Sample records for optimal decision rules
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A detailed comparison of optimality and simplicity in perceptual decision-making
Shen, Shan; Ma, Wei Ji
2017-01-01
Two prominent ideas in the study of decision-making have been that organisms behave near-optimally, and that they use simple heuristic rules. These principles might be operating in different types of tasks, but this possibility cannot be fully investigated without a direct, rigorous comparison within a single task. Such a comparison was lacking in most previous studies, because a) the optimal decision rule was simple; b) no simple suboptimal rules were considered; c) it was unclear what was optimal, or d) a simple rule could closely approximate the optimal rule. Here, we used a perceptual decision-making task in which the optimal decision rule is well-defined and complex, and makes qualitatively distinct predictions from many simple suboptimal rules. We find that all simple rules tested fail to describe human behavior, that the optimal rule accounts well for the data, and that several complex suboptimal rules are indistinguishable from the optimal one. Moreover, we found evidence that the optimal model is close to the true model: first, the better the trial-to-trial predictions of a suboptimal model agree with those of the optimal model, the better that suboptimal model fits; second, our estimate of the Kullback-Leibler divergence between the optimal model and the true model is not significantly different from zero. When observers receive no feedback, the optimal model still describes behavior best, suggesting that sensory uncertainty is implicitly represented and taken into account. Beyond the task and models studied here, our results have implications for best practices of model comparison. PMID:27177259
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A Swarm Optimization approach for clinical knowledge mining.
Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A
2015-10-01
Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald
2015-01-01
In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process haven fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. PMID:25584425
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Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald
2015-11-01
In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process have fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been the most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Adaptive decision rules for the acquisition of nature reserves.
Turner, Will R; Wilcove, David S
2006-04-01
Although reserve-design algorithms have shown promise for increasing the efficiency of conservation planning, recent work casts doubt on the usefulness of some of these approaches in practice. Using three data sets that vary widely in size and complexity, we compared various decision rules for acquiring reserve networks over multiyear periods. We explored three factors that are often important in real-world conservation efforts: uncertain availability of sites for acquisition, degradation of sites, and overall budget constraints. We evaluated the relative strengths and weaknesses of existing optimal and heuristic decision rules and developed a new set of adaptive decision rules that combine the strengths of existing optimal and heuristic approaches. All three of the new adaptive rules performed better than the existing rules we tested under virtually all scenarios of site availability, site degradation, and budget constraints. Moreover, the adaptive rules required no additional data beyond what was readily available and were relatively easy to compute.
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Interpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, Himabindu; Bach, Stephen H.; Jure, Leskovec
2016-01-01
One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model’s prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency. PMID:27853627
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Sendi, Pedram; Al, Maiwenn J; Gafni, Amiram; Birch, Stephen
2004-05-01
Bridges and Terris (Soc. Sci. Med. (2004)) critique our paper on the alternative decision rule of economic evaluation in the presence of uncertainty and constrained resources within the context of a portfolio of health care programs (Sendi et al. Soc. Sci. Med. 57 (2003) 2207). They argue that by not adopting a formal portfolio theory approach we overlook the optimal solution. We show that these arguments stem from a fundamental misunderstanding of the alternative decision rule of economic evaluation. In particular, the portfolio theory approach advocated by Bridges and Terris is based on the same theoretical assumptions that the alternative decision rule set out to relax. Moreover, Bridges and Terris acknowledge that the proposed portfolio theory approach may not identify the optimal solution to resource allocation problems. Hence, it provides neither theoretical nor practical improvements to the proposed alternative decision rule.
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A Bayesian model averaging method for the derivation of reservoir operating rules
NASA Astrophysics Data System (ADS)
Zhang, Jingwen; Liu, Pan; Wang, Hao; Lei, Xiaohui; Zhou, Yanlai
2015-09-01
Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China's the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions.
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Schaafsma, Murk; van der Deijl, Wilfred; Smits, Jacqueline M; Rahmel, Axel O; de Vries Robbé, Pieter F; Hoitsma, Andries J
2011-05-01
Organ allocation systems have become complex and difficult to comprehend. We introduced decision tables to specify the rules of allocation systems for different organs. A rule engine with decision tables as input was tested for the Kidney Allocation System (ETKAS). We compared this rule engine with the currently used ETKAS by running 11,000 historical match runs and by running the rule engine in parallel with the ETKAS on our allocation system. Decision tables were easy to implement and successful in verifying correctness, completeness, and consistency. The outcomes of the 11,000 historical matches in the rule engine and the ETKAS were exactly the same. Running the rule engine simultaneously in parallel and in real time with the ETKAS also produced no differences. Specifying organ allocation rules in decision tables is already a great step forward in enhancing the clarity of the systems. Yet, using these tables as rule engine input for matches optimizes the flexibility, simplicity and clarity of the whole process, from specification to the performed matches, and in addition this new method allows well controlled simulations. © 2011 The Authors. Transplant International © 2011 European Society for Organ Transplantation.
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Bayesian design of decision rules for failure detection
NASA Technical Reports Server (NTRS)
Chow, E. Y.; Willsky, A. S.
1984-01-01
The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is potentially a useful one.
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Simultaneous Optimization of Decisions Using a Linear Utility Function.
ERIC Educational Resources Information Center
Vos, Hans J.
1990-01-01
An approach is presented to simultaneously optimize decision rules for combinations of elementary decisions through a framework derived from Bayesian decision theory. The developed linear utility model for selection-mastery decisions was applied to a sample of 43 first year medical students to illustrate the procedure. (SLD)
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Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks
Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Zhong, Xionghu
2015-01-01
Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones. PMID:26251908
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NASA Astrophysics Data System (ADS)
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
2016-04-01
This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to foresee future inflows depending on present and past hydrological and meteorological variables actually used by the reservoir managers to define likely inflow scenarios. A Decision Support System (DSS) was created coupling the FRB systems and the inflow prediction scheme in order to give the user a set of possible optimal releases in response to the reservoir states at the beginning of the irrigation season and the fuzzy inflow projections made using hydrological and meteorological information. The results show that the optimal DSS created using the FRB operating policies are able to increase the amount of water allocated to the users in 20 to 50 Mm3 per irrigation season with respect to the current policies. Consequently, the mechanism used to define optimal operating rules and transform them into a DSS is able to increase the water deliveries in the Jucar River Basin, combining expert criteria and optimization algorithms in an efficient way. This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds. It also has received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811).
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Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis
NASA Astrophysics Data System (ADS)
Wang, M.; Hu, N. Q.; Qin, G. J.
2011-07-01
In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.
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Micheyl, Christophe; Dai, Huanping
2010-01-01
The equal-variance Gaussian signal-detection-theory (SDT) decision model for the dual-pair change-detection (or “4IAX”) paradigm has been described in earlier publications. In this note, we consider the equal-variance Gaussian SDT model for the related dual-pair AB vs BA identification paradigm. The likelihood ratios, optimal decision rules, receiver operating characteristics (ROCs), and relationships between d' and proportion-correct (PC) are analyzed for two special cases: that of statistically independent observations, which is likely to apply in constant-stimuli experiments, and that of highly correlated observations, which is likely to apply in experiments where stimuli are roved widely across trials or pairs. A surprising outcome of this analysis is that although these two situations lead to different optimal decision rules, the predicted ROCs and proportions of correct responses (PCs) for these two cases are not substantially different, and are either identical or similar to those observed in the basic Yes-No paradigm. PMID:19633356
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NASA Astrophysics Data System (ADS)
ShiouWei, L.
2014-12-01
Reservoirs are the most important water resources facilities in Taiwan.However,due to the steep slope and fragile geological conditions in the mountain area,storm events usually cause serious debris flow and flood,and the flood then will flush large amount of sediment into reservoirs.The sedimentation caused by flood has great impact on the reservoirs life.Hence,how to operate a reservoir during flood events to increase the efficiency of sediment desilting without risk the reservoir safety and impact the water supply afterward is a crucial issue in Taiwan. Therefore,this study developed a novel optimization planning model for reservoir flood operation considering flood control and sediment desilting,and proposed easy to use operating rules represented by decision trees.The decision trees rules have considered flood mitigation,water supply and sediment desilting.The optimal planning model computes the optimal reservoir release for each flood event that minimum water supply impact and maximum sediment desilting without risk the reservoir safety.Beside the optimal flood operation planning model,this study also proposed decision tree based flood operating rules that were trained by the multiple optimal reservoir releases to synthesis flood scenarios.The synthesis flood scenarios consists of various synthesis storm events,reservoir's initial storage and target storages at the end of flood operating. Comparing the results operated by the decision tree operation rules(DTOR) with that by historical operation for Krosa Typhoon in 2007,the DTOR removed sediment 15.4% more than that of historical operation with reservoir storage only8.38×106m3 less than that of historical operation.For Jangmi Typhoon in 2008,the DTOR removed sediment 24.4% more than that of historical operation with reservoir storage only 7.58×106m3 less than that of historical operation.The results show that the proposed DTOR model can increase the sediment desilting efficiency and extend the reservoir life.
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C-learning: A new classification framework to estimate optimal dynamic treatment regimes.
Zhang, Baqun; Zhang, Min
2017-12-11
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.
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A Compensatory Approach to Optimal Selection with Mastery Scores. Research Report 94-2.
ERIC Educational Resources Information Center
van der Linden, Wim J.; Vos, Hans J.
This paper presents some Bayesian theories of simultaneous optimization of decision rules for test-based decisions. Simultaneous decision making arises when an institution has to make a series of selection, placement, or mastery decisions with respect to subjects from a population. An obvious example is the use of individualized instruction in…
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Form and Objective of the Decision Rule in Absolute Identification
NASA Technical Reports Server (NTRS)
Balakrishnan, J. D.
1997-01-01
In several conditions of a line length identification experiment, the subjects' decision making strategies were systematically biased against the responses on the edges of the stimulus range. When the range and number of the stimuli were small, the bias caused the percentage of correct responses to be highest in the center and lowest on the extremes of the range. Two general classes of decision rules that would explain these results are considered. The first class assumes that subjects intend to adopt an optimal decision rule, but systematically misrepresent one or more parameters of the decision making context. The second class assumes that subjects use a different measure of performance than the one assumed by the experimenter: instead of maximizing the chances of a correct response, the subject attempts to minimize the expected size of the response error (a "fidelity criterion"). In a second experiment, extended experience and feedback did not diminish the bias effect, but explicitly penalizing all response errors equally, regardless of their size, did reduce or eliminate it in some subjects. Both results favor the fidelity criterion over the optimal rule.
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Optimal Sequential Rules for Computer-Based Instruction.
ERIC Educational Resources Information Center
Vos, Hans J.
1998-01-01
Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Berman, D.W.; Allen, B.C.; Van Landingham, C.B.
1998-12-31
The decision rules commonly employed to determine the need for cleanup are evaluated both to identify conditions under which they lead to erroneous conclusions and to quantify the rate that such errors occur. Their performance is also compared with that of other applicable decision rules. The authors based the evaluation of decision rules on simulations. Results are presented as power curves. These curves demonstrate that the degree of statistical control achieved is independent of the form of the null hypothesis. The loss of statistical control that occurs when a decision rule is applied to a data set that does notmore » satisfy the rule`s validity criteria is also clearly demonstrated. Some of the rules evaluated do not offer the formal statistical control that is an inherent design feature of other rules. Nevertheless, results indicate that such informal decision rules may provide superior overall control of error rates, when their application is restricted to data exhibiting particular characteristics. The results reported here are limited to decision rules applied to uncensored and lognormally distributed data. To optimize decision rules, it is necessary to evaluate their behavior when applied to data exhibiting a range of characteristics that bracket those common to field data. The performance of decision rules applied to data sets exhibiting a broader range of characteristics is reported in the second paper of this study.« less
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ERIC Educational Resources Information Center
Vos, Hans J.
An approach to simultaneous optimization of assignments of subjects to treatments followed by an end-of-mastery test is presented using the framework of Bayesian decision theory. Focus is on demonstrating how rules for the simultaneous optimization of sequences of decisions can be found. The main advantages of the simultaneous approach, compared…
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Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.
Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K
2008-07-01
A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.
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ERIC Educational Resources Information Center
Vos, Hans J.
As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…
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ERIC Educational Resources Information Center
Lee, Seong-Soo
1982-01-01
Tenth-grade students (n=144) received training on one of three processing methods: coding-mapping (simultaneous), coding only, or decision tree (sequential). The induced simultaneous processing strategy worked optimally under rule learning, while the sequential strategy was difficult to induce and/or not optimal for rule-learning operations.…
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Irwin, R John; Irwin, Timothy C
2011-06-01
Making clinical decisions on the basis of diagnostic tests is an essential feature of medical practice and the choice of the decision threshold is therefore crucial. A test's optimal diagnostic threshold is the threshold that maximizes expected utility. It is given by the product of the prior odds of a disease and a measure of the importance of the diagnostic test's sensitivity relative to its specificity. Choosing this threshold is the same as choosing the point on the Receiver Operating Characteristic (ROC) curve whose slope equals this product. We contend that a test's likelihood ratio is the canonical decision variable and contrast diagnostic thresholds based on likelihood ratio with two popular rules of thumb for choosing a threshold. The two rules are appealing because they have clear graphical interpretations, but they yield optimal thresholds only in special cases. The optimal rule can be given similar appeal by presenting indifference curves, each of which shows a set of equally good combinations of sensitivity and specificity. The indifference curve is tangent to the ROC curve at the optimal threshold. Whereas ROC curves show what is feasible, indifference curves show what is desirable. Together they show what should be chosen. Copyright © 2010 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Kaune, Alexander; López, Patricia; Werner, Micha; de Fraiture, Charlotte
2017-04-01
Hydrological information on water availability and demand is vital for sound water allocation decisions in irrigation districts, particularly in times of water scarcity. However, sub-optimal water allocation decisions are often taken with incomplete hydrological information, which may lead to agricultural production loss. In this study we evaluate the benefit of additional hydrological information from earth observations and reanalysis data in supporting decisions in irrigation districts. Current water allocation decisions were emulated through heuristic operational rules for water scarce and water abundant conditions in the selected irrigation districts. The Dynamic Water Balance Model based on the Budyko framework was forced with precipitation datasets from interpolated ground measurements, remote sensing and reanalysis data, to determine the water availability for irrigation. Irrigation demands were estimated based on estimates of potential evapotranspiration and coefficient for crops grown, adjusted with the interpolated precipitation data. Decisions made using both current and additional hydrological information were evaluated through the rate at which sub-optimal decisions were made. The decisions made using an amended set of decision rules that benefit from additional information on demand in the districts were also evaluated. Results show that sub-optimal decisions can be reduced in the planning phase through improved estimates of water availability. Where there are reliable observations of water availability through gauging stations, the benefit of the improved precipitation data is found in the improved estimates of demand, equally leading to a reduction of sub-optimal decisions.
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[Clinical economics: a concept to optimize healthcare services].
Porzsolt, F; Bauer, K; Henne-Bruns, D
2012-03-01
Clinical economics strives to support healthcare decisions by economic considerations. Making economic decisions does not mean saving costs but rather comparing the gained added value with the burden which has to be accepted. The necessary rules are offered in various disciplines, such as economy, epidemiology and ethics. Medical doctors have recognized these rules but are not applying them in daily clinical practice. This lacking orientation leads to preventable errors. Examples of these errors are shown for diagnosis, screening, prognosis and therapy. As these errors can be prevented by application of clinical economic principles the possible consequences for optimization of healthcare are discussed.
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Cost-effectiveness on a local level: whether and when to adopt a new technology.
Woertman, Willem H; Van De Wetering, Gijs; Adang, Eddy M M
2014-04-01
Cost-effectiveness analysis has become a widely accepted tool for decision making in health care. The standard textbook cost-effectiveness analysis focuses on whether to make the switch from an old or common practice technology to an innovative technology, and in doing so, it takes a global perspective. In this article, we are interested in a local perspective, and we look at the questions of whether and when the switch from old to new should be made. A new approach to cost-effectiveness from a local (e.g., a hospital) perspective, by means of a mathematical model for cost-effectiveness that explicitly incorporates time, is proposed. A decision rule is derived for establishing whether a new technology should be adopted, as well as a general rule for establishing when it pays to postpone adoption by 1 more period, and a set of decision rules that can be used to determine the optimal timing of adoption. Finally, a simple example is presented to illustrate our model and how it leads to optimal decision making in a number of cases.
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Ertefaie, Ashkan; Shortreed, Susan; Chakraborty, Bibhas
2016-06-15
Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to optimally individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been largely overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple assignment randomized trial of patients with schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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Search asymmetries: parallel processing of uncertain sensory information.
Vincent, Benjamin T
2011-08-01
What is the mechanism underlying search phenomena such as search asymmetry? Two-stage models such as Feature Integration Theory and Guided Search propose parallel pre-attentive processing followed by serial post-attentive processing. They claim search asymmetry effects are indicative of finding pairs of features, one processed in parallel, the other in serial. An alternative proposal is that a 1-stage parallel process is responsible, and search asymmetries occur when one stimulus has greater internal uncertainty associated with it than another. While the latter account is simpler, only a few studies have set out to empirically test its quantitative predictions, and many researchers still subscribe to the 2-stage account. This paper examines three separate parallel models (Bayesian optimal observer, max rule, and a heuristic decision rule). All three parallel models can account for search asymmetry effects and I conclude that either people can optimally utilise the uncertain sensory data available to them, or are able to select heuristic decision rules which approximate optimal performance. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Beyond Decision Making: Cultural Ideology as Heuristic Paradigmatic Models.
ERIC Educational Resources Information Center
Whitley, L. Darrell
A paradigmatic model of cultural ideology provides a context for understanding the relationship between decision-making and personal and cultural rationality. Cultural rules or heuristics exist which indicate that many decisions can be made on the basis of established strategy rather than continual analytical calculations. When an optimal solution…
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An automated approach to the design of decision tree classifiers
NASA Technical Reports Server (NTRS)
Argentiero, P.; Chin, R.; Beaudet, P.
1982-01-01
An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.
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Failure detection system design methodology. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Chow, E. Y.
1980-01-01
The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.
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Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space
NASA Astrophysics Data System (ADS)
Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.
2014-10-01
Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.
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Optimal joint detection and estimation that maximizes ROC-type curves
Wunderlich, Adam; Goossens, Bart; Abbey, Craig K.
2017-01-01
Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation. PMID:27093544
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Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves.
Wunderlich, Adam; Goossens, Bart; Abbey, Craig K
2016-09-01
Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.
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Age Effects and Heuristics in Decision Making*
Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael
2011-01-01
Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects. PMID:22544977
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Age Effects and Heuristics in Decision Making.
Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael
2012-05-01
Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects.
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Rule-based optimization and multicriteria decision support for packaging a truck chassis
NASA Astrophysics Data System (ADS)
Berger, Martin; Lindroth, Peter; Welke, Richard
2017-06-01
Trucks are highly individualized products where exchangeable parts are flexibly combined to suit different customer requirements, this leading to a great complexity in product development. Therefore, an optimization approach based on constraint programming is proposed for automatically packaging parts of a truck chassis by following packaging rules expressed as constraints. A multicriteria decision support system is developed where a database of truck layouts is computed, among which interactive navigation then can be performed. The work has been performed in cooperation with Volvo Group Trucks Technology (GTT), from which specific rules have been used. Several scenarios are described where the methods developed can be successfully applied and lead to less time-consuming manual work, fewer mistakes, and greater flexibility in configuring trucks. A numerical evaluation is also presented showing the efficiency and practical relevance of the methods, which are implemented in a software tool.
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A two-stage stochastic rule-based model to determine pre-assembly buffer content
NASA Astrophysics Data System (ADS)
Gunay, Elif Elcin; Kula, Ufuk
2018-01-01
This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.
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Optimal Policies for the Management of a Plug-In Hybrid Electric Vehicle Swap Station
2015-03-26
occurring for many other vehicle manufacturers. Honda, BMW, Chevrolet, Ford, Nissan, Cadillac, Fiat, Mercedes, Mitsubishi, SMART, Volkswagon, Kia, and Toyota ...rules depend on the current state of the system and not the entire history of states, Markovian decision rules [16] are considered. Furthermore, the
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Yu, Yang; Wang, Sihan; Tang, Jiafu; Kaku, Ikou; Sun, Wei
2016-01-01
Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms.
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Characterizing Rule-Based Category Learning Deficits in Patients with Parkinson's Disease
ERIC Educational Resources Information Center
Filoteo, J. Vincent; Maddox, W. Todd; Ing, A. David; Song, David D.
2007-01-01
Parkinson's disease (PD) patients and normal controls were tested in three category learning experiments to determine if previously observed rule-based category learning impairments in PD patients were due to deficits in selective attention or working memory. In Experiment 1, optimal categorization required participants to base their decision on a…
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Multiobjective hedging rules for flood water conservation
NASA Astrophysics Data System (ADS)
Ding, Wei; Zhang, Chi; Cai, Ximing; Li, Yu; Zhou, Huicheng
2017-03-01
Flood water conservation can be beneficial for water uses especially in areas with water stress but also can pose additional flood risk. The potential of flood water conservation is affected by many factors, especially decision makers' preference for water conservation and reservoir inflow forecast uncertainty. This paper discusses the individual and joint effects of these two factors on the trade-off between flood control and water conservation, using a multiobjective, two-stage reservoir optimal operation model. It is shown that hedging between current water conservation and future flood control exists only when forecast uncertainty or decision makers' preference is within a certain range, beyond which, hedging is trivial and the multiobjective optimization problem is reduced to a single objective problem with either flood control or water conservation. Different types of hedging rules are identified with different levels of flood water conservation preference, forecast uncertainties, acceptable flood risk, and reservoir storage capacity. Critical values of decision preference (represented by a weight) and inflow forecast uncertainty (represented by standard deviation) are identified. These inform reservoir managers with a feasible range of their preference to water conservation and thresholds of forecast uncertainty, specifying possible water conservation within the thresholds. The analysis also provides inputs for setting up an optimization model by providing the range of objective weights and the choice of hedging rule types. A case study is conducted to illustrate the concepts and analyses.
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The WHI offers an opportunity to evaluate ovarian cancer markers and screening decision rules developed and validated in EDRN CVC Studies 2 and 3 in women who were not being screened. It is particularly well suited to validation of risk markers, since many serum samples were drawn well before clinical diagnosis of cancer in the WHI cohorts. A strategy is needed to identify from among the general population of women over the age of 50 those at high-risk for a diagnosis of ovarian/fallopian tube cancer so that they can be referred for appropriate surveillance, imaging or surgical consult. Tools to identify high-risk women will be investigated including serum markers CA125, HE4, MSLN, and MMP7 and epidemiologic risk factors. We will optimize decision rules using stored serum samples from the WHI OS and conduct a simulated prospective validation using stored serum samples from the WHI CT. Decision rules to select women for ovarian cancer screening will be investigated as well as decision rules for use in ovarian cancer screening.
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Understanding Optimal Decision-Making in Wargaming
2013-09-01
of which is a better understanding of the impact of decisions as a part of combat processes. However, using wargaming to understand decision-making...Raymond, 1989). In the aviation domain, pilots exhibit different visual scanning patterns during various phases of flying under instrument flight rules ( IFR ...human neuro- science, 7, 2013. Anna Skinner, Chris Berka, Lindsay Ohara-Long, and Marc Sebrechts. Impact of virtual en- vironment fidelity on behavioral
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Combined Economic and Hydrologic Modeling to Support Collaborative Decision Making Processes
NASA Astrophysics Data System (ADS)
Sheer, D. P.
2008-12-01
For more than a decade, the core concept of the author's efforts in support of collaborative decision making has been a combination of hydrologic simulation and multi-objective optimization. The modeling has generally been used to support collaborative decision making processes. The OASIS model developed by HydroLogics Inc. solves a multi-objective optimization at each time step using a mixed integer linear program (MILP). The MILP can be configured to include any user defined objective, including but not limited too economic objectives. For example, an estimated marginal value for water for crops and M&I use were included in the objective function to drive trades in a model of the lower Rio Grande. The formulation of the MILP, constraints and objectives, in any time step is conditional: it changes based on the value of state variables and dynamic external forcing functions, such as rainfall, hydrology, market prices, arrival of migratory fish, water temperature, etc. It therefore acts as a dynamic short term multi-objective economic optimization for each time step. MILP is capable of solving a general problem that includes a very realistic representation of the physical system characteristics in addition to the normal multi-objective optimization objectives and constraints included in economic models. In all of these models, the short term objective function is a surrogate for achieving long term multi-objective results. The long term performance for any alternative (especially including operating strategies) is evaluated by simulation. An operating rule is the combination of conditions, parameters, constraints and objectives used to determine the formulation of the short term optimization in each time step. Heuristic wrappers for the simulation program have been developed improve the parameters of an operating rule, and are initiating research on a wrapper that will allow us to employ a genetic algorithm to improve the form of the rule (conditions, constraints, and short term objectives) as well. In the models operating rules represent different models of human behavior, and the objective of the modeling is to find rules for human behavior that perform well in terms of long term human objectives. The conceptual model used to represent human behavior incorporates economic multi-objective optimization for surrogate objectives, and rules that set those objectives based on current conditions and accounting for uncertainty, at least implicitly. The author asserts that real world operating rules follow this form and have evolved because they have been perceived as successful in the past. Thus, the modeling efforts focus on human behavior in much the same way that economic models focus on human behavior. This paper illustrates the above concepts with real world examples.
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Pricing and Enrollment Planning.
ERIC Educational Resources Information Center
Martin, Robert E.
2003-01-01
Presents a management model for pricing and enrollment planning that yields optimal pricing decisions relative to student fees and average scholarship, the institution's financial ability to support students, and an average cost-pricing rule. (SLD)
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New Directions for Military Decision Making Research in Combat and Operational Settings
1991-12-01
information; their search rules emphasize feasibility more than optimality; decisions depend on the order in which alternatives are presented...12:76-90. Easterbrook, J.A. "The effect of Emotion on Cue Utilization and the Organization of Behaviour." Psychological Review, 1959, 66:183-201...Acquisition and Affective State on Halo, Accuracy, Information Retrival , and Evaluations." Organizational Behavior and Human Decision Processes, 1988, 42:22
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Learning Optimal Individualized Treatment Rules from Electronic Health Record Data
Wang, Yuanjia; Wu, Peng; Liu, Ying; Weng, Chunhua; Zeng, Donglin
2016-01-01
Medical research is experiencing a paradigm shift from “one-size-fits-all” strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR. We transform the estimation of optimal ITR into a classification problem and account for the non-experimental features of the EHR data and confounding by clinical indication. We create a broad range of feature variables that reflect both patient health status and healthcare data collection process. Using EHR data collected at Columbia University clinical data warehouse, we construct a decision tree for choosing the best second line therapy for treating type 2 diabetes patients. PMID:28503676
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Collaborative Brain-Computer Interface for Aiding Decision-Making
Poli, Riccardo; Valeriani, Davide; Cinel, Caterina
2014-01-01
We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making. PMID:25072739
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The cost-effectiveness of diagnostic management strategies for adults with minor head injury.
Holmes, M W; Goodacre, S; Stevenson, M D; Pandor, A; Pickering, A
2012-09-01
To estimate the cost-effectiveness of diagnostic management strategies for adults with minor head injury. A mathematical model was constructed to evaluate the incremental costs and effectiveness (Quality Adjusted Life years Gained, QALYs) of ten diagnostic management strategies for adults with minor head injuries. Secondary analyses were undertaken to determine the cost-effectiveness of hospital admission compared to discharge home and to explore the cost-effectiveness of strategies when no responsible adult was available to observe the patient after discharge. The apparent optimal strategy was based on the high and medium risk Canadian CT Head Rule (CCHRhm), although the costs and outcomes associated with each strategy were broadly similar. Hospital admission for patients with non-neurosurgical injury on CT dominated discharge home, whilst hospital admission for clinically normal patients with a normal CT was not cost-effective compared to discharge home with or without a responsible adult at £39 and £2.5 million per QALY, respectively. A selective CT strategy with discharge home if the CT scan was normal remained optimal compared to not investigating or CT scanning all patients when there was no responsible adult available to observe them after discharge. Our economic analysis confirms that the recent extension of access to CT scanning for minor head injury is appropriate. Liberal use of CT scanning based on a high sensitivity decision rule is not only effective but also cost-saving. The cost of CT scanning is very small compared to the estimated cost of caring for patients with brain injury worsened by delayed treatment. It is recommended therefore that all hospitals receiving patients with minor head injury should have unrestricted access to CT scanning for use in conjunction with evidence based guidelines. Provisionally the CCHRhm decision rule appears to be the best strategy although there is considerable uncertainty around the optimal decision rule. However, the CCHRhm rule appears to be the most widely validated and it therefore seems appropriate to conclude that the CCHRhm rule has the best evidence to support its use. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Flexible functional regression methods for estimating individualized treatment regimes.
Ciarleglio, Adam; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
2016-01-01
A major focus of personalized medicine is on the development of individualized treatment rules. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods for developing such rules are progressing rapidly, but few methods have considered the use of pre-treatment functional data to guide in decision-making. Furthermore, those methods that do allow for the incorporation of functional pre-treatment covariates typically make strong assumptions about the relationships between the functional covariates and the response of interest. We propose two approaches for using functional data to select an optimal treatment that address some of the shortcomings of previously developed methods. Specifically, we combine the flexibility of functional additive regression models with Q -learning or A -learning in order to obtain treatment decision rules. Properties of the corresponding estimators are discussed. Our approaches are evaluated in several realistic settings using synthetic data and are applied to real data arising from a clinical trial comparing two treatments for major depressive disorder in which baseline imaging data are available for subjects who are subsequently treated.
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Automated control of hierarchical systems using value-driven methods
NASA Technical Reports Server (NTRS)
Pugh, George E.; Burke, Thomas E.
1990-01-01
An introduction is given to the Value-driven methodology, which has been successfully applied to solve a variety of difficult decision, control, and optimization problems. Many real-world decision processes (e.g., those encountered in scheduling, allocation, and command and control) involve a hierarchy of complex planning considerations. For such problems it is virtually impossible to define a fixed set of rules that will operate satisfactorily over the full range of probable contingencies. Decision Science Applications' value-driven methodology offers a systematic way of automating the intuitive, common-sense approach used by human planners. The inherent responsiveness of value-driven systems to user-controlled priorities makes them particularly suitable for semi-automated applications in which the user must remain in command of the systems operation. Three examples of the practical application of the approach in the automation of hierarchical decision processes are discussed: the TAC Brawler air-to-air combat simulation is a four-level computerized hierarchy; the autonomous underwater vehicle mission planning system is a three-level control system; and the Space Station Freedom electrical power control and scheduling system is designed as a two-level hierarchy. The methodology is compared with rule-based systems and with other more widely-known optimization techniques.
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Gimbel, Ronald W; Pirrallo, Ronald G; Lowe, Steven C; Wright, David W; Zhang, Lu; Woo, Min-Jae; Fontelo, Paul; Liu, Fang; Connor, Zachary
2018-03-12
The frequency of head computed tomography (CT) imaging for mild head trauma patients has raised safety and cost concerns. Validated clinical decision rules exist in the published literature and on-line sources to guide medical image ordering but are often not used by emergency department (ED) clinicians. Using simulation, we explored whether the presentation of a clinical decision rule (i.e. Canadian CT Head Rule - CCHR), findings from malpractice cases related to clinicians not ordering CT imaging in mild head trauma cases, and estimated patient out-of-pocket cost might influence clinician brain CT ordering. Understanding what type and how information may influence clinical decision making in the ordering advanced medical imaging is important in shaping the optimal design and implementation of related clinical decision support systems. Multi-center, double-blinded simulation-based randomized controlled trial. Following standardized clinical vignette presentation, clinicians made an initial imaging decision for the patient. This was followed by additional information on decision support rules, malpractice outcome review, and patient cost; each with opportunity to modify their initial order. The malpractice and cost information differed by assigned group to test the any temporal relationship. The simulation closed with a second vignette and an imaging decision. One hundred sixteen of the 167 participants (66.9%) initially ordered a brain CT scan. After CCHR presentation, the number of clinicians ordering a CT dropped to 76 (45.8%), representing a 21.1% reduction in CT ordering (P = 0.002). This reduction in CT ordering was maintained, in comparison to initial imaging orders, when presented with malpractice review information (p = 0.002) and patient cost information (p = 0.002). About 57% of clinicians changed their order during study, while 43% never modified their imaging order. This study suggests that ED clinician brain CT imaging decisions may be influenced by clinical decision support rules, patient out-of-pocket cost information and findings from malpractice case review. NCT03449862 , February 27, 2018, Retrospectively registered.
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Optimal tactics for close support operations. III - Degraded intelligence and communications
NASA Astrophysics Data System (ADS)
Hess, J.; Kalaba, R.; Kagiwada, H.; Spingarn, K.; Tsokos, C.
1980-04-01
A new generation of C3 (command, control, and communication) models for military cybernetics is developed. Recursive equations for the solution of the C3 problem are derived for an amphibious campaign with linear time-varying dynamics. Air and ground commanders are assumed to have no intelligence and no communications. Numerical results are given for the optimal decision rules.
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Converse, Sarah J.; Shelley, Kevin J.; Morey, Steve; Chan, Jeffrey; LaTier, Andrea; Scafidi, Carolyn; Crouse, Deborah T.; Runge, Michael C.
2011-01-01
The resources available to support conservation work, whether time or money, are limited. Decision makers need methods to help them identify the optimal allocation of limited resources to meet conservation goals, and decision analysis is uniquely suited to assist with the development of such methods. In recent years, a number of case studies have been described that examine optimal conservation decisions under fiscal constraints; here we develop methods to look at other types of constraints, including limited staff and regulatory deadlines. In the US, Section Seven consultation, an important component of protection under the federal Endangered Species Act, requires that federal agencies overseeing projects consult with federal biologists to avoid jeopardizing species. A benefit of consultation is negotiation of project modifications that lessen impacts on species, so staff time allocated to consultation supports conservation. However, some offices have experienced declining staff, potentially reducing the efficacy of consultation. This is true of the US Fish and Wildlife Service's Washington Fish and Wildlife Office (WFWO) and its consultation work on federally-threatened bull trout (Salvelinus confluentus). To improve effectiveness, WFWO managers needed a tool to help allocate this work to maximize conservation benefits. We used a decision-analytic approach to score projects based on the value of staff time investment, and then identified an optimal decision rule for how scored projects would be allocated across bins, where projects in different bins received different time investments. We found that, given current staff, the optimal decision rule placed 80% of informal consultations (those where expected effects are beneficial, insignificant, or discountable) in a short bin where they would be completed without negotiating changes. The remaining 20% would be placed in a long bin, warranting an investment of seven days, including time for negotiation. For formal consultations (those where expected effects are significant), 82% of projects would be placed in a long bin, with an average time investment of 15. days. The WFWO is using this decision-support tool to help allocate staff time. Because workload allocation decisions are iterative, we describe a monitoring plan designed to increase the tool's efficacy over time. This work has general application beyond Section Seven consultation, in that it provides a framework for efficient investment of staff time in conservation when such time is limited and when regulatory deadlines prevent an unconstrained approach. ?? 2010.
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Understanding Optimal Decision-Making in Wargaming
2013-10-01
beneficial outcomes from wargaming, one of which is a better understanding of the impact of decisions as a part of combat processes. However, using...under instrument flight rules ( IFR ) (Bellenkes et al., 1997; Katoh, 1997). Of note, eye-tracking technology also has been applied to investigate...Neuroscience, 7 . Skinner, A., Berka, C., Ohara-Long, L., & Sebrechts, M. (2010). Impact of Virtual En- vironment Fidelity on Behavioral and
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Goal-Directed Decision Making with Spiking Neurons.
Friedrich, Johannes; Lengyel, Máté
2016-02-03
Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.
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Goal-Directed Decision Making with Spiking Neurons
Lengyel, Máté
2016-01-01
Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636
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NASA Astrophysics Data System (ADS)
Pulido-Velazquez, Manuel; Macian-Sorribes, Hector; María Benlliure-Moreno, Jose; Fullana-Montoro, Juan
2015-04-01
Water resources systems in areas with a strong tradition in water use are complex to manage by the high amount of constraints that overlap in time and space, creating a complicated framework in which past, present and future collide between them. In addition, it is usual to find "hidden constraints" in system operations, which condition operation decisions being unnoticed by anyone but the river managers and users. Being aware of those hidden constraints requires usually years of experience and a degree of involvement in that system's management operations normally beyond the possibilities of technicians. However, their impact in the management decisions is strongly imprinted in the historical data records available. The purpose of this contribution is to present a methodology capable of assessing operating rules in complex water resources systems combining historical records and expert criteria. Both sources are coupled using fuzzy logic. The procedure stages are: 1) organize expert-technicians preliminary meetings to let the first explain how they manage the system; 2) set up a fuzzy rule-based system (FRB) structure according to the way the system is managed; 3) use the historical records available to estimate the inputs' fuzzy numbers, to assign preliminary output values to the FRB rules and to train and validate these rules; 4) organize expert-technician meetings to discuss the rule structure and the input's quantification, returning if required to the second stage; 5) once the FRB structure is accepted, its output values must be refined and completed with the aid of the experts by using meetings, workshops or surveys; 6) combine the FRB with a Decision Support System (DSS) to simulate the effect of those management decisions; 7) compare its results with the ones offered by the historical records and/or simulation or optimization models; and 8) discuss with the stakeholders the model performance returning, if it's required, to the fifth or the second stage. The methodology proposed has been applied to the Jucar River Basin (Spain). This basin has 3 reservoirs, 4 headwaters, 11 demands and 5 environmental flows; which form together a complex constraint set. After the preliminary meetings, one 81-rule FRB was created, using as inputs the system state variables at the start of the hydrologic year, and as outputs the target reservoir release schedule. The inputs' fuzzy numbers were estimated jointly using surveys. Fifteen years of historical records were used to train the system's outputs. The obtained FRB was then refined during additional expert-technician meetings. After that, the resulting FRB was introduced into a DSS simulating the effect of those management rules for different hydrological conditions. Three additional FRB's were created using: 1) exclusively the historical records; 2) a stochastic optimization model; and 3) a deterministic optimization model. The results proved to be consistent with the expectations, with the stakeholder's FRB performance located between the data-driven simulation and the stochastic optimization FRB's; and reflect the stakeholders' major goals and concerns about the river management. ACKNOWLEDGEMENT: This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) funds.
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Linearly Adjustable International Portfolios
NASA Astrophysics Data System (ADS)
Fonseca, R. J.; Kuhn, D.; Rustem, B.
2010-09-01
We present an approach to multi-stage international portfolio optimization based on the imposition of a linear structure on the recourse decisions. Multiperiod decision problems are traditionally formulated as stochastic programs. Scenario tree based solutions however can become intractable as the number of stages increases. By restricting the space of decision policies to linear rules, we obtain a conservative tractable approximation to the original problem. Local asset prices and foreign exchange rates are modelled separately, which allows for a direct measure of their impact on the final portfolio value.
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Hager, Rebecca; Tsiatis, Anastasios A; Davidian, Marie
2018-05-18
Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. We propose a method for estimation of an optimal regime involving two decision points when the outcome of interest is a censored survival time, which is based on maximizing a locally efficient, doubly robust, augmented inverse probability weighted estimator for average outcome over a class of regimes. By casting this optimization as a classification problem, we exploit well-studied classification techniques such as support vector machines to characterize the class of regimes and facilitate implementation via a backward iterative algorithm. Simulation studies of performance and application of the method to data from a sequential, multiple assignment randomized clinical trial in acute leukemia are presented. © 2018, The International Biometric Society.
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How Family Status and Social Security Claiming Options Shape Optimal Life Cycle Portfolios
Hubener, Andreas; Maurer, Raimond; Mitchell, Olivia S.
2017-01-01
We show how optimal household decisions regarding work, retirement, saving, portfolio allocations, and life insurance are shaped by the complex financial options embedded in U.S. Social Security rules and uncertain family transitions. Our life cycle model predicts sharp consumption drops on retirement, an age-62 peak in claiming rates, and earlier claiming by wives versus husbands and single women. Moreover, life insurance is mainly purchased on men’s lives. Our model, which takes Social Security rules seriously, generates wealth and retirement outcomes that are more consistent with the data, in contrast to earlier and less realistic models. PMID:28659659
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An automated approach to the design of decision tree classifiers
NASA Technical Reports Server (NTRS)
Argentiero, P.; Chin, P.; Beaudet, P.
1980-01-01
The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.
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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.
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The anatomy of choice: dopamine and decision-making
Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.
2014-01-01
This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making. PMID:25267823
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The anatomy of choice: dopamine and decision-making.
Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J
2014-11-05
This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.
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Automating the design of scientific computing software
NASA Technical Reports Server (NTRS)
Kant, Elaine
1992-01-01
SINAPSE is a domain-specific software design system that generates code from specifications of equations and algorithm methods. This paper describes the system's design techniques (planning in a space of knowledge-based refinement and optimization rules), user interaction style (user has option to control decision making), and representation of knowledge (rules and objects). It also summarizes how the system knowledge has evolved over time and suggests some issues in building software design systems to facilitate reuse.
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Orellana, Liliana; Rotnitzky, Andrea; Robins, James M
2010-01-01
Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.
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Policy Tree Optimization for Adaptive Management of Water Resources Systems
NASA Astrophysics Data System (ADS)
Herman, J. D.; Giuliani, M.
2016-12-01
Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points", which are threshold values of indicator variables that signal a change in policy. However, there remains a need for a general method to optimize the choice of indicators and their threshold values in a way that is easily interpretable for decision makers. Here we propose a conceptual framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. We demonstrate the approach using Folsom Reservoir, California as a case study, in which operating policies must balance the risk of both floods and droughts. Given a set of feature variables, such as reservoir level, inflow observations and forecasts, and time of year, the resulting policy defines the conditions under which flood control and water supply hedging operations should be triggered. Importantly, the tree-based rule sets are easy to interpret for decision making, and can be compared to historical operating policies to understand the adaptations needed under possible climate change scenarios. Several remaining challenges are discussed, including the empirical convergence properties of the method, and extensions to irreversible decisions such as infrastructure. Policy tree optimization, and corresponding open-source software, provide a generalizable, interpretable approach to designing adaptive policies under uncertainty for water resources systems.
