, Jussi

2005-12-01

We examined whether the combination of uncertainty (lack of work-time control, and negative changes at work) and organizational justice (i.e., justice of decision-making procedures and interpersonal treatment at work) contributes to sickness absence. A total of 7083 male and 24,317 female Finnish public sector employees completed questionnaires designed to assess organizational justice, workload and other factors. Hierarchical regression showed that after adjustment for age, income, and health behaviors low procedural and interactional justice were related to long sickness absence spells. In accordance with the uncertainty management model, these associations were dependent on experienced work-time control and perceived changes at work.

Uncertainty and control in the context of a category-five tornado.

PubMed

Afifi, Walid A; Afifi, Tamara D; Merrill, Annie

2014-10-01

The purpose of this qualitative descriptive study was to illuminate the experience and management of uncertainty during a natural disaster. Interviews were conducted with 26 survivors of a category-five tornado that entirely demolished the small, rural town of Greensburg, Kansas. Three primary themes were found in the survivors' accounts. First, the survivors experienced rapidly shifting levels and kinds of uncertainty as they proceeded through the stages of the disaster. Second, the fluidity of much-needed information added to uncertainty. Third, the feeling of lack of control over outcomes of the disaster and its aftermath was pervasive and was often managed through reliance on communal coping. Recommendations for disaster-related intervention programs are suggested. © 2014 Wiley Periodicals, Inc.

Experiences of Uncertainty in Men With an Elevated PSA

PubMed Central

Biddle, Caitlin; Brasel, Alicia; Underwood, Willie; Orom, Heather

2016-01-01

A significant proportion of men, ages 50 to 70 years, have, and continue to receive prostate specific antigen (PSA) tests to screen for prostate cancer (PCa). Approximately 70% of men with an elevated PSA level will not subsequently be diagnosed with PCa. Semistructured interviews were conducted with 13 men with an elevated PSA level who had not been diagnosed with PCa. Uncertainty was prominent in men’s reactions to the PSA results, stemming from unanswered questions about the PSA test, PCa risk, and confusion about their management plan. Uncertainty was exacerbated or reduced depending on whether health care providers communicated in lay and empathetic ways, and provided opportunities for question asking. To manage uncertainty, men engaged in information and health care seeking, self-monitoring, and defensive cognition. Results inform strategies for meeting informational needs of men with an elevated PSA and confirm the primary importance of physician communication behavior for open information exchange and uncertainty reduction. PMID:25979635

An application of information theory to stochastic classical gravitational fields

NASA Astrophysics Data System (ADS)

Angulo, J.; Angulo, J. C.; Angulo, J. M.

2018-06-01

The objective of this study lies on the incorporation of the concepts developed in the Information Theory (entropy, complexity, etc.) with the aim of quantifying the variation of the uncertainty associated with a stochastic physical system resident in a spatiotemporal region. As an example of application, a relativistic classical gravitational field has been considered, with a stochastic behavior resulting from the effect induced by one or several external perturbation sources. One of the key concepts of the study is the covariance kernel between two points within the chosen region. Using this concept and the appropriate criteria, a methodology is proposed to evaluate the change of uncertainty at a given spatiotemporal point, based on available information and efficiently applying the diverse methods that Information Theory provides. For illustration, a stochastic version of the Einstein equation with an added Gaussian Langevin term is analyzed.

The Mathematics of Dispatchability Revisited

NASA Technical Reports Server (NTRS)

Morris, Paul

2016-01-01

Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatchable STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, the dispatchability property has both theoretical and practical interest. One thing that hampers further work in this area is the underdeveloped theory. The existing definitions are expressed in terms of algorithms, and are less suitable for mathematical proofs. In this paper, we develop a new formal theory of dispatchability in terms of execution sequences. We exploit this to prove a characterization of dispatchability involving the structural properties of the STN graph. This facilitates the potential application of the theory to uncertainty reasoning.

Role of price and enforcement in water allocation: Insights from Game Theory

NASA Astrophysics Data System (ADS)

Souza Filho, Francisco Assis; Lall, Upmanu; Porto, Rubem La Laina

2008-12-01

As many countries are moving toward water sector reforms, practical issues of how water management institutions can better effect allocation, regulation, and enforcement of water rights have emerged. The problem of nonavailability of water to tailenders on an irrigation system in developing countries, due to unlicensed upstream diversions is well documented. The reliability of access or equivalently the uncertainty associated with water availability at their diversion point becomes a parameter that is likely to influence the application by users for water licenses, as well as their willingness to pay for licensed use. The ability of a water agency to reduce this uncertainty through effective water rights enforcement is related to the fiscal ability of the agency to monitor and enforce licensed use. In this paper, this interplay across the users and the agency is explored, considering the hydraulic structure or sequence of water use and parameters that define the users and the agency's economics. The potential for free rider behavior by the users, as well as their proposals for licensed use are derived conditional on this setting. The analyses presented are developed in the framework of the theory of "Law and Economics," with user interactions modeled as a game theoretic enterprise. The state of Ceara, Brazil, is used loosely as an example setting, with parameter values for the experiments indexed to be approximately those relevant for current decisions. The potential for using the ideas in participatory decision making is discussed. This paper is an initial attempt to develop a conceptual framework for analyzing such situations but with a focus on the reservoir-canal system water rights enforcement.

An integrated modeling approach to support management decisions of coupled groundwater-agricultural systems under multiple uncertainties

NASA Astrophysics Data System (ADS)

Hagos Subagadis, Yohannes; Schütze, Niels; Grundmann, Jens

2015-04-01

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational frameworks. Such integrative research to link different knowledge domains faces several practical challenges. Such complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. A fuzzy-stochastic multiple criteria decision analysis tool was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrated physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approach is applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structure. Results from the developed method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach has enabled to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. Sensitivity analysis applied within the developed tool has shown that the decision makers' risk aversion and risk taking attitude may yield in different ranking of decision alternatives. The developed approach can be applied to address the complexities and uncertainties inherent in water resources systems to support management decisions, while serving as a platform for stakeholder participation.

On the role of budget sufficiency, cost efficiency, and uncertainty in species management

USGS Publications Warehouse

van der Burg, Max Post; Bly, Bartholomew B.; Vercauteren, Tammy; Grand, James B.; Tyre, Andrew J.

2014-01-01

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one's budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.« less

Covariant information-density cutoff in curved space-time.

PubMed

Kempf, Achim

2004-06-04

In information theory, the link between continuous information and discrete information is established through well-known sampling theorems. Sampling theory explains, for example, how frequency-filtered music signals are reconstructible perfectly from discrete samples. In this Letter, sampling theory is generalized to pseudo-Riemannian manifolds. This provides a new set of mathematical tools for the study of space-time at the Planck scale: theories formulated on a differentiable space-time manifold can be equivalent to lattice theories. There is a close connection to generalized uncertainty relations which have appeared in string theory and other studies of quantum gravity.

Negotiating uncertainty: the transitional process of adapting to life with HIV.

PubMed

Perrett, Stephanie E; Biley, Francis C

2013-01-01

Glaser's (1978) grounded-theory method was used to investigate the transitional process of adapting to life with HIV. Semistructured interviews took place with 8 male HIV-infected participants recruited from a clinic in South Wales, United Kingdom. Data analysis used open, substantive, and theoretical coding. Adapting to a life with HIV infection emerged as a process of adapting to uncertainty with "negotiating uncertainty" as a core concept. Seven subcategories represented movements between bipolar opposites labeled "anticipating hopelessness" and "regaining optimism." This work progresses the theoretical concepts of transitions, uncertainty, and adaptation in relation to the HIV experience. Copyright © 2013 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Forecasting Distributional Responses of Limber Pine to Climate Change at Management-Relevant Scales in Rocky Mountain National Park

PubMed Central

Monahan, William B.; Cook, Tammy; Melton, Forrest; Connor, Jeff; Bobowski, Ben

2013-01-01

Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m2) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to help guide adaptive management. PMID:24391742

Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park.

PubMed

Monahan, William B; Cook, Tammy; Melton, Forrest; Connor, Jeff; Bobowski, Ben

2013-01-01

Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2)) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to help guide adaptive management.