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Multisensor fusion with non-optimal decision rules: the challenges of open world sensing
NASA Astrophysics Data System (ADS)
Minor, Christian; Johnson, Kevin
2014-05-01
In this work, simple, generic models of chemical sensing are used to simulate sensor array data and to illustrate the impact on overall system performance that specific design choices impart. The ability of multisensor systems to perform multianalyte detection (i.e., distinguish multiple targets) is explored by examining the distinction between fundamental design-related limitations stemming from mismatching of mixture composition to fused sensor measurement spaces, and limitations that arise from measurement uncertainty. Insight on the limits and potential of sensor fusion to robustly address detection tasks in realistic field conditions can be gained through an examination of a) the underlying geometry of both the composition space of sources one hopes to elucidate and the measurement space a fused sensor system is capable of generating, and b) the informational impact of uncertainty on both of these spaces. For instance, what is the potential impact on sensor fusion in an open world scenario where unknown interferants may contaminate target signals? Under complex and dynamic backgrounds, decision rules may implicitly become non-optimal and adding sensors may increase the amount of conflicting information observed. This suggests that the manner in which a decision rule handles sensor conflict can be critical in leveraging sensor fusion for effective open world sensing, and becomes exponentially more important as more sensors are added. Results and design considerations for handling conflicting evidence in Bayes and Dempster-Shafer fusion frameworks are presented. Bayesian decision theory is used to provide an upper limit on detector performance of simulated sensor systems.
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NASA Astrophysics Data System (ADS)
Reinert, K. A.
The use of linear decision rules (LDR) and chance constrained programming (CCP) to optimize the performance of wind energy conversion clusters coupled to storage systems is described. Storage is modelled by LDR and output by CCP. The linear allocation rule and linear release rule prescribe the size and optimize a storage facility with a bypass. Chance constraints are introduced to explicitly treat reliability in terms of an appropriate value from an inverse cumulative distribution function. Details of deterministic programming structure and a sample problem involving a 500 kW and a 1.5 MW WECS are provided, considering an installed cost of $1/kW. Four demand patterns and three levels of reliability are analyzed for optimizing the generator choice and the storage configuration for base load and peak operating conditions. Deficiencies in ability to predict reliability and to account for serial correlations are noted in the model, which is concluded useful for narrowing WECS design options.
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Van der Pol, L M; Mairuhu, A T A; Tromeur, C; Couturaud, F; Huisman, M V; Klok, F A
2017-03-01
Because pregnant women have an increased risk of venous thromboembolism (VTE) and at the same time normal pregnancy is associated with symptoms, mimicking those present in the setting of acute pulmonary embolism (PE), the latter diagnosis is frequently suspected in this patient category. Since imaging tests expose both mother and foetus to ionizing radiation, the ability to rule out PE based on non-radiological diagnostic tests is of paramount importance. However, clinical decision rules have only been scarcely evaluated in the pregnant population with suspected PE, while D-dimer levels lose diagnostic accuracy due to a physiological increase during normal pregnancy. Consequently, clinical guidelines provide contradicting and weak recommendations on this subject and the optimal diagnostic strategy remains highly debated. With this systematic review, we aimed to summarize current evidence on the safety and efficacy of clinical decision rules and biomarkers used in the diagnostic management of suspected acute PE in pregnant patients. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Efficient discovery of risk patterns in medical data.
Li, Jiuyong; Fu, Ada Wai-chee; Fahey, Paul
2009-01-01
This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.
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Advancing reservoir operation description in physically based hydrological models
NASA Astrophysics Data System (ADS)
Anghileri, Daniela; Giudici, Federico; Castelletti, Andrea; Burlando, Paolo
2016-04-01
Last decades have seen significant advances in our capacity of characterizing and reproducing hydrological processes within physically based models. Yet, when the human component is considered (e.g. reservoirs, water distribution systems), the associated decisions are generally modeled with very simplistic rules, which might underperform in reproducing the actual operators' behaviour on a daily or sub-daily basis. For example, reservoir operations are usually described by a target-level rule curve, which represents the level that the reservoir should track during normal operating conditions. The associated release decision is determined by the current state of the reservoir relative to the rule curve. This modeling approach can reasonably reproduce the seasonal water volume shift due to reservoir operation. Still, it cannot capture more complex decision making processes in response, e.g., to the fluctuations of energy prices and demands, the temporal unavailability of power plants or varying amount of snow accumulated in the basin. In this work, we link a physically explicit hydrological model with detailed hydropower behavioural models describing the decision making process by the dam operator. In particular, we consider two categories of behavioural models: explicit or rule-based behavioural models, where reservoir operating rules are empirically inferred from observational data, and implicit or optimization based behavioural models, where, following a normative economic approach, the decision maker is represented as a rational agent maximising a utility function. We compare these two alternate modelling approaches on the real-world water system of Lake Como catchment in the Italian Alps. The water system is characterized by the presence of 18 artificial hydropower reservoirs generating almost 13% of the Italian hydropower production. Results show to which extent the hydrological regime in the catchment is affected by different behavioural models and reservoir operating strategies.
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Urban Rain Gauge Siting Selection Based on Gis-Multicriteria Analysis
NASA Astrophysics Data System (ADS)
Fu, Yanli; Jing, Changfeng; Du, Mingyi
2016-06-01
With the increasingly rapid growth of urbanization and climate change, urban rainfall monitoring as well as urban waterlogging has widely been paid attention. In the light of conventional siting selection methods do not take into consideration of geographic surroundings and spatial-temporal scale for the urban rain gauge site selection, this paper primarily aims at finding the appropriate siting selection rules and methods for rain gauge in urban area. Additionally, for optimization gauge location, a spatial decision support system (DSS) aided by geographical information system (GIS) has been developed. In terms of a series of criteria, the rain gauge optimal site-search problem can be addressed by a multicriteria decision analysis (MCDA). A series of spatial analytical techniques are required for MCDA to identify the prospective sites. With the platform of GIS, using spatial kernel density analysis can reflect the population density; GIS buffer analysis is used to optimize the location with the rain gauge signal transmission character. Experiment results show that the rules and the proposed method are proper for the rain gauge site selection in urban areas, which is significant for the siting selection of urban hydrological facilities and infrastructure, such as water gauge.
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The future of decision-making in critical care after Cuthbertson v. Rasouli.
Hawryluck, Laura; Baker, Andrew J; Faith, Andrew; Singh, Jeffrey M
2014-10-01
The Supreme Court of Canada (SCC) ruling on Cuthbertson v. Rasouli has implications for all acute healthcare providers. This well-publicized case involved a disagreement between healthcare providers and a patient's family regarding the principles surrounding withdrawal of life support, which the physicians involved considered no longer of medical benefit and outside the standard of care, and whether consent was required for such withdrawals. Our objective in writing this article is to clarify the implications of this ruling on the care of critically ill patients. SCC ruling Cuthbertson v. Rasouli. The SCC ruled that consent must be obtained for all treatments that serve a "health-related purpose", including withdrawal of such treatments. The SCC did not fully consider what the standard of care should be. Health-related purpose is not sufficient in and of itself to mandate treatment, and clinicians must still ensure that their patients or decision-makers are aware of the possible medical benefits, risks, and expected outcomes of treatments. The provision of treatments that have no potential to provide medical benefit and carry only risks would still fall outside the standard of care. Nevertheless, due to their health-related purpose, physicians must seek consent for the discontinuation of these treatments. The SCC ruled that due to the legal definition of "health-related purpose", which is distinct from medical benefit, consent is required to withdraw life-support and outlined the steps to be taken should conflict arise. The SCC decision did not directly address the role of medical standard of care in these situations. In order to ensure optimal decision-making and communication with patients and their families, it is critical for healthcare providers to have a clear understanding of the implications of this legal ruling on medical practice.
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INDEXABILITY AND OPTIMAL INDEX POLICIES FOR A CLASS OF REINITIALISING RESTLESS BANDITS.
Villar, Sofía S
2016-01-01
Motivated by a class of Partially Observable Markov Decision Processes with application in surveillance systems in which a set of imperfectly observed state processes is to be inferred from a subset of available observations through a Bayesian approach, we formulate and analyze a special family of multi-armed restless bandit problems. We consider the problem of finding an optimal policy for observing the processes that maximizes the total expected net rewards over an infinite time horizon subject to the resource availability. From the Lagrangian relaxation of the original problem, an index policy can be derived, as long as the existence of the Whittle index is ensured. We demonstrate that such a class of reinitializing bandits in which the projects' state deteriorates while active and resets to its initial state when passive until its completion possesses the structural property of indexability and we further show how to compute the index in closed form. In general, the Whittle index rule for restless bandit problems does not achieve optimality. However, we show that the proposed Whittle index rule is optimal for the problem under study in the case of stochastically heterogenous arms under the expected total criterion, and it is further recovered by a simple tractable rule referred to as the 1-limited Round Robin rule. Moreover, we illustrate the significant suboptimality of other widely used heuristic: the Myopic index rule, by computing in closed form its suboptimality gap. We present numerical studies which illustrate for the more general instances the performance advantages of the Whittle index rule over other simple heuristics.
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INDEXABILITY AND OPTIMAL INDEX POLICIES FOR A CLASS OF REINITIALISING RESTLESS BANDITS
Villar, Sofía S.
2016-01-01
Motivated by a class of Partially Observable Markov Decision Processes with application in surveillance systems in which a set of imperfectly observed state processes is to be inferred from a subset of available observations through a Bayesian approach, we formulate and analyze a special family of multi-armed restless bandit problems. We consider the problem of finding an optimal policy for observing the processes that maximizes the total expected net rewards over an infinite time horizon subject to the resource availability. From the Lagrangian relaxation of the original problem, an index policy can be derived, as long as the existence of the Whittle index is ensured. We demonstrate that such a class of reinitializing bandits in which the projects’ state deteriorates while active and resets to its initial state when passive until its completion possesses the structural property of indexability and we further show how to compute the index in closed form. In general, the Whittle index rule for restless bandit problems does not achieve optimality. However, we show that the proposed Whittle index rule is optimal for the problem under study in the case of stochastically heterogenous arms under the expected total criterion, and it is further recovered by a simple tractable rule referred to as the 1-limited Round Robin rule. Moreover, we illustrate the significant suboptimality of other widely used heuristic: the Myopic index rule, by computing in closed form its suboptimality gap. We present numerical studies which illustrate for the more general instances the performance advantages of the Whittle index rule over other simple heuristics. PMID:27212781
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NASA Astrophysics Data System (ADS)
Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd
2016-04-01
This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.
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Newsvendor problem under complete uncertainty: a case of innovative products.
Gaspars-Wieloch, Helena
2017-01-01
The paper presents a new scenario-based decision rule for the classical version of the newsvendor problem (NP) under complete uncertainty (i.e. uncertainty with unknown probabilities). So far, NP has been analyzed under uncertainty with known probabilities or under uncertainty with partial information (probabilities known incompletely). The novel approach is designed for the sale of new, innovative products, where it is quite complicated to define probabilities or even probability-like quantities, because there are no data available for forecasting the upcoming demand via statistical analysis. The new procedure described in the contribution is based on a hybrid of Hurwicz and Bayes decision rules. It takes into account the decision maker's attitude towards risk (measured by coefficients of optimism and pessimism) and the dispersion (asymmetry, range, frequency of extremes values) of payoffs connected with particular order quantities. It does not require any information about the probability distribution.
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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.
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Procedures for Empirical Determination of En-Route Criterion Levels.
ERIC Educational Resources Information Center
Moncrief, Michael H.
En-route Criterion Levels (ECLs) are defined as decision rules for predicting pupil readiness to advance through an instructional sequence. This study investigated the validity of present ELCs in an individualized mathematics program and tested procedures for empirically determining optimal ECLs. Retest scores and subsequent progress were…
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Assessing predation risk: optimal behaviour and rules of thumb.
Welton, Nicky J; McNamara, John M; Houston, Alasdair I
2003-12-01
We look at a simple model in which an animal makes behavioural decisions over time in an environment in which all parameters are known to the animal except predation risk. In the model there is a trade-off between gaining information about predation risk and anti-predator behaviour. All predator attacks lead to death for the prey, so that the prey learns about predation risk by virtue of the fact that it is still alive. We show that it is not usually optimal to behave as if the current unbiased estimate of the predation risk is its true value. We consider two different ways to model reproduction; in the first scenario the animal reproduces throughout its life until it dies, and in the second scenario expected reproductive success depends on the level of energy reserves the animal has gained by some point in time. For both of these scenarios we find results on the form of the optimal strategy and give numerical examples which compare optimal behaviour with behaviour under simple rules of thumb. The numerical examples suggest that the value of the optimal strategy over the rules of thumb is greatest when there is little current information about predation risk, learning is not too costly in terms of predation, and it is energetically advantageous to learn about predation. We find that for the model and parameters investigated, a very simple rule of thumb such as 'use the best constant control' performs well.
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NASA Astrophysics Data System (ADS)
Chaubey, I.; Vema, V. K.; Sudheer, K.
2016-12-01
Site suitability evaluation of water conservation structures in water scarce rainfed agricultural areas consist of assessment of various landscape characteristics and various criterion. Many of these landscape characteristic attributes are conveyed through linguistic terms rather than precise numeric values. Fuzzy rule based system are capable of incorporating uncertainty and vagueness, when various decision making criteria expressed in linguistic terms are expressed as fuzzy rules. In this study a fuzzy rule based decision support system is developed, for optimal site selection of water harvesting technologies. Water conservation technologies like farm ponds, Check dams, Rock filled dams and percolation ponds aid in conserving water for irrigation and recharging aquifers and development of such a system will aid in improving the efficiency of the structures. Attributes and criteria involved in decision making are classified into different groups to estimate the suitability of the particular technology. The developed model is applied and tested on an Indian watershed. The input attributes are prepared in raster format in ArcGIS software and suitability of each raster cell is calculated and output is generated in the form of a thematic map showing the suitability of the cells pertaining to different technologies. The output of the developed model is compared against the already existing structures and results are satisfactory. This developed model will aid in improving the sustainability and efficiency of the watershed management programs aimed at enhancing in situ moisture content.
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Design and development of bio-inspired framework for reservoir operation optimization
NASA Astrophysics Data System (ADS)
Asvini, M. Sakthi; Amudha, T.
2017-12-01
Frameworks for optimal reservoir operation play an important role in the management of water resources and delivery of economic benefits. Effective utilization and conservation of water from reservoirs helps to manage water deficit periods. The main challenge in reservoir optimization is to design operating rules that can be used to inform real-time decisions on reservoir release. We develop a bio-inspired framework for the optimization of reservoir release to satisfy the diverse needs of various stakeholders. In this work, single-objective optimization and multiobjective optimization problems are formulated using an algorithm known as "strawberry optimization" and tested with actual reservoir data. Results indicate that well planned reservoir operations lead to efficient deployment of the reservoir water with the help of optimal release patterns.
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Developing Novel Reservoir Rule Curves Using Seasonal Inflow Projections
NASA Astrophysics Data System (ADS)
Tseng, Hsin-yi; Tung, Ching-pin
2015-04-01
Due to significant seasonal rainfall variations, reservoirs and their flexible operational rules are indispensable to Taiwan. Furthermore, with the intensifying impacts of climate change on extreme climate, the frequency of droughts in Taiwan has been increasing in recent years. Drought is a creeping phenomenon, the slow onset character of drought makes it difficult to detect at an early stage, and causes delays on making the best decision of allocating water. For these reasons, novel reservoir rule curves using projected seasonal streamflow are proposed in this study, which can potentially reduce the adverse effects of drought. This study dedicated establishing new rule curves which consider both current available storage and anticipated monthly inflows with leading time of two months to reduce the risk of water shortage. The monthly inflows are projected based on the seasonal climate forecasts from Central Weather Bureau (CWB), which a weather generation model is used to produce daily weather data for the hydrological component of the GWLF. To incorporate future monthly inflow projections into rule curves, this study designs a decision flow index which is a linear combination of current available storage and inflow projections with leading time of 2 months. By optimizing linear relationship coefficients of decision flow index, the shape of rule curves and the percent of water supply in each zone, the best rule curves to decrease water shortage risk and impacts can be developed. The Shimen Reservoir in the northern Taiwan is used as a case study to demonstrate the proposed method. Existing rule curves (M5 curves) of Shimen Reservoir are compared with two cases of new rule curves, including hindcast simulations and historic seasonal forecasts. The results show new rule curves can decrease the total water shortage ratio, and in addition, it can also allocate shortage amount to preceding months to avoid extreme shortage events. Even though some uncertainties in historic forecasts would result unnecessary discounts of water supply, it still performs better than M5 curves during droughts.
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Ren, Yue; Li, Jinhai; Aswani Kumar, Cherukuri; Liu, Wenqi
2014-01-01
Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: "if conditions 1,2,…, and m hold, then decisions hold." In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency.
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Ren, Yue; Aswani Kumar, Cherukuri; Liu, Wenqi
2014-01-01
Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: “if conditions 1,2,…, and m hold, then decisions hold.” In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency. PMID:25165744
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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.
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Ant groups optimally amplify the effect of transiently informed individuals
NASA Astrophysics Data System (ADS)
Gelblum, Aviram; Pinkoviezky, Itai; Fonio, Ehud; Ghosh, Abhijit; Gov, Nir; Feinerman, Ofer
2015-07-01
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.
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Ant groups optimally amplify the effect of transiently informed individuals
Gelblum, Aviram; Pinkoviezky, Itai; Fonio, Ehud; Ghosh, Abhijit; Gov, Nir; Feinerman, Ofer
2015-01-01
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge. PMID:26218613
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NASA Astrophysics Data System (ADS)
Hurford, A. P.; Harou, J. J.
2014-01-01
Competition for water between key economic sectors and the environment means agreeing on allocation is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly we seek to show how trade-offs between achievable benefits shift with the implementation of new proposed rice, cotton and biofuel irrigation projects. To identify the Pareto-optimal trade-offs we link a water resources management model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for objectives covering provision of water supply and irrigation, energy generation and maintenance of ecosystem services which underpin tourism and local livelihoods. Visual analytic plots allow decision makers to assess multi-reservoir rule-sets by understanding their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against disturbance of the flow regime which supports ecosystem services. Full implementation of the proposed schemes is shown to be Pareto-optimal, but at high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of water-energy-food "nexus" challenges.
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Working-memory load and temporal myopia in dynamic decision making.
Worthy, Darrell A; Otto, A Ross; Maddox, W Todd
2012-11-01
We examined the role of working memory (WM) in dynamic decision making by having participants perform decision-making tasks under single-task or dual-task conditions. In 2 experiments participants performed dynamic decision-making tasks in which they chose 1 of 2 options on each trial. The decreasing option always gave a larger immediate reward but caused future rewards for both options to decrease. The increasing option always gave a smaller immediate reward but caused future rewards for both options to increase. In each experiment we manipulated the reward structure such that the decreasing option was the optimal choice in 1 condition and the increasing option was the optimal choice in the other condition. Behavioral results indicated that dual-task participants selected the immediately rewarding decreasing option more often, and single-task participants selected the increasing option more often, regardless of which option was optimal. Thus, dual-task participants performed worse on 1 type of task but better on the other type. Modeling results showed that single-task participants' data were most often best fit by a win-stay, lose-shift (WSLS) rule-based model that tracked differences across trials, and dual-task participants' data were most often best fit by a Softmax reinforcement learning model that tracked recency-weighted average rewards for each option. This suggests that manipulating WM load affects the degree to which participants focus on the immediate versus delayed consequences of their actions and whether they employ a rule-based WSLS strategy, but it does not necessarily affect how well people weigh the immediate versus delayed benefits when determining the long-term utility of each option.
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Huy, Nguyen Tien; Thao, Nguyen Thanh Hong; Tuan, Nguyen Anh; Khiem, Nguyen Tuan; Moore, Christopher C.; Thi Ngoc Diep, Doan; Hirayama, Kenji
2012-01-01
Background and Purpose Successful outcomes from bacterial meningitis require rapid antibiotic treatment; however, unnecessary treatment of viral meningitis may lead to increased toxicities and expense. Thus, improved diagnostics are required to maximize treatment and minimize side effects and cost. Thirteen clinical decision rules have been reported to identify bacterial from viral meningitis. However, few rules have been tested and compared in a single study, while several rules are yet to be tested by independent researchers or in pediatric populations. Thus, simultaneous test and comparison of these rules are required to enable clinicians to select an optimal diagnostic rule for bacterial meningitis in settings and populations similar to ours. Methods A retrospective cross-sectional study was conducted at the Infectious Department of Pediatric Hospital Number 1, Ho Chi Minh City, Vietnam. The performance of the clinical rules was evaluated by area under a receiver operating characteristic curve (ROC-AUC) using the method of DeLong and McNemar test for specificity comparison. Results Our study included 129 patients, of whom 80 had bacterial meningitis and 49 had presumed viral meningitis. Spanos's rule had the highest AUC at 0.938 but was not significantly greater than other rules. No rule provided 100% sensitivity with a specificity higher than 50%. Based on our calculation of theoretical sensitivity and specificity, we suggest that a perfect rule requires at least four independent variables that posses both sensitivity and specificity higher than 85–90%. Conclusions No clinical decision rules provided an acceptable specificity (>50%) with 100% sensitivity when applying our data set in children. More studies in Vietnam and developing countries are required to develop and/or validate clinical rules and more very good biomarkers are required to develop such a perfect rule. PMID:23209715
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Huy, Nguyen Tien; Thao, Nguyen Thanh Hong; Tuan, Nguyen Anh; Khiem, Nguyen Tuan; Moore, Christopher C; Thi Ngoc Diep, Doan; Hirayama, Kenji
2012-01-01
Successful outcomes from bacterial meningitis require rapid antibiotic treatment; however, unnecessary treatment of viral meningitis may lead to increased toxicities and expense. Thus, improved diagnostics are required to maximize treatment and minimize side effects and cost. Thirteen clinical decision rules have been reported to identify bacterial from viral meningitis. However, few rules have been tested and compared in a single study, while several rules are yet to be tested by independent researchers or in pediatric populations. Thus, simultaneous test and comparison of these rules are required to enable clinicians to select an optimal diagnostic rule for bacterial meningitis in settings and populations similar to ours. A retrospective cross-sectional study was conducted at the Infectious Department of Pediatric Hospital Number 1, Ho Chi Minh City, Vietnam. The performance of the clinical rules was evaluated by area under a receiver operating characteristic curve (ROC-AUC) using the method of DeLong and McNemar test for specificity comparison. Our study included 129 patients, of whom 80 had bacterial meningitis and 49 had presumed viral meningitis. Spanos's rule had the highest AUC at 0.938 but was not significantly greater than other rules. No rule provided 100% sensitivity with a specificity higher than 50%. Based on our calculation of theoretical sensitivity and specificity, we suggest that a perfect rule requires at least four independent variables that posses both sensitivity and specificity higher than 85-90%. No clinical decision rules provided an acceptable specificity (>50%) with 100% sensitivity when applying our data set in children. More studies in Vietnam and developing countries are required to develop and/or validate clinical rules and more very good biomarkers are required to develop such a perfect rule.
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Considering social and environmental concerns as reservoir operating objectives
NASA Astrophysics Data System (ADS)
Tilmant, A.; Georis, B.; Doulliez, P.
2003-04-01
Sustainability principles are now widely recognized as key criteria for water resource development schemes, such as hydroelectric and multipurpose reservoirs. Development decisions no longer rely solely on economic grounds, but also consider environmental and social concerns through the so-called environmental and social impact assessments. The objective of this paper is to show that environmental and social concerns can also be addressed in the management (operation) of existing or projected reservoir schemes. By either adequately exploiting the results of environmental and social impact assessments, or by carrying out survey of water users, experts and managers, efficient (Pareto optimal) reservoir operating rules can be derived using flexible mathematical programming techniques. By reformulating the problem as a multistage flexible constraint satisfaction problem, incommensurable and subjective operating objectives can contribute, along with classical economic objectives, to the determination of optimal release decisions. Employed in a simulation mode, the results can be used to assess the long-term impacts of various operating rules on the social well-being of affected populations as well as on the integrity of the environment. The methodology is illustrated with a reservoir reallocation problem in Chile.
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Rigorous ILT optimization for advanced patterning and design-process co-optimization
NASA Astrophysics Data System (ADS)
Selinidis, Kosta; Kuechler, Bernd; Cai, Howard; Braam, Kyle; Hoppe, Wolfgang; Domnenko, Vitaly; Poonawala, Amyn; Xiao, Guangming
2018-03-01
Despite the large difficulties involved in extending 193i multiple patterning and the slow ramp of EUV lithography to full manufacturing readiness, the pace of development for new technology node variations has been accelerating. Multiple new variations of new and existing technology nodes have been introduced for a range of device applications; each variation with at least a few new process integration methods, layout constructs and/or design rules. This had led to a strong increase in the demand for predictive technology tools which can be used to quickly guide important patterning and design co-optimization decisions. In this paper, we introduce a novel hybrid predictive patterning method combining two patterning technologies which have each individually been widely used for process tuning, mask correction and process-design cooptimization. These technologies are rigorous lithography simulation and inverse lithography technology (ILT). Rigorous lithography simulation has been extensively used for process development/tuning, lithography tool user setup, photoresist hot-spot detection, photoresist-etch interaction analysis, lithography-TCAD interactions/sensitivities, source optimization and basic lithography design rule exploration. ILT has been extensively used in a range of lithographic areas including logic hot-spot fixing, memory layout correction, dense memory cell optimization, assist feature (AF) optimization, source optimization, complex patterning design rules and design-technology co-optimization (DTCO). The combined optimization capability of these two technologies will therefore have a wide range of useful applications. We investigate the benefits of the new functionality for a few of these advanced applications including correction for photoresist top loss and resist scumming hotspots.
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Computational mate choice: theory and empirical evidence.
Castellano, Sergio; Cadeddu, Giorgia; Cermelli, Paolo
2012-06-01
The present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice. Copyright © 2012 Elsevier B.V. All rights reserved.
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Direct adaptive performance optimization of subsonic transports: A periodic perturbation technique
NASA Technical Reports Server (NTRS)
Espana, Martin D.; Gilyard, Glenn
1995-01-01
Aircraft performance can be optimized at the flight condition by using available redundancy among actuators. Effective use of this potential allows improved performance beyond limits imposed by design compromises. Optimization based on nominal models does not result in the best performance of the actual aircraft at the actual flight condition. An adaptive algorithm for optimizing performance parameters, such as speed or fuel flow, in flight based exclusively on flight data is proposed. The algorithm is inherently insensitive to model inaccuracies and measurement noise and biases and can optimize several decision variables at the same time. An adaptive constraint controller integrated into the algorithm regulates the optimization constraints, such as altitude or speed, without requiring and prior knowledge of the autopilot design. The algorithm has a modular structure which allows easy incorporation (or removal) of optimization constraints or decision variables to the optimization problem. An important part of the contribution is the development of analytical tools enabling convergence analysis of the algorithm and the establishment of simple design rules. The fuel-flow minimization and velocity maximization modes of the algorithm are demonstrated on the NASA Dryden B-720 nonlinear flight simulator for the single- and multi-effector optimization cases.
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Bhanji, Jamil P.; Beer, Jennifer S.; Bunge, Silvia A.
2014-01-01
A decision may be difficult because complex information processing is required to evaluate choices according to deterministic decision rules and/or because it is not certain which choice will lead to the best outcome in a probabilistic context. Factors that tax decision making such as decision rule complexity and low decision certainty should be disambiguated for a more complete understanding of the decision making process. Previous studies have examined the brain regions that are modulated by decision rule complexity or by decision certainty but have not examined these factors together in the context of a single task or study. In the present functional magnetic resonance imaging study, both decision rule complexity and decision certainty were varied in comparable decision tasks. Further, the level of certainty about which choice to make (choice certainty) was varied separately from certainty about the final outcome resulting from a choice (outcome certainty). Lateral prefrontal cortex, dorsal anterior cingulate cortex, and bilateral anterior insula were modulated by decision rule complexity. Anterior insula was engaged more strongly by low than high choice certainty decisions, whereas ventromedial prefrontal cortex showed the opposite pattern. These regions showed no effect of the independent manipulation of outcome certainty. The results disambiguate the influence of decision rule complexity, choice certainty, and outcome certainty on activity in diverse brain regions that have been implicated in decision making. Lateral prefrontal cortex plays a key role in implementing deterministic decision rules, ventromedial prefrontal cortex in probabilistic rules, and anterior insula in both. PMID:19781652
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Volume sharing of reservoir water
NASA Astrophysics Data System (ADS)
Dudley, Norman J.
1988-05-01
Previous models optimize short-, intermediate-, and long-run irrigation decision making in a simplified river valley system characterized by highly variable water supplies and demands for a single decision maker controlling both reservoir releases and farm water use. A major problem in relaxing the assumption of one decision maker is communicating the stochastic nature of supplies and demands between reservoir and farm managers. In this paper, an optimizing model is used to develop release rules for reservoir management when all users share equally in releases, and computer simulation is used to generate an historical time sequence of announced releases. These announced releases become a state variable in a farm management model which optimizes farm area-to-irrigate decisions through time. Such modeling envisages the use of growing area climatic data by the reservoir authority to gauge water demand and the transfer of water supply data from reservoir to farm managers via computer data files. Alternative model forms, including allocating water on a priority basis, are discussed briefly. Results show lower mean aggregate farm income and lower variance of aggregate farm income than in the single decision-maker case. This short-run economic efficiency loss coupled with likely long-run economic efficiency losses due to the attenuated nature of property rights indicates the need for quite different ways of integrating reservoir and farm management.
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NASA Astrophysics Data System (ADS)
Park, J.; Yoo, K.
2013-12-01
For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.
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A preference aggregation model and application in AHP-group decision making
NASA Astrophysics Data System (ADS)
Yang, Taiyi; Yang, De; Chao, Xiangrui
2018-04-01
Group decision making process integrate individual preferences to obtain the group preference by applying aggregation rules and preference relations. The two most useful approaches, the aggregation of individual judgements and the aggregation of individual priorities, traditionally are employed in the Analytic Hierarchy Process to deal with group decision making problems. In both cases, it is assumed that the group preference is approximate weighted mathematical expectation of individual judgements and individual priorities. We propose new preference aggregation methods using optimization models in order to obtain group preference which is close to all individual priorities. Some illustrative examples are finally examined to demonstrate proposed models for application.
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Prahl, Andrew; Dexter, Franklin; Braun, Michael T; Van Swol, Lyn
2013-11-01
Because operating room (OR) management decisions with optimal choices are made with ubiquitous biases, decisions are improved with decision-support systems. We reviewed experimental social-psychology studies to explore what an OR leader can do when working with stakeholders lacking interest in learning the OR management science but expressing opinions about decisions, nonetheless. We considered shared information to include the rules-of-thumb (heuristics) that make intuitive sense and often seem "close enough" (e.g., staffing is planned based on the average workload). We considered unshared information to include the relevant mathematics (e.g., staffing calculations). Multiple studies have shown that group discussions focus more on shared than unshared information. Quality decisions are more likely when all group participants share knowledge (e.g., have taken a course in OR management science). Several biases in OR management are caused by humans' limited abilities to estimate tails of probability distributions in their heads. Groups are more susceptible to analogous biases than are educated individuals. Since optimal solutions are not demonstrable without groups sharing common language, only with education of most group members can a knowledgeable individual influence the group. The appropriate model of decision-making is autocratic, with information obtained from stakeholders. Although such decisions are good quality, the leaders often are disliked and the decisions considered unjust. In conclusion, leaders will find the most success if they do not bring OR management operational decisions to groups, but instead act autocratically while obtaining necessary information in 1:1 conversations. The only known route for the leader making such decisions to be considered likable and for the decisions to be considered fair is through colleagues and subordinates learning the management science.
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Heuristics in Managing Complex Clinical Decision Tasks in Experts’ Decision Making
Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme
2016-01-01
Background Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. Objective The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. Method After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. Results We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Conclusion Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Application Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design. PMID:27275019
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Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making.
Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme
2014-09-01
Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design.
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NASA Astrophysics Data System (ADS)
Gorsevski, Pece V.; Jankowski, Piotr
2010-08-01
The Kalman recursive algorithm has been very widely used for integrating navigation sensor data to achieve optimal system performances. This paper explores the use of the Kalman filter to extend the aggregation of spatial multi-criteria evaluation (MCE) and to find optimal solutions with respect to a decision strategy space where a possible decision rule falls. The approach was tested in a case study in the Clearwater National Forest in central Idaho, using existing landslide datasets from roaded and roadless areas and terrain attributes. In this approach, fuzzy membership functions were used to standardize terrain attributes and develop criteria, while the aggregation of the criteria was achieved by the use of a Kalman filter. The approach presented here offers advantages over the classical MCE theory because the final solution includes both the aggregated solution and the areas of uncertainty expressed in terms of standard deviation. A comparison of this methodology with similar approaches suggested that this approach is promising for predicting landslide susceptibility and further application as a spatial decision support system.
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Optimal Hedging Rule for Reservoir Refill Operation
NASA Astrophysics Data System (ADS)
Wan, W.; Zhao, J.; Lund, J. R.; Zhao, T.; Lei, X.; Wang, H.
2015-12-01
This paper develops an optimal reservoir Refill Hedging Rule (RHR) for combined water supply and flood operation using mathematical analysis. A two-stage model is developed to formulate the trade-off between operations for conservation benefit and flood damage in the reservoir refill season. Based on the probability distribution of the maximum refill water availability at the end of the second stage, three zones are characterized according to the relationship among storage capacity, expected storage buffer (ESB), and maximum safety excess discharge (MSED). The Karush-Kuhn-Tucker conditions of the model show that the optimality of the refill operation involves making the expected marginal loss of conservation benefit from unfilling (i.e., ending storage of refill period less than storage capacity) as nearly equal to the expected marginal flood damage from levee overtopping downstream as possible while maintaining all constraints. This principle follows and combines the hedging rules for water supply and flood management. A RHR curve is drawn analogously to water supply hedging and flood hedging rules, showing the trade-off between the two objectives. The release decision result has a linear relationship with the current water availability, implying the linearity of RHR for a wide range of water conservation functions (linear, concave, or convex). A demonstration case shows the impacts of factors. Larger downstream flood conveyance capacity and empty reservoir capacity allow a smaller current release and more water can be conserved. Economic indicators of conservation benefit and flood damage compete with each other on release, the greater economic importance of flood damage is, the more water should be released in the current stage, and vice versa. Below a critical value, improving forecasts yields less water release, but an opposing effect occurs beyond this critical value. Finally, the Danjiangkou Reservoir case study shows that the RHR together with a rolling horizon decision approach can lead to a gradual dynamic refilling, indicating its potential for practical use.
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A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker.
Leibfried, Felix; Braun, Daniel A
2015-08-01
Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.
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A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.
Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele
2017-01-01
Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.
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A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making
Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele
2017-01-01
Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks. PMID:28824512
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A cognitive prosthesis for complex decision-making.
Tremblay, Sébastien; Gagnon, Jean-François; Lafond, Daniel; Hodgetts, Helen M; Doiron, Maxime; Jeuniaux, Patrick P J M H
2017-01-01
While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies - using simplified as opposed to analytic decision rules - have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called "IMAGE" to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Evaluation of a rule base for decision making in general practice.
Essex, B; Healy, M
1994-01-01
BACKGROUND. Decision making in general practice relies heavily on judgmental expertise. It should be possible to codify this expertise into rules and principles. AIM. A study was undertaken to evaluate the effectiveness, of rules from a rule base designed to improve students' and trainees' management decisions relating to patients seen in general practice. METHOD. The rule base was developed after studying decisions about and management of thousands of patients seen in one general practice over an eight year period. Vignettes were presented to 93 fourth year medical students and 179 general practitioner trainees. They recorded their perception and management of each case before and after being presented with a selection of relevant rules. Participants also commented on their level of agreement with each of the rules provided with the vignettes. A panel of five independent assessors then rated as good, acceptable or poor, the participants' perception and management of each case before and after seeing the rules. RESULTS. Exposure to a few selected rules of thumb improved the problem perception and management decisions of both undergraduates and trainees. The degree of improvement was not related to previous experience or to the stated level of agreement with the proposed rules. The assessors identified difficulties students and trainees experienced in changing their perceptions and management decisions when the rules suggested options they had not considered. CONCLUSION. The rules developed to improve decision making skills in general practice are effective when used with vignettes. The next phase is to transform the rule base into an expert system to train students and doctors to acquire decision making skills. It could also be used to provide decision support when confronted with difficult management decisions in general practice. PMID:8204334
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Fuzzy multiobjective models for optimal operation of a hydropower system
NASA Astrophysics Data System (ADS)
Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.
2013-06-01
Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.
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Computer-aided diagnostic strategy selection.
Greenes, R A
1986-03-01
Determination of the optimal diagnostic work-up strategy for the patient is becoming a major concern for the practicing physician. Overlap of the indications for various diagnostic procedures, differences in their invasiveness or risk, and high costs have made physicians aware of the need to consider the choice of procedure carefully, as well as its relation to management actions available. In this article, the author discusses research approaches that aim toward development of formal decision analytic methods to allow the physician to determine optimal strategy; clinical algorithms or rules as guides to physician decisions; improved measures for characterizing the performance of diagnostic tests; educational tools for increasing the familiarity of physicians with the concepts underlying these measures and analytic procedures; and computer-based aids for facilitating the employment of these resources in actual clinical practice.
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Control Coordination of Multiple Agents Through Decision Theoretic and Economic Methods
2003-02-01
instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information...investigated the design of test data for benchmarking such optimization algorithms. Our other research on combinatorial auctions included I...average combination rule. We exemplified these theoretical results with experiments on stock market data , demonstrating how ensembles of classifiers can
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Liu, Yaolin; Peng, Jinjin; Jiao, Limin; Liu, Yanfang
2016-01-01
Optimizing land-use allocation is important to regional sustainable development, as it promotes the social equality of public services, increases the economic benefits of land-use activities, and reduces the ecological risk of land-use planning. Most land-use optimization models allocate land-use using cell-level operations that fragment land-use patches. These models do not cooperate well with land-use planning knowledge, leading to irrational land-use patterns. This study focuses on building a heuristic land-use allocation model (PSOLA) using particle swarm optimization. The model allocates land-use with patch-level operations to avoid fragmentation. The patch-level operations include a patch-edge operator, a patch-size operator, and a patch-compactness operator that constrain the size and shape of land-use patches. The model is also integrated with knowledge-informed rules to provide auxiliary knowledge of land-use planning during optimization. The knowledge-informed rules consist of suitability, accessibility, land use policy, and stakeholders' preference. To validate the PSOLA model, a case study was performed in Gaoqiao Town in Zhejiang Province, China. The results demonstrate that the PSOLA model outperforms a basic PSO (Particle Swarm Optimization) in the terms of the social, economic, ecological, and overall benefits by 3.60%, 7.10%, 1.53% and 4.06%, respectively, which confirms the effectiveness of our improvements. Furthermore, the model has an open architecture, enabling its extension as a generic tool to support decision making in land-use planning.
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Liu, Yaolin; Peng, Jinjin; Jiao, Limin; Liu, Yanfang
2016-01-01
Optimizing land-use allocation is important to regional sustainable development, as it promotes the social equality of public services, increases the economic benefits of land-use activities, and reduces the ecological risk of land-use planning. Most land-use optimization models allocate land-use using cell-level operations that fragment land-use patches. These models do not cooperate well with land-use planning knowledge, leading to irrational land-use patterns. This study focuses on building a heuristic land-use allocation model (PSOLA) using particle swarm optimization. The model allocates land-use with patch-level operations to avoid fragmentation. The patch-level operations include a patch-edge operator, a patch-size operator, and a patch-compactness operator that constrain the size and shape of land-use patches. The model is also integrated with knowledge-informed rules to provide auxiliary knowledge of land-use planning during optimization. The knowledge-informed rules consist of suitability, accessibility, land use policy, and stakeholders’ preference. To validate the PSOLA model, a case study was performed in Gaoqiao Town in Zhejiang Province, China. The results demonstrate that the PSOLA model outperforms a basic PSO (Particle Swarm Optimization) in the terms of the social, economic, ecological, and overall benefits by 3.60%, 7.10%, 1.53% and 4.06%, respectively, which confirms the effectiveness of our improvements. Furthermore, the model has an open architecture, enabling its extension as a generic tool to support decision making in land-use planning. PMID:27322619
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Data mining for multiagent rules, strategies, and fuzzy decision tree structure
NASA Astrophysics Data System (ADS)
Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin
2002-03-01
A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.
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NASA Astrophysics Data System (ADS)
Xu, Yan; Dong, Zhao Yang; Zhang, Rui; Wong, Kit Po
2014-02-01
Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system.
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Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.
Jiménez, Fernando; Sánchez, Gracia; Juárez, José M
2014-03-01
This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.
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2012-01-01
Clinical decision rules are an increasingly common presence in the biomedical literature and represent one strategy of enhancing clinical-decision making with the goal of improving the efficiency and effectiveness of healthcare delivery. In the context of rehabilitation research, clinical decision rules have been predominantly aimed at classifying patients by predicting their treatment response to specific therapies. Traditionally, recommendations for developing clinical decision rules propose a multistep process (derivation, validation, impact analysis) using defined methodology. Research efforts aimed at developing a “diagnosis-based clinical decision rule” have departed from this convention. Recent publications in this line of research have used the modified terminology “diagnosis-based clinical decision guide.” Modifications to terminology and methodology surrounding clinical decision rules can make it more difficult for clinicians to recognize the level of evidence associated with a decision rule and understand how this evidence should be implemented to inform patient care. We provide a brief overview of clinical decision rule development in the context of the rehabilitation literature and two specific papers recently published in Chiropractic and Manual Therapies. PMID:22726639
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AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT
NASA Astrophysics Data System (ADS)
Shimomura, Taizo; Fujimori, Yuji; Kaito, Kiyoyuki; Obama, Kengo; Kobayashi, Kiyoshi
In this paper, an optimal management model is formulated for the performance-based rehabilitation/maintenance contract for airport concrete pavement, whereby two types of life cycle cost risks, i.e., ground consolidation risk and concrete depreciation risk, are explicitly considered. The non-homogenous Markov chain model is formulated to represent the deterioration processes of concrete pavement which are conditional upon the ground consolidation processes. The optimal non-homogenous Markov decision model with multiple types of risk is presented to design the optimal rehabilitation/maintenance plans. And the methodology to revise the optimal rehabilitation/maintenance plans based upon the monitoring data by the Bayesian up-to-dating rules. The validity of the methodology presented in this paper is examined based upon the case studies carried out for the H airport.
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Pedersen, Kine; Sørbye, Sveinung Wergeland; Burger, Emily Annika; Lönnberg, Stefan; Kristiansen, Ivar Sønbø
2015-12-01
Decision makers often need to simultaneously consider multiple criteria or outcomes when deciding whether to adopt new health interventions. Using decision analysis within the context of cervical cancer screening in Norway, we aimed to aid decision makers in identifying a subset of relevant strategies that are simultaneously efficient, feasible, and optimal. We developed an age-stratified probabilistic decision tree model following a cohort of women attending primary screening through one screening round. We enumerated detected precancers (i.e., cervical intraepithelial neoplasia of grade 2 or more severe (CIN2+)), colposcopies performed, and monetary costs associated with 10 alternative triage algorithms for women with abnormal cytology results. As efficiency metrics, we calculated incremental cost-effectiveness, and harm-benefit, ratios, defined as the additional costs, or the additional number of colposcopies, per additional CIN2+ detected. We estimated capacity requirements and uncertainty surrounding which strategy is optimal according to the decision rule, involving willingness to pay (monetary or resources consumed per added benefit). For ages 25 to 33 years, we eliminated four strategies that did not fall on either efficiency frontier, while one strategy was efficient with respect to both efficiency metrics. Compared with current practice in Norway, two strategies detected more precancers at lower monetary costs, but some required more colposcopies. Similar results were found for women aged 34 to 69 years. Improving the effectiveness and efficiency of cervical cancer screening may necessitate additional resources. Although efficient and feasible, both society and individuals must specify their willingness to accept the additional resources and perceived harms required to increase effectiveness before a strategy can be considered optimal. Copyright © 2015. Published by Elsevier Inc.
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A universal hybrid decision tree classifier design for human activity classification.
Chien, Chieh; Pottie, Gregory J
2012-01-01
A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. The system was tested using seven subjects each monitored by 14 triaxial accelerometers. Each subject performed fourteen different activities typical of daily life. Using leave-one-out cross validation, our decision tree produced average classification accuracies of 89.9%. In contrast, the MATLAB personalized tree classifiers using Gini's diversity index as the split criterion followed by optimally tuning the thresholds for each subject yielded 69.2%.
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Maximizing the Predictive Value of Production Rules
1988-08-31
Clancev, 1985] Clancey, W. "Heuristic Classification." Artifcial Intelligence . 27 (1985) 289-350. [Crawford, 19881 Crawford, S. "Extensions to the CART...Optimality 16 6.1.2. Comparative Analysis for Normally Distributed Data 17 6.2. Comparison with Alternative Machine Learning Methods 18 6.2.1. Alternative...are reported on data sets previously analyzed in the Al literature using alternative classification techniques. 1. Introduction MIanv decision-making
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Pipeline Optimization Program (PLOP)
2006-08-01
the framework of the Dredging Operations Decision Support System (DODSS, https://dodss.wes.army.mil/wiki/0). PLOP compiles industry standards and...efficiency point ( BEP ). In the interest of acceptable wear rate on the pump, industrial standards dictate that the flow Figure 2. Pump class as a function of...percentage of the flow rate corresponding to the BEP . Pump Acceptability Rules. The facts for pump performance, industrial standards and pipeline and
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On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems
NASA Technical Reports Server (NTRS)
Tunstel, Edward; Jamshidi, Mo
1997-01-01
Intelligent control of complex multi-variable systems can be a challenge for single fuzzy rule-based controllers. This class of problems cam often be managed with less difficulty by distributing intelligent decision-making amongst a collection of rule-bases. Such an approach requires that a mechanism be chosen to ensure goal-oriented interaction between the multiple rule-bases. In this paper, a hierarchical rule-based approach is described. Decision-making mechanisms based on generalized concepts from single-rule-based fuzzy control are described. Finally, the effects of different aggregation operators on multi-rule-base decision-making are examined in a navigation control problem for mobile robots.
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Scheduling Software for Complex Scenarios
NASA Technical Reports Server (NTRS)
2006-01-01
Preparing a vehicle and its payload for a single launch is a complex process that involves thousands of operations. Because the equipment and facilities required to carry out these operations are extremely expensive and limited in number, optimal assignment and efficient use are critically important. Overlapping missions that compete for the same resources, ground rules, safety requirements, and the unique needs of processing vehicles and payloads destined for space impose numerous constraints that, when combined, require advanced scheduling. Traditional scheduling systems use simple algorithms and criteria when selecting activities and assigning resources and times to each activity. Schedules generated by these simple decision rules are, however, frequently far from optimal. To resolve mission-critical scheduling issues and predict possible problem areas, NASA historically relied upon expert human schedulers who used their judgment and experience to determine where things should happen, whether they will happen on time, and whether the requested resources are truly necessary.
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Using fuzzy rule-based knowledge model for optimum plating conditions search
NASA Astrophysics Data System (ADS)
Solovjev, D. S.; Solovjeva, I. A.; Litovka, Yu V.; Arzamastsev, A. A.; Glazkov, V. P.; L’vov, A. A.
2018-03-01
The paper discusses existing approaches to plating process modeling in order to decrease the distribution thickness of plating surface cover. However, these approaches do not take into account the experience, knowledge, and intuition of the decision-makers when searching the optimal conditions of electroplating technological process. The original approach to optimal conditions search for applying the electroplating coatings, which uses the rule-based model of knowledge and allows one to reduce the uneven product thickness distribution, is proposed. The block diagrams of a conventional control system of a galvanic process as well as the system based on the production model of knowledge are considered. It is shown that the fuzzy production model of knowledge in the control system makes it possible to obtain galvanic coatings of a given thickness unevenness with a high degree of adequacy to the experimental data. The described experimental results confirm the theoretical conclusions.
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Liu, Ying; ZENG, Donglin; WANG, Yuanjia
2014-01-01
Summary Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient’s time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual’s response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data. PMID:25642116
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Decision Rules and Group Rationality: Cognitive Gain or Standstill?
Curşeu, Petru Lucian; Jansen, Rob J. G.; Chappin, Maryse M. H.
2013-01-01
Recent research in group cognition points towards the existence of collective cognitive competencies that transcend individual group members’ cognitive competencies. Since rationality is a key cognitive competence for group decision making, and group cognition emerges from the coordination of individual cognition during social interactions, this study tests the extent to which collaborative and consultative decision rules impact the emergence of group rationality. Using a set of decision tasks adapted from the heuristics and biases literature, we evaluate rationality as the extent to which individual choices are aligned with a normative ideal. We further operationalize group rationality as cognitive synergy (the extent to which collective rationality exceeds average or best individual rationality in the group), and we test the effect of collaborative and consultative decision rules in a sample of 176 groups. Our results show that the collaborative decision rule has superior synergic effects as compared to the consultative decision rule. The ninety one groups working in a collaborative fashion made more rational choices (above and beyond the average rationality of their members) than the eighty five groups working in a consultative fashion. Moreover, the groups using a collaborative decision rule were closer to the rationality of their best member than groups using consultative decision rules. Nevertheless, on average groups did not outperformed their best member. Therefore, our results reveal how decision rules prescribing interpersonal interactions impact on the emergence of collective cognitive competencies. They also open potential venues for further research on the emergence of collective rationality in human decision-making groups. PMID:23451050
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Decision rules and group rationality: cognitive gain or standstill?
Curşeu, Petru Lucian; Jansen, Rob J G; Chappin, Maryse M H
2013-01-01
Recent research in group cognition points towards the existence of collective cognitive competencies that transcend individual group members' cognitive competencies. Since rationality is a key cognitive competence for group decision making, and group cognition emerges from the coordination of individual cognition during social interactions, this study tests the extent to which collaborative and consultative decision rules impact the emergence of group rationality. Using a set of decision tasks adapted from the heuristics and biases literature, we evaluate rationality as the extent to which individual choices are aligned with a normative ideal. We further operationalize group rationality as cognitive synergy (the extent to which collective rationality exceeds average or best individual rationality in the group), and we test the effect of collaborative and consultative decision rules in a sample of 176 groups. Our results show that the collaborative decision rule has superior synergic effects as compared to the consultative decision rule. The ninety one groups working in a collaborative fashion made more rational choices (above and beyond the average rationality of their members) than the eighty five groups working in a consultative fashion. Moreover, the groups using a collaborative decision rule were closer to the rationality of their best member than groups using consultative decision rules. Nevertheless, on average groups did not outperformed their best member. Therefore, our results reveal how decision rules prescribing interpersonal interactions impact on the emergence of collective cognitive competencies. They also open potential venues for further research on the emergence of collective rationality in human decision-making groups.
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An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts.
Barghash, Mahmoud
2015-01-01
Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN's performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.
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Bressan, Silvia; Romanato, Sabrina; Mion, Teresa; Zanconato, Stefania; Da Dalt, Liviana
2012-07-01
Of the currently published clinical decision rules for the management of minor head injury (MHI) in children, the Pediatric Emergency Care Applied Research Network (PECARN) rule, derived and validated in a large multicenter prospective study cohort, with high methodologic standards, appears to be the best clinical decision rule to accurately identify children at very low risk of clinically important traumatic brain injuries (ciTBI) in the pediatric emergency department (PED). This study describes the implementation of an adapted version of the PECARN rule in a tertiary care academic PED in Italy and evaluates implementation success, in terms of medical staff adherence and satisfaction, as well as its effects on clinical practice. The adapted PECARN decision rule algorithms for children (one for those younger than 2 years and one for those older than 2 years) were actively implemented in the PED of Padova, Italy, for a 6-month testing period. Adherence and satisfaction of medical staff to the new rule were calculated. Data from 356 visits for MHI during PECARN rule implementation and those of 288 patients attending the PED for MHI in the previous 6 months were compared for changes in computed tomography (CT) scan rate, ciTBI rate (defined as death, neurosurgery, intubation for longer than 24 hours, or hospital admission at least for two nights associated with TBI) and return visits for symptoms or signs potentially related to MHI. The safety and efficacy of the adapted PECARN rule in clinical practice were also calculated. Adherence to the adapted PECARN rule was 93.5%. The percentage of medical staff satisfied with the new rule, in terms of usefulness and ease of use for rapid decision-making, was significantly higher (96% vs. 51%, p<0.0001) compared to the previous, more complex, internal guideline. CT scan was performed in 30 patients (8.4%, 95% confidence interval [CI]=6% to 11.8%) in the implementation period versus 21 patients (7.3%, 95% CI=4.8% to 10.9%) before implementation. A ciTBI occurred in three children (0.8%, 95% CI=0.3 to 2.5) during the implementation period and in two children (0.7%, 95% CI=0.2 to 2.5) in the prior 6 months. There were five return visits (1.4%) postimplementation and seven (2.4%) before implementation (p=0.506). The safety of use of the adapted PECARN rule in clinical practice was 100% (95% CI=36.8 to 100; three of three patients with ciTBI who received CT scan at first evaluation), while efficacy was 92.3% (95% CI=89 to 95; 326 of 353 patients without ciTBI who did not receive a CT scan). The adapted PECARN rule was successfully implemented in an Italian tertiary care academic PED, achieving high adherence and satisfaction of medical staff. Its use determined a low CT scan rate that was unchanged compared to previous clinical practice and showed an optimal safety and high efficacy profile. Strict monitoring is mandatory to evaluate the long-lasting benefit in patient care and/or resource utilization. © 2012 by the Society for Academic Emergency Medicine.
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46 CFR 201.3 - Authentication of rules, orders, determinations and decisions of the Administration.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 46 Shipping 8 2010-10-01 2010-10-01 false Authentication of rules, orders, determinations and decisions of the Administration. 201.3 Section 201.3 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF....3 Authentication of rules, orders, determinations and decisions of the Administration. All rules...
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Affective and cognitive decision-making in adolescents.
van Duijvenvoorde, Anna C K; Jansen, Brenda R J; Visser, Ingmar; Huizenga, Hilde M
2010-01-01
Adolescents demonstrate impaired decision-making in emotionally arousing situations, yet they appear to exhibit relatively mature decision-making skills in predominantly cognitive, low-arousal situations. In this study we compared adolescents' (13-15 years) performance on matched affective and cognitive decision-making tasks, in order to determine (1) their performance level on each task and (2) whether performance on the cognitive task was associated with performance on the affective task. Both tasks required a comparison of choice dimensions characterized by frequency of loss, amount of loss, and constant gain. Results indicated that in the affective task, adolescents performed sub-optimally by considering only the frequency of loss, whereas in the cognitive task adolescents used relatively mature decision rules by considering two or all three choice dimensions. Performance on the affective task was not related to performance on the cognitive task. These results are discussed in light of neural developmental trajectories observed in adolescence.
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48 CFR 6103.306 - Decisions [Rule 306].
Code of Federal Regulations, 2010 CFR
2010-10-01
... 48 Federal Acquisition Regulations System 7 2010-10-01 2010-10-01 false Decisions [Rule 306]. 6103.306 Section 6103.306 Federal Acquisition Regulations System CIVILIAN BOARD OF CONTRACT APPEALS, GENERAL SERVICES ADMINISTRATION TRANSPORTATION RATE CASES 6103.306 Decisions [Rule 306]. The judge will issue a written decision based upon the record,...
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When to stop managing or surveying cryptic threatened species
Chadès, Iadine; McDonald-Madden, Eve; McCarthy, Michael A.; Wintle, Brendan; Linkie, Matthew; Possingham, Hugh P.
2008-01-01
Threatened species become increasingly difficult to detect as their populations decline. Managers of such cryptic threatened species face several dilemmas: if they are not sure the species is present, should they continue to manage for that species or invest the limited resources in surveying? We find optimal solutions to this problem using a Partially Observable Markov Decision Process and rules of thumb derived from an analytical approximation. We discover that managing a protected area for a cryptic threatened species can be optimal even if we are not sure the species is present. The more threatened and valuable the species is, relative to the costs of management, the more likely we are to manage this species without determining its continued persistence by using surveys. If a species remains unseen, our belief in the persistence of the species declines to a point where the optimal strategy is to shift resources from saving the species to surveying for it. Finally, when surveys lead to a sufficiently low belief that the species is extant, we surrender resources to other conservation actions. We illustrate our findings with a case study using parameters based on the critically endangered Sumatran tiger (Panthera tigris sumatrae), and we generate rules of thumb on how to allocate conservation effort for any cryptic species. Using Partially Observable Markov Decision Processes in conservation science, we determine the conditions under which it is better to abandon management for that species because our belief that it continues to exist is too low. PMID:18779594
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Environmental statistics and optimal regulation.
Sivak, David A; Thomson, Matt
2014-09-01
Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies--such as constitutive expression or graded response--for regulating protein levels in response to environmental inputs. We propose a general framework-here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient-to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.
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When to stop managing or surveying cryptic threatened species.
Chadès, Iadine; McDonald-Madden, Eve; McCarthy, Michael A; Wintle, Brendan; Linkie, Matthew; Possingham, Hugh P
2008-09-16
Threatened species become increasingly difficult to detect as their populations decline. Managers of such cryptic threatened species face several dilemmas: if they are not sure the species is present, should they continue to manage for that species or invest the limited resources in surveying? We find optimal solutions to this problem using a Partially Observable Markov Decision Process and rules of thumb derived from an analytical approximation. We discover that managing a protected area for a cryptic threatened species can be optimal even if we are not sure the species is present. The more threatened and valuable the species is, relative to the costs of management, the more likely we are to manage this species without determining its continued persistence by using surveys. If a species remains unseen, our belief in the persistence of the species declines to a point where the optimal strategy is to shift resources from saving the species to surveying for it. Finally, when surveys lead to a sufficiently low belief that the species is extant, we surrender resources to other conservation actions. We illustrate our findings with a case study using parameters based on the critically endangered Sumatran tiger (Panthera tigris sumatrae), and we generate rules of thumb on how to allocate conservation effort for any cryptic species. Using Partially Observable Markov Decision Processes in conservation science, we determine the conditions under which it is better to abandon management for that species because our belief that it continues to exist is too low.
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NASA Astrophysics Data System (ADS)
Hurford, A. P.; Harou, J. J.
2014-08-01
Competition for water between key economic sectors and the environment means agreeing allocations is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks firstly to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly, it seeks to show how trade-offs between achievable benefits shift with the implementation of proposed new rice, cotton and biofuel irrigation projects. To approximate the Pareto-optimal trade-offs we link a water resources management simulation model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume-dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for eight objectives covering the provision of water supply and irrigation, energy generation and maintenance of ecosystem services. Trade-off plots allow decision-makers to assess multi-reservoir rule-sets and irrigation investment options by visualising their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against the disturbance of ecosystems and local livelihoods that depend on them. Full implementation of the proposed schemes is shown to come at a high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of "water-energy-food nexus" resource security issues.
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Ni, Kuei-Jung
2013-01-01
Most international health-related standards are voluntary per se. However, the incorporation of international standard-making into WTO agreements like the SPS Agreement has drastically changed the status and effectiveness of the standards. WTO members are urged to follow international standards, even when not required to comply fully with them. Indeed, such standards have attained great influence in the trade system. Yet evidence shows that the credibility of the allegedly scientific approach of these international standard-setting institutions, especially the Codex Alimentarius Commission (Codex) governing food safety standards, has been eroded and diluted by industrial and political influences. Its decision-making is no longer based on consensus, but voting. The adoption of new safety limits for the veterinary drug ractopamine in 2012, by a very close vote, is simply another instance of the problematic operations of the Codex. These dynamics have led skeptics to question the legitimacy of the standard setting body and to propose solutions to rectify the situation. Prior WTO rulings have yet to pay attention to the defect in the decision-making processes of the Codex. Nevertheless, the recent Appellate Body decision on Hormones II is indicative of a deferential approach to national measures that are distinct from Codex formulas. The ruling also rejects the reliance on those experts who authored the Codex standards to assess new measures of the European Community. This approach provides an opportunity to contemplate what the proper relationship between the WTO and Codex ought to be. Through a critical review of WTO rulings and academic proposals, this article aims to analyze how the WTO ought to define such interactions and respond to the politicized standard-making process in an optimal manner. This article argues that building a more systematic approach and normative basis for WTO judicial review of standard-setting decisions and the selection of technical experts would be instrumental to strengthening the mutual supports between the WTO and international standard-setting organizations, and may help avoid the introduction of a prejudice toward a justified science finding.
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Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L
2013-12-01
Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Improved multi-objective ant colony optimization algorithm and its application in complex reasoning
NASA Astrophysics Data System (ADS)
Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing
2013-09-01
The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.
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Gao, Dashan; Vasconcelos, Nuno
2009-01-01
A decision-theoretic formulation of visual saliency, first proposed for top-down processing (object recognition) (Gao & Vasconcelos, 2005a), is extended to the problem of bottom-up saliency. Under this formulation, optimality is defined in the minimum probability of error sense, under a constraint of computational parsimony. The saliency of the visual features at a given location of the visual field is defined as the power of those features to discriminate between the stimulus at the location and a null hypothesis. For bottom-up saliency, this is the set of visual features that surround the location under consideration. Discrimination is defined in an information-theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images. It is shown that under the assumption that saliency is driven by linear filtering, the optimal detector consists of what is usually referred to as the standard architecture of V1: a cascade of linear filtering, divisive normalization, rectification, and spatial pooling. The optimal detector is also shown to replicate the fundamental properties of the psychophysics of saliency: stimulus pop-out, saliency asymmetries for stimulus presence versus absence, disregard of feature conjunctions, and Weber's law. Finally, it is shown that the optimal saliency architecture can be applied to the solution of generic inference problems. In particular, for the class of stimuli studied, it performs the three fundamental operations of statistical inference: assessment of probabilities, implementation of Bayes decision rule, and feature selection.
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Lynn, Spencer K.; Ibagon, Camila; Bui, Eric; Palitz, Sophie A.; Simon, Naomi M.; Barrett, Lisa Feldman
2017-01-01
Emotion perception, inferring the emotional state of another person, is a frequent judgment made under perceptual uncertainty (e.g., a scowling facial expression can indicate anger or concentration) and behavioral risk (e.g., incorrect judgment can be costly to the perceiver). Working memory capacity (WMC), the ability to maintain controlled processing in the face of competing demands, is an important component of many decisions. We investigated the association of WMC and anger perception in a task in which “angry” and “not angry” categories comprised overlapping ranges of scowl intensity, and correct and incorrect responses earned and lost points, respectively. Participants attempted to earn as many points as they could; adopting an optimal response bias would maximize decision utility. Participants with higher WMC more optimally tuned their anger perception response bias to accommodate their perceptual sensitivity (their ability to discriminate the categories) than did participants with lower WMC. Other factors that influence response bias (i.e., the relative base rate of angry vs. not angry faces and the decision costs & benefits) were ruled out as contributors to the WMC-bias relationship. Our results suggest that WMC optimizes emotion perception by contributing to perceivers’ ability to adjust their response bias to account for their level of perceptual sensitivity, likely an important component of adapting emotion perception to dynamic social interactions and changing circumstances. PMID:26461251
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NASA Astrophysics Data System (ADS)
Huang, Yin; Chen, Jianhua; Xiong, Shaojun
2009-07-01
Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.
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Regan, Tracey J; Taylor, Barbara L; Thompson, Grant G; Cochrane, Jean Fitts; Ralls, Katherine; Runge, Michael C; Merrick, Richard
2013-08-01
Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by increasing transparency and consistency. Conservation Biology © 2013 Society for Conservation Biology No claim to original US government works.
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Future Costs, Fixed Healthcare Budgets, and the Decision Rules of Cost-Effectiveness Analysis.
van Baal, Pieter; Meltzer, David; Brouwer, Werner
2016-02-01
Life-saving medical technologies result in additional demand for health care due to increased life expectancy. However, most economic evaluations do not include all medical costs that may result from this additional demand in health care and include only future costs of related illnesses. Although there has been much debate regarding the question to which extent future costs should be included from a societal perspective, the appropriate role of future medical costs in the widely adopted but more narrow healthcare perspective has been neglected. Using a theoretical model, we demonstrate that optimal decision rules for cost-effectiveness analyses assuming fixed healthcare budgets dictate that future costs of both related and unrelated medical care should be included. Practical relevance of including the costs of future unrelated medical care is illustrated using the example of transcatheter aortic valve implantation. Our findings suggest that guidelines should prescribe inclusion of these costs. Copyright © 2014 John Wiley & Sons, Ltd.
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The optimum decision rules for the oddity task.
Versfeld, N J; Dai, H; Green, D M
1996-01-01
This paper presents the optimum decision rule for an m-interval oddity task in which m-1 intervals contain the same signal and one is different or odd. The optimum decision rule depends on the degree of correlation among observations. The present approach unifies the different strategies that occur with "roved" or "fixed" experiments (Macmillan & Creelman, 1991, p. 147). It is shown that the commonly used decision rule for an m-interval oddity task corresponds to the special case of highly correlated observations. However, as is also true for the same-different paradigm, there exists a different optimum decision rule when the observations are independent. The relation between the probability of a correct response and d' is derived for the three-interval oddity task. Tables are presented of this relation for the three-, four-, and five-interval oddity task. Finally, an experimental method is proposed that allows one to determine the decision rule used by the observer in an oddity experiment.
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Rosi, Alessia; Bruine de Bruin, Wändi; Del Missier, Fabio; Cavallini, Elena; Russo, Riccardo
2017-12-28
Older adults perform worse than younger adults when applying decision rules to choose between options that vary along multiple attributes. Although previous studies have shown that general fluid cognitive abilities contribute to the accurate application of decision rules, relatively little is known about which specific cognitive abilities play the most important role. We examined the independent roles of working memory, verbal fluency, semantic knowledge, and components of executive functioning. We found that age-related decline in applying decision rules was statistically mediated by age-related decline in working memory and verbal fluency. Our results have implications for theories of aging and decision-making.
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1980-12-01
Inventories and Work Force, Prentice-Hall, Englewood Cliffs, New Jersey (1960). Horngren , C. T. Cost Accounting : A Managerial Emphasis, Fourth Edition...achieved/received by virtue of AFIT performing the research. Can you estimate what this research would have cost if it had been accomplished under...future potential, or other value of this research. Please use the bottom part of this questionnaire for your statement( s ). NAME GRADE POSITION
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A Predictive Analysis of the Department of Defense Distribution System Utilizing Random Forests
2016-06-01
resources capable of meeting both customer and individual resource constraints and goals while also maximizing the global benefit to the supply...and probability rules to determine the optimal red wine distribution network for an Italian-based wine producer. The decision support model for...combinations of factors that will result in delivery of the highest quality wines . The model’s first stage inputs basic logistics information to look
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Zhang, Wei; Swinton, Scott M
2012-04-15
By suppressing pest populations, natural enemies provide an important ecosystem service that maintains the stability of agricultural ecosystems systems and potentially mitigates producers' pest control costs. Integrating natural control services into decisions about pesticide-based control has the potential to significantly improve the economic efficiency of pesticide use, with socially desirable outcomes. Two gaps have hindered the incorporation of natural enemies into pest management decision rules: (1) insufficient knowledge of pest and predator population dynamics and (2) lack of a decision framework for the economic tradeoffs among pest control options. Using a new intra-seasonal, dynamic bioeconomic optimization model, this study assesses how predation by natural enemies contributes to profit-maximizing pest management strategies. The model is applied to the management of the invasive soybean aphid, the most significant serious insect threat to soybean production in North America. The resulting lower bound estimate of the value of natural pest control ecosystem services was estimated at $84 million for the states of Illinois, Indiana, Iowa, Michigan and Minnesota in 2005. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse
2014-01-01
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.
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A Decision Making Methodology in Support of the Business Rules Lifecycle
NASA Technical Reports Server (NTRS)
Wild, Christopher; Rosca, Daniela
1998-01-01
The business rules that underlie an enterprise emerge as a new category of system requirements that represent decisions about how to run the business, and which are characterized by their business-orientation and their propensity for change. In this report, we introduce a decision making methodology which addresses several aspects of the business rules lifecycle: acquisition, deployment and evolution. We describe a meta-model for representing business rules in terms of an enterprise model, and also a decision support submodel for reasoning about and deriving the rules. The possibility for lifecycle automated assistance is demonstrated in terms of the automatic extraction of business rules from the decision structure. A system based on the metamodel has been implemented, including the extraction algorithm. This is the final report for Daniela Rosca's PhD fellowship. It describes the work we have done over the past year, current research and the list of publications associated with her thesis topic.
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Water-resources optimization model for Santa Barbara, California
Nishikawa, Tracy
1998-01-01
A simulation-optimization model has been developed for the optimal management of the city of Santa Barbara's water resources during a drought. The model, which links groundwater simulation with linear programming, has a planning horizon of 5 years. The objective is to minimize the cost of water supply subject to: water demand constraints, hydraulic head constraints to control seawater intrusion, and water capacity constraints. The decision variables are montly water deliveries from surface water and groundwater. The state variables are hydraulic heads. The drought of 1947-51 is the city's worst drought on record, and simulated surface-water supplies for this period were used as a basis for testing optimal management of current water resources under drought conditions. The simulation-optimization model was applied using three reservoir operation rules. In addition, the model's sensitivity to demand, carry over [the storage of water in one year for use in the later year(s)], head constraints, and capacity constraints was tested.
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Neural decoding of collective wisdom with multi-brain computing.
Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry
2012-01-02
Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally, our methods highlight the potential of multi-brain computing as a technique to rapidly and in parallel gather increased information about the environment as well as to access collective perceptual/cognitive choices and mental states. Copyright © 2011 Elsevier Inc. All rights reserved.
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38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.
Code of Federal Regulations, 2010 CFR
2010-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...
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38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.
Code of Federal Regulations, 2011 CFR
2011-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2011-07-01 2011-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...
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38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.
Code of Federal Regulations, 2013 CFR
2013-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2013-07-01 2013-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...
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38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.
Code of Federal Regulations, 2014 CFR
2014-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2014-07-01 2014-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...
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38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.
Code of Federal Regulations, 2012 CFR
2012-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2012-07-01 2012-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...
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19 CFR 177.10 - Publication of decisions.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 19 Customs Duties 2 2014-04-01 2014-04-01 false Publication of decisions. 177.10 Section 177.10... TREASURY (CONTINUED) ADMINISTRATIVE RULINGS General Ruling Procedure § 177.10 Publication of decisions. (a....8(a)(3). (b) [Reserved] (c) Changes of practice. Before the publication of a ruling which has the...
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Model-Based Design of Tree WSNs for Decentralized Detection.
Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam
2015-08-20
The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.
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Environmental Statistics and Optimal Regulation
2014-01-01
Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies–such as constitutive expression or graded response–for regulating protein levels in response to environmental inputs. We propose a general framework–here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient–to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones. PMID:25254493
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Conformance Testing: Measurement Decision Rules
NASA Technical Reports Server (NTRS)
Mimbs, Scott M.
2010-01-01
The goal of a Quality Management System (QMS) as specified in ISO 9001 and AS9100 is to provide assurance to the customer that end products meet specifications. Measuring devices, often called measuring and test equipment (MTE), are used to provide the evidence of product conformity to specified requirements. Unfortunately, processes that employ MTE can become a weak link to the overall QMS if proper attention is not given to the measurement process design, capability, and implementation. Documented "decision rules" establish the requirements to ensure measurement processes provide the measurement data that supports the needs of the QMS. Measurement data are used to make the decisions that impact all areas of technology. Whether measurements support research, design, production, or maintenance, ensuring the data supports the decision is crucial. Measurement data quality can be critical to the resulting consequences of measurement-based decisions. Historically, most industries required simplistic, one-size-fits-all decision rules for measurements. One-size-fits-all rules in some cases are not rigorous enough to provide adequate measurement results, while in other cases are overly conservative and too costly to implement. Ideally, decision rules should be rigorous enough to match the criticality of the parameter being measured, while being flexible enough to be cost effective. The goal of a decision rule is to ensure that measurement processes provide data with a sufficient level of quality to support the decisions being made - no more, no less. This paper discusses the basic concepts of providing measurement-based evidence that end products meet specifications. Although relevant to all measurement-based conformance tests, the target audience is the MTE end-user, which is anyone using MTE other than calibration service providers. Topics include measurement fundamentals, the associated decision risks, verifying conformance to specifications, and basic measurement decisions rules.
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Automated rule-base creation via CLIPS-Induce
NASA Technical Reports Server (NTRS)
Murphy, Patrick M.
1994-01-01
Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.
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A mobile asset sharing policy for hospitals with real time locating systems.
Demircan-Yıldız, Ece Arzu; Fescioglu-Unver, Nilgun
2016-01-01
Each year, hospitals lose a considerable amount of time and money due to misplaced mobile assets. In addition the assets which remain in departments that frequently use them depreciate early, while other assets of the same type in different departments are rarely used. A real time locating system can prevent these losses when used with appropriate asset sharing policies. This research quantifies the amount of time a medium size hospital saves by using real time locating system and proposes an asset selection rule to eliminate the asset usage imbalance problem. The asset selection rule proposed is based on multi objective optimization techniques. The effectiveness of this rule on asset to patient time and asset utilization rate variance performance measures were tested using discrete event simulation method. Results show that the proposed asset selection rule improved the usage balance significantly. Sensitivity analysis showed that the proposed rule is robust to changes in demand rates and user preferences. Real time locating systems enable saving considerable amount of time in hospitals, and they can still be improved by integrating decision support mechanisms. Combining tracking technology and asset selection rules helps improve healthcare services.
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Perry, Jeffrey J; Stiell, Ian G
2006-12-01
Traumatic injuries to the ankle/foot, knee, cervical spine, and head are very commonly seen in emergency and accident departments around the world. There has been much interest in the development of clinical decision rules to help guide the investigations of these patients in a standardised and cost-effective manner. In this article we reviewed the impact of the Ottawa ankle rules, Ottawa knee rules, Canadian C-spine rule and the Canadian CT head rule. The studies conducted have confirmed that the use of well developed clinical decision rules results in less radiography, less time spent in the emergency department and does not decrease patient satisfaction or result in misdiagnosis. Emergency physicians around the world should adopt the use of clinical decision rules for ankle/foot, knee, cervical spine and minor head injuries. With relatively simple implementation strategies, care can be standardized and costs reduced while providing excellent clinical care.
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2014-07-16
been properly screened and evaluated, and certified as possessing exemplary moral character. Any decision-making construct that knowingly and...to generate optimal outcomes in the grip of chaos.34 In his book Corps Business (2000), David Freeman isolates this idea even further: What sets...and Robb Webb, Battle Ready, Pg. 142. 35 Freedman, David H., Corps Business : The 30 Management Principles of the U.S. Marines, Pg. 68. 36 Interview
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A decision-theoretic approach to identifying future high-cost patients.
Pietz, Kenneth; Byrne, Margaret M; Petersen, Laura A
2006-09-01
The objective of this study was to develop and evaluate a method of allocating funding for very-high-cost (VHC) patients among hospitals. Diagnostic cost groups (DCGs) were used for risk adjustment. The patient population consisted of 253,013 veterans who used Department of Veterans Affairs (VA) medical care services in fiscal year (FY) 2003 (October 1, 2002-September 30, 2003) in a network of 8 VA hospitals. We defined VHC as greater than 75,000 dollars (0.81%). The upper fifth percentile was also used for comparison. A Bayesian decision rule for classifying patients as VHC/not VHC using DCGs was developed and evaluated. The method uses FY 2003 DCGs to allocate VHC funds for FY 2004. We also used FY 2002 DCGs to allocate VHC funds for FY 2003 for comparison. The resulting allocation was compared with using the allocation of VHC patients among the hospitals in the previous year. The decision rule identified DCG 17 as the optimal cutoff for identifying VHC patients for the next year. The previous year's allocation came closest to the actual distribution of VHC patients. The decision-theoretic approach may provide insight into the economic consequences of classifying a patient as VHC or not VHC. More research is needed into methods of identifying future VHC patients so that capitation plans can fairly reimburse healthcare systems for appropriately treating these patients.
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Expected p-values in light of an ROC curve analysis applied to optimal multiple testing procedures.
Vexler, Albert; Yu, Jihnhee; Zhao, Yang; Hutson, Alan D; Gurevich, Gregory
2017-01-01
Many statistical studies report p-values for inferential purposes. In several scenarios, the stochastic aspect of p-values is neglected, which may contribute to drawing wrong conclusions in real data experiments. The stochastic nature of p-values makes their use to examine the performance of given testing procedures or associations between investigated factors to be difficult. We turn our focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we prove that the EPV can be considered in the context of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. The ROC-based framework provides a new and efficient methodology for investigating and constructing statistical decision-making procedures, including: (1) evaluation and visualization of properties of the testing mechanisms, considering, e.g. partial EPVs; (2) developing optimal tests via the minimization of EPVs; (3) creation of novel methods for optimally combining multiple test statistics. We demonstrate that the proposed EPV-based approach allows us to maximize the integrated power of testing algorithms with respect to various significance levels. In an application, we use the proposed method to construct the optimal test and analyze a myocardial infarction disease dataset. We outline the usefulness of the "EPV/ROC" technique for evaluating different decision-making procedures, their constructions and properties with an eye towards practical applications.
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Parental investment: how an equity motive can produce inequality.
Hertwig, Ralph; Davis, Jennifer Nerissa; Sulloway, Frank J
2002-09-01
The equity heuristic is a decision rule specifying that parents should attempt to subdivide resources more or less equally among their children. This investment rule coincides with the prescription from optimality models in economics and biology in cases in which expected future return for each offspring is equal. In this article, the authors present a counterintuitive implication of the equity heuristic: Whereas an equity motive produces a fair distribution at any given point in time, it yields a cumulative distribution of investments that is unequal. The authors test this analytical observation against evidence reported in studies exploring parental investment and show how the equity heuristic can provide an explanation of why the literature reports a diversity of birth order effects with respect to parental resource allocation.
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Decision Rules Used in Academic Program Closure: Where the Rubber Meets the Road.
ERIC Educational Resources Information Center
Eckel, Peter D.
This study examines, from an organizational perspective, decision rules guiding program discontinuance, testing the framework of decision rule rationality versus action rationality. A multi-site case study method was used; interviews were conducted with 11-16 individuals at each of four research I or II universities that had discontinued at least…
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Fific, Mario; Little, Daniel R; Nosofsky, Robert M
2010-04-01
We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT-distribution data associated with individual stimuli in tasks of speeded perceptual classification. PsycINFO Database Record (c) 2010 APA, all rights reserved.
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14 CFR 16.227 - Standard of proof.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Standard of proof. 16.227 Section 16.227 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES OF... hearing officer shall issue an initial decision or rule in a party's favor only if the decision or ruling...
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Presenting Germany's drug pricing rule as a cost-per-QALY rule.
Gandjour, Afschin
2012-06-01
In Germany, the Institute for Quality and Efficiency in Health Care (IQWiG) makes recommendations for ceiling prices of drugs based on an evaluation of the relationship between costs and effectiveness. To set ceiling prices, IQWiG uses the following decision rule: the incremental cost-effectiveness ratio of a new drug compared with the next effective intervention should not be higher than that of the next effective intervention compared to its comparator. The purpose of this paper is to show that IQWiG's decision rule can be presented as a cost-per-QALY rule by using equity-weighted QALYs. This transformation shows where both rules share commonalities. Furthermore, it makes the underlying ethical implications of IQWiG's decision rule transparent and open to debate.
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Califf, Robert M; Rasiel, Emma B; Schulman, Kevin A
2008-11-01
The pharmaceutical and medical device industries function in a business environment in which shareholders expect companies to optimize profit within legal and ethical standards. A fundamental tool used to optimize decision making is the net present value calculation, which estimates the current value of cash flows relating to an investment. We examined 3 prototypical research investment decisions that have been the source of public scrutiny to illustrate how policy decisions can be better understood when their impact on societally desirable investments by industry are viewed from the standpoint of their impact on net present value. In the case of direct, comparative clinical trials, a simple net present value calculation provides insight into why companies eschew such investments. In the case of pediatric clinical trials, the Pediatric Extension Rule changed the net present value calculation from unattractive to potentially very attractive by allowing patent extensions; thus, the dramatic increase in pediatric clinical trials can be explained by the financial return on investment. In the case of products for small markets, the fixed costs of development make this option financially unattractive. Policy decisions can be better understood when their impact on societally desirable investments by the pharmaceutical and medical device industries are viewed from the standpoint of their impact on net present value.
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Emergency physicians' attitudes toward and use of clinical decision rules for radiography.
Graham, I D; Stiell, I G; Laupacis, A; O'Connor, A M; Wells, G A
1998-02-01
1) To assess Canadian emergency physicians' (EPs') use of and attitudes toward 2 radiographic clinical decision rules that have recently been developed and to identify physician characteristics associated with decision rule use; 2) to determine the use of CT head and cervical spine radiography by EPs and their beliefs about the appropriateness of expert recommendations supporting the routine use of these radiographic procedures; and 3) to determine the potential acceptance of clinical decision rules for CT scan in patients with minor head injury and cervical spine radiography in trauma patients. A cross-sectional anonymous mail survey of a random sample of 300 members of the Canadian Association of Emergency Physicians using Dillman's Total Design Method for mail surveys. Of 288 eligible physicians, 232 (81%) responded. More than 95% of the respondents stated they currently used the Ottawa Ankle Rules and were willing to consider using the newly developed Ottawa Knee Rule. Physician characteristics related to frequent use of the Ottawa Ankle Rules were younger age, fewer years since graduating from medical school, part time or resident employment status, working in a hospital without a CT scanner, and believing that decision rules are not oversimplified cookbook medicine or too rigid to apply. Eighty-five percent did not agree that all patients with minor head injuries should receive a CT head scan and only 3.5% stated they always refer such patients for CT scan. Similarly, 78.5% of the respondents did not agree that all trauma patients should receive cervical spine radiography and only 13.2% said they always refer such patients for cervical spine radiography. Ninety-seven and 98% stated they would be willing to consider using well-validated decision rules for CT scan of the head and cervical spine radiography, respectively. Fifty-two percent and 67% of the respondents required the proposed CT and C-spine to be 100% sensitive for identifying serious injuries, respectively. Canadian EPs are generally supportive of clinical decision rules and, in particular, have very positive attitudes toward the Ottawa Ankle and Knee Rules. Furthermore, EPs disagree with recommendations for routine use of CT head and cervical spine radiography and strongly support the development of well-validated decision rules for the use of CT head and cervical spine radiography. Most EPs expected the latter rules to be 100% sensitive for acute clinically significant lesions.
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Graham, I D; Stiell, I G; Laupacis, A; McAuley, L; Howell, M; Clancy, M; Durieux, P; Simon, N; Emparanza, J I; Aginaga, J R; O'connor, A; Wells, G
2001-03-01
We evaluate the international diffusion of the Ottawa Ankle and Knee Rules and determine emergency physicians' attitudes toward clinical decision rules in general. We conducted a cross-sectional, self-administered mail survey of random samples of 500 members each of the American College of Emergency Physicians, Canadian Association of Emergency Physicians, British Association for Accident and Emergency Medicine, Spanish Society for Emergency Medicine, and all members (n=1,350) of the French Speaking Society of Emergency Physicians, France. Main outcome measures were awareness of the Ottawa Ankle and Knee Rules, reported use of these rules, and attitudes toward clinical decision rules in general. A total of 1,769 (57%) emergency physicians responded, with country-specific response rates between 49% (United States and France) and 79% (Canada). More than 69% of physicians in all countries, except Spain, were aware of the Ottawa Ankle Rules. Use of the Ottawa Ankle Rules differed by country with more than 70% of all responding Canadian and United Kingdom physicians reporting frequent use of the rules compared with fewer than one third of US, French, and Spanish physicians. The Ottawa Knee Rule was less well known and less used by physicians in all countries. Most physicians in all countries viewed decision rules as intended to improve the quality of health care (>78%), a convenient source of advice (>67%), and good educational tools (>61%). Of all physicians, those from the United States held the least positive attitudes toward decision rules. This constitutes the largest international survey of emergency physicians' attitudes toward and use of clinical decision rules. Striking differences were apparent among countries with regard to knowledge and use of decision rules. Despite similar awareness in the United States, Canada, and the United Kingdom, US physicians appeared much less likely to use the Ottawa Ankle Rules. Future research should investigate factors leading to differences in rates of diffusion among countries and address strategies to enhance dissemination and implementation of such rules in the emergency department.
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Estimating Classification Accuracy for Complex Decision Rules Based on Multiple Scores
ERIC Educational Resources Information Center
Douglas, Karen M.; Mislevy, Robert J.
2010-01-01
Important decisions about students are made by combining multiple measures using complex decision rules. Although methods for characterizing the accuracy of decisions based on a single measure have been suggested by numerous researchers, such methods are not useful for estimating the accuracy of decisions based on multiple measures. This study…
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Connecting clinical and actuarial prediction with rule-based methods.
Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H
2015-06-01
Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).
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Model-Based Design of Tree WSNs for Decentralized Detection †
Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam
2015-01-01
The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches. PMID:26307989
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Narrative dynamics in social groups: A discrete choice model
NASA Astrophysics Data System (ADS)
Antoci, A.; Bellanca, N.; Galdi, G.; Sodini, M.