Models of science-policy interaction: exploring approaches to Bisphenol A management in the EU.

PubMed

Udovyk, O

2014-07-01

This study investigated science-policy interaction models and their limitations under conditions of uncertainty. In detail, it looked at the management of the suspected endocrine-disrupting chemical Bisphenol A (BPA). Despite growing evidence that BPA is hazardous to human and environmental health, the level of scientific uncertainty is still high and, as a result, there is significant disagreement on the actual extent and type of risk. Analysis of decision-making processes at different regulatory levels (EU, Sweden, and the Swedish municipality of Gothenburg) exposed chemicals risk management and associated science-policy interaction under uncertainty. The results of the study show that chemicals management and associated science-policy interaction follow the modern model of science-policy interaction, where science is assumed to 'speak truth to policy' and highlights existing limitations of this model under conditions of uncertainty. The study not only explores alternative models (precautionary, consensus, science-policy demarcation. and extended participation) but also shows their limitations. The study concludes that all models come with their particular underlying assumptions, strengths, and limitations. At the same time, by exposing serious limitations of the modern model, the study calls for a rethinking of the relationship between science, policy, and management. Copyright © 2014 Elsevier B.V. All rights reserved.

Systems Reliability Framework for Surface Water Sustainability and Risk Management

NASA Astrophysics Data System (ADS)

Myers, J. R.; Yeghiazarian, L.

2016-12-01

With microbial contamination posing a serious threat to the availability of clean water across the world, it is necessary to develop a framework that evaluates the safety and sustainability of water systems in respect to non-point source fecal microbial contamination. The concept of water safety is closely related to the concept of failure in reliability theory. In water quality problems, the event of failure can be defined as the concentration of microbial contamination exceeding a certain standard for usability of water. It is pertinent in watershed management to know the likelihood of such an event of failure occurring at a particular point in space and time. Microbial fate and transport are driven by environmental processes taking place in complex, multi-component, interdependent environmental systems that are dynamic and spatially heterogeneous, which means these processes and therefore their influences upon microbial transport must be considered stochastic and variable through space and time. A physics-based stochastic model of microbial dynamics is presented that propagates uncertainty using a unique sampling method based on artificial neural networks to produce a correlation between watershed characteristics and spatial-temporal probabilistic patterns of microbial contamination. These results are used to address the question of water safety through several sustainability metrics: reliability, vulnerability, resilience and a composite sustainability index. System reliability is described uniquely though the temporal evolution of risk along watershed points or pathways. Probabilistic resilience describes how long the system is above a certain probability of failure, and the vulnerability metric describes how the temporal evolution of risk changes throughout a hierarchy of failure levels. Additionally our approach allows for the identification of contributions in microbial contamination and uncertainty from specific pathways and sources. We expect that this framework will significantly improve the efficiency and precision of sustainable watershed management strategies through providing a better understanding of how watershed characteristics and environmental parameters affect surface water quality and sustainability. With microbial contamination posing a serious threat to the availability of clean water across the world, it is necessary to develop a framework that evaluates the safety and sustainability of water systems in respect to non-point source fecal microbial contamination. The concept of water safety is closely related to the concept of failure in reliability theory. In water quality problems, the event of failure can be defined as the concentration of microbial contamination exceeding a certain standard for usability of water. It is pertinent in watershed management to know the likelihood of such an event of failure occurring at a particular point in space and time. Microbial fate and transport are driven by environmental processes taking place in complex, multi-component, interdependent environmental systems that are dynamic and spatially heterogeneous, which means these processes and therefore their influences upon microbial transport must be considered stochastic and variable through space and time. A physics-based stochastic model of microbial dynamics is presented that propagates uncertainty using a unique sampling method based on artificial neural networks to produce a correlation between watershed characteristics and spatial-temporal probabilistic patterns of microbial contamination. These results are used to address the question of water safety through several sustainability metrics: reliability, vulnerability, resilience and a composite sustainability index. System reliability is described uniquely though the temporal evolution of risk along watershed points or pathways. Probabilistic resilience describes how long the system is above a certain probability of failure, and the vulnerability metric describes how the temporal evolution of risk changes throughout a hierarchy of failure levels. Additionally our approach allows for the identification of contributions in microbial contamination and uncertainty from specific pathways and sources. We expect that this framework will significantly improve the efficiency and precision of sustainable watershed management strategies through providing a better understanding of how watershed characteristics and environmental parameters affect surface water quality and sustainability.