2018-05-01
Individuals follow different rules for action: they react swiftly, grasping the short-term advantages in sight, or they waste cognitive resources to complete otherwise easy tasks, but they are able to plan ahead future complex decisions. Scholars from different disciplines studied the conditions under which either decision rule may enhance the fitness of its adopters, with a focus on the environmental features. However, we here propose that a crucial feature of the evolution of populations and their decision rules is rather inter-group interactions. Indeed, we study what happens when two groups support different decision rules, encapsulated in narratives, and their populations interact with each other. In particular, we assume that the payoff of each rule depends on the share of both social groups which adopt such rules. We then describe the most salient dynamics scenarios and identify the conditions which lead to chaotic dynamics and multistability regimes.
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NASA Astrophysics Data System (ADS)
Raseman, W. J.; Kasprzyk, J. R.; Rosario-Ortiz, F.; Summers, R. S.; Stewart, J.; Livneh, B.
2016-12-01
To promote public health, the United States Environmental Protection Agency (US EPA), and similar entities around the world enact strict laws to regulate drinking water quality. These laws, such as the Stage 1 and 2 Disinfectants and Disinfection Byproducts (D/DBP) Rules, come at a cost to water treatment plants (WTPs) which must alter their operations and designs to meet more stringent standards and the regulation of new contaminants of concern. Moreover, external factors such as changing influent water quality due to climate extremes and climate change, may force WTPs to adapt their treatment methods. To grapple with these issues, decision support systems (DSSs) have been developed to aid WTP operation and planning. However, there is a critical need to better address long-term decision making for WTPs. In this poster, we propose a DSS framework for WTPs for long-term planning, which improves upon the current treatment of deep uncertainties within the overall potable water system including the impact of climate on influent water quality and uncertainties in treatment process efficiencies. We present preliminary results exploring how a multi-objective evolutionary algorithm (MOEA) search can be coupled with models of WTP processes to identify high-performing plans for their design and operation. This coupled simulation-optimization technique uses Borg MOEA, an auto-adaptive algorithm, and the Water Treatment Plant Model, a simulation model developed by the US EPA to assist in creating the D/DBP Rules. Additionally, Monte Carlo sampling methods were used to study the impact of uncertainty of influent water quality on WTP decision-making and generate plans for robust WTP performance.
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The US Court of Appeals for the D.C. Circuit Ruling to Stay the CSAPR
The United States Court of Appeals for the D.C. Circuit issued its ruling to stay the CSAPR pending judicial review. The court's decision is not a decision on the merits of the rule. EPA is ensuring the transition back to the Clean Air Interstate Rule.
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A Decision Analysis Perspective on Multiple Response Robust Optimization
2012-03-01
the utility function in question is monotonically increasing and is twice differentiable . If γ(y) = 0, the utility function is describing risk neutral...twice differentiable , the risk aversion function with respect to a single attribute, yi, i = 1, . . . , n, is given in Equation 2.9, γUyi = − U ′′yi U...UV (V (y1, y2)) and fol- lowing the chain rule of differentiation , Matheson and Abbas [31] show that the risk aversion with respect to a single
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Is expected utility theory normative for medical decision making?
Cohen, B J
1996-01-01
Expected utility theory is felt by its proponents to be a normative theory of decision making under uncertainty. The theory starts with some simple axioms that are held to be rules that any rational person would follow. It can be shown that if one adheres to these axioms, a numerical quantity, generally referred to as utility, can be assigned to each possible outcome, with the preferred course of action being that which has the highest expected utility. One of these axioms, the independence principle, is controversial, and is frequently violated in experimental situations. Proponents of the theory hold that these violations are irrational. The independence principle is simply an axiom dictating consistency among preferences, in that it dictates that a rational agent should hold a specified preference given another stated preference. When applied to preferences between lotteries, the independence principle can be demonstrated to be a rule that is followed only when preferences are formed in a particular way. The logic of expected utility theory is that this demonstration proves that preferences should be formed in this way. An alternative interpretation is that this demonstrates that the independence principle is not a valid general rule of consistency, but in particular, is a rule that must be followed if one is to consistently apply the decision rule "choose the lottery that has the highest expected utility." This decision rule must be justified on its own terms as a valid rule of rationality by demonstration that violation would lead to decisions that conflict with the decision maker's goals. This rule does not appear to be suitable for medical decisions because often these are one-time decisions in which expectation, a long-run property of a random variable, would not seem to be applicable. This is particularly true for those decisions involving a non-trivial risk of death.
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The Analysis of Forward and Backward Dynamic Programming for Multistage Graph
NASA Astrophysics Data System (ADS)
Sitinjak, Anna Angela; Pasaribu, Elvina; Simarmata, Justin E.; Putra, Tedy; Mawengkang, Herman
2018-01-01
Dynamic programming is an optimization approach that divides the complex problems into the simple sequences of problems in which they are interrelated leading to decisions. In the dynamic programming, there is no standard formula that can be used to make a certain formulation. In this paper we use forward and backward method to find path which have the minimum cost and to know whether they make the same final decision. Convert the problem into several successive sequential stages starting on from stages 1,2,3 and 4 for forward dynamic programming and the step back from stage 4.3,2,1 for backward dynamic programming and interconnected with a decision rule in each stage. Find the optimal solution with cost principle at next stage. Based on the characteristics of the dynamic programming, the case is divided into several stages and the decision is has to be made (xk) at each stage. The results obtained at a stage are used for the states in the next stage so that at the forward stage 1, f1 (s) is obtained and used as a consideration of the decision in the next stage. In the backward, used firstly stage 4, f4 (s) is obtained and used as a consideration of the decision in the next stage. Cost forward and backward always increase steadily, because the cost in the next stage depends on the cost in the previous stage and formed the decision of each stage by taking the smallest fk value. Therefore the forward and backward approaches have the same result.
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NASA Astrophysics Data System (ADS)
Neves de Campos, Thiago
This research examines the distortionary effects of a discovered and undeveloped sequential modular offshore project under five different designs for a production-sharing agreement (PSA). The model differs from previous research by looking at the effect of taxation from the perspective of a host government, where the objective is to maximize government utility over government revenue generated by the project and the non-pecuniary benefits to society. This research uses Modern Asset Pricing (MAP) theory, which is able to provide a good measure of the asset value accruing to various stakeholders in the project combined with the optimal decision rule for the development of the investment opportunity. Monte Carlo simulation was also applied to incorporate into the model the most important sources of risk associated with the project and to account for non-linearity in the cash flows. For a complete evaluation of how the fiscal system affects the project development, an investor's behavioral model was constructed, incorporating three operational decisions: investment timing, capacity size and early abandonment. The model considers four sources of uncertainty that affect the project value and the firm's optimal decision: the long run oil price and short-run deviations from that price, cost escalation and the reservoir recovery rate. The optimizations outcomes show that all fiscal systems evaluated produce distortion over the companies' optimal decisions, and companies adjust their choices to avoid taxation in different ways according to the fiscal system characteristics. Moreover, it is revealed that fiscal systems with tax provisions that try to capture additional project profits based on production profitability measures leads to stronger distortions in the project investment and output profile. It is also shown that a model based on a fixed percentage rate is the system that creates the least distortion. This is because companies will be subjected to the same government share of profit oil independently of any operational decision which they can make to change the production profile to evade taxation.
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Modeling Limited Foresight in Water Management Systems
NASA Astrophysics Data System (ADS)
Howitt, R.
2005-12-01
The inability to forecast future water supplies means that their management inevitably occurs under situations of limited foresight. Three modeling problems arise, first what type of objective function is a manager with limited foresight optimizing? Second how can we measure these objectives? Third can objective functions that incorporate uncertainty be integrated within the structure of optimizing water management models? The paper reviews the concepts of relative risk aversion and intertemporal substitution that underlie stochastic dynamic preference functions. Some initial results from the estimation of such functions for four different dam operations in northern California are presented and discussed. It appears that the path of previous water decisions and states influences the decision-makers willingness to trade off water supplies between periods. A compromise modeling approach that incorporates carry-over value functions under limited foresight within a broader net work optimal water management model is developed. The approach uses annual carry-over value functions derived from small dimension stochastic dynamic programs embedded within a larger dimension water allocation network. The disaggregation of the carry-over value functions to the broader network is extended using the space rule concept. Initial results suggest that the solution of such annual nonlinear network optimizations is comparable to, or faster than, the solution of linear network problems over long time series.
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Cerrada, Christian Jules; Dzubur, Eldin; Blackman, Kacie C. A.; Mays, Vickie; Shoptaw, Steven; Huh, Jimi
2017-01-01
Purpose Cigarette smoking is a preventable risk factor that contributes to unnecessary lung cancer burden among Korean Americans and there is limited research on effective smoking cessation strategies for this population. Smartphone-based smoking cessation apps that leverage just-in-time adaptive interventions (JITAIs) hold promise for smokers attempting to quit. However, little is known about how to develop and tailor a smoking cessation JITAI for Korean American emerging adult (KAEA) smokers. Method This paper documents the development process of MyQuit USC according to design guidelines for JITAI. Our development process builds on findings from a prior ecological momentary assessment study by using qualitative research methods. Semi-structured interviews and a focus group were conducted to inform which intervention options to offer and the decision rules that dictate their delivery. Results Qualitative findings highlighted that (1) smoking episodes are highly context-driven and that (2) KAEA smokers believe they need personalized cessation strategies tailored to different contexts. Thus, MyQuit USC operates via decision rules that guide the delivery of personalized implementation intentions, which are contingent on dynamic factors, to be delivered “just in time” at user-scheduled, high-risk smoking situations. Conclusion Through an iterative design process, informed by quantitative and qualitative formative research, we developed a smoking cessation JITAI tailored specifically for KAEA smokers. Further testing is under way to optimize future versions of the app with the most effective intervention strategies and decision rules. MyQuit USC has the potential to provide cessation support in real-world settings, when KAEAs need them the most. PMID:28070868
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Cerrada, Christian Jules; Dzubur, Eldin; Blackman, Kacie C A; Mays, Vickie; Shoptaw, Steven; Huh, Jimi
2017-10-01
Cigarette smoking is a preventable risk factor that contributes to unnecessary lung cancer burden among Korean Americans and there is limited research on effective smoking cessation strategies for this population. Smartphone-based smoking cessation apps that leverage just-in-time adaptive interventions (JITAIs) hold promise for smokers attempting to quit. However, little is known about how to develop and tailor a smoking cessation JITAI for Korean American emerging adult (KAEA) smokers. This paper documents the development process of MyQuit USC according to design guidelines for JITAI. Our development process builds on findings from a prior ecological momentary assessment study by using qualitative research methods. Semi-structured interviews and a focus group were conducted to inform which intervention options to offer and the decision rules that dictate their delivery. Qualitative findings highlighted that (1) smoking episodes are highly context-driven and that (2) KAEA smokers believe they need personalized cessation strategies tailored to different contexts. Thus, MyQuit USC operates via decision rules that guide the delivery of personalized implementation intentions, which are contingent on dynamic factors, to be delivered "just in time" at user-scheduled, high-risk smoking situations. Through an iterative design process, informed by quantitative and qualitative formative research, we developed a smoking cessation JITAI tailored specifically for KAEA smokers. Further testing is under way to optimize future versions of the app with the most effective intervention strategies and decision rules. MyQuit USC has the potential to provide cessation support in real-world settings, when KAEAs need them the most.
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Economic modelling with low-cognition agents
NASA Astrophysics Data System (ADS)
Ormerod, Paul
2006-10-01
The standard socio-economic model (SSSM) postulates very considerable cognitive powers on the part of its agents. They are able to gather all relevant information in any given situation, and to take the optimal decision on the basis of it, given their tastes and preferences. This behavioural rule is postulated to be universal. The concept of bounded rationality relaxes this somewhat, by permitting agents to have access to only limited amounts of information. But agents still optimise subject to their information set and tastes. Empirical work in economics over the past 20 years or so has shown that in general these behavioural postulates lack empirical validity. Instead, agents appear to have limited ability to gather information, and use simple rules of thumb to process the information which they have in order to take decisions. Building theoretical models on these realistic foundations which give better accounts of empirical phenomena than does the SSSM is an important challenge to both economists and econophysicists. Considerable progress has already been made in a short space of time, and examples are given in this paper.
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46 CFR 201.159 - Decisions; contents and service.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 46 Shipping 8 2012-10-01 2012-10-01 false Decisions; contents and service. 201.159 Section 201.159 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION POLICY, PRACTICE AND PROCEDURE RULES OF PRACTICE AND PROCEDURE Briefs, Requests for Findings, Decisions, Exceptions (Rule 16) § 201.159 Decisions; contents and service. All initial,...
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Mellers, B A; Schwartz, A; Cooke, A D
1998-01-01
For many decades, research in judgment and decision making has examined behavioral violations of rational choice theory. In that framework, rationality is expressed as a single correct decision shared by experimenters and subjects that satisfies internal coherence within a set of preferences and beliefs. Outside of psychology, social scientists are now debating the need to modify rational choice theory with behavioral assumptions. Within psychology, researchers are debating assumptions about errors for many different definitions of rationality. Alternative frameworks are being proposed. These frameworks view decisions as more reasonable and adaptive that previously thought. For example, "rule following." Rule following, which occurs when a rule or norm is applied to a situation, often minimizes effort and provides satisfying solutions that are "good enough," though not necessarily the best. When rules are ambiguous, people look for reasons to guide their decisions. They may also let their emotions take charge. This chapter presents recent research on judgment and decision making from traditional and alternative frameworks.
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Extraneous factors in judicial decisions
Danziger, Shai; Levav, Jonathan; Avnaim-Pesso, Liora
2011-01-01
Are judicial rulings based solely on laws and facts? Legal formalism holds that judges apply legal reasons to the facts of a case in a rational, mechanical, and deliberative manner. In contrast, legal realists argue that the rational application of legal reasons does not sufficiently explain the decisions of judges and that psychological, political, and social factors influence judicial rulings. We test the common caricature of realism that justice is “what the judge ate for breakfast” in sequential parole decisions made by experienced judges. We record the judges’ two daily food breaks, which result in segmenting the deliberations of the day into three distinct “decision sessions.” We find that the percentage of favorable rulings drops gradually from ≈65% to nearly zero within each decision session and returns abruptly to ≈65% after a break. Our findings suggest that judicial rulings can be swayed by extraneous variables that should have no bearing on legal decisions. PMID:21482790
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Acute knee injuries: use of decision rules for selective radiograph ordering.
Tandeter, H B; Shvartzman, P; Stevens, Max A
1999-12-01
Family physicians often encounter patients with acute knee trauma. Radiographs of injured knees are commonly ordered, even though fractures are found in only 6 percent of such patients and emergency department physicians can usually discriminate clinically between fracture and nonfracture. Decision rules have been developed to reduce the unnecessary use of radiologic studies in patients with acute knee injury. The Ottawa knee rules and the Pittsburgh decision rules are the latest guidelines for the selective use of radiographs in knee trauma. Application of these rules may lead to a more efficient evaluation of knee injuries and a reduction in health costs without an increase in adverse outcomes.
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Collective decision making and social interaction rules in mixed-species flocks of songbirds
Farine, Damien R.; Aplin, Lucy M.; Garroway, Colin J.; Mann, Richard P.; Sheldon, Ben C.
2014-01-01
Associations in mixed-species foraging groups are common in animals, yet have rarely been explored in the context of collective behaviour. Despite many investigations into the social and ecological conditions under which individuals should form groups, we still know little about the specific behavioural rules that individuals adopt in these contexts, or whether these can be generalized to heterospecifics. Here, we studied collective behaviour in flocks in a community of five species of woodland passerine birds. We adopted an automated data collection protocol, involving visits by RFID-tagged birds to feeding stations equipped with antennae, over two winters, recording 91 576 feeding events by 1904 individuals. We demonstrated highly synchronized feeding behaviour within patches, with birds moving towards areas of the patch with the largest proportion of the flock. Using a model of collective decision making, we then explored the underlying decision rule birds may be using when foraging in mixed-species flocks. The model tested whether birds used a different decision rule for conspecifics and heterospecifics, and whether the rules used by individuals of different species varied. We found that species differed in their response to the distribution of conspecifics and heterospecifics across foraging patches. However, simulating decisions using the different rules, which reproduced our data well, suggested that the outcome of using different decision rules by each species resulted in qualitatively similar overall patterns of movement. It is possible that the decision rules each species uses may be adjusted to variation in mean species abundance in order for individuals to maintain the same overall flock-level response. This is likely to be important for maintaining coordinated behaviour across species, and to result in quick and adaptive flock responses to food resources that are patchily distributed in space and time. PMID:25214653
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Utility of Decision Rules for Transcutaneous Bilirubin Measurements
Burgos, Anthony E.; Flaherman, Valerie; Chung, Esther K.; Simpson, Elizabeth A.; Goyal, Neera K.; Von Kohorn, Isabelle; Dhepyasuwan, Niramol
2016-01-01
BACKGROUND: Transcutaneous bilirubin (TcB) meters are widely used for screening newborns for jaundice, with a total serum bilirubin (TSB) measurement indicated when the TcB value is classified as “positive” by using a decision rule. The goal of our study was to assess the clinical utility of 3 recommended TcB screening decision rules. METHODS: Paired TcB/TSB measurements were collected at 34 newborn nursery sites. At 27 sites (sample 1), newborns were routinely screened with a TcB measurement. For sample 2, sites that typically screen with TSB levels also obtained a TcB measurement for the study. Three decision rules to define a positive TcB measurement were evaluated: ≥75th percentile on the Bhutani nomogram, 70% of the phototherapy level, and within 3 mg/dL of the phototherapy threshold. The primary outcome was a TSB level at/above the phototherapy threshold. The rate of false-negative TcB screens and percentage of blood draws avoided were calculated for each decision rule. RESULTS: For sample 1, data were analyzed on 911 paired TcB-TSB measurements from a total of 8316 TcB measurements. False-negative rates were <10% with all decision rules; none identified all 31 newborns with a TSB level at/above the phototherapy threshold. The percentage of blood draws avoided ranged from 79.4% to 90.7%. In sample 2, each rule correctly identified all 8 newborns with TSB levels at/above the phototherapy threshold. CONCLUSIONS: Although all of the decision rules can be used effectively to screen newborns for jaundice, each will “miss” some infants with a TSB level at/above the phototherapy threshold. PMID:27244792
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A knowledge authoring tool for clinical decision support.
Dunsmuir, Dustin; Daniels, Jeremy; Brouse, Christopher; Ford, Simon; Ansermino, J Mark
2008-06-01
Anesthesiologists in the operating room are unable to constantly monitor all data generated by physiological monitors. They are further distracted by clinical and educational tasks. An expert system would ideally provide assistance to the anesthesiologist in this data-rich environment. Clinical monitoring expert systems have not been widely adopted, as traditional methods of knowledge encoding require both expert medical and programming skills, making knowledge acquisition difficult. A software application was developed for use as a knowledge authoring tool for physiological monitoring. This application enables clinicians to create knowledge rules without the need of a knowledge engineer or programmer. These rules are designed to provide clinical diagnosis, explanations and treatment advice for optimal patient care to the clinician in real time. By intelligently combining data from physiological monitors and demographical data sources the expert system can use these rules to assist in monitoring the patient. The knowledge authoring process is simplified by limiting connective relationships between rules. The application is designed to allow open collaboration between communities of clinicians to build a library of rules for clinical use. This design provides clinicians with a system for parameter surveillance and expert advice with a transparent pathway of reasoning. A usability evaluation demonstrated that anesthesiologists can rapidly develop useful rules for use in a predefined clinical scenario.
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Modeling joint restoration strategies for interdependent infrastructure systems.
Zhang, Chao; Kong, Jingjing; Simonovic, Slobodan P
2018-01-01
Life in the modern world depends on multiple critical services provided by infrastructure systems which are interdependent at multiple levels. To effectively respond to infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent infrastructure systems following a disruptive event. First, models for (i) describing structure of interdependent infrastructure system and (ii) their interaction process, are presented. Both models are considering the failure types, infrastructure operating rules and interdependencies among systems. Second, an optimization model for determining an optimal joint restoration strategy at infrastructure component level by minimizing the economic loss from the infrastructure failures, is proposed. The utility of the model is illustrated using a case study of electric-water systems. Results show that a small number of failed infrastructure components can trigger high level failures in interdependent systems; the optimal joint restoration strategy varies with failure occurrence time. The proposed models can help decision makers to understand the mechanisms of infrastructure interactions and search for optimal joint restoration strategy, which can significantly enhance safety of infrastructure systems.
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Zhang, Wenyu; Zhang, Zhenjiang
2015-01-01
Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule. PMID:26295399
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NASA Astrophysics Data System (ADS)
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
2013-04-01
Water resources systems are operated, mostly, using a set of pre-defined rules not regarding, usually, to an optimal allocation in terms of water use or economic benefits, but to historical and institutional reasons. These operating policies are reproduced, commonly, as hedging rules, pack rules or zone-based operations, and simulation models can be used to test their performance under a wide range of hydrological and/or socio-economic hypothesis. Despite the high degree of acceptation and testing that these models have achieved, the actual operation of water resources systems hardly follows all the time the pre-defined rules with the consequent uncertainty on the system performance. Real-world reservoir operation is very complex, affected by input uncertainty (imprecision in forecast inflow, seepage and evaporation losses, etc.), filtered by the reservoir operator's experience and natural risk-aversion, while considering the different physical and legal/institutional constraints in order to meet the different demands and system requirements. The aim of this work is to expose a fuzzy logic approach to derive and assess the historical operation of a system. This framework uses a fuzzy rule-based system to reproduce pre-defined rules and also to match as close as possible the actual decisions made by managers. After built up, the fuzzy rule-based system can be integrated in a water resources management model, making possible to assess the system performance at the basin scale. The case study of the Mijares basin (eastern Spain) is used to illustrate the method. A reservoir operating curve regulates the two main reservoir releases (operated in a conjunctive way) with the purpose of guaranteeing a high realiability of supply to the traditional irrigation districts with higher priority (more senior demands that funded the reservoir construction). A fuzzy rule-based system has been created to reproduce the operating curve's performance, defining the system state (total water stored in the reservoirs) and the month of the year as inputs; and the demand deliveries as outputs. The developed simulation management model integrates the fuzzy-ruled system of the operation of the two main reservoirs of the basin with the corresponding mass balance equations, the physical or boundary conditions and the water allocation rules among the competing demands. Historical information on inflow time series is used as inputs to the model simulation, being trained and validated using historical information on reservoir storage level and flow in several streams of the Mijares river. This methodology provides a more flexible and close to real policies approach. The model is easy to develop and to understand due to its rule-based structure, which mimics the human way of thinking. This can improve cooperation and negotiation between managers, decision-makers and stakeholders. The approach can be also applied to analyze the historical operation of the reservoir (what we have called a reservoir operation "audit").
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NASA Astrophysics Data System (ADS)
Hurford, Anthony; Harou, Julien
2014-05-01
Water related eco-system services are important to the livelihoods of the poorest sectors of society in developing countries. Degradation or loss of these services can increase the vulnerability of people decreasing their capacity to support themselves. New approaches to help guide water resources management decisions are needed which account for the non-market value of ecosystem goods and services. In case studies from Brazil and Kenya we demonstrate the capability of many objective Pareto-optimal trade-off analysis to help decision makers balance economic and non-market benefits from the management of existing multi-reservoir systems. A multi-criteria search algorithm is coupled to a water resources management simulator of each basin to generate a set of Pareto-approximate trade-offs representing the best case management decisions. In both cases, volume dependent reservoir release rules are the management decisions being optimised. In the Kenyan case we further assess the impacts of proposed irrigation investments, and how the possibility of new investments impacts the system's trade-offs. During the multi-criteria search (optimisation), performance of different sets of management decisions (policies) is assessed against case-specific objective functions representing provision of water supply and irrigation, hydropower generation and maintenance of ecosystem services. Results are visualised as trade-off surfaces to help decision makers understand the impacts of different policies on a broad range of stakeholders and to assist in decision-making. These case studies show how the approach can reveal unexpected opportunities for win-win solutions, and quantify the trade-offs between investing to increase agricultural revenue and negative impacts on protected ecosystems which support rural livelihoods.
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Voting systems for environmental decisions.
Burgman, Mark A; Regan, Helen M; Maguire, Lynn A; Colyvan, Mark; Justus, James; Martin, Tara G; Rothley, Kris
2014-04-01
Voting systems aggregate preferences efficiently and are often used for deciding conservation priorities. Desirable characteristics of voting systems include transitivity, completeness, and Pareto optimality, among others. Voting systems that are common and potentially useful for environmental decision making include simple majority, approval, and preferential voting. Unfortunately, no voting system can guarantee an outcome, while also satisfying a range of very reasonable performance criteria. Furthermore, voting methods may be manipulated by decision makers and strategic voters if they have knowledge of the voting patterns and alliances of others in the voting populations. The difficult properties of voting systems arise in routine decision making when there are multiple criteria and management alternatives. Because each method has flaws, we do not endorse one method. Instead, we urge organizers to be transparent about the properties of proposed voting systems and to offer participants the opportunity to approve the voting system as part of the ground rules for operation of a group. © 2014 The Authors. Conservation Biology published by Wiley Periodicals, Inc., on behalf of the Society for Conservation Biology.
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A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
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16 CFR 3.51 - Initial decision.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Initial decision. 3.51 Section 3.51 Commercial Practices FEDERAL TRADE COMMISSION ORGANIZATION, PROCEDURES AND RULES OF PRACTICE RULES OF PRACTICE FOR ADJUDICATIVE PROCEEDINGS Decision § 3.51 Initial decision. (a) When filed and when effective. The Administrative Law Judge shall file an...
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16 CFR 3.52 - Appeal from initial decision.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Appeal from initial decision. 3.52 Section 3.52 Commercial Practices FEDERAL TRADE COMMISSION ORGANIZATION, PROCEDURES AND RULES OF PRACTICE RULES OF PRACTICE FOR ADJUDICATIVE PROCEEDINGS Decision § 3.52 Appeal from initial decision. (a) Automatic review of cases in which the Commission sough...
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49 CFR 1503.631 - Interlocutory appeals.
Code of Federal Regulations, 2010 CFR
2010-10-01
... Rules of Practice in TSA Civil Penalty Actions § 1503.631 Interlocutory appeals. (a) General. Unless otherwise provided in this subpart, a party may not appeal a ruling or decision of the ALJ to the TSA decision maker until the initial decision has been entered on the record. A decision or order of the TSA...
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Is there a need for a clinical decision rule in blunt wrist trauma?
van den Brand, Crispijn L; van Leerdam, Roderick H; van Ufford, Jet H M E Quarles; Rhemrev, Steven J
2013-11-01
Blunt wrist trauma is a very common injury in emergency medicine. However, in contrast to other extremity trauma, there is no clinical decision rule for radiography in patients with blunt wrist trauma. The purpose of this study is to describe current practice and to assess the need and feasibility for a clinical decision rule for radiography in patients with blunt wrist trauma. All patients with blunt wrist trauma who presented to our Emergency Department (ED) during a 6-month period were included in this study. Basic demographics were analysed and the radiography ratio was determined. The radiography results were compared for different demographic groups. Current practice and the need and feasibility for a decision rule were evaluated using Stiell's checklist for clinical decision rules. A total of 1019 patients with 1032 blunt wrist injuries presented at our ED in a period of 6 months. In 91.4% of patients, radiographs were taken. In 41.6% of those radiographed, a fracture was visible on plain radiography. Fractures were most common in the paediatric and senior age groups. However, even in the lower-risk groups we observed a fracture incidence of about 20%. There is no need for a clinical decision rule for radiography in patients with blunt wrist trauma because the fracture ratio is high. Neither does it seem feasible to develop a highly sensitive and efficient decision rule. Therefore, the authors recommend radiography in all patients with blunt wrist trauma presenting to the ED. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Edwards, W; Fasolo, B
2001-01-01
This review is about decision technology-the rules and tools that help us make wiser decisions. First, we review the three rules that are at the heart of most traditional decision technology-multi-attribute utility, Bayes' theorem, and subjective expected utility maximization. Since the inception of decision research, these rules have prescribed how we should infer values and probabilities and how we should combine them to make better decisions. We suggest how to make best use of all three rules in a comprehensive 19-step model. The remainder of the review explores recently developed tools of decision technology. It examines the characteristics and problems of decision-facilitating sites on the World Wide Web. Such sites now provide anyone who can use a personal computer with access to very sophisticated decision-aiding tools structured mainly to facilitate consumer decision making. It seems likely that the Web will be the mode by means of which decision tools will be distributed to lay users. But methods for doing such apparently simple things as winnowing 3000 options down to a more reasonable number, like 10, contain traps for unwary decision technologists. The review briefly examines Bayes nets and influence diagrams-judgment and decision-making tools that are available as computer programs. It very briefly summarizes the state of the art of eliciting probabilities from experts. It concludes that decision tools will be as important in the 21st century as spreadsheets were in the 20th.
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A study of some nine-element decision rules. [for multispectral recognition of remote sensing
NASA Technical Reports Server (NTRS)
Richardson, W.
1974-01-01
A nine-element rule is one that makes a classification decision for each pixel based on data from that pixel and its eight immediate neighbors. Three such rules, all fast and simple to use, are defined and tested. All performed substantially better on field interiors than the best one-point rule. Qualitative results indicate that fine detail and contradictory testimony tend to be overlooked by the rules.
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Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction
NASA Astrophysics Data System (ADS)
Zhang, W.; Li, X.; Xiao, W.
2018-05-01
The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.
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Optimality versus stability in water resource allocation.
Read, Laura; Madani, Kaveh; Inanloo, Bahareh
2014-01-15
Water allocation is a growing concern in a developing world where limited resources like fresh water are in greater demand by more parties. Negotiations over allocations often involve multiple groups with disparate social, economic, and political status and needs, who are seeking a management solution for a wide range of demands. Optimization techniques for identifying the Pareto-optimal (social planner solution) to multi-criteria multi-participant problems are commonly implemented, although often reaching agreement for this solution is difficult. In negotiations with multiple-decision makers, parties who base decisions on individual rationality may find the social planner solution to be unfair, thus creating a need to evaluate the willingness to cooperate and practicality of a cooperative allocation solution, i.e., the solution's stability. This paper suggests seeking solutions for multi-participant resource allocation problems through an economics-based power index allocation method. This method can inform on allocation schemes that quantify a party's willingness to participate in a negotiation rather than opt for no agreement. Through comparison of the suggested method with a range of distance-based multi-criteria decision making rules, namely, least squares, MAXIMIN, MINIMAX, and compromise programming, this paper shows that optimality and stability can produce different allocation solutions. The mismatch between the socially-optimal alternative and the most stable alternative can potentially result in parties leaving the negotiation as they may be too dissatisfied with their resource share. This finding has important policy implications as it justifies why stakeholders may not accept the socially optimal solution in practice, and underlies the necessity of considering stability where it may be more appropriate to give up an unstable Pareto-optimal solution for an inferior stable one. Authors suggest assessing the stability of an allocation solution as an additional component to an analysis that seeks to distribute water in a negotiated process. Copyright © 2013 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Sindt, Nathan M.; Robison, Faith; Brick, Mark A.; Schwartz, Howard F.; Heuberger, Adam L.; Prenni, Jessica E.
2018-02-01
Matrix-assisted desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) is a fast and effective tool for microbial species identification. However, current approaches are limited to species-level identification even when genetic differences are known. Here, we present a novel workflow that applies the statistical method of partial least squares discriminant analysis (PLS-DA) to MALDI-TOF-MS protein fingerprint data of Xanthomonas axonopodis, an important bacterial plant pathogen of fruit and vegetable crops. Mass spectra of 32 X. axonopodis strains were used to create a mass spectral library and PLS-DA was employed to model the closely related strains. A robust workflow was designed to optimize the PLS-DA model by assessing the model performance over a range of signal-to-noise ratios (s/n) and mass filter (MF) thresholds. The optimized parameters were observed to be s/n = 3 and MF = 0.7. The model correctly classified 83% of spectra withheld from the model as a test set. A new decision rule was developed, termed the rolled-up Maximum Decision Rule (ruMDR), and this method improved identification rates to 92%. These results demonstrate that MALDI-TOF-MS protein fingerprints of bacterial isolates can be utilized to enable identification at the strain level. Furthermore, the open-source framework of this workflow allows for broad implementation across various instrument platforms as well as integration with alternative modeling and classification algorithms.
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Sleep-dependent modulation of affectively guided decision-making.
Pace-Schott, Edward F; Nave, Genevieve; Morgan, Alexandra; Spencer, Rebecca M C
2012-02-01
A question of great interest in current sleep research is whether and how sleep might facilitate complex cognitive skills such as decision-making. The Iowa Gambling Task (IGT) was used to investigate effects of sleep on affect-guided decision-making. After a brief standardized preview of the IGT that was insufficient to learn its underlying rule, participants underwent a 12-h delay containing either a normal night's sleep (Sleep group; N = 28) or continuous daytime wake (Wake group; N = 26). Following the delay, both groups performed the full IGT. To control for circadian effects, two additional groups performed both the preview and the full task either in the morning (N = 17) or the evening (N = 21). In the IGT, four decks of cards were presented. Draws from two 'advantageous decks' yielded low play-money rewards, occasional low losses and, over multiple draws, a net gain. Draws from 'disadvantageous' decks yielded high rewards, occasional high losses and, over multiple draws, a net loss. Participants were instructed to win and avoid losing as much as possible, and better performance was defined as more advantageous draws. Relative to the wake group, the sleep group showed both superior behavioral outcome (more advantageous draws) and superior rule understanding (blindly judged from statements written at task completion). Neither measure differentiated the two control groups. These results illustrate a role of sleep in optimizing decision-making, a benefit that may be brought about by changes in underlying emotional or cognitive processes. © 2011 European Sleep Research Society.
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Concurrent approach for evolving compact decision rule sets
NASA Astrophysics Data System (ADS)
Marmelstein, Robert E.; Hammack, Lonnie P.; Lamont, Gary B.
1999-02-01
The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.
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NASA Astrophysics Data System (ADS)
Hu, Y.; Quinn, C.; Cai, X.
2015-12-01
One major challenge of agent-based modeling is to derive agents' behavioral rules due to behavioral uncertainty and data scarcity. This study proposes a new approach to combine a data-driven modeling based on the directed information (i.e., machine intelligence) with expert domain knowledge (i.e., human intelligence) to derive the behavioral rules of agents considering behavioral uncertainty. A directed information graph algorithm is applied to identifying the causal relationships between agents' decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional factors. A case study is conducted for the High Plains aquifer hydrological observatory (HO) area, U.S. Preliminary results show that four factors, corn price (CP), underlying groundwater level (GWL), monthly mean temperature (T) and precipitation (P) have causal influences on agents' decisions on groundwater irrigation depth (GWID) to various extents. Based on the similarity of the directed information graph for each agent, five clusters of graphs are further identified to represent all the agents' behaviors in the study area as shown in Figure 1. Using these five representative graphs, agents' monthly optimal groundwater pumping rates are derived through the probabilistic inference. Such data-driven relationships and probabilistic quantifications are then coupled with a physically-based groundwater model to investigate the interactions between agents' pumping behaviors and the underlying groundwater system in the context of coupled human and natural systems.
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Systematic methods for knowledge acquisition and expert system development
NASA Technical Reports Server (NTRS)
Belkin, Brenda L.; Stengel, Robert F.
1991-01-01
Nine cooperating rule-based systems, collectively called AUTOCREW, were designed to automate functions and decisions associated with a combat aircraft's subsystem. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base, and to assess the cooperation between the rule-bases. Each AUTOCREW subsystem is composed of several expert systems that perform specific tasks. AUTOCREW's NAVIGATOR was analyzed in detail to understand the difficulties involved in designing the system and to identify tools and methodologies that ease development. The NAVIGATOR determines optimal navigation strategies from a set of available sensors. A Navigation Sensor Management (NSM) expert system was systematically designed from Kalman filter covariance data; four ground-based, a satellite-based, and two on-board INS-aiding sensors were modeled and simulated to aid an INS. The NSM Expert was developed using the Analysis of Variance (ANOVA) and the ID3 algorithm. Navigation strategy selection is based on an RSS position error decision metric, which is computed from the covariance data. Results show that the NSM Expert predicts position error correctly between 45 and 100 percent of the time for a specified navaid configuration and aircraft trajectory. The NSM Expert adapts to new situations, and provides reasonable estimates of hybrid performance. The systematic nature of the ANOVA/ID3 method makes it broadly applicable to expert system design when experimental or simulation data is available.
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EXADS - EXPERT SYSTEM FOR AUTOMATED DESIGN SYNTHESIS
NASA Technical Reports Server (NTRS)
Rogers, J. L.
1994-01-01
The expert system called EXADS was developed to aid users of the Automated Design Synthesis (ADS) general purpose optimization program. Because of the general purpose nature of ADS, it is difficult for a nonexpert to select the best choice of strategy, optimizer, and one-dimensional search options from the one hundred or so combinations that are available. EXADS aids engineers in determining the best combination based on their knowledge of the problem and the expert knowledge previously stored by experts who developed ADS. EXADS is a customized application of the AESOP artificial intelligence program (the general version of AESOP is available separately from COSMIC. The ADS program is also available from COSMIC.) The expert system consists of two main components. The knowledge base contains about 200 rules and is divided into three categories: constrained, unconstrained, and constrained treated as unconstrained. The EXADS inference engine is rule-based and makes decisions about a particular situation using hypotheses (potential solutions), rules, and answers to questions drawn from the rule base. EXADS is backward-chaining, that is, it works from hypothesis to facts. The rule base was compiled from sources such as literature searches, ADS documentation, and engineer surveys. EXADS will accept answers such as yes, no, maybe, likely, and don't know, or a certainty factor ranging from 0 to 10. When any hypothesis reaches a confidence level of 90% or more, it is deemed as the best choice and displayed to the user. If no hypothesis is confirmed, the user can examine explanations of why the hypotheses failed to reach the 90% level. The IBM PC version of EXADS is written in IQ-LISP for execution under DOS 2.0 or higher with a central memory requirement of approximately 512K of 8 bit bytes. This program was developed in 1986.
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Automatic rule generation for high-level vision
NASA Technical Reports Server (NTRS)
Rhee, Frank Chung-Hoon; Krishnapuram, Raghu
1992-01-01
A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.
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Simple heuristics and rules of thumb: where psychologists and behavioural biologists might meet.
Hutchinson, John M C; Gigerenzer, Gerd
2005-05-31
The Centre for Adaptive Behaviour and Cognition (ABC) has hypothesised that much human decision-making can be described by simple algorithmic process models (heuristics). This paper explains this approach and relates it to research in biology on rules of thumb, which we also review. As an example of a simple heuristic, consider the lexicographic strategy of Take The Best for choosing between two alternatives: cues are searched in turn until one discriminates, then search stops and all other cues are ignored. Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics. Simulations show that the statistical structures of different environments affect which heuristics perform better, a relationship referred to as ecological rationality. We contrast ecological rationality with the stronger claim of adaptation. Rules of thumb from biology provide clearer examples of adaptation because animals can be studied in the environments in which they evolved. The range of examples is also much more diverse. To investigate them, biologists have sometimes used similar simulation techniques to ABC, but many examples depend on empirically driven approaches. ABC's theoretical framework can be useful in connecting some of these examples, particularly the scattered literature on how information from different cues is integrated. Optimality modelling is usually used to explain less detailed aspects of behaviour but might more often be redirected to investigate rules of thumb.
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Code of Federal Regulations, 2011 CFR
2011-01-01
... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Decision. 14.27 Section 14.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES IMPLEMENTING THE EQUAL ACCESS TO JUSTICE ACT OF 1980 Procedures for Considering Applications § 14.27 Decision...
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Code of Federal Regulations, 2014 CFR
2014-01-01
... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Decision. 14.27 Section 14.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES IMPLEMENTING THE EQUAL ACCESS TO JUSTICE ACT OF 1980 Procedures for Considering Applications § 14.27 Decision...
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Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Decision. 14.27 Section 14.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES IMPLEMENTING THE EQUAL ACCESS TO JUSTICE ACT OF 1980 Procedures for Considering Applications § 14.27 Decision...
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Code of Federal Regulations, 2013 CFR
2013-01-01
... 14 Aeronautics and Space 1 2013-01-01 2013-01-01 false Decision. 14.27 Section 14.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES IMPLEMENTING THE EQUAL ACCESS TO JUSTICE ACT OF 1980 Procedures for Considering Applications § 14.27 Decision...
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Code of Federal Regulations, 2012 CFR
2012-01-01
... 14 Aeronautics and Space 1 2012-01-01 2012-01-01 false Decision. 14.27 Section 14.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES IMPLEMENTING THE EQUAL ACCESS TO JUSTICE ACT OF 1980 Procedures for Considering Applications § 14.27 Decision...
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14 CFR 16.227 - Standard of proof.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Standard of proof. 16.227 Section 16.227 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES OF... hearing officer shall issue an initial decision or shall rule in a party's favor only if the decision or...
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14 CFR 16.227 - Standard of proof.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Standard of proof. 16.227 Section 16.227 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES OF... hearing officer shall issue an initial decision or shall rule in a party's favor only if the decision or...
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14 CFR 16.227 - Standard of proof.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 14 Aeronautics and Space 1 2012-01-01 2012-01-01 false Standard of proof. 16.227 Section 16.227 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES OF... hearing officer shall issue an initial decision or shall rule in a party's favor only if the decision or...
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14 CFR 16.227 - Standard of proof.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 14 Aeronautics and Space 1 2013-01-01 2013-01-01 false Standard of proof. 16.227 Section 16.227 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES OF... hearing officer shall issue an initial decision or shall rule in a party's favor only if the decision or...
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A simple threshold rule is sufficient to explain sophisticated collective decision-making.
Robinson, Elva J H; Franks, Nigel R; Ellis, Samuel; Okuda, Saki; Marshall, James A R
2011-01-01
Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.
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Linan, Margaret K; Sottara, Davide; Freimuth, Robert R
2015-01-01
Pharmacogenomics (PGx) guidelines contain drug-gene relationships, therapeutic and clinical recommendations from which clinical decision support (CDS) rules can be extracted, rendered and then delivered through clinical decision support systems (CDSS) to provide clinicians with just-in-time information at the point of care. Several tools exist that can be used to generate CDS rules that are based on computer interpretable guidelines (CIG), but none have been previously applied to the PGx domain. We utilized the Unified Modeling Language (UML), the Health Level 7 virtual medical record (HL7 vMR) model, and standard terminologies to represent the semantics and decision logic derived from a PGx guideline, which were then mapped to the Health eDecisions (HeD) schema. The modeling and extraction processes developed here demonstrate how structured knowledge representations can be used to support the creation of shareable CDS rules from PGx guidelines.
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Collaboration pathway(s) using new tools for optimizing `operational' climate monitoring from space
NASA Astrophysics Data System (ADS)
Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.
2015-09-01
Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a long term solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the collective needs of policy makers, scientific communities and global academic users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent rule-based expert system (RBES) optimization modeling of the intended NPOESS architecture becomes a surrogate for global operational climate monitoring architecture(s). These rulebased systems tools provide valuable insight for global climate architectures, by comparison/evaluation of alternatives and the sheer range of trade space explored. Optimization of climate monitoring architecture(s) for a partial list of ECV (essential climate variables) is explored and described in detail with dialogue on appropriate rule-based valuations. These optimization tool(s) suggest global collaboration advantages and elicit responses from the audience and climate science community. This paper will focus on recent research exploring joint requirement implications of the high profile NPOESS architecture and extends the research and tools to optimization for a climate centric case study. This reflects work from SPIE RS Conferences 2013 and 2014, abridged for simplification30, 32. First, the heavily securitized NPOESS architecture; inspired the recent research question - was Complexity (as a cost/risk factor) overlooked when considering the benefits of aggregating different missions into a single platform. Now years later a complete reversal; should agencies considering Disaggregation as the answer. We'll discuss what some academic research suggests. Second, using the GCOS requirements of earth climate observations via ECV (essential climate variables) many collected from space-based sensors; and accepting their definitions of global coverages intended to ensure the needs of major global and international organizations (UNFCCC and IPCC) are met as a core objective. Consider how new optimization tools like rule-based engines (RBES) offer alternative methods of evaluating collaborative architectures and constellations? What would the trade space of optimized operational climate monitoring architectures of ECV look like? Third, using the RBES tool kit (2014) demonstrate with application to a climate centric rule-based decision engine - optimizing architectural trades of earth observation satellite systems, allowing comparison(s) to existing architectures and gaining insights for global collaborative architectures. How difficult is it to pull together an optimized climate case study - utilizing for example 12 climate based instruments on multiple existing platforms and nominal handful of orbits; for best cost and performance benefits against the collection requirements of representative set of ECV. How much effort and resources would an organization expect to invest to realize these analysis and utility benefits?
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Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.
2014-01-01
Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187
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Determining rules for closing customer service centers: A public utility company's fuzzy decision
NASA Technical Reports Server (NTRS)
Dekorvin, Andre; Shipley, Margaret F.
1992-01-01
In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert.
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Monahan, M; Ensor, J; Moore, D; Fitzmaurice, D; Jowett, S
2017-08-01
Essentials Correct duration of treatment after a first unprovoked venous thromboembolism (VTE) is unknown. We assessed when restarting anticoagulation was worthwhile based on patient risk of recurrent VTE. When the risk over a one-year period is 17.5%, restarting is cost-effective. However, sensitivity analyses indicate large uncertainty in the estimates. Background Following at least 3 months of anticoagulation therapy after a first unprovoked venous thromboembolism (VTE), there is uncertainty about the duration of therapy. Further anticoagulation therapy reduces the risk of having a potentially fatal recurrent VTE but at the expense of a higher risk of bleeding, which can also be fatal. Objective An economic evaluation sought to estimate the long-term cost-effectiveness of using a decision rule for restarting anticoagulation therapy vs. no extension of therapy in patients based on their risk of a further unprovoked VTE. Methods A Markov patient-level simulation model was developed, which adopted a lifetime time horizon with monthly time cycles and was from a UK National Health Service (NHS)/Personal Social Services (PSS) perspective. Results Base-case model results suggest that treating patients with a predicted 1 year VTE risk of 17.5% or higher may be cost-effective if decision makers are willing to pay up to £20 000 per quality adjusted life year (QALY) gained. However, probabilistic sensitivity analysis shows that the model was highly sensitive to overall parameter uncertainty and caution is warranted in selecting the optimal decision rule on cost-effectiveness grounds. Univariate sensitivity analyses indicate variables such as anticoagulation therapy disutility and mortality risks were very influential in driving model results. Conclusion This represents the first economic model to consider the use of a decision rule for restarting therapy for unprovoked VTE patients. Better data are required to predict long-term bleeding risks during therapy in this patient group. © 2017 International Society on Thrombosis and Haemostasis.
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14 CFR 302.607 - Decision by administrative law judge.
Code of Federal Regulations, 2014 CFR
2014-01-01
... (AVIATION PROCEEDINGS) PROCEDURAL REGULATIONS RULES OF PRACTICE IN PROCEEDINGS Rules Applicable to... judge shall issue a decision recommending a disposition of a complaint or request for determination...
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14 CFR 302.607 - Decision by administrative law judge.
Code of Federal Regulations, 2013 CFR
2013-01-01
... (AVIATION PROCEEDINGS) PROCEDURAL REGULATIONS RULES OF PRACTICE IN PROCEEDINGS Rules Applicable to... judge shall issue a decision recommending a disposition of a complaint or request for determination...
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14 CFR 302.607 - Decision by administrative law judge.
Code of Federal Regulations, 2010 CFR
2010-01-01
... (AVIATION PROCEEDINGS) PROCEDURAL REGULATIONS RULES OF PRACTICE IN PROCEEDINGS Rules Applicable to... judge shall issue a decision recommending a disposition of a complaint or request for determination...
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14 CFR 302.607 - Decision by administrative law judge.
Code of Federal Regulations, 2011 CFR
2011-01-01
... (AVIATION PROCEEDINGS) PROCEDURAL REGULATIONS RULES OF PRACTICE IN PROCEEDINGS Rules Applicable to... judge shall issue a decision recommending a disposition of a complaint or request for determination...
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14 CFR 302.607 - Decision by administrative law judge.
Code of Federal Regulations, 2012 CFR
2012-01-01
... (AVIATION PROCEEDINGS) PROCEDURAL REGULATIONS RULES OF PRACTICE IN PROCEEDINGS Rules Applicable to... judge shall issue a decision recommending a disposition of a complaint or request for determination...
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Jacob, Louis; Uvarova, Maria; Boulet, Sandrine; Begaj, Inva; Chevret, Sylvie
2016-06-02
Multi-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure. We redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds. Our findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3). Adaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates. ClinicalTrials.gov Identifier: NCT01342692 .
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ERIC Educational Resources Information Center
Shockley-Zalabak, Pamela
A study of decision making processes and communication rules, in a corporate setting undergoing change as a result of organizational ineffectiveness, examined whether (1) decisions about formal communication reporting systems were linked to management assumptions about technical creativity/effectiveness, (2) assumptions about…
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38 CFR 20.1401 - Rule 1401. Definitions.
Code of Federal Regulations, 2012 CFR
2012-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2012-07-01 2012-07-01 false Rule 1401. Definitions... Unmistakable Error § 20.1401 Rule 1401. Definitions. (a) Issue. Unless otherwise specified, the term “issue” in this subpart means a matter upon which the Board made a final decision (other than a decision under...
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38 CFR 20.1401 - Rule 1401. Definitions.
Code of Federal Regulations, 2011 CFR
2011-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2011-07-01 2011-07-01 false Rule 1401. Definitions... Unmistakable Error § 20.1401 Rule 1401. Definitions. (a) Issue. Unless otherwise specified, the term “issue” in this subpart means a matter upon which the Board made a final decision (other than a decision under...
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38 CFR 20.1401 - Rule 1401. Definitions.
Code of Federal Regulations, 2014 CFR
2014-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2014-07-01 2014-07-01 false Rule 1401. Definitions... Unmistakable Error § 20.1401 Rule 1401. Definitions. (a) Issue. Unless otherwise specified, the term “issue” in this subpart means a matter upon which the Board made a final decision (other than a decision under...
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Inferring the rules of social interaction in migrating caribou.
Torney, Colin J; Lamont, Myles; Debell, Leon; Angohiatok, Ryan J; Leclerc, Lisa-Marie; Berdahl, Andrew M
2018-05-19
Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa.This article is part of the theme issue 'Collective movement ecology'. © 2018 The Authors.
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NASA Astrophysics Data System (ADS)
Müller, Ruben; Schütze, Niels
2014-05-01
Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A climate change assessment is performed for climate change scenarios based on the SRES emission scenarios A1B, B1 and A2 for a set of statistically downscaled meteorological data. The future performance of the multi-purpose multi-reservoir system is quantified and possible intensifications of trade-offs between management goals or reservoir utilizations are shown.
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Colonius, Hans; Diederich, Adele
2011-07-01
The concept of a "time window of integration" holds that information from different sensory modalities must not be perceived too far apart in time in order to be integrated into a multisensory perceptual event. Empirical estimates of window width differ widely, however, ranging from 40 to 600 ms depending on context and experimental paradigm. Searching for theoretical derivation of window width, Colonius and Diederich (Front Integr Neurosci 2010) developed a decision-theoretic framework using a decision rule that is based on the prior probability of a common source, the likelihood of temporal disparities between the unimodal signals, and the payoff for making right or wrong decisions. Here, this framework is extended to the focused attention task where subjects are asked to respond to signals from a target modality only. Evoking the framework of the time-window-of-integration (TWIN) model, an explicit expression for optimal window width is obtained. The approach is probed on two published focused attention studies. The first is a saccadic reaction time study assessing the efficiency with which multisensory integration varies as a function of aging. Although the window widths for young and older adults differ by nearly 200 ms, presumably due to their different peripheral processing speeds, neither of them deviates significantly from the optimal values. In the second study, head saccadic reactions times to a perfectly aligned audiovisual stimulus pair had been shown to depend on the prior probability of spatial alignment. Intriguingly, they reflected the magnitude of the time-window widths predicted by our decision-theoretic framework, i.e., a larger time window is associated with a higher prior probability.
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Implementation of clinical decision rules in the emergency department.
Stiell, Ian G; Bennett, Carol
2007-11-01
Clinical decision rules (CDRs) are tools designed to help clinicians make bedside diagnostic and therapeutic decisions. The development of a CDR involves three stages: derivation, validation, and implementation. Several criteria need to be considered when designing and evaluating the results of an implementation trial. In this article, the authors review the results of implementation studies evaluating the effect of four CDRs: the Ottawa Ankle Rules, the Ottawa Knee Rule, the Canadian C-Spine Rule, and the Canadian CT Head Rule. Four implementation studies demonstrated that the implementation of CDRs in the emergency department (ED) safely reduced the use of radiography for ankle, knee, and cervical spine injuries. However, a recent trial failed to demonstrate an impact on computed tomography imaging rates. Well-developed and validated CDRs can be successfully implemented into practice, efficiently standardizing ED care. However, further research is needed to identify barriers to implementation in order to achieve improved uptake in the ED.
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Kashyap, Vipul; Morales, Alfredo; Hongsermeier, Tonya
2006-01-01
We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.
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Clean Air Interstate Rule: Changes and Modeling in AEO2010 (released in AEO2010)
2010-01-01
On December 23, 2008, the D.C. Circuit Court remanded but did not vacate the Clean Air Interstate Rule (CAIR), overriding its previous decision on February 8, 2008, to remand and vacate CAIR. The December decision, which is reflected in Annual Energy Outlook 2010 (AEO) , allows CAIR to remain in effect, providing time for the Environmental Protection Agency to modify the rule in order to address objections raised by the Court in its earlier decision. A similar rule, referred to as the Clean Air Mercury Rule (CAMR), which was to set up a cap-and-trade system for reducing mercury emissions by approximately 70%, is not represented in the AEO2010 projections, because it was vacated by the D.C. Circuit Court in February 2008.
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Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules
van der Laan, Mark J.; Petersen, Maya L.
2008-01-01
Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus. PMID:19122792
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A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea.
Rodsutti, Julvit; Hensley, Michael; Thakkinstian, Ammarin; D'Este, Catherine; Attia, John
2004-06-15
To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography, Prospective data collection on consecutive patients referred to a sleep center. The Newcastle Sleep Disorders Centre, University of Newcastle, NSW, Australia. Consecutive adult patients who had been scheduled for initial diagnostic polysomnography. Eight hundred and thirty-seven patients were used for derivation of the decision rule. An apnea-hypopnoea index of at least 5 was used as the cutoff point to diagnose sleep apnea. Fifteen clinical features were included in the analyses using logistic regression to construct a model from the derivation data set. Only 5 variables--age, sex, body mass index, snoring, and stopping breathing during sleep--were significantly associated with sleep apnea. A scoring scheme based on regression coefficients was developed, and the total score was trichotomized into low-, moderate-, and high-risk groups with prevalence of sleep apnea of 8%, 51%, and 82%, respectively. Color-coded tables were developed for ease of use. The clinical decision rule was validated on a separate set of 243 patients. Receiver operating characteristic analysis confirmed that the decision rule performed well, with the area under the curve being similar for both the derivation and validation sets: 0.81 and 0.79, P =.612. We conclude that this decision rule was able to accurately classify the risk of sleep apnea and will be useful for prioritizing patients with suspected sleep apnea who are on waiting lists for polysomnography.
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18 CFR 385.2201 - Rules governing off-the-record communications (Rule 2201).
Code of Federal Regulations, 2010 CFR
2010-04-01
...) Relevant to the merits means capable of affecting the outcome of a proceeding, or of influencing a decision, or providing an opportunity to influence a decision, on any issue in the proceeding, but does not... Commission in a manner that permits fully informed decision making by the Commission while ensuring the...
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18 CFR 385.704 - Rights of participants before initial decision (Rule 704).
Code of Federal Regulations, 2010 CFR
2010-04-01
... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Rights of participants... PROCEDURE Decisions § 385.704 Rights of participants before initial decision (Rule 704). After testimony is... good cause, deny opportunity for reply or limit the issues which may be addressed in any reply. ...
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17 CFR 201.410 - Appeal of initial decisions by hearing officers.
Code of Federal Regulations, 2010 CFR
2010-04-01
... COMMISSION RULES OF PRACTICE Rules of Practice Appeal to the Commission and Commission Review § 201.410 Appeal of initial decisions by hearing officers. (a) Petition for review; when available. In any... would have been entitled to judicial review of the decision entered therein if the Commission itself had...
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Wolf, Max; Krause, Jens; Carney, Patricia A; Bogart, Andy; Kurvers, Ralf H J M
2015-01-01
While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules ("majority", "quorum", and "weighted quorum") when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.
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Evolving optimised decision rules for intrusion detection using particle swarm paradigm
NASA Astrophysics Data System (ADS)
Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.
2012-12-01
The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.
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Modeling joint restoration strategies for interdependent infrastructure systems
Simonovic, Slobodan P.
2018-01-01
Life in the modern world depends on multiple critical services provided by infrastructure systems which are interdependent at multiple levels. To effectively respond to infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent infrastructure systems following a disruptive event. First, models for (i) describing structure of interdependent infrastructure system and (ii) their interaction process, are presented. Both models are considering the failure types, infrastructure operating rules and interdependencies among systems. Second, an optimization model for determining an optimal joint restoration strategy at infrastructure component level by minimizing the economic loss from the infrastructure failures, is proposed. The utility of the model is illustrated using a case study of electric-water systems. Results show that a small number of failed infrastructure components can trigger high level failures in interdependent systems; the optimal joint restoration strategy varies with failure occurrence time. The proposed models can help decision makers to understand the mechanisms of infrastructure interactions and search for optimal joint restoration strategy, which can significantly enhance safety of infrastructure systems. PMID:29649300
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Supervision of dynamic systems: Monitoring, decision-making and control
NASA Technical Reports Server (NTRS)
White, T. N.
1982-01-01
Effects of task variables on the performance of the human supervisor by means of modelling techniques are discussed. The task variables considered are: The dynamics of the system, the task to be performed, the environmental disturbances and the observation noise. A relationship between task variables and parameters of a supervisory model is assumed. The model consists of three parts: (1) The observer part is thought to be a full order optimal observer, (2) the decision-making part is stated as a set of decision rules, and (3) the controller part is given by a control law. The observer part generates, on the basis of the system output and the control actions, an estimate of the state of the system and its associated variance. The outputs of the observer part are then used by the decision-making part to determine the instants in time of the observation actions on the one hand and the controls actions on the other. The controller part makes use of the estimated state to derive the amplitude(s) of the control action(s).
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Code of Federal Regulations, 2010 CFR
2010-07-01
... of 38 U.S.C. 6104 and 6105, issues involved in a survivor's claim for death benefits will be decided... death benefits by survivor-prior unfavorable decisions during veteran's lifetime. 20.1106 Section 20... VETERANS' APPEALS: RULES OF PRACTICE Finality § 20.1106 Rule 1106. Claim for death benefits by survivor...
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48 CFR 6101.26 - Reconsideration; amendment of decisions; new hearings [Rule 26].
Code of Federal Regulations, 2012 CFR
2012-10-01
... in 6101.27(a) (Rule 27(a)) and the reasons established by the rules of common law or equity... for granting a new hearing. Upon granting a motion for a new hearing, the Board will take additional testimony and, if a decision has been issued, either amend its findings of fact and conclusions or law or...
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-10-25
... provide input to decision-making for updating the Waste Confidence Decision and Rule and would not involve... Commission's tentative planning and decision-making schedule; g. Identify any cooperating agencies and, as... #0;notices is to give interested persons an opportunity to participate in #0;the rule making prior to...
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Order of Verdict Consideration and Decision Rule Effects on Mock Jury Decision Making.
ERIC Educational Resources Information Center
Olaye, Imafidon M.
A study investigated the effects of order verdict consideration and decision rule on jury verdicts. After reading the summary of an actual trial, 240 mock jurors drawn from undergraduate communications classes were randomly assigned to six-member juries. Jury assignments were made under two verdict orders (ascending and descending order of…
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ERIC Educational Resources Information Center
Fific, Mario; Little, Daniel R.; Nosofsky, Robert M.
2010-01-01
We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli…
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Federal Register 2010, 2011, 2012, 2013, 2014
2013-04-01
... Reconsideration (MO&O) denies or dismisses petitions seeking reconsideration of certain decisions made by the Commission in the 700 MHz Second Report and Order, relating to the 698-806 MHz Band, including decisions..., public safety narrowband relocation procedures, and the decisions not to impose wholesale requirements...
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When More Is Less: Feedback Effects in Perceptual Category Learning
ERIC Educational Resources Information Center
Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent
2008-01-01
Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether…
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2014-01-01
Background Previous efforts such as Assessing Care of Vulnerable Elders (ACOVE) provide quality indicators for assessing the care of elderly patients, but thus far little has been done to leverage this knowledge to improve care for these patients. We describe a clinical decision support system to improve general practitioner (GP) adherence to ACOVE quality indicators and a protocol for investigating impact on GPs’ adherence to the rules. Design We propose two randomized controlled trials among a group of Dutch GP teams on adherence to ACOVE quality indicators. In both trials a clinical decision support system provides un-intrusive feedback appearing as a color-coded, dynamically updated, list of items needing attention. The first trial pertains to real-time automatically verifiable rules. The second trial concerns non-automatically verifiable rules (adherence cannot be established by the clinical decision support system itself, but the GPs report whether they will adhere to the rules). In both trials we will randomize teams of GPs caring for the same patients into two groups, A and B. For the automatically verifiable rules, group A GPs receive support only for a specific inter-related subset of rules, and group B GPs receive support only for the remainder of the rules. For non-automatically verifiable rules, group A GPs receive feedback framed as actions with positive consequences, and group B GPs receive feedback framed as inaction with negative consequences. GPs indicate whether they adhere to non-automatically verifiable rules. In both trials, the main outcome measure is mean adherence, automatically derived or self-reported, to the rules. Discussion We relied on active end-user involvement in selecting the rules to support, and on a model for providing feedback displayed as color-coded real-time messages concerning the patient visiting the GP at that time, without interrupting the GP’s workflow with pop-ups. While these aspects are believed to increase clinical decision support system acceptance and its impact on adherence to the selected clinical rules, systems with these properties have not yet been evaluated. Trial registration Controlled Trials NTR3566 PMID:24642339
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Wang, Yong; Wang, Bing-Chuan; Li, Han-Xiong; Yen, Gary G
2016-12-01
When solving constrained optimization problems by evolutionary algorithms, an important issue is how to balance constraints and objective function. This paper presents a new method to address the above issue. In our method, after generating an offspring for each parent in the population by making use of differential evolution (DE), the well-known feasibility rule is used to compare the offspring and its parent. Since the feasibility rule prefers constraints to objective function, the objective function information has been exploited as follows: if the offspring cannot survive into the next generation and if the objective function value of the offspring is better than that of the parent, then the offspring is stored into a predefined archive. Subsequently, the individuals in the archive are used to replace some individuals in the population according to a replacement mechanism. Moreover, a mutation strategy is proposed to help the population jump out of a local optimum in the infeasible region. Note that, in the replacement mechanism and the mutation strategy, the comparison of individuals is based on objective function. In addition, the information of objective function has also been utilized to generate offspring in DE. By the above processes, this paper achieves an effective balance between constraints and objective function in constrained evolutionary optimization. The performance of our method has been tested on two sets of benchmark test functions, namely, 24 test functions at IEEE CEC2006 and 18 test functions with 10-D and 30-D at IEEE CEC2010. The experimental results have demonstrated that our method shows better or at least competitive performance against other state-of-the-art methods. Furthermore, the advantage of our method increases with the increase of the number of decision variables.
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Rejecting the Baby Doe rules and defending a "negative" analysis of the Best Interests Standard.
Kopelman, Loretta M
2005-08-01
Two incompatible policies exist for guiding medical decisions for extremely premature, sick, or terminally ill infants, the Best Interests Standard and the newer, 20-year old "Baby Doe" Rules. The background, including why there were two sets of Baby Doe Rules, and their differences with the Best Interests Standard, are illustrated. Two defenses of the Baby Doe Rules are considered and rejected. The first, held by Reagan, Koop, and others, is a "right-to-life" defense. The second, held by some leaders of the American Academy of Pediatrics, is that the Baby Doe Rules are benign and misunderstood. The Baby Doe Rules should be rejected since they can thwart compassionate and individualized decision-making, undercut duties to minimize unnecessary suffering, and single out one group for treatment adults would not want for themselves. In these ways, they are inferior to the older Best Interests Standard. A "negative" analysis of the Best Interests Standard is articulated and defended for decision-making for all incompetent individuals.
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Schiebener, Johannes; Brand, Matthias
2017-06-01
Previous literature has explained older individuals' disadvantageous decision-making under ambiguity in the Iowa Gambling Task (IGT) by reduced emotional warning signals preceding decisions. We argue that age-related reductions in IGT performance may also be explained by reductions in certain cognitive abilities (reasoning, executive functions). In 210 participants (18-86 years), we found that the age-related variance on IGT performance occurred only in the last 60 trials. The effect was mediated by cognitive abilities and their relation with decision-making performance under risk with explicit rules (Game of Dice Task). Thus, reductions in cognitive functions in older age may be associated with both a reduced ability to gain explicit insight into the rules of the ambiguous decision situation and with failure to choose the less risky options consequently after the rules have been understood explicitly. Previous literature may have underestimated the relevance of cognitive functions for age-related decline in decision-making performance under ambiguity.
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Code of Federal Regulations, 2010 CFR
2010-07-01
... 30 Mineral Resources 2 2010-07-01 2010-07-01 false Where are the rules concerning the effect of the Department not issuing a decision in my appeal within the statutory time frame? 290.107 Section... PROCEDURES Minerals Revenue Management Appeal Procedures § 290.107 Where are the rules concerning the effect...
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-04-15
... package to OMB for its review and approval because the changes in this rule making do not affect the...: Final rule. SUMMARY: The United States Court of Appeals for the Federal Circuit issued a decision in Agilent Technologies, Inc. v. Affymetrix, Inc., 567 F.3d 1366 (Fed. Cir. 2009). That decision impacted the...
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Design of exploration and minerals-data-collection programs in developing areas
Attanasi, E.D.
1981-01-01
This paper considers the practical problem of applying economic analysis to designing minerals exploration and data collection strategies for developing countries. Formal decision rules for the design of government exploration and minerals-data-collection programs are derived by using a minerals-industry planning model that has been extended to include an exploration function. Rules derived are applicable to centrally planned minerals industries as well as market-oriented minerals sectors. They pertain to the spatial allocation of exploration effort and to the allocation of activities between government and private concerns for market-oriented economies. Programs characterized by uniform expenditures, uniform information coverage across regions, or uniform-density grid drilling progrmas are shown to be inferior to the strategy derived. Moreover, for market-oriented economies, the economically optimal mix in exploration activities between private and government data collection would require that only private firms assess local sites and that government agencies carry out regional surveys. ?? 1981.
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An overview of bipolar qualitative decision rules
NASA Astrophysics Data System (ADS)
Bonnefon, Jean-Francois; Dubois, Didier; Fargier, Hélène
Making a good decision is often a matter of listing and comparing positive and negative arguments, as studies in cognitive psychology have shown. In such cases, the evaluation scale should be considered bipolar, that is, negative and positive values are explicitly distinguished. Generally, positive and negative features are evaluated separately, as done in Cumulative Prospect Theory. However, contrary to the latter framework that presupposes genuine numerical assessments, decisions are often made on the basis of an ordinal ranking of the pros and the cons, and focusing on the most salient features, i.e., the decision process is qualitative. In this paper, we report on a project aiming at characterizing several decision rules, based on possibilistic order of magnitude reasoning, and tailored for the joint handling of positive and negative affects, and at testing their empirical validity. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case and, like them, suffer from a lack of discrimination power. More decisive rules that refine them are also proposed. They account for both the principle of Pareto-efficiency and the notion of order of magnitude reasoning. The most decisive one uses a lexicographic ranking of the pros and cons. It comes down to a special case of Cumulative Prospect Theory, and subsumes the “Take the best” heuristic.
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A neural network model of foraging decisions made under predation risk.
Coleman, Scott L; Brown, Vincent R; Levine, Daniel S; Mellgren, Roger L
2005-12-01
This article develops the cognitive-emotional forager (CEF) model, a novel application of a neural network to dynamical processes in foraging behavior. The CEF is based on a neural network known as the gated dipole, introduced by Grossberg, which is capable of representing short-term affective reactions in a manner similar to Solomon and Corbit's (1974) opponent process theory. The model incorporates a trade-off between approach toward food and avoidance of predation under varying levels of motivation induced by hunger. The results of simulations in a simple patch selection paradigm, using a lifetime fitness criterion for comparison, indicate that the CEF model is capable of nearly optimal foraging and outperforms a run-of-luck rule-of-thumb model. Models such as the one presented here can illuminate the underlying cognitive and motivational components of animal decision making.
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Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm
NASA Astrophysics Data System (ADS)
Mittal, Ruchi; Kaur, Magandeep
2010-11-01
In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.
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Federal Register 2010, 2011, 2012, 2013, 2014
2013-01-24
... which is delegated the authority to review disciplinary decisions on behalf of the Exchange Board of... organization. Under the rule, the decision of a majority of the Hearing Panel is the decision of the Hearing..., may request a determination of guilt by default, and may recommend a penalty to be imposed. If the...
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-07-12
... that a refusal by a member to take action necessary to effectuate a final decision of a FINRA officer... necessary to effectuate a final decision of a FINRA officer or the UPC Committee under the UPC Code (FINRA Rule 11000 Series) or other FINRA rules that permit review of FINRA decisions by the UPC Committee...
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48 CFR 6101.26 - Reconsideration; amendment of decisions; new hearings [Rule 26].
Code of Federal Regulations, 2010 CFR
2010-10-01
... amend a decision or order for any reason that would justify such action on motion of a party. (d) Effect... for granting a new hearing. Upon granting a motion for a new hearing, the Board will take additional... issue a new decision. (b) Procedure. Any motion under 6101.26 (Rule 26) shall comply with the provisions...
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48 CFR 6101.26 - Reconsideration; amendment of decisions; new hearings [Rule 26].
Code of Federal Regulations, 2011 CFR
2011-10-01
... amend a decision or order for any reason that would justify such action on motion of a party. (d) Effect... for granting a new hearing. Upon granting a motion for a new hearing, the Board will take additional... issue a new decision. (b) Procedure. Any motion under 6101.26 (Rule 26) shall comply with the provisions...
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An Autonomous Flight Safety System
2008-11-01
are taken. AFSS can take vehicle navigation data from redundant onboard sensors and make flight termination decisions using software-based rules...implemented on redundant flight processors. By basing these decisions on actual Instantaneous Impact Predictions and by providing for an arbitrary...number of mission rules, it is the contention of the AFSS development team that the decision making process used by Missile Flight Control Officers
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Castellano, Sergio; Cermelli, Paolo
2011-04-07
Mate choice depends on mating preferences and on the manner in which mate-quality information is acquired and used to make decisions. We present a model that describes how these two components of mating decision interact with each other during a comparative evaluation of prospective mates. The model, with its well-explored precedents in psychology and neurophysiology, assumes that decisions are made by the integration over time of noisy information until a stopping-rule criterion is reached. Due to this informational approach, the model builds a coherent theoretical framework for developing an integrated view of functions and mechanisms of mating decisions. From a functional point of view, the model allows us to investigate speed-accuracy tradeoffs in mating decision at both population and individual levels. It shows that, under strong time constraints, decision makers are expected to make fast and frugal decisions and to optimally trade off population-sampling accuracy (i.e. the number of sampled males) against individual-assessment accuracy (i.e. the time spent for evaluating each mate). From the proximate-mechanism point of view, the model makes testable predictions on the interactions of mating preferences and choosiness in different contexts and it might be of compelling empirical utility for a context-independent description of mating preference strength. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Determining rules for closing customer service centers: A public utility company's fuzzy decision
NASA Technical Reports Server (NTRS)
Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.
1992-01-01
In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.
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78 FR 36434 - Revisions to Rules of Practice
Federal Register 2010, 2011, 2012, 2013, 2014
2013-06-18
... federal holidays, make grammatical corrections, and remove the reference to part-day holidays. Rule 3001... section, the following categories of persons are designated ``decision-making personnel'': (i) The.... The following categories of person are designated ``non-decision-making personnel'': (i) All...
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Extracting decision rules from police accident reports through decision trees.
de Oña, Juan; López, Griselda; Abellán, Joaquín
2013-01-01
Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules. Copyright © 2012 Elsevier Ltd. All rights reserved.
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2012-01-01
Background Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs), Software Engineers (SEs), and Subject Matter Experts (SMEs) to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE) in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. Methods The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools. Results While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS) interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users. Conclusions A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR) systems, testing, and reporting. PMID:23145874
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NASA Astrophysics Data System (ADS)
Cheng, Wei-Chen; Hsu, Nien-Sheng; Cheng, Wen-Ming; Yeh, William W.-G.
2011-10-01
This paper develops alternative strategies for European call options for water purchase under hydrological uncertainties that can be used by water resources managers for decision making. Each alternative strategy maximizes its own objective over a selected sequence of future hydrology that is characterized by exceedance probability. Water trade provides flexibility and enhances water distribution system reliability. However, water trade between two parties in a regional water distribution system involves many issues, such as delivery network, reservoir operation rules, storage space, demand, water availability, uncertainty, and any existing contracts. An option is a security giving the right to buy or sell an asset; in our case, the asset is water. We extend a flow path-based water distribution model to include reservoir operation rules. The model simultaneously considers both the physical distribution network as well as the relationships between water sellers and buyers. We first test the model extension. Then we apply the proposed optimization model for European call options to the Tainan water distribution system in southern Taiwan. The formulation lends itself to a mixed integer linear programming model. We use the weighing method to formulate a composite function for a multiobjective problem. The proposed methodology provides water resources managers with an overall picture of water trade strategies and the consequence of each strategy. The results from the case study indicate that the strategy associated with a streamflow exceedence probability of 50% or smaller should be adopted as the reference strategy for the Tainan water distribution system.
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Design technology co-optimization for 14/10nm metal1 double patterning layer
NASA Astrophysics Data System (ADS)
Duan, Yingli; Su, Xiaojing; Chen, Ying; Su, Yajuan; Shao, Feng; Zhang, Recco; Lei, Junjiang; Wei, Yayi
2016-03-01
Design and technology co-optimization (DTCO) can satisfy the needs of the design, generate robust design rule, and avoid unfriendly patterns at the early stage of design to ensure a high level of manufacturability of the product by the technical capability of the present process. The DTCO methodology in this paper includes design rule translation, layout analysis, model validation, hotspots classification and design rule optimization mainly. The correlation of the DTCO and double patterning (DPT) can optimize the related design rule and generate friendlier layout which meets the requirement of the 14/10nm technology node. The experiment demonstrates the methodology of DPT-compliant DTCO which is applied to a metal1 layer from the 14/10nm node. The DTCO workflow proposed in our job is an efficient solution for optimizing the design rules for 14/10 nm tech node Metal1 layer. And the paper also discussed and did the verification about how to tune the design rule of the U-shape and L-shape structures in a DPT-aware metal layer.
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Van Norman, Ethan R; Christ, Theodore J
2016-10-01
Curriculum based measurement of oral reading (CBM-R) is used to monitor the effects of academic interventions for individual students. Decisions to continue, modify, or terminate these interventions are made by interpreting time series CBM-R data. Such interpretation is founded upon visual analysis or the application of decision rules. The purpose of this study was to compare the accuracy of visual analysis and decision rules. Visual analysts interpreted 108 CBM-R progress monitoring graphs one of three ways: (a) without graphic aids, (b) with a goal line, or (c) with a goal line and a trend line. Graphs differed along three dimensions, including trend magnitude, variability of observations, and duration of data collection. Automated trend line and data point decision rules were also applied to each graph. Inferential analyses permitted the estimation of the probability of a correct decision (i.e., the student is improving - continue the intervention, or the student is not improving - discontinue the intervention) for each evaluation method as a function of trend magnitude, variability of observations, and duration of data collection. All evaluation methods performed better when students made adequate progress. Visual analysis and decision rules performed similarly when observations were less variable. Results suggest that educators should collect data for more than six weeks, take steps to control measurement error, and visually analyze graphs when data are variable. Implications for practice and research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.
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FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects.
Lagorce, David; Sperandio, Olivier; Galons, Hervé; Miteva, Maria A; Villoutreix, Bruno O
2008-09-24
Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.
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NASA Astrophysics Data System (ADS)
Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing
2017-09-01
The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.
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Application of decision rules for empowering of Indonesian telematics services SMEs
NASA Astrophysics Data System (ADS)
Tosida, E. T.; Hairlangga, O.; Amirudin, F.; Ridwanah, M.
2018-03-01
The independence of the field of telematics became one of Indonesia's vision in 2024. One effort to achieve it can be done by empowering SMEs in the field of telematics. Empowerment carried out need a practical mechanism by utilizing data centered, including through the National Economic Census database (Susenas). Based on the Susenas can be formulated the decision rules of determining the provision of assistance for SMEs in the field of telematics. The way it did by generating the rule base through the classification technique. The CART algorithm-based decision rule model performs better than C45 and ID3 models. The high level of performance model is also in line with the regulations applied by the government. This becomes one of the strengths of research, because the resulting model is consistent with the existing conditions in Indonesia. The rules base generated from the three classification techniques show different rules. The CART technique has pattern matching with the realization of activities in The Ministry of Cooperatives and SMEs. So far, the government has difficulty in referring data related to the empowerment of SMEs telematics services. Therefore, the findings resulting from this research can be used as an alternative decision support system related to the program of empowerment of SMEs in telematics.
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2015-12-30
This final rule establishes a prior authorization program for certain durable medical equipment, prosthetics, orthotics, and supplies (DMEPOS) items that are frequently subject to unnecessary utilization. This rule defines unnecessary utilization and creates a new requirement that claims for certain DMEPOS items must have an associated provisional affirmed prior authorization decision as a condition of payment. This rule also adds the review contractor's decision regarding prior authorization of coverage of DMEPOS items to the list of actions that are not initial determinations and therefore not appealable.
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Proposed Clinical Decision Rules to Diagnose Acute Rhinosinusitis Among Adults in Primary Care.
Ebell, Mark H; Hansen, Jens Georg
2017-07-01
To reduce inappropriate antibiotic prescribing, we sought to develop a clinical decision rule for the diagnosis of acute rhinosinusitis and acute bacterial rhinosinusitis. Multivariate analysis and classification and regression tree (CART) analysis were used to develop clinical decision rules for the diagnosis of acute rhinosinusitis, defined using 3 different reference standards (purulent antral puncture fluid or abnormal finding on a computed tomographic (CT) scan; for acute bacterial rhinosinusitis, we used a positive bacterial culture of antral fluid). Signs, symptoms, C-reactive protein (CRP), and reference standard tests were prospectively recorded in 175 Danish patients aged 18 to 65 years seeking care for suspected acute rhinosinusitis. For each reference standard, we developed 2 clinical decision rules: a point score based on a logistic regression model and an algorithm based on a CART model. We identified low-, moderate-, and high-risk groups for acute rhinosinusitis or acute bacterial rhinosinusitis for each clinical decision rule. The point scores each had between 5 and 6 predictors, and an area under the receiver operating characteristic curve (AUROCC) between 0.721 and 0.767. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a 16%, 49%, and 73% likelihood of acute bacterial rhinosinusitis, respectively. CART models had an AUROCC ranging from 0.783 to 0.827. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a likelihood of acute bacterial rhinosinusitis of 6%, 31%, and 59% respectively. We have developed a series of clinical decision rules integrating signs, symptoms, and CRP to diagnose acute rhinosinusitis and acute bacterial rhinosinusitis with good accuracy. They now require prospective validation and an assessment of their effect on clinical and process outcomes. © 2017 Annals of Family Medicine, Inc.
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Watzek, Julia; Brosnan, Sarah F
2018-05-28
Human and animal decision-making is known to violate rational expectations in a variety of contexts. Previous models suggest that statistical structures of real-world environments can favor such seemingly irrational behavior, but this has not been tested empirically. We tested 16 capuchin monkeys, seven rhesus monkeys, and 30 humans in a computerized experiment that implemented such stochastic environments. Subjects chose from among up to three options of different value that disappeared and became available again with different probabilities. All species overwhelmingly chose transitively (A > B > C) in the control condition, where doing so maximized overall gain. Most subjects also adhered to transitivity in the test condition, where it was suboptimal, but ultimately led to negligible losses compared to the optimal, non-transitive strategy. We used a modelling approach to show that differences in temporal discounting may account for this pattern of choices on a proximate level. Specifically, when short- and long-term goals are valued similarly, near-optimal decision rules can map onto rational choice principles. Such cognitive shortcuts have been argued to have evolved to preserve mental resources without sacrificing good decision-making, and here we provide evidence that these heuristics can provide almost identical outcomes even in situations in which they lead to suboptimal choices. Copyright © 2018 Elsevier B.V. All rights reserved.
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Geometry-based ensembles: toward a structural characterization of the classification boundary.
Pujol, Oriol; Masip, David
2009-06-01
This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.
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Dynamic optimization of CELSS crop photosynthetic rate by computer-assisted feedback control
NASA Astrophysics Data System (ADS)
Chun, C.; Mitchell, C. A.
1997-01-01
A procedure for dynamic optimization of net photosynthetic rate (Pn) for crop production in Controlled Ecological Life-Support Systems (CELSS) was developed using leaf lettuce as a model crop. Canopy Pn was measured in real time and fed back for environmental control. Setpoints of photosynthetic photon flux (PPF) and CO_2 concentration for each hour of the crop-growth cycle were decided by computer to reach a targeted Pn each day. Decision making was based on empirical mathematical models combined with rule sets developed from recent experimental data. Comparisons showed that dynamic control resulted in better yield per unit energy input to the growth system than did static control. With comparable productivity parameters and potential for significant energy savings, dynamic control strategies will contribute greatly to the sustainability of space-deployed CELSS.
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The need for speed: informed land acquisitions for conservation in a dynamic property market.
McDonald-Madden, Eve; Bode, Michael; Game, Edward T; Grantham, Hedley; Possingham, Hugh P
2008-11-01
Land acquisition is a common approach to biodiversity conservation but is typically subject to property availability on the public market. Consequently, conservation plans are often unable to be implemented as intended. When properties come on the market, conservation agencies must make a choice: purchase immediately, often without a detailed knowledge of its biodiversity value; survey the parcel and accept the risk that it may be removed from the market during this process; or not purchase and hope a better parcel comes on the market at a later date. We describe both an optimal method, using stochastic dynamic programming, and a simple rule of thumb for making such decisions. The solutions to this problem illustrate how optimal conservation is necessarily dynamic and requires explicit consideration of both the time period allowed for implementation and the availability of properties.
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Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman
2018-06-01
Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.
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Rands, Sean A.
2011-01-01
Functional explanations of behaviour often propose optimal strategies for organisms to follow. These ‘best’ strategies could be difficult to perform given biological constraints such as neural architecture and physiological constraints. Instead, simple heuristics or ‘rules-of-thumb’ that approximate these optimal strategies may instead be performed. From a modelling perspective, rules-of-thumb are also useful tools for considering how group behaviour is shaped by the behaviours of individuals. Using simple rules-of-thumb reduces the complexity of these models, but care needs to be taken to use rules that are biologically relevant. Here, we investigate the similarity between the outputs of a two-player dynamic foraging game (which generated optimal but complex solutions) and a computational simulation of the behaviours of the two members of a foraging pair, who instead followed a rule-of-thumb approximation of the game's output. The original game generated complex results, and we demonstrate here that the simulations following the much-simplified rules-of-thumb also generate complex results, suggesting that the rule-of-thumb was sufficient to make some of the model outcomes unpredictable. There was some agreement between both modelling techniques, but some differences arose – particularly when pair members were not identical in how they gained and lost energy. We argue that exploring how rules-of-thumb perform in comparison to their optimal counterparts is an important exercise for biologically validating the output of agent-based models of group behaviour. PMID:21765938
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Rands, Sean A
2011-01-01
Functional explanations of behaviour often propose optimal strategies for organisms to follow. These 'best' strategies could be difficult to perform given biological constraints such as neural architecture and physiological constraints. Instead, simple heuristics or 'rules-of-thumb' that approximate these optimal strategies may instead be performed. From a modelling perspective, rules-of-thumb are also useful tools for considering how group behaviour is shaped by the behaviours of individuals. Using simple rules-of-thumb reduces the complexity of these models, but care needs to be taken to use rules that are biologically relevant. Here, we investigate the similarity between the outputs of a two-player dynamic foraging game (which generated optimal but complex solutions) and a computational simulation of the behaviours of the two members of a foraging pair, who instead followed a rule-of-thumb approximation of the game's output. The original game generated complex results, and we demonstrate here that the simulations following the much-simplified rules-of-thumb also generate complex results, suggesting that the rule-of-thumb was sufficient to make some of the model outcomes unpredictable. There was some agreement between both modelling techniques, but some differences arose - particularly when pair members were not identical in how they gained and lost energy. We argue that exploring how rules-of-thumb perform in comparison to their optimal counterparts is an important exercise for biologically validating the output of agent-based models of group behaviour.
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Textural features for image classification
NASA Technical Reports Server (NTRS)
Haralick, R. M.; Dinstein, I.; Shanmugam, K.