Intolerance of uncertainty, causal uncertainty, causal importance, self-concept clarity and their relations to generalized anxiety disorder.

PubMed

Kusec, Andrea; Tallon, Kathleen; Koerner, Naomi

2016-06-01

Although numerous studies have provided support for the notion that intolerance of uncertainty plays a key role in pathological worry (the hallmark feature of generalized anxiety disorder (GAD)), other uncertainty-related constructs may also have relevance for the understanding of individuals who engage in pathological worry. Three constructs from the social cognition literature, causal uncertainty, causal importance, and self-concept clarity, were examined in the present study to assess the degree to which these explain unique variance in GAD, over and above intolerance of uncertainty. N = 235 participants completed self-report measures of trait worry, GAD symptoms, and uncertainty-relevant constructs. A subgroup was subsequently classified as low in GAD symptoms (n = 69) or high in GAD symptoms (n = 54) based on validated cut scores on measures of trait worry and GAD symptoms. In logistic regressions, only elevated intolerance of uncertainty and lower self-concept clarity emerged as unique correlates of high (vs. low) GAD symptoms. The possible role of self-concept uncertainty in GAD and the utility of integrating social cognition theories and constructs into clinical research on intolerance of uncertainty are discussed.

Position-momentum uncertainty relations in the presence of quantum memory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Furrer, Fabian, E-mail: furrer@eve.phys.s.u-tokyo.ac.jp; Berta, Mario; Institute for Theoretical Physics, ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zürich

2014-12-15

A prominent formulation of the uncertainty principle identifies the fundamental quantum feature that no particle may be prepared with certain outcomes for both position and momentum measurements. Often the statistical uncertainties are thereby measured in terms of entropies providing a clear operational interpretation in information theory and cryptography. Recently, entropic uncertainty relations have been used to show that the uncertainty can be reduced in the presence of entanglement and to prove security of quantum cryptographic tasks. However, much of this recent progress has been focused on observables with only a finite number of outcomes not including Heisenberg’s original setting ofmore » position and momentum observables. Here, we show entropic uncertainty relations for general observables with discrete but infinite or continuous spectrum that take into account the power of an entangled observer. As an illustration, we evaluate the uncertainty relations for position and momentum measurements, which is operationally significant in that it implies security of a quantum key distribution scheme based on homodyne detection of squeezed Gaussian states.« less

Implementation uncertainty when using recreational hunting to manage carnivores

PubMed Central

Bischof, Richard; Nilsen, Erlend B; Brøseth, Henrik; Männil, Peep; Ozoliņš, Jaānis; Linnell, John D C; Bode, Michael

2012-01-01

1. Wildlife managers often rely on resource users, such as recreational or commercial hunters, to achieve management goals. The use of hunters to control wildlife populations is especially common for predators and ungulates, but managers cannot assume that hunters will always fill annual quotas set by the authorities. It has been advocated that resource management models should account for uncertainty in how harvest rules are realized, requiring that this implementation uncertainty be estimated. 2. We used a survival analysis framework and long-term harvest data from large carnivore management systems in three countries (Estonia, Latvia and Norway) involving four species (brown bear, grey wolf, Eurasian lynx and wolverine) to estimate the performance of hunters with respect to harvest goals set by managers. 3. Variation in hunter quota-filling performance was substantial, ranging from 40% for wolverine in Norway to nearly 100% for lynx in Latvia. Seasonal and regional variation was also high within country–species pairs. We detected a positive relationship between the instantaneous potential to fill a quota slot and the relative availability of the target species for both wolverine and lynx in Norway. 4. Survivor curves and hazards – with survival time measured as the time from the start of a season until a quota slot is filled – can indicate the extent to which managers can influence harvest through adjustments of season duration and quota limits. 5. Synthesis and applications. We investigated seven systems where authorities use recreational hunting to manage large carnivore populations. The variation and magnitude of deviation from harvest goals was substantial, underlining the need to incorporate implementation uncertainty into resource management models and decisions-making. We illustrate how survival analysis can be used by managers to estimate the performance of resource users with respect to achieving harvest goals set by managers. The findings in this study come at an opportune time given the growing popularity of management strategy evaluation (MSE) models in fisheries and a push towards incorporating MSE into terrestrial harvest management. PMID:23197878

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

NASA Astrophysics Data System (ADS)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.