1973-01-01
Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
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19 CFR 177.13 - Inconsistent customs decisions.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 19 Customs Duties 2 2010-04-01 2010-04-01 false Inconsistent customs decisions. 177.13 Section 177.13 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) ADMINISTRATIVE RULINGS General Ruling Procedure § 177.13 Inconsistent customs...
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19 CFR 177.13 - Inconsistent customs decisions.
Code of Federal Regulations, 2012 CFR
2012-04-01
... 19 Customs Duties 2 2012-04-01 2012-04-01 false Inconsistent customs decisions. 177.13 Section 177.13 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) ADMINISTRATIVE RULINGS General Ruling Procedure § 177.13 Inconsistent customs...
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19 CFR 177.13 - Inconsistent customs decisions.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 19 Customs Duties 2 2011-04-01 2011-04-01 false Inconsistent customs decisions. 177.13 Section 177.13 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) ADMINISTRATIVE RULINGS General Ruling Procedure § 177.13 Inconsistent customs...
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Reliability Constrained Priority Load Shedding for Aerospace Power System Automation
NASA Technical Reports Server (NTRS)
Momoh, James A.; Zhu, Jizhong; Kaddah, Sahar S.; Dolce, James L. (Technical Monitor)
2000-01-01
The need for improving load shedding on board the space station is one of the goals of aerospace power system automation. To accelerate the optimum load-shedding functions, several constraints must be involved. These constraints include congestion margin determined by weighted probability contingency, component/system reliability index, generation rescheduling. The impact of different faults and indices for computing reliability were defined before optimization. The optimum load schedule is done based on priority, value and location of loads. An optimization strategy capable of handling discrete decision making, such as Everett optimization, is proposed. We extended Everett method to handle expected congestion margin and reliability index as constraints. To make it effective for real time load dispatch process, a rule-based scheme is presented in the optimization method. It assists in selecting which feeder load to be shed, the location of the load, the value, priority of the load and cost benefit analysis of the load profile is included in the scheme. The scheme is tested using a benchmark NASA system consisting of generators, loads and network.
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Optimizing Diagnostic Imaging in the Emergency Department
Mills, Angela M.; Raja, Ali S.; Marin, Jennifer R.
2015-01-01
While emergency diagnostic imaging use has increased significantly, there is a lack of evidence for corresponding improvements in patient outcomes. Optimizing emergency department (ED) diagnostic imaging has the potential to improve the quality, safety, and outcomes of ED patients, but to date, there have not been any coordinated efforts to further our evidence-based knowledge in this area. The objective of this article is to discuss six aspects of diagnostic imaging in order to provide background information on the underlying framework for the 2015 Academic Emergency Medicine consensus conference, “Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization.” The consensus conference aims to generate a high priority research agenda for emergency diagnostic imaging that will inform the design of future investigations. The six components herein will serve as the group topics for the conference: 1) patient-centered outcomes research; 2) clinical decision rules; 3) training, education, and competency; 4) knowledge translation and barriers to image optimization; 5) use of administrative data; and 6) comparative effectiveness research: alternatives to traditional CT use. PMID:25731864
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Optimizing diagnostic imaging in the emergency department.
Mills, Angela M; Raja, Ali S; Marin, Jennifer R
2015-05-01
While emergency diagnostic imaging use has increased significantly, there is a lack of evidence for corresponding improvements in patient outcomes. Optimizing emergency department (ED) diagnostic imaging has the potential to improve the quality, safety, and outcomes of ED patients, but to date, there have not been any coordinated efforts to further our evidence-based knowledge in this area. The objective of this article is to discuss six aspects of diagnostic imaging to provide background information on the underlying framework for the 2015 Academic Emergency Medicine consensus conference, "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization." The consensus conference aims to generate a high priority research agenda for emergency diagnostic imaging that will inform the design of future investigations. The six components herein will serve as the group topics for the conference: 1) patient-centered outcomes research; 2) clinical decision rules; 3) training, education, and competency; 4) knowledge translation and barriers to image optimization; 5) use of administrative data; and 6) comparative effectiveness research: alternatives to traditional CT use. © 2015 by the Society for Academic Emergency Medicine.
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Joint optimization of green vehicle scheduling and routing problem with time-varying speeds
Zhang, Dezhi; Wang, Xin; Ni, Nan; Zhang, Zhuo
2018-01-01
Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions. PMID:29466370
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Joint optimization of green vehicle scheduling and routing problem with time-varying speeds.
Zhang, Dezhi; Wang, Xin; Li, Shuangyan; Ni, Nan; Zhang, Zhuo
2018-01-01
Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions.
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Learning stage-dependent effect of M1 disruption on value-based motor decisions.
Derosiere, Gerard; Vassiliadis, Pierre; Demaret, Sophie; Zénon, Alexandre; Duque, Julie
2017-11-15
The present study aimed at characterizing the impact of M1 disruption on the implementation of implicit value information in motor decisions, at both early stages (during reinforcement learning) and late stages (after consolidation) of action value encoding. Fifty subjects performed, over three consecutive days, a task that required them to select between two finger responses according to the color (instruction) and to the shape (implicit, undisclosed rule) of an imperative signal: considering the implicit rule in addition to the instruction allowed subjects to earn more money. We investigated the functional contribution of M1 to the implementation of the implicit rule in subjects' motor decisions. Continuous theta burst stimulation (cTBS) was applied over M1 either on Day 1 or on Day 3, producing a temporary lesion either during reinforcement learning (cTBS Learning group) or after consolidation of the implicit rule, during decision-making (cTBS Decision group), respectively. Interestingly, disrupting M1 activity on Day 1 improved the reliance on the implicit rule, plausibly because M1 cTBS increased dopamine release in the putamen in an indirect way. This finding corroborates the view that cTBS may affect activity in unstimulated areas, such as the basal ganglia. Notably, this effect was short-lasting; it did not persist overnight, suggesting that the functional integrity of M1 during learning is a prerequisite for the consolidation of implicit value information to occur. Besides, cTBS over M1 did not impact the use of the implicit rule when applied on Day 3, although it did so when applied on Day 2 in a recent study where the reliance on the implicit rule declined following cTBS (Derosiere et al., 2017). Overall, these findings indicate that the human M1 is functionally involved in the consolidation and implementation of implicit value information underlying motor decisions. However, M1 contribution seems to vanish as subjects become more experienced in using the implicit value information to make their motor decisions. Copyright © 2017 Elsevier Inc. All rights reserved.
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Optimization of a matched-filter receiver for frequency hopping code acquisition in jamming
NASA Astrophysics Data System (ADS)
Pawlowski, P. R.; Polydoros, A.
A matched-filter receiver for frequency hopping (FH) code acquisition is optimized when either partial-band tone jamming or partial-band Gaussian noise jamming is present. The receiver is matched to a segment of the FH code sequence, sums hard per-channel decisions to form a test, and uses multiple tests to verify acquisition. The length of the matched filter and the number of verification tests are fixed. Optimization is then choosing thresholds to maximize performance based upon the receiver's degree of knowledge about the jammer ('side-information'). Four levels of side-information are considered, ranging from none to complete. The latter level results in a constant-false-alarm-rate (CFAR) design. At each level, performance sensitivity to threshold choice is analyzed. Robust thresholds are chosen to maximize performance as the jammer varies its power distribution, resulting in simple design rules which aid threshold selection. Performance results, which show that optimum distributions for the jammer power over the total FH bandwidth exist, are presented.
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Environmental statistics and optimal regulation
NASA Astrophysics Data System (ADS)
Sivak, David; Thomson, Matt
2015-03-01
The precision with which an organism can detect its environment, and the timescale for and statistics of environmental change, will affect the suitability of different strategies for regulating protein levels in response to environmental inputs. We propose a general framework--here applied to the enzymatic regulation of metabolism in response to changing nutrient concentrations--to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, and the costs associated with enzyme production. We find: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.
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The Cape Town Clinical Decision Rule for Streptococcal Pharyngitis in Children
Engel, Mark Emmanuel; Cohen, Karen; Gounden, Ronald; Kengne, Andre P.; Barth, Dylan Dominic; Whitelaw, Andrew C; Francis, Veronica; Badri, Motasim; Stewart, Annemie; Dale, James B.; Mayosi, Bongani M.; Maartens, Gary
2016-01-01
Background Existing clinical decision rules (CDR) to diagnose group A streptococcal (GAS) pharyngitis have not been validated in sub-Saharan Africa. We developed a locally applicable CDR while evaluating existing CDRs for diagnosing GAS pharyngitis in South African children. Methods We conducted a prospective cohort study and enrolled 997 children aged 3-15 years presenting to primary care clinics with a complaint of sore throat, and whose parents provided consent. Main outcome measures were signs and symptoms of pharyngitis, and a positive GAS culture from a throat swab. Bivariate and multivariate analyses were used to develop the clinical decision rule. In addition, the diagnostic effectiveness of six existing rules for predicting a positive culture in our cohort was assessed. Results 206 of 982 children (21%) had a positive GAS culture. Tonsillar swelling, tonsillar exudates, tender or enlarged anterior cervical lymph nodes, absence of cough and absence of rhinorrhea were associated with positive cultures in bivariate and multivariate analyses. Four variables (tonsillar swelling and one of tonsillar exudate, no rhinorrhea, no cough), when used in a cumulative score, showed 83.7% sensitivity and 32.2% specificity for GAS pharyngitis. Of existing rules tested, the McIsaac rule had the highest positive predictive value (28%), but missed 49% of the culture-positive children who should have been treated. Conclusion The new four-variable clinical decision rule for GAS pharyngitis (i.e., tonsillar swelling and one of tonsillar exudate, no rhinorrhea, no cough) outperformed existing rules for GAS pharyngitis diagnosis in children with symptomatic sore throat in Cape Town. PMID:27870815
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MacGillivray, Brian H
2017-08-01
In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.
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Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls
NASA Technical Reports Server (NTRS)
Anastasiadis, Stergios
1991-01-01
Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.
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NASA Astrophysics Data System (ADS)
Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.
2016-05-01
A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.
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Web-based Weather Expert System (WES) for Space Shuttle Launch
NASA Technical Reports Server (NTRS)
Bardina, Jorge E.; Rajkumar, T.
2003-01-01
The Web-based Weather Expert System (WES) is a critical module of the Virtual Test Bed development to support 'go/no go' decisions for Space Shuttle operations in the Intelligent Launch and Range Operations program of NASA. The weather rules characterize certain aspects of the environment related to the launching or landing site, the time of the day or night, the pad or runway conditions, the mission durations, the runway equipment and landing type. Expert system rules are derived from weather contingency rules, which were developed over years by NASA. Backward chaining, a goal-directed inference method is adopted, because a particular consequence or goal clause is evaluated first, and then chained backward through the rules. Once a rule is satisfied or true, then that particular rule is fired and the decision is expressed. The expert system is continuously verifying the rules against the past one-hour weather conditions and the decisions are made. The normal procedure of operations requires a formal pre-launch weather briefing held on Launch minus 1 day, which is a specific weather briefing for all areas of Space Shuttle launch operations. In this paper, the Web-based Weather Expert System of the Intelligent Launch and range Operations program is presented.
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Clinical Decision Rules for Diagnostic Imaging in the Emergency Department: A Research Agenda.
Finnerty, Nathan M; Rodriguez, Robert M; Carpenter, Christopher R; Sun, Benjamin C; Theyyunni, Nik; Ohle, Robert; Dodd, Kenneth W; Schoenfeld, Elizabeth M; Elm, Kendra D; Kline, Jeffrey A; Holmes, James F; Kuppermann, Nathan
2015-12-01
Major gaps persist in the development, validation, and implementation of clinical decision rules (CDRs) for diagnostic imaging. The objective of this working group and article was to generate a consensus-based research agenda for the development and implementation of CDRs for diagnostic imaging in the emergency department (ED). The authors followed consensus methodology, as outlined by the journal Academic Emergency Medicine (AEM), combining literature review, electronic surveys, telephonic communications, and a modified nominal group technique. Final discussions occurred in person at the 2015 AEM consensus conference. A research agenda was developed, prioritizing the following questions: 1) what are the optimal methods to justify the derivation and validation of diagnostic imaging CDRs, 2) what level of evidence is required before disseminating CDRs for widespread implementation, 3) what defines a successful CDR, 4) how should investigators best compare CDRs to clinical judgment, and 5) what disease states are amenable (and highest priority) to development of CDRs for diagnostic imaging in the ED? The concepts discussed herein demonstrate the need for further research on CDR development and implementation regarding diagnostic imaging in the ED. Addressing this research agenda should have direct applicability to patients, clinicians, and health care systems. © 2015 by the Society for Academic Emergency Medicine.
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Advanced Information Technology in Simulation Based Life Cycle Design
NASA Technical Reports Server (NTRS)
Renaud, John E.
2003-01-01
In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.
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Validation of the Ottawa Knee Rules.
Emparanza, J I; Aginaga, J R
2001-10-01
We sought to validate the Ottawa Knee Rules for determining the need for radiography in patients with acute knee injury. A prospective cohort study was performed in emergency departments of 11 hospitals of the Osakidetza-Basque Country Health Service. The patient population was composed of a convenience sample of 1,522 eligible adults of 2,315 patients with acute knee injuries. The attending emergency physicians assessed each patient for standardized clinical variables and determined the need for radiography according to the decision rule. Radiography was performed in each patient, irrespective of the determination of the rule, after clinical evaluation findings were recorded. The rule was assessed for the ability to correctly identify fracture of the knee. The decision rule had a sensitivity of 1.0 (95% confidence interval [CI] 0.96 to 1.0), identifying 89 patients with clinically important fractures. The potential reduction in use of radiography was estimated to be 49%. The probability of fracture, if the decision rules were negative, is estimated to be 0% (95% CI 0% to 0.5%). Prospective validation has shown the Ottawa Knee Rules to be 100% sensitive for identifying fractures of the knee and to have the potential to allow physicians to reduce the use of radiography in patients with acute knee injuries.
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-06-01
... Association decisions in disciplinary, membership denial, registration, and member responsibility actions... and disciplinary actions. The Commission estimates the burden of this collection of information as... Renew Collection: Rules Relating To Review of National Futures Association Decisions in Disciplinary...
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19 CFR 177.1 - General ruling practice and definitions.
Code of Federal Regulations, 2010 CFR
2010-04-01
... in applicable Treasury Decisions, rulings, opinions, or court decisions published in the Customs... in response to a written request therefor and set forth in a letter addressed to the person making... more than call attention to a well-established interpretation or principle of Customs law, without...
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Schmid, Verena
2012-01-01
Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests’ site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions. PMID:25540476
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McGinn, Thomas G; McCullagh, Lauren; Kannry, Joseph; Knaus, Megan; Sofianou, Anastasia; Wisnivesky, Juan P; Mann, Devin M
2013-09-23
There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be demonstrated in clinical trials. To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care. In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia. INTERVENTIONS AND MAIN OUTCOMES AND MEASURES: The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care. The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P= .03). The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01386047.
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Methods, systems, and computer program products for network firewall policy optimization
Fulp, Errin W [Winston-Salem, NC; Tarsa, Stephen J [Duxbury, MA
2011-10-18
Methods, systems, and computer program products for firewall policy optimization are disclosed. According to one method, a firewall policy including an ordered list of firewall rules is defined. For each rule, a probability indicating a likelihood of receiving a packet matching the rule is determined. The rules are sorted in order of non-increasing probability in a manner that preserves the firewall policy.
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The impact of the Rasouli decision: a Survey of Canadian intensivists.
Cape, David; Fox-Robichaud, Alison; Turgeon, Alexis F; Seely, Andrew; Hall, Richard; Burns, Karen; Singal, Rohit K; Dodek, Peter; Bagshaw, Sean; Sibbald, Robert; Downar, James
2016-03-01
In a landmark 2013 decision, the Supreme Court of Canada (SCC) ruled that the withdrawal of life support in certain circumstances is a treatment requiring patient or substitute decision maker (SDM) consent. How intensive care unit (ICU) physicians perceive this ruling is unknown. To determine physician knowledge of and attitudes towards the SCC decision, as well as the self-reported changes in practice attributed to the decision. We surveyed intensivists at university hospitals across Canada. We used a knowledge test and Likert-scale questions to measure respondent knowledge of and attitudes towards the ruling. We used vignettes to assess decision making in cases of intractable physician-SDM conflict over the management of patients with very poor prognoses. We compared management choices pre-SCC decision versus post-SCC decision versus the subjective, respondent-defined most appropriate choice. Responses were compared across predefined subgroups. We performed qualitative analysis on free-text responses. We received 82 responses (response rate=42%). Respondents reported providing high levels of self-defined inappropriate treatment. Although most respondents reported no change in practice, there was a significant overall shift towards higher intensity and less subjectively appropriate management after the SCC decision. Attitudes to the SCC decision and approaches to disputes over end-of-life (EoL) care in the ICU were highly variable. There were no significant differences among predefined subgroups. Many Canadian ICU physicians report providing a higher intensity of treatment, and less subjectively appropriate treatment, in situations of dispute over EoL care after the Supreme Court of Canada's ruling in Cuthbertson versus Rasouli. 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/
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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.
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Alterations in choice behavior by manipulations of world model.
Green, C S; Benson, C; Kersten, D; Schrater, P
2010-09-14
How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.
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Alterations in choice behavior by manipulations of world model
Green, C. S.; Benson, C.; Kersten, D.; Schrater, P.
2010-01-01
How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) “probability matching”—a consistent example of suboptimal choice behavior seen in humans—occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning. PMID:20805507
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Kim, Heejun; Bian, Jiantao; Mostafa, Javed; Jonnalagadda, Siddhartha; Del Fiol, Guilherme
2016-01-01
Motivation: Clinicians need up-to-date evidence from high quality clinical trials to support clinical decisions. However, applying evidence from the primary literature requires significant effort. Objective: To examine the feasibility of automatically extracting key clinical trial information from ClinicalTrials.gov. Methods: We assessed the coverage of ClinicalTrials.gov for high quality clinical studies that are indexed in PubMed. Using 140 random ClinicalTrials.gov records, we developed and tested rules for the automatic extraction of key information. Results: The rate of high quality clinical trial registration in ClinicalTrials.gov increased from 0.2% in 2005 to 17% in 2015. Trials reporting results increased from 3% in 2005 to 19% in 2015. The accuracy of the automatic extraction algorithm for 10 trial attributes was 90% on average. Future research is needed to improve the algorithm accuracy and to design information displays to optimally present trial information to clinicians.
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Mock jury trials in Taiwan--paving the ground for introducing lay participation.
Huang, Kuo-Chang; Lin, Chang-Ching
2014-08-01
The first mock jury study in Taiwan, in which 279 community members watched a videotaped trial, investigated how jurors' estimates of the relative undesirability of wrongful conviction versus wrongful acquittal predicted individual decisions and how decision rules affected outcomes. The percentage of jurors who viewed wrongful conviction as more undesirable increased from 50.9% to 60.9% after deliberation and jurors' postdeliberation acquittal rate (71.7%) was higher than predeliberation acquittal rate (58.8%). Jurors' estimates of the undesirability of wrongful conviction were not correlated with their predeliberation votes but became positively correlated with their postdeliberation decisions. The unanimous rule facilitated jurors' change of vote, predominantly from conviction to acquittal, than the simple majority rule. Jurors reaching a verdict under the unanimous rule viewed deliberation and the verdict more positively. This study indicates that deliberation can ameliorate the problem of most Taiwanese citizens not viewing wrongful conviction as more undesirable than wrongful acquittal. It also suggests that Taiwan should adopt a unanimous rule for its proposed lay participation system.
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12 CFR 263.38 - Recommended decision and filing of record.
Code of Federal Regulations, 2010 CFR
2010-01-01
... FEDERAL RESERVE SYSTEM RULES OF PRACTICE FOR HEARINGS Uniform Rules of Practice and Procedure § 263.38... expiration of the time allowed for filing reply briefs under § 263.37(b), the administrative law judge shall... the administrative law judge's recommended decision, recommended findings of fact, recommended...
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49 CFR 1115.1 - Scope of rule.
Code of Federal Regulations, 2010 CFR
2010-10-01
... other than initial decisions. For each category, this rule describes the types of appeal permitted, the requirements to be observed in filing an appeal, provisions for stay of the action, and the status of the action in the absence of a stay. (c) Appeals from the decisions of employees acting under authority...
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12 CFR 622.12 - Proposed findings and conclusions; recommended decision.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Proposed findings and conclusions; recommended decision. 622.12 Section 622.12 Banks and Banking FARM CREDIT ADMINISTRATION FARM CREDIT SYSTEM RULES OF PRACTICE AND PROCEDURE Rules Applicable to Formal Hearings § 622.12 Proposed findings and conclusions...
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Code of Federal Regulations, 2010 CFR
2010-01-01
... provisions of the Atomic Energy Act and to all applicable rules, regulations, decisions and orders of the... conditions when required by amendments of the Atomic Energy Act or other applicable law, or by other rules, regulations, decisions or orders issued in accordance with the terms of the Atomic Energy Act or other...
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19 CFR 177.2 - Submission of ruling requests.
Code of Federal Regulations, 2010 CFR
2010-04-01
... Headquarters Office will prepare final decisions under § 177.11 (Requests for Advice by Field Officers), or § 174.23 (Further Review of Protests), § 177.10 (Change of Practice), decisions under part 175 of this... Carrier rulings should be addressed to the Commissioner of Customs and Border Protection, Attention...
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17 CFR 201.411 - Commission consideration of initial decisions by hearing officers.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false Commission consideration of initial decisions by hearing officers. 201.411 Section 201.411 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION RULES OF PRACTICE Rules of Practice Appeal to the Commission and...
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76 FR 37274 - Outer Continental Shelf Air Regulations Consistency Update for Alaska
Federal Register 2010, 2011, 2012, 2013, 2014
2011-06-27
... and prevents EPA from making substantive changes to the requirements it incorporates. As a result, EPA... narrative discussing how EPA made the decision to include or exclude rules. The Alaska Eskimo Whaling Commission expressed specific concern with EPA's decision to exclude administrative and procedural rules and...
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Derosiere, Gerard; Zénon, Alexandre; Alamia, Andrea; Duque, Julie
2017-02-01
In the present study, we investigated the functional contribution of the human primary motor cortex (M1) to motor decisions. Continuous theta burst stimulation (cTBS) was used to alter M1 activity while participants performed a decision-making task in which the reward associated with the subjects' responses (right hand finger movements) depended on explicit and implicit value-based rules. Subjects performed the task over two consecutive days and cTBS occurred in the middle of Day 2, once the subjects were just about to implement implicit rules, in addition to the explicit instructions, to choose their responses, as evident in the control group (cTBS over the right somatosensory cortex). Interestingly, cTBS over the left M1 prevented subjects from implementing the implicit value-based rule while its implementation was enhanced in the group receiving cTBS over the right M1. Hence, cTBS had opposite effects depending on whether it was applied on the contralateral or ipsilateral M1. The use of the explicit value-based rule was unaffected by cTBS in the three groups of subject. Overall, the present study provides evidence for a functional contribution of M1 to the implementation of freshly acquired implicit rules, possibly through its involvement in a cortico-subcortical network controlling value-based motor decisions. Copyright © 2016 Elsevier Inc. All rights reserved.
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18 CFR 385.702 - Definitions (Rule 702).
Code of Federal Regulations, 2010 CFR
2010-04-01
... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Definitions (Rule 702). 385.702 Section 385.702 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY COMMISSION, DEPARTMENT OF ENERGY PROCEDURAL RULES RULES OF PRACTICE AND PROCEDURE Decisions § 385.702 Definitions (Rule...
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Optimal water resource allocation modelling in the Lowveld of Zimbabwe
NASA Astrophysics Data System (ADS)
Mhiribidi, Delight; Nobert, Joel; Gumindoga, Webster; Rwasoka, Donald T.
2018-05-01
The management and allocation of water from multi-reservoir systems is complex and thus requires dynamic modelling systems to achieve optimality. A multi-reservoir system in the Southern Lowveld of Zimbabwe is used for irrigation of sugarcane estates that produce sugar for both local and export consumption. The system is burdened with water allocation problems, made worse by decommissioning of dams. Thus the aim of this research was to develop an operating policy model for the Lowveld multi-reservoir system.The Mann Kendall Trend and Wilcoxon Signed-Rank tests were used to assess the variability of historic monthly rainfall and dam inflows for the period 1899-2015. The WEAP model was set up to evaluate the water allocation system of the catchment and come-up with a reference scenario for the 2015/2016 hydrologic year. Stochastic Dynamic Programming approach was used for optimisation of the multi-reservoirs releases.Results showed no significant trend in the rainfall but a significantly decreasing trend in inflows (p < 0.05). The water allocation model (WEAP) showed significant deficits ( ˜ 40 %) in irrigation water allocation in the reference scenario. The optimal rule curves for all the twelve months for each reservoir were obtained and considered to be a proper guideline for solving multi- reservoir management problems within the catchment. The rule curves are effective tools in guiding decision makers in the release of water without emptying the reservoirs but at the same time satisfying the demands based on the inflow, initial storage and end of month storage.
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McBride, Marissa F; Wilson, Kerrie A; Bode, Michael; Possingham, Hugh P
2007-12-01
Uncertainty in the implementation and outcomes of conservation actions that is not accounted for leaves conservation plans vulnerable to potential changes in future conditions. We used a decision-theoretic approach to investigate the effects of two types of investment uncertainty on the optimal allocation of global conservation resources for land acquisition in the Mediterranean Basin. We considered uncertainty about (1) whether investment will continue and (2) whether the acquired biodiversity assets are secure, which we termed transaction uncertainty and performance uncertainty, respectively. We also developed and tested the robustness of different rules of thumb for guiding the allocation of conservation resources when these sources of uncertainty exist. In the presence of uncertainty in future investment ability (transaction uncertainty), the optimal strategy was opportunistic, meaning the investment priority should be to act where uncertainty is highest while investment remains possible. When there was a probability that investments would fail (performance uncertainty), the optimal solution became a complex trade-off between the immediate biodiversity benefits of acting in a region and the perceived longevity of the investment. In general, regions were prioritized for investment when they had the greatest performance certainty, even if an alternative region was highly threatened or had higher biodiversity value. The improved performance of rules of thumb when accounting for uncertainty highlights the importance of explicitly incorporating sources of investment uncertainty and evaluating potential conservation investments in the context of their likely long-term success.
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de la Colina, M A; Pompilio, L; Hauber, M E; Reboreda, J C; Mahler, B
2018-03-01
Obligate avian brood parasites lay their eggs in nests of other host species, which assume all the costs of parental care for the foreign eggs and chicks. The most common defensive response to parasitism is the rejection of foreign eggs by hosts. Different cognitive mechanisms and decision-making rules may guide both egg recognition and rejection behaviors. Classical optimization models generally assume that decisions are based on the absolute properties of the options (i.e., absolute valuation). Increasing evidence shows instead that hosts' rejection decisions also depend on the context in which options are presented (i.e., context-dependent valuation). Here we study whether the chalk-browed mockingbird's (Mimus saturninus) rejection of parasitic shiny cowbird (Molothrus bonariensis) eggs is a fixed behavior or varies with the context of the clutch. We tested three possible context-dependent mechanisms: (1) range effect, (2) habituation to variation, and (3) sensitization to variation. We found that mockingbird rejection of parasitic eggs does not change according to the characteristics of the other eggs in the nest. Thus, rejection decisions may exclusively depend on the objective characteristics of the eggs, meaning that the threshold of acceptance or rejection of a foreign egg is context-independent in this system.
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NASA Astrophysics Data System (ADS)
Chen, Y. Y.; Ho, C. C.; Chang, L. C.
2017-12-01
The reservoir management in Taiwan faces lots of challenge. Massive sediment caused by landslide were flushed into reservoir, which will decrease capacity, rise the turbidity, and increase supply risk. Sediment usually accompanies nutrition that will cause eutrophication problem. Moreover, the unevenly distribution of rainfall cause water supply instability. Hence, how to ensure sustainable use of reservoirs has become an important task in reservoir management. The purpose of the study is developing an optimal planning model for reservoir sustainable management to find out an optimal operation rules of reservoir flood control and sediment sluicing. The model applies Genetic Algorithms to combine with the artificial neural network of hydraulic analysis and reservoir sediment movement. The main objective of operation rules in this study is to prevent reservoir outflow caused downstream overflow, minimum the gap between initial and last water level of reservoir, and maximum sluicing sediment efficiency. A case of Shihmen reservoir was used to explore the different between optimal operating rule and the current operation of the reservoir. The results indicate optimal operating rules tended to open desilting tunnel early and extend open duration during flood discharge period. The results also show the sluicing sediment efficiency of optimal operating rule is 36%, 44%, 54% during Typhoon Jangmi, Typhoon Fung-Wong, and Typhoon Sinlaku respectively. The results demonstrate the optimal operation rules do play a role in extending the service life of Shihmen reservoir and protecting the safety of downstream. The study introduces a low cost strategy, alteration of operation reservoir rules, into reservoir sustainable management instead of pump dredger in order to improve the problem of elimination of reservoir sediment and high cost.
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High Level Rule Modeling Language for Airline Crew Pairing
NASA Astrophysics Data System (ADS)
Mutlu, Erdal; Birbil, Ş. Ilker; Bülbül, Kerem; Yenigün, Hüsnü
2011-09-01
The crew pairing problem is an airline optimization problem where a set of least costly pairings (consecutive flights to be flown by a single crew) that covers every flight in a given flight network is sought. A pairing is defined by using a very complex set of feasibility rules imposed by international and national regulatory agencies, and also by the airline itself. The cost of a pairing is also defined by using complicated rules. When an optimization engine generates a sequence of flights from a given flight network, it has to check all these feasibility rules to ensure whether the sequence forms a valid pairing. Likewise, the engine needs to calculate the cost of the pairing by using certain rules. However, the rules used for checking the feasibility and calculating the costs are usually not static. Furthermore, the airline companies carry out what-if-type analyses through testing several alternate scenarios in each planning period. Therefore, embedding the implementation of feasibility checking and cost calculation rules into the source code of the optimization engine is not a practical approach. In this work, a high level language called ARUS is introduced for describing the feasibility and cost calculation rules. A compiler for ARUS is also implemented in this work to generate a dynamic link library to be used by crew pairing optimization engines.
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Cooperation and Defection in Ghetto
NASA Astrophysics Data System (ADS)
Kułakowski, Krzysztof
We consider ghetto as a community of people ruled against their will by an external power. Members of the community feel that their laws are broken. However, attempts to leave ghetto makes their situation worse. We discuss the relation of the ghetto inhabitants to the ruling power in context of their needs, organized according to the Maslow hierarchy. Decisions how to satisfy successive needs are undertaken in cooperation with or defection the ruling power. This issue allows to construct the tree of decisions and to adopt the pruning technique from the game theory. Dynamics of decisions can be described within the formalism of fundamental equations. The result is that the strategy of defection is stabilized by the estimated payoff.
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Learning about new products: an empirical study of physicians' behavior.
Ferreyra, Maria Marta; Kosenok, Grigory
2011-01-01
We develop and estimate a model of market demand for a new pharmaceutical, whose quality is learned through prescriptions by forward-looking physicians. We use a panel of antiulcer prescriptions from Italian physicians between 1990 and 1992 and focus on a new molecule available since 1990. We solve the model by calculating physicians' optimal decision rules as functions of their beliefs about the new pharmaceutical. According to our counterfactuals, physicians' initial pessimism and uncertainty can have large, negative effects on their propensity to prescribe the new drug and on expected health outcomes. In contrast, subsidizing the new good can mitigate informational losses.
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Sideline coverage: when to get radiographs? A review of clinical decision tools.
Gould, Sara J; Cardone, Dennis A; Munyak, John; Underwood, Philipp J; Gould, Stephen A
2014-05-01
Sidelines coverage presents unique challenges in the evaluation of injured athletes. Health care providers may be confronted with the question of when to obtain radiographs following an injury. Given that most sidelines coverage occurs outside the elite level, radiographs are not readily available at the time of injury, and the decision of when to send a player for radiographs must be made based on physical examination. Clinical tools have been developed to aid in identifying injuries that are likely to result in radiographically important fractures or dislocations. A search for the keywords x-ray and decision rule along with the anatomic locations shoulder, elbow, wrist, knee, and ankle was performed using the PubMed database. No limits were set regarding year of publication. We selected meta-analyses, randomized controlled trials, and survey results. Our selection focused on the largest, most well-studied published reports. We also attempted to include studies that reported the application of the rules to the field of sports medicine. Retrospective literature review. Level 4. The Ottawa Foot and Ankle Rules have been validated and implemented and are appropriate for use in both pediatric and adult populations. The Ottawa Knee Rules have been widely studied, validated, and accepted for evaluation of knee injuries. There are promising studies of decision rules for clinically important fractures of the wrist, but these studies have not been validated. The elbow has been evaluated with good outcomes via the elbow extension test, which has been validated in both single and multicenter studies. Currently, there are no reliable clinical decision tools for traumatic sports injuries to the shoulder to aid in the decision of when to obtain radiographs. Clinical decision tools have been developed to aid in the diagnosis and management of injuries commonly sustained during sporting events. Tools that have been appropriately validated in populations outside the initial study population can assist sports medicine physicians in the decision of when to get radiographs from the sidelines.
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NASA Astrophysics Data System (ADS)
Si, Y.; Li, X.; Li, T.; Huang, Y.; Yin, D.
2016-12-01
The cascade reservoirs in Upper Yellow River (UYR), one of the largest hydropower bases in China, play a vital role in peak load and frequency regulation for Northwest China Power Grid. The joint operation of this system has been put forward for years whereas has not come into effect due to management difficulties and inflow uncertainties, and thus there is still considerable improvement room for hydropower production. This study presents a decision support framework incorporating long- and short-term operation of the reservoir system. For long-term operation, we maximize hydropower production of the reservoir system using historical hydrological data of multiple years, and derive operating rule curves for storage reservoirs. For short-term operation, we develop a program consisting of three modules, namely hydrologic forecast module, reservoir operation module and coordination module. The coordination module is responsible for calling the hydrologic forecast module to acquire predicted inflow within a short-term horizon, and transferring the information to the reservoir operation module to generate optimal release decision. With the hydrologic forecast information updated, the rolling short-term optimization is iterated until the end of operation period, where the long-term operating curves serve as the ending storage target. As an application, the Digital Yellow River Integrated Model (referred to as "DYRIM", which is specially designed for runoff-sediment simulation in the Yellow River basin by Tsinghua University) is used in the hydrologic forecast module, and the successive linear programming (SLP) in the reservoir operation module. The application in the reservoir system of UYR demonstrates that the framework can effectively support real-time decision making, and ensure both computational accuracy and speed. Furthermore, it is worth noting that the general framework can be extended to any other reservoir system with any or combination of hydrological model(s) to forecast and any solver to optimize the operation of reservoir system.
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4 CFR 22.4 - Appeal File [Rule 4].
Code of Federal Regulations, 2010 CFR
2010-01-01
... contracting officer relied in making the decision, and any correspondence relating thereto; (v) Transcripts of... 4 Accounts 1 2010-01-01 2010-01-01 false Appeal File [Rule 4]. 22.4 Section 22.4 Accounts... consisting of all documents pertinent to the appeal, including: (i) The decision from which the appeal is...
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29 CFR 18.103 - Rulings on evidence.
Code of Federal Regulations, 2010 CFR
2010-07-01
... is more probably true than not true that the error did not materially contribute to the decision or... if explicitly not relied upon by the judge in support of the decision or order. (b) Record of offer... making of an offer in question and answer form. (c) Plain error. Nothing in this rule precludes taking...
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34 CFR 31.8 - Rules of decision.
Code of Federal Regulations, 2010 CFR
2010-07-01
... 34 Education 1 2010-07-01 2010-07-01 false Rules of decision. 31.8 Section 31.8 Education Office of the Secretary, Department of Education SALARY OFFSET FOR FEDERAL EMPLOYEES WHO ARE INDEBTED TO THE.... (3) The act or omission of an institution of higher education at which the employee was enrolled does...
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Student Expression: The Uncertain Future
ERIC Educational Resources Information Center
Bathon, Justin M.; McCarthy, Martha M.
2008-01-01
On June 25, 2007, the United States Supreme Court rendered its decision in "Morse v. Frederick", a long-awaited ruling regarding student speech in public schools. For nearly twenty years, the Supreme Court had been silent on the issue while lower courts attempted to apply the rules announced in previous Supreme Court decisions. It is…
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Hasenbein, U; Wallesch, C-W
2003-12-01
We investigated processes of and subjective reasons for resource allocation in three out of four rehabilitation specialists of a regional office of a major health insurance. Decisions of health insurance personnel include approval of and duration of rehabilitation treatment and choice of clinical provider. Insurance specialists are mainly involved in documentation and coordination, whereas decisions mainly follow expert recommendations, mainly of the medical service. Allocation is based primarily on somatic impairment and disability, psychosocial function, motivation and rehabilitation potential are regarded as secondary. Goals and expected results of rehabilitation are neither individually defined nor their achievement evaluated. Decision processes are dominated by routines and agreements. Only exceptionally, defined rules and procedures are applied. Active case management is hampered by a highly specialized internal structure of the investigated insurance fund. The optimal fulfillment of individual requirements for a limited-time rehabilitation treatment is the central criterion for decision making. However, the specialists lack detailed information concerning appropriateness, quality and efficacy of rehabilitation providers, especially when taking patient-related variables into account. Instead, they trust that only high-quality institutions are contracted. Systematic control and feedback of rehabilitation results is not available. The surveyed rehabilitation managers do not include cost aspects in their decision-making. They would regard this as alien to a member- and patient-oriented policy. Improvement potentials with respect to rehabilitation case management are being reviewed.
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Gubhaju, Bina; De Jong, Gordon F
2009-03-01
This research tests the thesis that the neoclassical micro-economic and the new household economic theoretical assumptions on migration decision-making rules are segmented by gender, marital status, and time frame of intention to migrate. Comparative tests of both theories within the same study design are relatively rare. Utilizing data from the Causes of Migration in South Africa national migration survey, we analyze how individually held "own-future" versus alternative "household well-being" migration decision rules effect the intentions to migrate of male and female adults in South Africa. Results from the gender and marital status specific logistic regressions models show consistent support for the different gender-marital status decision rule thesis. Specifically, the "maximizing one's own future" neoclassical microeconomic theory proposition is more applicable for never married men and women, the "maximizing household income" proposition for married men with short-term migration intentions, and the "reduce household risk" proposition for longer time horizon migration intentions of married men and women. Results provide new evidence on the way household strategies and individual goals jointly affect intentions to move or stay.
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Ventral striatum and the evaluation of memory retrieval strategies.
Badre, David; Lebrecht, Sophie; Pagliaccio, David; Long, Nicole M; Scimeca, Jason M
2014-09-01
Adaptive memory retrieval requires mechanisms of cognitive control that facilitate the recovery of goal-relevant information. Frontoparietal systems are known to support control of memory retrieval. However, the mechanisms by which the brain acquires, evaluates, and adapts retrieval strategies remain unknown. Here, we provide evidence that ventral striatal activation tracks the success of a retrieval strategy and correlates with subsequent reliance on that strategy. Human participants were scanned with fMRI while performing a lexical decision task. A rule was provided that indicated the likely semantic category of a target word given the category of a preceding prime. Reliance on the rule improved decision-making, as estimated within a drift diffusion framework. Ventral striatal activation tracked the benefit that relying on the rule had on decision-making. Moreover, activation in ventral striatum correlated with a participant's subsequent reliance on the rule. Taken together, these results support a role for ventral striatum in learning and evaluating declarative retrieval strategies.
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Sanfilippo, Paul G; Hewitt, Alex W; Mackey, David A
2017-04-01
To outline and detail the importance of conditional probability in clinical decision making and discuss the various diagnostic measures eye care practitioners should be aware of in order to improve the scope of their clinical practice. We conducted a review of the importance of conditional probability in diagnostic testing for the eye care practitioner. Eye care practitioners use diagnostic tests on a daily basis to assist in clinical decision making and optimizing patient care and management. These tests provide probabilistic information that can enable the clinician to increase (or decrease) their level of certainty about the presence of a particular condition. While an understanding of the characteristics of diagnostic tests are essential to facilitate proper interpretation of test results and disease risk, many practitioners either confuse or misinterpret these measures. In the interests of their patients, practitioners should be aware of the basic concepts associated with diagnostic testing and the simple mathematical rule that underpins them. Importantly, the practitioner needs to recognize that the prevalence of a disease in the population greatly determines the clinical value of a diagnostic test.
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Multi-tasking arbitration and behaviour design for human-interactive robots
NASA Astrophysics Data System (ADS)
Kobayashi, Yuichi; Onishi, Masaki; Hosoe, Shigeyuki; Luo, Zhiwei
2013-05-01
Robots that interact with humans in household environments are required to handle multiple real-time tasks simultaneously, such as carrying objects, collision avoidance and conversation with human. This article presents a design framework for the control and recognition processes to meet these requirements taking into account stochastic human behaviour. The proposed design method first introduces a Petri net for synchronisation of multiple tasks. The Petri net formulation is converted to Markov decision processes and processed in an optimal control framework. Three tasks (safety confirmation, object conveyance and conversation) interact and are expressed by the Petri net. Using the proposed framework, tasks that normally tend to be designed by integrating many if-then rules can be designed in a systematic manner in a state estimation and optimisation framework from the viewpoint of the shortest time optimal control. The proposed arbitration method was verified by simulations and experiments using RI-MAN, which was developed for interactive tasks with humans.
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Use of DandD for dose assessment under NRC`s radiological criteria for license termination rule
DOE Office of Scientific and Technical Information (OSTI.GOV)
Gallegos, D.P.; Brown, T.J.; Davis, P.A.
The Decontamination and Decommissioning (DandD) software package has been developed by Sandia National Laboratories for the Nuclear Regulatory Commission (NRC) specifically for the purpose of providing a user-friendly analytical tool to address the dose criteria contained in NRC`s Radiological Criteria for License Termination rule (10 CFR Part 20 Subpart E; NRC, 1997). Specifically, DandD embodies the NRC`s screening methodology to allow licensees to convert residual radioactivity contamination levels at their site to annual dose, in a manner consistent with both 10 CFR Part 20 and the corresponding implementation guidance developed by NRC. The screening methodology employs reasonably conservative scenarios, fatemore » and transport models, and default parameter values that have been developed to allow the NRC to quantitatively estimate the risk of releasing a site given only information about the level of contamination. Therefore, a licensee has the option of specifying only the level of contamination and running the code with the default parameter values, or in the case where site specific information is available to alter the appropriate parameter values and then calculate dose. DandD can evaluate dose for fur different scenarios: residential, building occupancy, building renovation, or drinking water. The screening methodology and DandD are part of a larger decision framework that allows and encourages licensees to optimize decisions on choice of alternative actions at their site, including collection of additional data and information. This decision framework is integrated into and documented in NRC`s technical guidance for decommissioning.« less
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Learning the opportunity cost of time in a patch-foraging task
Constantino, Sara; Daw, Nathaniel D.
2015-01-01
Although most decision research concerns choice between simultaneously presented options, in many situations options are encountered serially and the decision is whether to exploit an option or search for a better one. Such problems have a rich history in animal foraging but we know little about the psychological processes involved. In particular, it is unknown whether learning in these problems is supported by the well studied neurocomputational mechanisms involved in more conventional tasks. We investigated how humans learn in a foraging task, which requires deciding whether to harvest a depleting resource or switch to a replenished one. The optimal choice (given by the Marginal Value Theorem; MVT) requires comparing the immediate return from harvesting to the opportunity cost of time, which is given by the long-run average reward. In two experiments, we varied opportunity cost across blocks. Subjects adjusted their behavior to blockwise changes in environmental characteristics. We examined how subjects learned their choice strategies by comparing choice adjustments to a learning rule suggested by the MVT (where the opportunity cost threshold is estimated as an average over previous rewards) and to the predominant incremental learning theory in neuroscience, temporal-difference learning (TD). Trial-by-trial decisions were better explained by the MVT threshold learning rule. These findings expand on the foraging literature, which has focused on steady-state behavior, by elucidating a computational mechanism for learning in switching tasks that is distinct from those used in traditional tasks, and suggest connections to research on average reward rates in other domains of neuroscience. PMID:25917000
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Making sense of information in noisy networks: human communication, gossip, and distortion.
Laidre, Mark E; Lamb, Alex; Shultz, Susanne; Olsen, Megan
2013-01-21
Information from others can be unreliable. Humans nevertheless act on such information, including gossip, to make various social calculations, thus raising the question of whether individuals can sort through social information to identify what is, in fact, true. Inspired by empirical literature on people's decision-making when considering gossip, we built an agent-based simulation model to examine how well simple decision rules could make sense of information as it propagated through a network. Our simulations revealed that a minimalistic decision-rule 'Bit-wise mode' - which compared information from multiple sources and then sought a consensus majority for each component bit within the message - was consistently the most successful at converging upon the truth. This decision rule attained high relative fitness even in maximally noisy networks, composed entirely of nodes that distorted the message. The rule was also superior to other decision rules regardless of its frequency in the population. Simulations carried out with variable agent memory constraints, different numbers of observers who initiated information propagation, and a variety of network types suggested that the single most important factor in making sense of information was the number of independent sources that agents could consult. Broadly, our model suggests that despite the distortion information is subject to in the real world, it is nevertheless possible to make sense of it based on simple Darwinian computations that integrate multiple sources. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Combined rule extraction and feature elimination in supervised classification.
Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E
2012-09-01
There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.
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NASA Astrophysics Data System (ADS)
Seoud, Ahmed; Kim, Juhwan; Ma, Yuansheng; Jayaram, Srividya; Hong, Le; Chae, Gyu-Yeol; Lee, Jeong-Woo; Park, Dae-Jin; Yune, Hyoung-Soon; Oh, Se-Young; Park, Chan-Ha
2018-03-01
Sub-resolution assist feature (SRAF) insertion techniques have been effectively used for a long time now to increase process latitude in the lithography patterning process. Rule-based SRAF and model-based SRAF are complementary solutions, and each has its own benefits, depending on the objectives of applications and the criticality of the impact on manufacturing yield, efficiency, and productivity. Rule-based SRAF provides superior geometric output consistency and faster runtime performance, but the associated recipe development time can be of concern. Model-based SRAF provides better coverage for more complicated pattern structures in terms of shapes and sizes, with considerably less time required for recipe development, although consistency and performance may be impacted. In this paper, we introduce a new model-assisted template extraction (MATE) SRAF solution, which employs decision tree learning in a model-based solution to provide the benefits of both rule-based and model-based SRAF insertion approaches. The MATE solution is designed to automate the creation of rules/templates for SRAF insertion, and is based on the SRAF placement predicted by model-based solutions. The MATE SRAF recipe provides optimum lithographic quality in relation to various manufacturing aspects in a very short time, compared to traditional methods of rule optimization. Experiments were done using memory device pattern layouts to compare the MATE solution to existing model-based SRAF and pixelated SRAF approaches, based on lithographic process window quality, runtime performance, and geometric output consistency.
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Development and prospects of standardization in the German municipal wastewater sector.
Freimuth, Claudia; Oelmann, Mark; Amann, Erwin
2018-04-17
Given the significance of wastewater treatment and disposal for society and the economy together with the omnipresence of standards in the sector, we studied the development and prospects of the rules governing standardization in the German municipal wastewater sector. We thereby provide a detailed description of sector-specific committee-based standardization and significantly contribute to the understanding of this complex arena. We find that the German Association for Water Wastewater and Waste (DWA) has significantly improved its rules on standardization over time by aligning them closer to the generally accepted superordinate standardization principles. However, by focusing on theoretical findings of committee decision-making and committee composition, we argue that there is still scope for improvement with respect to rule reading and rule compliance. We show that the incentives at work in standardization committees are manifold, whereas the representation of the different stakeholder groups needs' remains unbalanced. Due to vested interests and potential strategic behavior of the various agents involved in standardization rule compliance does not necessarily happen naturally. To this end, we claim that the implementation of monitoring mechanisms can be a significant contribution to the institutional design of standardization and briefly discuss the advantages and disadvantages of different schemes. Finally, we show that there is ample need for future research on the optimal design of such a scheme. Even though the analysis relates specifically to the DWA our claims apply to a wide range of standards development organizations. Copyright © 2018 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Perry, Dan; Nakamoto, Mark; Verghese, Nishath; Hurat, Philippe; Rouse, Rich
2007-03-01
Model-based hotspot detection and silicon-aware parametric analysis help designers optimize their chips for yield, area and performance without the high cost of applying foundries' recommended design rules. This set of DFM/ recommended rules is primarily litho-driven, but cannot guarantee a manufacturable design without imposing overly restrictive design requirements. This rule-based methodology of making design decisions based on idealized polygons that no longer represent what is on silicon needs to be replaced. Using model-based simulation of the lithography, OPC, RET and etch effects, followed by electrical evaluation of the resulting shapes, leads to a more realistic and accurate analysis. This analysis can be used to evaluate intelligent design trade-offs and identify potential failures due to systematic manufacturing defects during the design phase. The successful DFM design methodology consists of three parts: 1. Achieve a more aggressive layout through limited usage of litho-related recommended design rules. A 10% to 15% area reduction is achieved by using more aggressive design rules. DFM/recommended design rules are used only if there is no impact on cell size. 2. Identify and fix hotspots using a model-based layout printability checker. Model-based litho and etch simulation are done at the cell level to identify hotspots. Violations of recommended rules may cause additional hotspots, which are then fixed. The resulting design is ready for step 3. 3. Improve timing accuracy with a process-aware parametric analysis tool for transistors and interconnect. Contours of diffusion, poly and metal layers are used for parametric analysis. In this paper, we show the results of this physical and electrical DFM methodology at Qualcomm. We describe how Qualcomm was able to develop more aggressive cell designs that yielded a 10% to 15% area reduction using this methodology. Model-based shape simulation was employed during library development to validate architecture choices and to optimize cell layout. At the physical verification stage, the shape simulator was run at full-chip level to identify and fix residual hotspots on interconnect layers, on poly or metal 1 due to interaction between adjacent cells, or on metal 1 due to interaction between routing (via and via cover) and cell geometry. To determine an appropriate electrical DFM solution, Qualcomm developed an experiment to examine various electrical effects. After reporting the silicon results of this experiment, which showed sizeable delay variations due to lithography-related systematic effects, we also explain how contours of diffusion, poly and metal can be used for silicon-aware parametric analysis of transistors and interconnect at the cell-, block- and chip-level.
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Leroy, S; Marc, E; Adamsbaum, C; Gendrel, D; Bréart, G; Chalumeau, M
2006-03-01
To test the reproducibility of a highly sensitive clinical decision rule proposed to predict vesicoureteral reflux (VUR) after a first febrile urinary tract infection in children. This rule combines clinical (family history of uropathology, male gender, young age), biological (raised C reactive protein), and radiological (urinary tract dilation on renal ultrasound) predictors in a score, and provides 100% sensitivity. A retrospective hospital based cohort study included all children, 1 month to 4 years old, with a first febrile urinary tract infection. The sensitivities and specificities of the rule at the two previously proposed score thresholds (< or =0 and < or =5) to predict respectively, all-grade or grade > or =3 VUR, were calculated. A total of 149 children were included. VUR prevalence was 25%. The rule yielded 100% sensitivity and 3% specificity for all-grade VUR, and 93% sensitivity and 13% specificity for grade > or =3 VUR. Some methodological weaknesses explain this lack of reproducibility. The reproducibility of the previously proposed decision rule was poor and its potential contribution to clinical management of children with febrile urinary tract infection seems to be modest.
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Leroy, S; Marc, E; Adamsbaum, C; Gendrel, D; Bréart, G; Chalumeau, M
2006-01-01
Aims To test the reproducibility of a highly sensitive clinical decision rule proposed to predict vesicoureteral reflux (VUR) after a first febrile urinary tract infection in children. This rule combines clinical (family history of uropathology, male gender, young age), biological (raised C reactive protein), and radiological (urinary tract dilation on renal ultrasound) predictors in a score, and provides 100% sensitivity. Methods A retrospective hospital based cohort study included all children, 1 month to 4 years old, with a first febrile urinary tract infection. The sensitivities and specificities of the rule at the two previously proposed score thresholds (⩽0 and ⩽5) to predict respectively, all‐grade or grade ⩾3 VUR, were calculated. Results A total of 149 children were included. VUR prevalence was 25%. The rule yielded 100% sensitivity and 3% specificity for all‐grade VUR, and 93% sensitivity and 13% specificity for grade ⩾3 VUR. Some methodological weaknesses explain this lack of reproducibility. Conclusions The reproducibility of the previously proposed decision rule was poor and its potential contribution to clinical management of children with febrile urinary tract infection seems to be modest. PMID:15890693
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Klaczynski, Paul A
2011-05-01
To examine age trends in precedent-setting decisions and the effects of these decisions on perceptions of authorities, preadolescents and adolescents were presented with deontic rule infractions that occurred in the absence or presence of mitigating circumstances. In Study 1, in the absence of mitigating circumstances, adolescents recommended punishing rule violations more than preadolescents; when mitigating circumstances were present, adolescents recommended punishing infractions less than preadolescents. In Study 2, before and after receiving information that authorities had punished or permitted rule violations, participants indicated their beliefs in authority legitimacy, rule strength, and rule deterrence value. In the absence of mitigating circumstances, beliefs strengthened when infractions were punished and beliefs weakened when infractions were permitted. When mitigating circumstances were present and authorities punished violations, preadolescents' legitimacy and deterrence beliefs strengthened. Adolescents' deterrence beliefs strengthened, but their beliefs in authority legitimacy weakened. When justifiable infractions were permitted, preadolescents' legitimacy and deterrence beliefs weakened, whereas adolescents' beliefs strengthened. Discussion focuses on age differences in legitimacy beliefs and understanding the consequences of setting precedents and on the relevance of the findings to theories of deontic reasoning, moral judgments, and epistemological development. Copyright © 2010 Elsevier Inc. All rights reserved.
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Extending rule-based methods to model molecular geometry and 3D model resolution.
Hoard, Brittany; Jacobson, Bruna; Manavi, Kasra; Tapia, Lydia
2016-08-01
Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.
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Babl, Franz E; Lyttle, Mark D; Bressan, Silvia; Borland, Meredith; Phillips, Natalie; Kochar, Amit; Dalziel, Stuart R; Dalton, Sarah; Cheek, John A; Furyk, Jeremy; Gilhotra, Yuri; Neutze, Jocelyn; Ward, Brenton; Donath, Susan; Jachno, Kim; Crowe, Louise; Williams, Amanda; Oakley, Ed
2014-06-13
Head injuries in children are responsible for a large number of emergency department visits. Failure to identify a clinically significant intracranial injury in a timely fashion may result in long term neurodisability and death. Whilst cranial computed tomography (CT) provides rapid and definitive identification of intracranial injuries, it is resource intensive and associated with radiation induced cancer. Evidence based head injury clinical decision rules have been derived to aid physicians in identifying patients at risk of having a clinically significant intracranial injury. Three rules have been identified as being of high quality and accuracy: the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) from Canada, the Children's Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE) from the UK, and the prediction rule for the identification of children at very low risk of clinically important traumatic brain injury developed by the Pediatric Emergency Care Applied Research Network (PECARN) from the USA. This study aims to prospectively validate and compare the performance accuracy of these three clinical decision rules when applied outside the derivation setting. This study is a prospective observational study of children aged 0 to less than 18 years presenting to 10 emergency departments within the Paediatric Research in Emergency Departments International Collaborative (PREDICT) research network in Australia and New Zealand after head injuries of any severity. Predictor variables identified in CATCH, CHALICE and PECARN clinical decision rules will be collected. Patients will be managed as per the treating clinicians at the participating hospitals. All patients not undergoing cranial CT will receive a follow up call 14 to 90 days after the injury. Outcome data collected will include results of cranial CTs (if performed) and details of admission, intubation, neurosurgery and death. The performance accuracy of each of the rules will be assessed using rule specific outcomes and inclusion and exclusion criteria. This study will allow the simultaneous comparative application and validation of three major paediatric head injury clinical decision rules outside their derivation setting. The study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR)- ACTRN12614000463673 (registered 2 May 2014).
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Wallace, Lorraine Silver; Ballard, Joyce E.; Holiday, David; Turner, Lori W.; Keenum, Amy J.; Pearman, Cynthia M.
2004-01-01
OBJECTIVE: While African-American women tend to have greater bone mineral density (BMD) than caucasian women, they are still at risk of developing osteoporosis later in life. Clinical decision rules (i.e., algorithms) have been developed to assist clinicians identify women at greatest risk of low BMD. However, such tools have only been validated in caucasian and Asian populations. Accordingly, the objective of this study was to compare the performance of five clinical decision rules in identifying postmenopausal African-American women at greatest risk for low femoral BMD. METHODOLOGY: One hundred-seventy-four (n=174) postmenopausal African-American women completed a valid and reliable oral questionnaire to assess lifestyle characteristics, and completed height and weight measures. BMD at the femoral neck was measured via dual energy x-ray absorptiometry (DXA). We calculated sensitivity, specificity, positive predictive value, and negative predictive value for identifying African-American women with low BMD (T-Score < or = -2.0 SD) using five clinical decision rules: Age, Body Size, No Estrogen (ABONE), Osteoporosis Risk Assessment Instrument (ORAI), Osteoporosis Self-Assessment Tool (OST), Simple Calculated Osteoporosis Risk Estimation (SCORE), and body weight less than 70 kg. RESULTS: Approximately 30% of African-American women had low BMD, half of whom had osteoporosis (BMD T-Score < or = -2.5 SD). Sensitivity for identifying women with a low BMD (T-Score < or = -2.0 SD) ranged from 65.57-83.61%, while specificity ranged from 53.85-78.85%. Positive predictive values ranged from 80.95-87.91%, while negative predictive values ranged from 48.44-58.33%. CONCLUSION: Our data suggest that the clinical decision rules analyzed in this study have some usefulness for identifying postmenopausal African-American women with low BMD. However, there is a need to establish cut-points for these clinical decision rules in a larger, more diverse sample of African-American women. PMID:15040510
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Heuristics: foundations for a novel approach to medical decision making.
Bodemer, Nicolai; Hanoch, Yaniv; Katsikopoulos, Konstantinos V
2015-03-01
Medical decision-making is a complex process that often takes place during uncertainty, that is, when knowledge, time, and resources are limited. How can we ensure good decisions? We present research on heuristics-simple rules of thumb-and discuss how medical decision-making can benefit from these tools. We challenge the common view that heuristics are only second-best solutions by showing that they can be more accurate, faster, and easier to apply in comparison to more complex strategies. Using the example of fast-and-frugal decision trees, we illustrate how heuristics can be studied and implemented in the medical context. Finally, we suggest how a heuristic-friendly culture supports the study and application of heuristics as complementary strategies to existing decision rules.
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Hess, Erik P; Wells, George A; Jaffe, Allan; Stiell, Ian G
2008-01-01
Background Chest pain is the second most common chief complaint in North American emergency departments. Data from the U.S. suggest that 2.1% of patients with acute myocardial infarction and 2.3% of patients with unstable angina are misdiagnosed, with slightly higher rates reported in a recent Canadian study (4.6% and 6.4%, respectively). Information obtained from the history, 12-lead ECG, and a single set of cardiac enzymes is unable to identify patients who are safe for early discharge with sufficient sensitivity. The 2007 ACC/AHA guidelines for UA/NSTEMI do not identify patients at low risk for adverse cardiac events who can be safely discharged without provocative testing. As a result large numbers of low risk patients are triaged to chest pain observation units and undergo provocative testing, at significant cost to the healthcare system. Clinical decision rules use clinical findings (history, physical exam, test results) to suggest a diagnostic or therapeutic course of action. Currently no methodologically robust clinical decision rule identifies patients safe for early discharge. Methods/design The goal of this study is to derive a clinical decision rule which will allow emergency physicians to accurately identify patients with chest pain who are safe for early discharge. The study will utilize a prospective cohort design. Standardized clinical variables will be collected on all patients at least 25 years of age complaining of chest pain prior to provocative testing. Variables strongly associated with the composite outcome acute myocardial infarction, revascularization, or death will be further analyzed with multivariable analysis to derive the clinical rule. Specific aims are to: i) apply standardized clinical assessments to patients with chest pain, incorporating results of early cardiac testing; ii) determine the inter-observer reliability of the clinical information; iii) determine the statistical association between the clinical findings and the composite outcome; and iv) use multivariable analysis to derive a highly sensitive clinical decision rule to guide triage decisions. Discussion The study will derive a highly sensitive clinical decision rule to identify low risk patients safe for early discharge. This will improve patient care, lower healthcare costs, and enhance flow in our busy and overcrowded emergency departments. PMID:18254973
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Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.
Sesen, M Berkan; Peake, Michael D; Banares-Alcantara, Rene; Tse, Donald; Kadir, Timor; Stanley, Roz; Gleeson, Fergus; Brady, Michael
2014-09-06
Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.
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Does the cost function matter in Bayes decision rule?
Schlü ter, Ralf; Nussbaum-Thom, Markus; Ney, Hermann
2012-02-01
In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.
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Flu Diagnosis System Using Jaccard Index and Rough Set Approaches
NASA Astrophysics Data System (ADS)
Efendi, Riswan; Azah Samsudin, Noor; Mat Deris, Mustafa; Guan Ting, Yip
2018-04-01
Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems based on two different approaches, such as, Jaccard index and rough set. These two different approaches are established using set operations concept, namely intersection and subset. The step-by-step procedure is demonstrated from each approach in diagnosing flu system. The similarity and dissimilarity indexes between conditional symptoms and decision are measured using Jaccard approach. Additionally, the rough set is used to build decision support rules. Moreover, the decision support rules are established using redundant data analysis and elimination of unclassified elements. A number data sets is considered to attempt the step-by-step procedure from each approach. The result has shown that rough set can be used to support Jaccard approaches in establishing decision support rules. Additionally, Jaccard index is better approach for investigating the worst condition of patients. While, the definitely and possibly patients with or without flu can be determined using rough set approach. The rules may improve the performance of medical diagnosis systems. Therefore, inexperienced doctors and patients are easier in preliminary flu diagnosis.
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Orthogonal search-based rule extraction for modelling the decision to transfuse.
Etchells, T A; Harrison, M J
2006-04-01
Data from an audit relating to transfusion decisions during intermediate or major surgery were analysed to determine the strengths of certain factors in the decision making process. The analysis, using orthogonal search-based rule extraction (OSRE) from a trained neural network, demonstrated that the risk of tissue hypoxia (ROTH) assessed using a 100-mm visual analogue scale, the haemoglobin value (Hb) and the presence or absence of on-going haemorrhage (OGH) were able to reproduce the transfusion decisions with a joint specificity of 0.96 and sensitivity of 0.93 and a positive predictive value of 0.9. The rules indicating transfusion were: 1. ROTH > 32 mm and Hb < 94 g x l(-1); 2. ROTH > 13 mm and Hb < 87 g x l(-1); 3. ROTH > 38 mm, Hb < 102 g x l(-1) and OGH; 4. Hb < 78 g x l(-1).
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Magid, Steven K; Pancoast, Paul E; Fields, Theodore; Bradley, Diane G; Williams, Robert B
2007-01-01
Clinical decision support can be employed to increase patient safety and improve workflow efficiencies for physicians and other healthcare providers. Physician input into the design and deployment of clinical decision support systems can increase the utility of the alerts and reduce the likelihood of "alert fatigue." The Hospital for Special Surgery is a 146-bed orthopedic facility that performs approximately 18,000 surgeries a year Efficient work processes are a necessity. The facility began implementing a new electronic health record system in June 2005 and plan to go live in summer 2007. This article reports on some of the clinical decision support rules and alerts being incorporated into the facility's system in the following categories--high-risk, high-frequency scenarios, rules that provide efficiencies and value from the presciber perspective, and rules that relate to patient safety.
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Schiebener, Johannes; Brand, Matthias
2015-11-01
In decisions under objective risk conditions information about the decision options' possible outcomes and the rules for outcomes' occurrence are provided. Thus, deciders can base decision-making strategies on probabilistic laws. In many laboratory decision-making tasks, choosing the option with the highest winning probability in all trials (=maximization strategy) is probabilistically regarded the most rational behavior. However, individuals often behave less optimal, especially in case the individuals have lower cognitive functions or in case no feedback about consequences is provided in the situation. It is still unclear which cognitive functions particularly predispose individuals for using successful strategies and which strategies profit from feedback. We investigated 195 individuals with two decision-making paradigms, the Game of Dice Task (GDT) (with and without feedback), and the Card Guessing Game. Thereafter, participants reported which strategies they had applied. Interaction effects (feedback × strategy), effect sizes, and uncorrected single group comparisons suggest that feedback in the GDT tended to be more beneficial to individuals reporting exploratory strategies (e.g., use intuition). In both tasks, the self-reported use of more principled and more rational strategies was accompanied by better decision-making performance and better performances in reasoning and executive functioning tasks. The strategy groups did not significantly differ in most short-term and working-memory tasks. Thus, particularly individual differences in reasoning and executive functions seem to predispose individuals toward particular decision-making strategies. Feedback seems to be useful for individuals who rather explore the decision-making situation instead of following a certain plan.
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Scott, A J; Mason, S E; Arunakirinathan, M; Reissis, Y; Kinross, J M; Smith, J J
2015-04-01
Current management of suspected appendicitis is hampered by the overadmission of patients with non-specific abdominal pain and a significant negative exploration rate. The potential benefits of risk stratification by the Appendicitis Inflammatory Response (AIR) score to guide clinical decision-making were assessed. During this 50-week prospective observational study at one institution, the AIR score was calculated for all patients admitted with suspected appendicitis. Appendicitis was diagnosed by histological examination, and patients were classified as having non-appendicitis pain if histological findings were negative or surgery was not performed. The diagnostic performance of the AIR score and the potential for risk stratification to reduce admissions, optimize imaging and prevent unnecessary explorations were quantified. A total of 464 patients were included, of whom 210 (63·3 per cent) with non-appendicitis pain were correctly classified as low risk. However, 13 low-risk patients had appendicitis. Low-risk patients accounted for 48·1 per cent of admissions (223 of 464), 57 per cent of negative explorations (48 of 84) and 50·7 per cent of imaging requests (149 of 294). An AIR score of 5 or more (intermediate and high risk) had high sensitivity for all severities of appendicitis (90 per cent) and also for advanced appendicitis (98 per cent). An AIR score of 9 or more (high risk) was very specific (97 per cent) for appendicitis, and the majority of patients with appendicitis in the high-risk group (21 of 30, 70 per cent) had perforation or gangrene. Ultrasound imaging could not exclude appendicitis in low-risk patients (negative likelihood ratio (LR) 1·0) but could rule-in the diagnosis in intermediate-risk patients (positive LR 10·2). CT could exclude appendicitis in low-risk patients (negative LR 0·0) and rule-in appendicitis in the intermediate group (positive LR 10·9). Risk stratification of patients with suspected appendicitis by the AIR score could guide decision-making to reduce admissions, optimize utility of diagnostic imaging and prevent negative explorations. © 2015 BJS Society Ltd Published by John Wiley & Sons Ltd.
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A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm.
Ronowicz, Joanna; Thommes, Markus; Kleinebudde, Peter; Krysiński, Jerzy
2015-06-20
The present study is focused on the thorough analysis of cause-effect relationships between pellet formulation characteristics (pellet composition as well as process parameters) and the selected quality attribute of the final product. The shape using the aspect ratio value expressed the quality of pellets. A data matrix for chemometric analysis consisted of 224 pellet formulations performed by means of eight different active pharmaceutical ingredients and several various excipients, using different extrusion/spheronization process conditions. The data set contained 14 input variables (both formulation and process variables) and one output variable (pellet aspect ratio). A tree regression algorithm consistent with the Quality by Design concept was applied to obtain deeper understanding and knowledge of formulation and process parameters affecting the final pellet sphericity. The clear interpretable set of decision rules were generated. The spehronization speed, spheronization time, number of holes and water content of extrudate have been recognized as the key factors influencing pellet aspect ratio. The most spherical pellets were achieved by using a large number of holes during extrusion, a high spheronizer speed and longer time of spheronization. The described data mining approach enhances knowledge about pelletization process and simultaneously facilitates searching for the optimal process conditions which are necessary to achieve ideal spherical pellets, resulting in good flow characteristics. This data mining approach can be taken into consideration by industrial formulation scientists to support rational decision making in the field of pellets technology. Copyright © 2015 Elsevier B.V. All rights reserved.
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How power influences moral thinking.
Lammers, Joris; Stapel, Diederik A
2009-08-01
The authors conducted 5 studies to test the idea that both thinking about and having power affects the way in which people resolve moral dilemmas. It is shown that high power increases the use of rule-based (deontological) moral thinking styles, whereas low power increases reliance on outcome-based (consequentialist) moral thinking. Stated differently, in determining whether an act is right or wrong, the powerful focus on whether rules and principles are violated, whereas the powerless focus on the consequences. For this reason, the powerful are also more inclined to stick to the rules, irrespective of whether this has positive or negative effects, whereas the powerless are more inclined to make exceptions. The first 3 experiments show that thinking about power increases rule-based thinking and decreases outcome-based thinking in participants' moral decision making. A 4th experiment shows the mediating role of moral orientation in the effect of power on moral decisions. The 5th experiment demonstrates the role of self-interest by showing that the power-moral link is reversed when rule-based decisions threaten participants' own self-interests.
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Comparative study of multimodal biometric recognition by fusion of iris and fingerprint.
Benaliouche, Houda; Touahria, Mohamed
2014-01-01
This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical weighted sum rule and the classical sum rule in order. The performance evaluation of each method is reported in terms of matching time, error rates, and accuracy after doing exhaustive tests on the public CASIA-Iris databases V1 and V2 and the FVC 2004 fingerprint database. Experimental results prior to fusion and after fusion are presented followed by their comparison with related works in the current literature. The fusion by fuzzy logic decision mimics the human reasoning in a soft and simple way and gives enhanced results.
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Comparative Study of Multimodal Biometric Recognition by Fusion of Iris and Fingerprint
Benaliouche, Houda; Touahria, Mohamed
2014-01-01
This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical weighted sum rule and the classical sum rule in order. The performance evaluation of each method is reported in terms of matching time, error rates, and accuracy after doing exhaustive tests on the public CASIA-Iris databases V1 and V2 and the FVC 2004 fingerprint database. Experimental results prior to fusion and after fusion are presented followed by their comparison with related works in the current literature. The fusion by fuzzy logic decision mimics the human reasoning in a soft and simple way and gives enhanced results. PMID:24605065
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Obesity: can behavioral economics help?
Just, David R; Payne, Collin R
2009-12-01
Consumers regularly and predictably behave in ways that contradict standard assumptions of economic analysis such that they make decisions that prevent them from reaching rationally intended goals. These contradictions play a significant role with respect to consumers' food decisions and the effect these decisions have on their health. Food decisions that are rationally derived include those that trade short-term gains of sensory pleasure (hedonic) for longer term gains of health and wellness (utilitarian). However, extra-rational food decisions are much more common. They can occur because of the contexts in which they are made--such as being distracted or pressed for time. In these contexts, heuristics (or rules of thumb) are used. Because food decisions are made with little cognitive involvement, food policies designed to appeal to highly cognitive thought (e.g., fat taxes, detailed information labels) are likely to have little impact. Furthermore, food marketing environments influence not only what foods consumers buy but also how much. As a general principle, when individuals do not behave in their own interest, markets will feed perverse and sub-optimal behaviors. Given the limited ability of individuals to retain and use accurate health information coupled with varying levels of self control, profit motivations of marketers can become predatory--though not necessarily malicious. Alternative policy options that do not restrict choice are outlined, which enable consumers to make better decisions. These options allow for profit motivations of marketers to align with the long-term well being of the consumer.
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-09-20
... regarding EPA's Zinc Fertilizer Rule in a separate final rule (following the proposed rule) as it... the Zinc Fertilizer Rule. Today's action responds to that comment but does not agree with it and, thus, finalizes the Agency's decision to authorize Rhode Island for EPA's Zinc Fertilizer Rule. In addition, the...
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ERIC Educational Resources Information Center
Torres, Mario S., Jr.
2012-01-01
This study examined federal and state court decisions related to student Fourth Amendment rights following the "New Jersey v. T.L.O." ruling in 1985. There has been minimal research in judicial treatment of students' Fourth Amendment rights across regions of the country and less to what extent regional rulings implicitly or explicitly…
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An Intelligent Decision Support System for Workforce Forecast
2011-01-01
ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models
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Brain Regions Involved in the Learning and Application of Reward Rules in a Two-Deck Gambling Task
ERIC Educational Resources Information Center
Hartstra, E.; Oldenburg, J. F. E.; Van Leijenhorst, L.; Rombouts, S. A. R. B.; Crone, E. A.
2010-01-01
Decision-making involves the ability to choose between competing actions that are associated with uncertain benefits and penalties. The Iowa Gambling Task (IGT), which mimics real-life decision-making, involves learning a reward-punishment rule over multiple trials. Patients with damage to ventromedial prefrontal cortex (VMPFC) show deficits…
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We will collaborate with investigators from University College London to test a screening decision rule in preclinical serial samples from the U.K. Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) to learn if the panel can do better than CA125 alone. The UKCTOCS is an ideal setting for retrospective validation of an early detection marker panel and decision rule because it offers serial samples collected annually and use of imaging in women with rising CA125. Multi-modal strategies using serum markers HE4, MSLN, MMP7, and CA125 will be compared to strategies relying exclusively on CA125 and transvaginal sonography (TVS).
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Decision rules for unbiased inventory estimates
NASA Technical Reports Server (NTRS)
Argentiero, P. D.; Koch, D.
1979-01-01
An efficient and accurate procedure for estimating inventories from remote sensing scenes is presented. In place of the conventional and expensive full dimensional Bayes decision rule, a one-dimensional feature extraction and classification technique was employed. It is shown that this efficient decision rule can be used to develop unbiased inventory estimates and that for large sample sizes typical of satellite derived remote sensing scenes, resulting accuracies are comparable or superior to more expensive alternative procedures. Mathematical details of the procedure are provided in the body of the report and in the appendix. Results of a numerical simulation of the technique using statistics obtained from an observed LANDSAT scene are included. The simulation demonstrates the effectiveness of the technique in computing accurate inventory estimates.
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Real-time reservoir operation considering non-stationary inflow prediction
NASA Astrophysics Data System (ADS)
Zhao, J.; Xu, W.; Cai, X.; Wang, Z.
2011-12-01
Stationarity of inflow has been a basic assumption for reservoir operation rule design, which is now facing challenges due to climate change and human interferences. This paper proposes a modeling framework to incorporate non-stationary inflow prediction for optimizing the hedging operation rule of large reservoirs with multiple-year flow regulation capacity. A multi-stage optimization model is formulated and a solution algorithm based on the optimality conditions is developed to incorporate non-stationary annual inflow prediction through a rolling, dynamic framework that updates the prediction from period to period and adopt the updated prediction in reservoir operation decision. The prediction model is ARIMA(4,1,0), in which parameter 4 stands for the order of autoregressive, 1 represents a linear trend, and 0 is the order of moving average. The modeling framework and solution algorithm is applied to the Miyun reservoir in China, determining a yearly operating schedule during the period from 1996 to 2009, during which there was a significant declining trend of reservoir inflow. Different operation policy scenarios are modeled, including standard operation policy (SOP, matching the current demand as much as possible), hedging rule (i.e., leaving a certain amount of water for future to avoid large risk of water deficit) with forecast from ARIMA (HR-1), hedging (HR) with perfect forecast (HR-2 ). Compared to the results of these scenarios to that of the actual reservoir operation (AO), the utility of the reservoir operation under HR-1 is 3.0% lower than HR-2, but 3.7% higher than the AO and 14.4% higher than SOP. Note that the utility under AO is 10.3% higher than that under SOP, which shows that a certain level of hedging under some inflow prediction or forecast was used in the real-world operation. Moreover, the impacts of discount rate and forecast uncertainty level on the operation will be discussed.
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NASA Astrophysics Data System (ADS)
Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.
2017-12-01
Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters. Overall, the probability of detection of an event with a return period of 10 years is 62%. 44% of all 10-year flood peaks can be detected with a timing error of 2 hours or less. These results indicate that the modeling system can provide useful information about the timing and magnitude of flood events at ungauged sites.
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Simply criminal: predicting burglars' occupancy decisions with a simple heuristic.
Snook, Brent; Dhami, Mandeep K; Kavanagh, Jennifer M
2011-08-01
Rational choice theories of criminal decision making assume that offenders weight and integrate multiple cues when making decisions (i.e., are compensatory). We tested this assumption by comparing how well a compensatory strategy called Franklin's Rule captured burglars' decision policies regarding residence occupancy compared to a non-compensatory strategy (i.e., Matching Heuristic). Forty burglars each decided on the occupancy of 20 randomly selected photographs of residences (for which actual occupancy was known when the photo was taken). Participants also provided open-ended reports on the cues that influenced their decisions in each case, and then rated the importance of eight cues (e.g., deadbolt visible) over all decisions. Burglars predicted occupancy beyond chance levels. The Matching Heuristic was a significantly better predictor of burglars' decisions than Franklin's Rule, and cue use in the Matching Heuristic better corresponded to the cue ecological validities in the environment than cue use in Franklin's Rule. The most important cue in burglars' models was also the most ecologically valid or predictive of actual occupancy (i.e., vehicle present). The majority of burglars correctly identified the most important cue in their models, and the open-ended technique showed greater correspondence between self-reported and captured cue use than the rating over decision technique. Our findings support a limited rationality perspective to understanding criminal decision making, and have implications for crime prevention.
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A fuzzy neural network for intelligent data processing
NASA Astrophysics Data System (ADS)
Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam
2005-03-01
In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.
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The expert explorer: a tool for hospital data visualization and adverse drug event rules validation.
Băceanu, Adrian; Atasiei, Ionuţ; Chazard, Emmanuel; Leroy, Nicolas
2009-01-01
An important part of adverse drug events (ADEs) detection is the validation of the clinical cases and the assessment of the decision rules to detect ADEs. For that purpose, a software called "Expert Explorer" has been designed by Ideea Advertising. Anonymized datasets have been extracted from hospitals into a common repository. The tool has 3 main features. (1) It can display hospital stays in a visual and comprehensive way (diagnoses, drugs, lab results, etc.) using tables and pretty charts. (2) It allows designing and executing dashboards in order to generate knowledge about ADEs. (3) It finally allows uploading decision rules obtained from data mining. Experts can then review the rules, the hospital stays that match the rules, and finally give their advice thanks to specialized forms. Then the rules can be validated, invalidated, or improved (knowledge elicitation phase).
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Gould, Sara J.; Cardone, Dennis A.; Munyak, John; Underwood, Philipp J.; Gould, Stephen A.
2014-01-01
Context: Sidelines coverage presents unique challenges in the evaluation of injured athletes. Health care providers may be confronted with the question of when to obtain radiographs following an injury. Given that most sidelines coverage occurs outside the elite level, radiographs are not readily available at the time of injury, and the decision of when to send a player for radiographs must be made based on physical examination. Clinical tools have been developed to aid in identifying injuries that are likely to result in radiographically important fractures or dislocations. Evidence Acquisition: A search for the keywords x-ray and decision rule along with the anatomic locations shoulder, elbow, wrist, knee, and ankle was performed using the PubMed database. No limits were set regarding year of publication. We selected meta-analyses, randomized controlled trials, and survey results. Our selection focused on the largest, most well-studied published reports. We also attempted to include studies that reported the application of the rules to the field of sports medicine. Study Design: Retrospective literature review. Level of Evidence: Level 4. Results: The Ottawa Foot and Ankle Rules have been validated and implemented and are appropriate for use in both pediatric and adult populations. The Ottawa Knee Rules have been widely studied, validated, and accepted for evaluation of knee injuries. There are promising studies of decision rules for clinically important fractures of the wrist, but these studies have not been validated. The elbow has been evaluated with good outcomes via the elbow extension test, which has been validated in both single and multicenter studies. Currently, there are no reliable clinical decision tools for traumatic sports injuries to the shoulder to aid in the decision of when to obtain radiographs. Conclusion: Clinical decision tools have been developed to aid in the diagnosis and management of injuries commonly sustained during sporting events. Tools that have been appropriately validated in populations outside the initial study population can assist sports medicine physicians in the decision of when to get radiographs from the sidelines. PMID:24790698
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Empirical study on voting power in participatory forest planning.
Vainikainen, N; Kangas, A; Kangas, J
2008-07-01
Multicriteria decision support systems are applied in natural resource management in order to clarify the planning process for the stakeholders, to make all available information usable and all objectives manageable. Especially when the public is involved in planning, the decision support system should be easy to comprehend, transparent and fair. Social choice theory has recently been applied to group decision-making in natural resources management to accomplish these objectives. Although voting forms the basis of democracy, and is usually taken as a fair method, the influence of voters over the outcome may vary. It is also possible to vote strategically to improve the results from each stakeholder's point of view. This study examines the use of social choice theory in revealing stakeholders' preferences in participatory forest planning, and the influence of different voters on the outcome. The positional voting rules examined were approval voting and Borda count, but both rules were slightly modified for the purposes of this study. The third rule examined, cumulative rule, resembles utilitarian voting rules. The voting rules were tested in a real participatory forest planning situation in eastern Lapland, Finland. All voting rules resulted in a different joint order of importance of the criteria. Yet, the preference orders produced had also a lot in common and the criteria could be divided into three quite distinct groups according to their importance. The influence of individual voters varied between the voting rules, and in each case different voter was the most influential.
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Marin, Jennifer R; Mills, Angela M
2015-12-01
The 2015 Academic Emergency Medicine consensus conference, "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization" was held on May 12, 2015, with the goal of developing a high-priority research agenda on which to base future research. The specific aims of the conference were to (1) understand the current state of evidence regarding emergency department (ED) diagnostic imaging use and identify key opportunities, limitations, and gaps in knowledge; (2) develop a consensus-driven research agenda emphasizing priorities and opportunities for research in ED diagnostic imaging; and (3) explore specific funding mechanisms available to facilitate research in ED diagnostic imaging. Over a 2-year period, the executive committee and other experts in the field convened regularly to identify specific areas in need of future research. Six content areas within emergency diagnostic imaging were identified before the conference and served as the breakout groups on which consensus was achieved: clinical decision rules; use of administrative data; patient-centered outcomes research; training, education, and competency; knowledge translation and barriers to imaging optimization; and comparative effectiveness research in alternatives to traditional computed tomography use. The executive committee invited key stakeholders to assist with the planning and to participate in the consensus conference to generate a multidisciplinary agenda. There were a total of 164 individuals involved in the conference and spanned various specialties, including general emergency medicine, pediatric emergency medicine, radiology, surgery, medical physics, and the decision sciences.
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Study on key technologies of optimization of big data for thermal power plant performance
NASA Astrophysics Data System (ADS)
Mao, Mingyang; Xiao, Hong
2018-06-01
Thermal power generation accounts for 70% of China's power generation, the pollutants accounted for 40% of the same kind of emissions, thermal power efficiency optimization needs to monitor and understand the whole process of coal combustion and pollutant migration, power system performance data show explosive growth trend, The purpose is to study the integration of numerical simulation of big data technology, the development of thermal power plant efficiency data optimization platform and nitrogen oxide emission reduction system for the thermal power plant to improve efficiency, energy saving and emission reduction to provide reliable technical support. The method is big data technology represented by "multi-source heterogeneous data integration", "large data distributed storage" and "high-performance real-time and off-line computing", can greatly enhance the energy consumption capacity of thermal power plants and the level of intelligent decision-making, and then use the data mining algorithm to establish the boiler combustion mathematical model, mining power plant boiler efficiency data, combined with numerical simulation technology to find the boiler combustion and pollutant generation rules and combustion parameters of boiler combustion and pollutant generation Influence. The result is to optimize the boiler combustion parameters, which can achieve energy saving.
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Qi, Xuewei; Wu, Guoyuan; Boriboonsomsin, Kanok; ...
2016-01-01
Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off betweenmore » real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.« less
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Process Approach for Modeling of Machine and Tractor Fleet Structure
NASA Astrophysics Data System (ADS)
Dokin, B. D.; Aletdinova, A. A.; Kravchenko, M. S.; Tsybina, Y. S.
2018-05-01
The existing software complexes on modelling of the machine and tractor fleet structure are mostly aimed at solving the task of optimization. However, the creators, choosing only one optimization criterion and incorporating it in their software, provide grounds on why it is the best without giving a decision maker the opportunity to choose it for their enterprise. To analyze “bottlenecks” of machine and tractor fleet modelling, the authors of this article created a process model, in which they included adjustment to the plan of using machinery based on searching through alternative technologies. As a result, the following recommendations for software complex development have been worked out: the introduction of a database of alternative technologies; the possibility for a user to change the timing of the operations even beyond the allowable limits and in that case the calculation of the incurred loss; the possibility to rule out the solution of an optimization task, and if there is a necessity in it - the possibility to choose an optimization criterion; introducing graphical display of an annual complex of works, which could be enough for the development and adjustment of a business strategy.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Qi, Xuewei; Wu, Guoyuan; Boriboonsomsin, Kanok
Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off betweenmore » real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.« less
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Wright, Adam; Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W
2011-01-01
Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.