Fuzzy-Arden-Syntax-based, Vendor-agnostic, Scalable Clinical Decision Support and Monitoring Platform.

PubMed

Adlassnig, Klaus-Peter; Fehre, Karsten; Rappelsberger, Andrea

2015-01-01

This study's objective is to develop and use a scalable genuine technology platform for clinical decision support based on Arden Syntax, which was extended by fuzzy set theory and fuzzy logic. Arden Syntax is a widely recognized formal language for representing clinical and scientific knowledge in an executable format, and is maintained by Health Level Seven (HL7) International and approved by the American National Standards Institute (ANSI). Fuzzy set theory and logic permit the representation of knowledge and automated reasoning under linguistic and propositional uncertainty. These forms of uncertainty are a common feature of patients' medical data, the body of medical knowledge, and deductive clinical reasoning.

Mean-variance model for portfolio optimization with background risk based on uncertainty theory

NASA Astrophysics Data System (ADS)

Zhai, Jia; Bai, Manying

2018-04-01

The aim of this paper is to develop a mean-variance model for portfolio optimization considering the background risk, liquidity and transaction cost based on uncertainty theory. In portfolio selection problem, returns of securities and assets liquidity are assumed as uncertain variables because of incidents or lacking of historical data, which are common in economic and social environment. We provide crisp forms of the model and a hybrid intelligent algorithm to solve it. Under a mean-variance framework, we analyze the portfolio frontier characteristic considering independently additive background risk. In addition, we discuss some effects of background risk and liquidity constraint on the portfolio selection. Finally, we demonstrate the proposed models by numerical simulations.

The application of probabilistic design theory to high temperature low cycle fatigue

NASA Technical Reports Server (NTRS)

Wirsching, P. H.

1981-01-01

Metal fatigue under stress and thermal cycling is a principal mode of failure in gas turbine engine hot section components such as turbine blades and disks and combustor liners. Designing for fatigue is subject to considerable uncertainty, e.g., scatter in cycles to failure, available fatigue test data and operating environment data, uncertainties in the models used to predict stresses, etc. Methods of analyzing fatigue test data for probabilistic design purposes are summarized. The general strain life as well as homo- and hetero-scedastic models are considered. Modern probabilistic design theory is reviewed and examples are presented which illustrate application to reliability analysis of gas turbine engine components.

Benefits of the uncertainty management intervention for African American and White older breast cancer survivors: 20-month outcomes.

PubMed

Gil, Karen M; Mishel, Merle H; Belyea, Michael; Germino, Barbara; Porter, Laura S; Clayton, Margaret

2006-01-01

In a 2 x 2 randomized block repeated measure design, this study evaluated the follow-up efficacy of the uncertainty management intervention at 20 months. The sample included 483 recurrence-free women (342 White, 141 African American women; mean age = 64 years) who were 5-9 years posttreatment for breast cancer. Women were randomly assigned to either the intervention or usual care control condition. The intervention was delivered during 4 weekly telephone sessions in which survivors were guided in the use of audiotaped cognitive-behavioral strategies and a self-help manual. Repeated measures MANOVAs evaluating treatment group, ethnic group, and treatment by ethnic interaction effects at 20 months indicated that training in uncertainty management resulted in improvements in cognitive reframing, cancer knowledge, and a variety of coping skills. Importantly, the 20-month outcomes also demonstrated benefits for women in the intervention condition in terms of declines in illness uncertainty and stable effects in personal growth over time.

Uncertainty, learning, and the optimal management of wildlife

USGS Publications Warehouse

Williams, B.K.

2001-01-01

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.