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NASA Astrophysics Data System (ADS)
Herd, A.; Wolff, M.
2012-01-01
Extended mission operations, such as human spaceflight to Mars provide an opportunity for take current human exploration beyond Low Earth Orbit, such as the operations undertaken on the International Space Station (ISS). This opportunity also presents a challenge in terms of extending what we currently understand as "remote operations" performed on ISS, offering learning beyond that gained from the successful moon- lander expeditions. As such there is a need to assess how the existing operations concept of ground support teams directing (and supporting) on-orbit ISS operations can be applied in the extended mission concept. The current mission support concept involves three interacting operations products - a short term plan, crew procedures and flight rules. Flight rules (for ISS operations) currently provide overall planning, engineering and operations constraints (including those derived from a safety perspective) in the form of a rule book. This paper will focus specifically on flight rules, and describe the current use of them, and assess the future role of flight rules to support exploration, including the deployment of decision support tools (DSTs) to ensure flight rule compliancy for missions with minimal ground support. Taking consideration of the historical development of pre-planned decisions, and their manifestation within the operations environment, combined with the extended remoteness of human exploration missions, we will propose a future development of this product and a platform on which it could be presented.
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NASA Astrophysics Data System (ADS)
van Rooijen, Wim-Jan; Rodriguez, Ben
2002-12-01
A complex production mask-house faces the issue of handling and understanding the logistics information from the production process of the masks. We managed to control key performance indicators like cycle-time, flow-factor, line-speed, WIP, etc. To improve the line flow, we set-up rules for optimising batching at operations and forbid batching between operations, we defined maximum and minimum WIP at the operations, scheduled urgency of the different lots and built rules for bottleneck management. Also we restricted the number of "hot lots". By migrating to the modern MES (manufacturing execution system) MaTISSe, which manages the shopfloor control, and a reporting database, we are able to eliminate the time deviations within our data, caused by data-extraction for different reports at different moments. This gives us a better understanding of our fixed bottleneck and a faster recognition of the temporarily bottlenecks caused by missing availability of machines or men. In this paper we describe the features and advantages of our new MES, as well as the migration process. We have already achieved considerable benefits. Our plan is to extend decision support within the MES, to help both managers and operators to make the right decisions. The project behind this paper reaped major benefits described here and we are looking forward to further challenges and successes.
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-01-05
... decisions. The MSRB is firmly of the belief that the proposed rule change is within its statutory authority... Proposed Rule Change Relating to Rule G-32, on Disclosures in Connection With Primary Offerings, Form G-32... proposed rule change relating to Rule G-32, on disclosures in connection with primary offerings, Form G-32...
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McCoyd, Judith L M
2009-10-01
The sociology of emotion is rapidly evolving and has implications for medical settings. Advancing medical technologies create new contexts for decision-making and emotional reaction that are framed by "feeling rules." Feeling rules guide not only behavior, but also how one believes one should feel, thereby causing one to attempt to bring one's authentic feelings into line with perceived feeling rules. Using qualitative data, the theoretical existence of feeling rules in pregnancy and prenatal testing is confirmed. Further examination extends this analysis: at times of technological development feeling rules are often discrepant, leaving patients with unscripted emotion work. Data from a study of women who interrupted anomalous pregnancies indicate that feeling rules are unclear when competing feeling rules are operating during times of societal and technological change. Because much of this occurs below the level of consciousness, medical and psychological services providers need to be aware of potential discrepancies in feeling rules and assist patients in identifying the salient feeling rules. Patients' struggles ease when they can recognize the discrepancies and assess their implications for decision-making and emotional response. (c) 2009 APA, all rights reserved.
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-09-14
... specified in the proposed rule: i. Clarify the time frames within which members must take action to effect... a final decision of a FINRA officer or the UPC Committee under NASD Rule 11890 (Clearly Erroneous... refusal by a member to take action necessary to effectuate a final decision of a FINRA officer or the UPC...
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The Supreme Court Spanking Ruling: An Issue in Debate.
ERIC Educational Resources Information Center
Welsh, Ralph S.; And Others
Few issues have polarized the educational community so completely as the 1975 and 1977 decisions by the U.S. Supreme Court to allow corporal punishment in the schools. The symposium reported here was organized and conducted following the 1975 decision but prior to the 1977 one. Three papers in support and three papers against the ruling were read,…
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Code of Federal Regulations, 2010 CFR
2010-01-01
... a conference will advance its evaluation of the request. (b) Criteria. Except where modification or... rule or order. (a) OFE will consider the entire administrative record in its evaluation of the decision... request under this subpart and all interested persons will be afforded an opportunity to respond to these...
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Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.
Naso, David; Turchiano, Biagio
2005-04-01
In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.
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Working dogs cooperate among one another by generalised reciprocity.
Gfrerer, Nastassja; Taborsky, Michael
2017-03-06
Cooperation by generalised reciprocity implies that individuals apply the decision rule "help anyone if helped by someone". This mechanism has been shown to generate evolutionarily stable levels of cooperation, but as yet it is unclear how widely this cooperation mechanism is applied among animals. Dogs (Canis familiaris) are highly social animals with considerable cognitive potential and the ability to differentiate between individual social partners. But although dogs can solve complex problems, they may use simple rules for behavioural decisions. Here we show that dogs trained in an instrumental cooperative task to provide food to a social partner help conspecifics more often after receiving help from a dog before. Remarkably, in so doing they show no distinction between partners that had helped them before and completely unfamiliar conspecifics. Apparently, dogs use the simple decision rule characterizing generalised reciprocity, although they are probably capable of using the more complex decision rule of direct reciprocity: "help someone who has helped you". However, generalized reciprocity involves lower information processing costs and is therefore a cheaper cooperation strategy. Our results imply that generalised reciprocity might be applied more commonly than direct reciprocity also in other mutually cooperating animals.
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Working dogs cooperate among one another by generalised reciprocity
Gfrerer, Nastassja; Taborsky, Michael
2017-01-01
Cooperation by generalised reciprocity implies that individuals apply the decision rule “help anyone if helped by someone”. This mechanism has been shown to generate evolutionarily stable levels of cooperation, but as yet it is unclear how widely this cooperation mechanism is applied among animals. Dogs (Canis familiaris) are highly social animals with considerable cognitive potential and the ability to differentiate between individual social partners. But although dogs can solve complex problems, they may use simple rules for behavioural decisions. Here we show that dogs trained in an instrumental cooperative task to provide food to a social partner help conspecifics more often after receiving help from a dog before. Remarkably, in so doing they show no distinction between partners that had helped them before and completely unfamiliar conspecifics. Apparently, dogs use the simple decision rule characterizing generalised reciprocity, although they are probably capable of using the more complex decision rule of direct reciprocity: “help someone who has helped you”. However, generalized reciprocity involves lower information processing costs and is therefore a cheaper cooperation strategy. Our results imply that generalised reciprocity might be applied more commonly than direct reciprocity also in other mutually cooperating animals. PMID:28262722
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Gubhaju, Bina; De Jong, Gordon F.
2009-01-01
This research tests the thesis that the neoclassical micro-economic and the new household economic theoretical assumptions on migration decision-making rules are segmented by gender, marital status, and time frame of intention to migrate. Comparative tests of both theories within the same study design are relatively rare. Utilizing data from the Causes of Migration in South Africa national migration survey, we analyze how individually held “own-future” versus alternative “household well-being” migration decision rules effect the intentions to migrate of male and female adults in South Africa. Results from the gender and marital status specific logistic regressions models show consistent support for the different gender-marital status decision rule thesis. Specifically, the “maximizing one’s own future” neoclassical microeconomic theory proposition is more applicable for never married men and women, the “maximizing household income” proposition for married men with short-term migration intentions, and the “reduce household risk” proposition for longer time horizon migration intentions of married men and women. Results provide new evidence on the way household strategies and individual goals jointly affect intentions to move or stay. PMID:20161187
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Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.
Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab
2017-09-01
Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.
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NASA Astrophysics Data System (ADS)
Smith, R.; Kasprzyk, J. R.; Zagona, E. A.
2013-12-01
Population growth and climate change, combined with difficulties in building new infrastructure, motivate portfolio-based solutions to ensuring sufficient water supply. Powerful simulation models with graphical user interfaces (GUI) are often used to evaluate infrastructure portfolios; these GUI based models require manual modification of the system parameters, such as reservoir operation rules, water transfer schemes, or system capacities. Multiobjective evolutionary algorithm (MOEA) based optimization can be employed to balance multiple objectives and automatically suggest designs for infrastructure systems, but MOEA based decision support typically uses a fixed problem formulation (i.e., a single set of objectives, decisions, and constraints). This presentation suggests a dynamic framework for linking GUI-based infrastructure models with MOEA search. The framework begins with an initial formulation which is solved using a MOEA. Then, stakeholders can interact with candidate solutions, viewing their properties in the GUI model. This is followed by changes in the formulation which represent users' evolving understanding of exigent system properties. Our case study is built using RiverWare, an object-oriented, data-centered model that facilitates the representation of a diverse array of water resources systems. Results suggest that assumptions within the initial MOEA search are violated after investigating tradeoffs and reveal how formulations should be modified to better capture stakeholders' preferences.
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Wind offering in energy and reserve markets
NASA Astrophysics Data System (ADS)
Soares, T.; Pinson, P.; Morais, H.
2016-09-01
The increasing penetration of wind generation in power systems to fulfil the ambitious European targets will make wind power producers to play an even more important role in the future power system. Wind power producers are being incentivized to participate in reserve markets to increase their revenue, since currently wind turbine/farm technologies allow them to provide ancillary services. Thus, wind power producers are to develop offering strategies for participation in both energy and reserve markets, accounting for market rules, while ensuring optimal revenue. We consider a proportional offering strategy to optimally decide upon participation in both markets by maximizing expected revenue from day-ahead decisions while accounting for estimated regulation costs for failing to provide the services. An evaluation of considering the same proportional splitting of energy and reserve in both day- ahead and balancing market is performed. A set of numerical examples illustrate the behavior of such strategy. An important conclusion is that the optimal split of the available wind power between energy and reserve strongly depends upon prices and penalties on both market trading floors.
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76 FR 18383 - Extension of Sunset Date for Attorney Advisor Program
Federal Register 2010, 2011, 2012, 2013, 2014
2011-04-04
... rule. SUMMARY: We are extending for 2 years our rule authorizing attorney advisors to conduct certain prehearing procedures and to issue fully favorable decisions. The current rule will expire on August 10, 2011... would end on August 10, 2009, unless we decided either to terminate the rule earlier or to extend it...
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Ventral Striatum and the Evaluation of Memory Retrieval Strategies
Badre, David; Lebrecht, Sophie; Pagliaccio, David; Long, Nicole M.; Scimeca, Jason M.
2015-01-01
Adaptive memory retrieval requires mechanisms of cognitive control that facilitate the recovery of goal-relevant information. Frontoparietal systems are known to support control of memory retrieval. However, the mechanisms by which the brain acquires, evaluates, and adapts retrieval strategies remain unknown. Here, we provide evidence that ventral striatal activation tracks the success of a retrieval strategy and correlates with subsequent reliance on that strategy. Human participants were scanned with fMRI while performing a lexical decision task. A rule was provided that indicated the likely semantic category of a target word given the category of a preceding prime. Reliance on the rule improved decision-making, as estimated within a drift diffusion framework. Ventral striatal activation tracked the benefit that relying on the rule had on decision-making. Moreover, activation in ventral striatum correlated with a participant’s subsequent reliance on the rule. Taken together, these results support a role for ventral striatum in learning and evaluating declarative retrieval strategies. PMID:24564466
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Li, Feilong; Li, Zhiqiang; Li, Guangxia; Dong, Feihong; Zhang, Wei
2017-01-01
The usable satellite spectrum is becoming scarce due to static spectrum allocation policies. Cognitive radio approaches have already demonstrated their potential towards spectral efficiency for providing more spectrum access opportunities to secondary user (SU) with sufficient protection to licensed primary user (PU). Hence, recent scientific literature has been focused on the tradeoff between spectrum reuse and PU protection within narrowband spectrum sensing (SS) in terrestrial wireless sensing networks. However, those narrowband SS techniques investigated in the context of terrestrial CR may not be applicable for detecting wideband satellite signals. In this paper, we mainly investigate the problem of joint designing sensing time and hard fusion scheme to maximize SU spectral efficiency in the scenario of low earth orbit (LEO) mobile satellite services based on wideband spectrum sensing. Compressed detection model is established to prove that there indeed exists one optimal sensing time achieving maximal spectral efficiency. Moreover, we propose novel wideband cooperative spectrum sensing (CSS) framework where each SU reporting duration can be utilized for its following SU sensing. The sensing performance benefits from the novel CSS framework because the equivalent sensing time is extended by making full use of reporting slot. Furthermore, in respect of time-varying channel, the spatiotemporal CSS (ST-CSS) is presented to attain space and time diversity gain simultaneously under hard decision fusion rule. Computer simulations show that the optimal sensing settings algorithm of joint optimization of sensing time, hard fusion rule and scheduling strategy achieves significant improvement in spectral efficiency. Additionally, the novel ST-CSS scheme performs much higher spectral efficiency than that of general CSS framework. PMID:28117712
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Optimal policy for value-based decision-making.
Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre
2016-08-18
For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.
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Optimal policy for value-based decision-making
Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre
2016-01-01
For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down. PMID:27535638
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Heuristic rules embedded genetic algorithm for in-core fuel management optimization
NASA Astrophysics Data System (ADS)
Alim, Fatih
The objective of this study was to develop a unique methodology and a practical tool for designing loading pattern (LP) and burnable poison (BP) pattern for a given Pressurized Water Reactor (PWR) core. Because of the large number of possible combinations for the fuel assembly (FA) loading in the core, the design of the core configuration is a complex optimization problem. It requires finding an optimal FA arrangement and BP placement in order to achieve maximum cycle length while satisfying the safety constraints. Genetic Algorithms (GA) have been already used to solve this problem for LP optimization for both PWR and Boiling Water Reactor (BWR). The GA, which is a stochastic method works with a group of solutions and uses random variables to make decisions. Based on the theories of evaluation, the GA involves natural selection and reproduction of the individuals in the population for the next generation. The GA works by creating an initial population, evaluating it, and then improving the population by using the evaluation operators. To solve this optimization problem, a LP optimization package, GARCO (Genetic Algorithm Reactor Code Optimization) code is developed in the framework of this thesis. This code is applicable for all types of PWR cores having different geometries and structures with an unlimited number of FA types in the inventory. To reach this goal, an innovative GA is developed by modifying the classical representation of the genotype. To obtain the best result in a shorter time, not only the representation is changed but also the algorithm is changed to use in-core fuel management heuristics rules. The improved GA code was tested to demonstrate and verify the advantages of the new enhancements. The developed methodology is explained in this thesis and preliminary results are shown for the VVER-1000 reactor hexagonal geometry core and the TMI-1 PWR. The improved GA code was tested to verify the advantages of new enhancements. The core physics code used for VVER in this research is Moby-Dick, which was developed to analyze the VVER by SKODA Inc. The SIMULATE-3 code, which is an advanced two-group nodal code, is used to analyze the TMI-1.
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Précis of Simple heuristics that make us smart.
Todd, P M; Gigerenzer, G
2000-10-01
How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics--simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data--that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.
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NASA Astrophysics Data System (ADS)
Zakoldaev, D. A.; Shukalov, A. V.; Zharinov, I. O.; Zharinov, O. O.
2018-05-01
The task of the algorithm of choosing the type of mechanical assembly production of instrument making enterprises of Industry 4.0 is being studied. There is a comparison of two project algorithms for Industry 3.0 and Industry 4.0. The algorithm of choosing the type of mechanical assembly production of instrument making enterprises of Industry 4.0 is based on the technological route analysis of the manufacturing process in a company equipped with cyber and physical systems. This algorithm may give some project solutions selected from the primary part or the auxiliary one of the production. The algorithm decisive rules are based on the optimal criterion.
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Hierarchical fuzzy control of low-energy building systems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yu, Zhen; Dexter, Arthur
2010-04-15
A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profilemore » can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)« less
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Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.
Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon
2017-01-01
In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.
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Kriegeskorte, Nikolaus; Carlin, Johan D.; Rowe, James B.
2013-01-01
Behavior is governed by rules that associate stimuli with responses and outcomes. Human and monkey studies have shown that rule-specific information is widely represented in the frontoparietal cortex. However, it is not known how establishing a rule under different contexts affects its neural representation. Here, we use event-related functional MRI (fMRI) and multivoxel pattern classification methods to investigate the human brain's mechanisms of establishing and maintaining rules for multiple perceptual decision tasks. Rules were either chosen by participants or specifically instructed to them, and the fMRI activation patterns representing rule-specific information were compared between these contexts. We show that frontoparietal regions differ in the properties of their rule representations during active maintenance before execution. First, rule-specific information maintained in the dorsolateral and medial frontal cortex depends on the context in which it was established (chosen vs specified). Second, rule representations maintained in the ventrolateral frontal and parietal cortex are independent of the context in which they were established. Furthermore, we found that the rule-specific coding maintained in anticipation of stimuli may change with execution of the rule: representations in context-independent regions remain invariant from maintenance to execution stages, whereas rule representations in context-dependent regions do not generalize to execution stage. The identification of distinct frontoparietal systems with context-independent and context-dependent task rule representations, and the distinction between anticipatory and executive rule representations, provide new insights into the functional architecture of goal-directed behavior. PMID:23864675
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A novel medical information management and decision model for uncertain demand optimization.
Bi, Ya
2015-01-01
Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions. To establish a novel medical information management and decision model for uncertain demand optimization. A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm. The optimal management and decision model can effectively reduce the medicine inventory cost. The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.
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49 CFR 1503.655 - Initial decision.
Code of Federal Regulations, 2010 CFR
2010-10-01
... Rules of Practice in TSA Civil Penalty Actions § 1503.655 Initial decision. (a) Contents. The ALJ may... copies of that initial decision available to all parties and the TSA decision maker. (b) Written decision... ALJ is persuasive authority in any other civil penalty action, unless appealed and reversed by the TSA...
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20 CFR 501.6 - Decisions and orders.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Decisions and orders. 501.6 Section 501.6 Employees' Benefits EMPLOYEES' COMPENSATION APPEALS BOARD, DEPARTMENT OF LABOR RULES OF PROCEDURE § 501.6 Decisions and orders. (a) Decisions. A decision of the Board will contain a written opinion setting forth...
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Decentralisation of Health Services in Fiji: A Decision Space Analysis.
Mohammed, Jalal; North, Nicola; Ashton, Toni
2015-11-15
Decentralisation aims to bring services closer to the community and has been advocated in the health sector to improve quality, access and equity, and to empower local agencies, increase innovation and efficiency and bring healthcare and decision-making as close as possible to where people live and work. Fiji has attempted two approaches to decentralisation. The current approach reflects a model of deconcentration of outpatient services from the tertiary level hospital to the peripheral health centres in the Suva subdivision. Using a modified decision space approach developed by Bossert, this study measures decision space created in five broad categories (finance, service organisation, human resources, access rules, and governance rules) within the decentralised services. Fiji's centrally managed historical-based allocation of financial resources and management of human resources resulted in no decision space for decentralised agents. Narrow decision space was created in the service organisation category where, with limited decision space created over access rules, Fiji has seen greater usage of its decentralised health centres. There remains limited decision space in governance. The current wave of decentralisation reveals that, whilst the workload has shifted from the tertiary hospital to the peripheral health centres, it has been accompanied by limited transfer of administrative authority, suggesting that Fiji's deconcentration reflects the transfer of workload only with decision-making in the five functional areas remaining largely centralised. As such, the benefits of decentralisation for users and providers are likely to be limited. © 2016 by Kerman University of Medical Sciences.
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VanGeest, Jonathan; Weiner, Saul; Johnson, Timothy; Cummins, Deborah
2007-07-01
To develop and test an explanatory model of the impact of managed care on physicians' decisions to manipulate reimbursement rules for patients. A self-administered mailed questionnaire of a national random sample of 1124 practicing physicians in the USA. Structural equation modelling was used. The main outcome measure assessed whether or not physicians had manipulated reimbursement rules (such as exaggerated the severity of patients conditions, changed billing diagnoses, or reported signs or symptoms that the patients did not have) to help patients secure coverage for needed treatment or services. The response rate was 64% (n = 720). Physicians' decisions to manipulate reimbursement rules for patients are directly driven not only by ethical beliefs about gaming the system but also by requests from patients, the perception of insufficient time to deliver care, and the proportion of Medicaid patients. Covert advocacy is also the indirect result of utilization review hassles, primary care specialty, and practice environment. Managed care is not just a set of rules that physicians choose to follow or disobey, but an environment of competing pressures from patients, purchasers, and high workload. Reimbursement manipulation is a response to that environment, rather than simply a reflection of individual physicians' values.
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-06
...'s Republic of China: Notice of Court Decision Not in Harmony With Final Scope Ruling and Notice of... Corporation's toolkits from the People's Republic of China (``PRC''), pursuant to the CIT's remand order in... judgment in this case is not in harmony with the Department's final scope ruling and is amending its final...
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ERIC Educational Resources Information Center
Van Norman, Ethan R.; Parker, David C.
2018-01-01
Recent simulations suggest that trend line decision rules applied to curriculum-based measurement of reading progress monitoring data may lead to inaccurate interpretations unless data are collected for upward of 3 months. The authors of those studies did not manipulate goal line slope or account for a student's level of initial performance when…
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Hampson, Lisa V; Williamson, Paula R; Wilby, Martin J; Jaki, Thomas
2017-01-01
Just over half of publicly funded trials recruit their target sample size within the planned study duration. When recruitment targets are missed, the funder of a trial is faced with the decision of either committing further resources to the study or risk that a worthwhile treatment effect may be missed by an underpowered final analysis. To avoid this challenging situation, when there is insufficient prior evidence to support predicted recruitment rates, funders now require feasibility assessments to be performed in the early stages of trials. Progression criteria are usually specified and agreed with the funder ahead of time. To date, however, the progression rules used are typically ad hoc. In addition, rules routinely permit adaptations to recruitment strategies but do not stipulate criteria for evaluating their effectiveness. In this paper, we develop a framework for planning and designing internal pilot studies which permit a trial to be stopped early if recruitment is disappointing or to continue to full recruitment if enrolment during the feasibility phase is adequate. This framework enables a progression rule to be pre-specified and agreed upon prior to starting a trial. The novel two-stage designs stipulate that if neither of these situations arises, adaptations to recruitment should be made and subsequently evaluated to establish whether they have been successful. We derive optimal progression rules for internal pilot studies which minimise the expected trial overrun and maintain a high probability of completing the study when the recruitment rate is adequate. The advantages of this procedure are illustrated using a real trial example.
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A programmable rules engine to provide clinical decision support using HTML forms.
Heusinkveld, J; Geissbuhler, A; Sheshelidze, D; Miller, R
1999-01-01
The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser.
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A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters
Wang, Zhihao; Yi, Jing
2016-01-01
For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291
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The anatomy of choice: active inference and agency.
Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J
2013-01-01
This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.
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NASA Astrophysics Data System (ADS)
Zaremotlagh, S.; Hezarkhani, A.
2017-04-01
Some evidences of rare earth elements (REE) concentrations are found in iron oxide-apatite (IOA) deposits which are located in Central Iranian microcontinent. There are many unsolved problems about the origin and metallogenesis of IOA deposits in this district. Although it is considered that felsic magmatism and mineralization were simultaneous in the district, interaction of multi-stage hydrothermal-magmatic processes within the Early Cambrian volcano-sedimentary sequence probably caused some epigenetic mineralizations. Secondary geological processes (e.g., multi-stage mineralization, alteration, and weathering) have affected on variations of major elements and possible redistribution of REE in IOA deposits. Hence, the geochemical behaviors and distribution patterns of REE are expected to be complicated in different zones of these deposits. The aim of this paper is recognizing LREE distribution patterns based on whole-rock chemical compositions and automatic discovery of their geochemical rules. For this purpose, the pattern recognition techniques including decision tree and neural network were applied on a high-dimensional geochemical dataset from Choghart IOA deposit. Because some data features were irrelevant or redundant in recognizing the distribution patterns of each LREE, a greedy attribute subset selection technique was employed to select the best subset of predictors used in classification tasks. The decision trees (CART algorithm) were pruned optimally to more accurately categorize independent test data than unpruned ones. The most effective classification rules were extracted from the pruned tree to describe the meaningful relationships between the predictors and different concentrations of LREE. A feed-forward artificial neural network was also applied to reliably predict the influence of various rock compositions on the spatial distribution patterns of LREE with a better performance than the decision tree induction. The findings of this study could be effectively used to visualize the LREE distribution patterns as geochemical maps.
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Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W
2011-01-01
Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy. Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts. PMID:21613643
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Salzmann-Erikson, Martin
2017-11-01
Ward rules in psychiatric care aim to promote safety for both patients and staff. Simultaneously, ward rules are associated with increased patient violence, leading to neither a safe work environment nor a safe caring environment. Although ward rules are routinely used, few studies have explicitly accounted for their impact. To describe the process of a team development project considering ward rule issues, and to develop a working model to empower staff in their daily in-patient psychiatric nursing practices. The design of this study is explorative and descriptive. Participatory action research methodology was applied to understand ward rules. Data consists of audio-recorded group discussions, observations and field notes, together creating a data set of 556 text pages. More than 100 specific ward rules were identified. In this process, the word rules was relinquished in favor of adopting the term principles, since rules are inconsistent with a caring ideology. A linguistic transition led to the development of a framework embracing the (1) Principle of Safety, (2) Principle of Structure and (3) Principle of Interplay. The principles were linked to normative guidelines and applied ethical theories: deontology, consequentialism and ethics of care. The work model reminded staff about the principles, empowered their professional decision-making, decreased collegial conflicts because of increased acceptance for individual decisions, and, in general, improved well-being at work. Furthermore, the work model also empowered staff to find support for their decisions based on principles that are grounded in the ethics of totality.
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Sendi, Pedram
2008-06-01
When choosing from a menu of treatment alternatives, the optimal treatment depends on the objective function and the assumptions of the model. The classical decision rule of cost-effectiveness analysis may be formulated via two different objective functions: (i) maximising health outcomes subject to the budget constraint or (ii) maximising the net benefit of the intervention with the budget being determined ex post. We suggest a more general objective function of (iii) maximising return on investment from available resources with consideration of health and non-health investments. The return on investment approach allows to adjust the analysis for the benefits forgone by alternative non-health investments from a societal or subsocietal perspective. We show that in the presence of positive returns on non-health investments the decision-maker's willingness to pay per unit of effect for a treatment program needs to be higher than its incremental cost-effectiveness ratio to be considered cost-effective.
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An Integrated Product Environment
NASA Technical Reports Server (NTRS)
Higgins, Chuck
1997-01-01
Mechanical Advantage is a mechanical design decision support system. Unlike our CAD/CAM cousins, Mechanical Advantage addresses true engineering processes, not just the form and fit of geometry. If we look at a traditional engineering environment, we see that an engineer starts with two things - performance goals and design rules. The intent is to have a product perform specific functions and accomplish that within a designated environment. Geometry should be a simple byproduct of that engineering process - not the controller of it. Mechanical Advantage is a performance modeler allowing engineers to consider all these criteria in making their decisions by providing such capabilities as critical parameter analysis, tolerance and sensitivity analysis, math driven Geometry, and automated design optimizations. If you should desire an industry standard solid model, we would produce an ACIS-based solid model. If you should desire an ANSI/ISO standard drawing, we would produce this as well with a virtual push of the button. For more information on this and other Advantage Series products, please contact the author.
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Kim, Heejun; Bian, Jiantao; Mostafa, Javed; Jonnalagadda, Siddhartha; Del Fiol, Guilherme
2016-01-01
Motivation: Clinicians need up-to-date evidence from high quality clinical trials to support clinical decisions. However, applying evidence from the primary literature requires significant effort. Objective: To examine the feasibility of automatically extracting key clinical trial information from ClinicalTrials.gov. Methods: We assessed the coverage of ClinicalTrials.gov for high quality clinical studies that are indexed in PubMed. Using 140 random ClinicalTrials.gov records, we developed and tested rules for the automatic extraction of key information. Results: The rate of high quality clinical trial registration in ClinicalTrials.gov increased from 0.2% in 2005 to 17% in 2015. Trials reporting results increased from 3% in 2005 to 19% in 2015. The accuracy of the automatic extraction algorithm for 10 trial attributes was 90% on average. Future research is needed to improve the algorithm accuracy and to design information displays to optimally present trial information to clinicians. PMID:28269867
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A Framework for Including Family Health Spillovers in Economic Evaluation
Al-Janabi, Hareth; van Exel, Job; Brouwer, Werner; Coast, Joanna
2016-01-01
Health care interventions may affect the health of patients’ family networks. It has been suggested that these “health spillovers” should be included in economic evaluation, but there is not a systematic method for doing this. In this article, we develop a framework for including health spillovers in economic evaluation. We focus on extra-welfarist economic evaluations where the objective is to maximize health benefits from a health care budget (the “health care perspective”). Our framework involves adapting the conventional cost-effectiveness decision rule to include 2 multiplier effects to internalize the spillover effects. These multiplier effects express the ratio of total health effects (for patients and their family networks) to patient health effects. One multiplier effect is specified for health benefit generated from providing a new intervention, one for health benefit displaced by funding this intervention. We show that using multiplier effects to internalize health spillovers could change the optimal funding decisions and generate additional health benefits to society. PMID:26377370
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Bendability optimization of flexible optical nanoelectronics via neutral axis engineering
2012-01-01
The enhancement of bendability of flexible nanoelectronics is critically important to realize future portable and wearable nanoelectronics for personal and military purposes. Because there is an enormous variety of materials and structures that are used for flexible nanoelectronic devices, a governing design rule for optimizing the bendability of these nanodevices is required. In this article, we suggest a design rule to optimize the bendability of flexible nanoelectronics through neutral axis (NA) engineering. In flexible optical nanoelectronics, transparent electrodes such as indium tin oxide (ITO) are usually the most fragile under an external load because of their brittleness. Therefore, we representatively focus on the bendability of ITO which has been widely used as transparent electrodes, and the NA is controlled by employing a buffer layer on the ITO layer. First, we independently investigate the effect of the thickness and elastic modulus of a buffer layer on the bendability of an ITO film. Then, we develop a design rule for the bendability optimization of flexible optical nanoelectronics. Because NA is determined by considering both the thickness and elastic modulus of a buffer layer, the design rule is conceived to be applicable regardless of the material and thickness that are used for the buffer layer. Finally, our design rule is applied to optimize the bendability of an organic solar cell, which allows the bending radius to reach about 1 mm. Our design rule is thus expected to provide a great strategy to enhance the bending performance of a variety of flexible nanoelectronics. PMID:22587757
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Bendability optimization of flexible optical nanoelectronics via neutral axis engineering.
Lee, Sangmin; Kwon, Jang-Yeon; Yoon, Daesung; Cho, Handong; You, Jinho; Kang, Yong Tae; Choi, Dukhyun; Hwang, Woonbong
2012-05-15
The enhancement of bendability of flexible nanoelectronics is critically important to realize future portable and wearable nanoelectronics for personal and military purposes. Because there is an enormous variety of materials and structures that are used for flexible nanoelectronic devices, a governing design rule for optimizing the bendability of these nanodevices is required. In this article, we suggest a design rule to optimize the bendability of flexible nanoelectronics through neutral axis (NA) engineering. In flexible optical nanoelectronics, transparent electrodes such as indium tin oxide (ITO) are usually the most fragile under an external load because of their brittleness. Therefore, we representatively focus on the bendability of ITO which has been widely used as transparent electrodes, and the NA is controlled by employing a buffer layer on the ITO layer. First, we independently investigate the effect of the thickness and elastic modulus of a buffer layer on the bendability of an ITO film. Then, we develop a design rule for the bendability optimization of flexible optical nanoelectronics. Because NA is determined by considering both the thickness and elastic modulus of a buffer layer, the design rule is conceived to be applicable regardless of the material and thickness that are used for the buffer layer. Finally, our design rule is applied to optimize the bendability of an organic solar cell, which allows the bending radius to reach about 1 mm. Our design rule is thus expected to provide a great strategy to enhance the bending performance of a variety of flexible nanoelectronics.
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Paradis, Michelle; Stiell, Ian; Atkinson, Katherine M; Guerinet, Julien; Sequeira, Yulric; Salter, Laura; Forster, Alan J; Murphy, Malia Sq; Wilson, Kumanan
2018-06-11
The Ottawa Ankle Rules, Ottawa Knee Rule, and Canadian C-Spine Rule-together known as The Ottawa Rules-are a set of internationally validated clinical decision rules developed to decrease unnecessary diagnostic imaging in the emergency department. In this study, we sought to develop and evaluate the use of a mobile app version of The Ottawa Rules. The primary objective of this study was to determine acceptability of The Ottawa Rules app among emergency department clinicians. The secondary objective was to evaluate the effect of publicity efforts on uptake of The Ottawa Rules app. The Ottawa Rules app was developed and publicly released for free on iOS and Android operating systems in April 2016. Local and national news and academic media coverage coincided with app release. This study was conducted at a large tertiary trauma care center in Ottawa, Canada. The study was advertised through posters and electronically by email. Emergency department clinicians were approached in person to enroll via in-app consent for a 1-month study during which time they were encouraged to use the app when evaluating patients with suspected knee, foot, or neck injuries. A 23-question survey was administered at the end of the study period via email to determine self-reported frequency, perceived ease of use of the app, and participant Technology Readiness Index scores. A total of 108 emergency department clinicians completed the study including 42 nurses, 33 residents, 20 attending physicians, and 13 medical students completing emergency department rotations. The median Technology Readiness Index for this group was 3.56, indicating a moderate degree of openness for technological adoption. The majority of survey respondents indicated favorable receptivity to the app including finding it helpful to applying the rules (73/108, 67.6%), that they would recommend the app to colleagues (81/108, 75.0%), and that they would continue using the app (73/108, 67.6%). Feedback from study participants highlighted a desire for access to more clinical decision rules and a higher degree of interactivity of the app. Between April 21, 2016, and June 1, 2017, The Ottawa Rules app was downloaded approximately 4000 times across 89 countries. We have found The Ottawa Rules app to be an effective means to disseminate the Ottawa Ankle Rules, Ottawa Knee Rule, and Canadian C-Spine Rule among all levels of emergency department clinicians. We have been successful in monitoring uptake and access of the rules in the app as a result of our publicity efforts. Mobile technology can be leveraged to improve the accessibility of clinical decision tools to health professionals. ©Michelle Paradis, Ian Stiell, Katherine M Atkinson, Julien Guerinet, Yulric Sequeira, Laura Salter, Alan J Forster, Malia SQ Murphy, Kumanan Wilson. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.06.2018.
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Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges.
Klabunde, Anna; Willekens, Frans
We review agent-based models (ABM) of human migration with respect to their decision-making rules. The most prominent behavioural theories used as decision rules are the random utility theory, as implemented in the discrete choice model, and the theory of planned behaviour. We identify the critical choices that must be made in developing an ABM, namely the modelling of decision processes and social networks. We also discuss two challenges that hamper the widespread use of ABM in the study of migration and, more broadly, demography and the social sciences: (a) the choice and the operationalisation of a behavioural theory (decision-making and social interaction) and (b) the selection of empirical evidence to validate the model. We offer advice on how these challenges might be overcome.
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Code of Federal Regulations, 2010 CFR
2010-04-01
... limited to any advantage, preference, privilege, license, permit, favorable decision, ruling, status, or... United States Government. Individual means a natural person. Initial decision means the written decision of the ALJ required by § 224.10 or § 224.37, and includes a revised initial decision issued following...
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Code of Federal Regulations, 2010 CFR
2010-07-01
... advantage, preference, privilege, license, permit, favorable decision, ruling, status, or loan guarantee.... Initial Decision means the written decision of the ALJ required by § 42.10 or § 42.37 of this part, and includes a revised initial decision issued following a remand or a motion for reconsideration...
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76 FR 28209 - Committee on Regulation
Federal Register 2010, 2011, 2012, 2013, 2014
2011-05-16
... #0; #0;Notices #0; Federal Register #0; #0; #0;This section of the FEDERAL REGISTER contains documents other than rules #0;or proposed rules that are applicable to the public. Notices of hearings #0;and investigations, committee meetings, agency decisions and rulings, #0;delegations of authority...
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78 FR 22842 - Nicolet Resource Advisory Committee
Federal Register 2010, 2011, 2012, 2013, 2014
2013-04-17
... #0; #0;Notices #0; Federal Register #0; #0; #0;This section of the FEDERAL REGISTER contains documents other than rules #0;or proposed rules that are applicable to the public. Notices of hearings #0;and investigations, committee meetings, agency decisions and rulings, #0;delegations of authority...
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Bereby-Meyer, Yoella; Meyer, Joachim; Budescu, David V
2003-02-01
This paper assesses framing effects on decision making with internal uncertainty, i.e., partial knowledge, by focusing on examinees' behavior in multiple-choice (MC) tests with different scoring rules. In two experiments participants answered a general-knowledge MC test that consisted of 34 solvable and 6 unsolvable items. Experiment 1 studied two scoring rules involving Positive (only gains) and Negative (only losses) scores. Although answering all items was the dominating strategy for both rules, the results revealed a greater tendency to answer under the Negative scoring rule. These results are in line with the predictions derived from Prospect Theory (PT) [Econometrica 47 (1979) 263]. The second experiment studied two scoring rules, which allowed respondents to exhibit partial knowledge. Under the Inclusion-scoring rule the respondents mark all answers that could be correct, and under the Exclusion-scoring rule they exclude all answers that might be incorrect. As predicted by PT, respondents took more risks under the Inclusion rule than under the Exclusion rule. The results illustrate that the basic process that underlies choice behavior under internal uncertainty and especially the effect of framing is similar to the process of choice under external uncertainty and can be described quite accurately by PT. Copyright 2002 Elsevier Science B.V.
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Newgard, C D; Hedges, J R; Stone, J V; Lenfesty, B; Diggs, B; Arthur, M; Mullins, R J
2005-12-01
To derive a clinical decision rule for people with traumatic brain injury (TBI) that enables early identification of patients requiring specialised trauma care. We collected data from 1999 through 2003 on a retrospective cohort of consecutive people aged 18-65 years with a serious head injury (AIS > or =3), transported directly from the scene of injury, and evaluated in the ED. Information on 22 demographical, physiological, radiographic, and lab variables was collected. Resource based "high therapeutic intensity" measures occurring within 72 hours of ED arrival (the outcome measure) were identified a priori and included: neurosurgical intervention, exploratory laparotomy, intensive care interventions, or death. We used classification and regression tree analysis to derive and cross validate the decision rule. 504 consecutive trauma patients were identified as having a serious head injury: 246 (49%) required at least one of the HTI measures. Five ED variables (GCS, respiratory rate, age, temperature, and pulse rate) identified subjects requiring at least one of the HTI measures with 94% sensitivity (95% CI 91 to 97%) and 63% specificity (95% CI 57 to 69%) in the derivation sample, and 90% sensitivity and 55% specificity using cross validation. This decision rule identified among a cohort of head injured patients evaluated in the ED the majority of those who urgently required specialised trauma care. The rule will require prospective validation in injured people presenting to non-tertiary care hospitals before implementation can be recommended.
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Irrational decision-making in an amoeboid organism: transitivity and context-dependent preferences.
Latty, Tanya; Beekman, Madeleine
2011-01-22
Most models of animal foraging and consumer choice assume that individuals make choices based on the absolute value of items and are therefore 'economically rational'. However, frequent violations of rationality by animals, including humans, suggest that animals use comparative valuation rules. Are comparative valuation strategies a consequence of the way brains process information, or are they an intrinsic feature of biological decision-making? Here, we examine the principles of rationality in an organism with radically different information-processing mechanisms: the brainless, unicellular, slime mould Physarum polycephalum. We offered P. polycephalum amoebas a choice between food options that varied in food quality and light exposure (P. polycephalum is photophobic). The use of an absolute valuation rule will lead to two properties: transitivity and independence of irrelevant alternatives (IIA). Transitivity is satisfied if preferences have a consistent, linear ordering, while IIA states that a decision maker's preference for an item should not change if the choice set is expanded. A violation of either of these principles suggests the use of comparative rather than absolute valuation rules. Physarum polycephalum satisfied transitivity by having linear preference rankings. However, P. polycephalum's preference for a focal alternative increased when a third, inferior quality option was added to the choice set, thus violating IIA and suggesting the use of a comparative valuation process. The discovery of comparative valuation rules in a unicellular organism suggests that comparative valuation rules are ubiquitous, if not universal, among biological decision makers.
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Heuristic and optimal policy computations in the human brain during sequential decision-making.
Korn, Christoph W; Bach, Dominik R
2018-01-23
Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.
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Rules, Schema, and Decision Making.
1987-09-01
discussion ............................................................ 47 6. GENERAL DISCUSSION...65 6.3. Generality of results................................................................ 68 6.4. Implications for...3 2-1 General model of decision making
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Mate choice when males are in patches: optimal strategies and good rules of thumb.
Hutchinson, John M C; Halupka, Konrad
2004-11-07
In standard mate-choice models, females encounter males sequentially and decide whether to inspect the quality of another male or to accept a male already inspected. What changes when males are clumped in patches and there is a significant cost to travel between patches? We use stochastic dynamic programming to derive optimum strategies under various assumptions. With zero costs to returning to a male in the current patch, the optimal strategy accepts males above a quality threshold which is constant whenever one or more males in the patch remain uninspected; this threshold drops when inspecting the last male in the patch, so returns may occur only then and are never to a male in a previously inspected patch. With non-zero within-patch return costs, such a two-threshold rule still performs extremely well, but a more gradual decline in acceptance threshold is optimal. Inability to return at all need not decrease performance by much. The acceptance threshold should also decline if it gets harder to discover the last males in a patch. Optimal strategies become more complex when mean male quality varies systematically between patches or years, and females estimate this in a Bayesian manner through inspecting male qualities. It can then be optimal to switch patch before inspecting all males on a patch, or, exceptionally, to return to an earlier patch. We compare performance of various rules of thumb in these environments and in ones without a patch structure. A two-threshold rule performs excellently, as do various simplifications of it. The best-of-N rule outperforms threshold rules only in non-patchy environments with between-year quality variation. The cutoff rule performs poorly.
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Diagnostic accuracy and reproducibility of the Ottawa Knee Rule vs the Pittsburgh Decision Rule.
Cheung, Tung C; Tank, Yeliz; Breederveld, Roelf S; Tuinebreijer, Wim E; de Lange-de Klerk, Elly S M; Derksen, Robert J
2013-04-01
The aim of this present study was to compare the diagnostic accuracy and reproducibility of 2 clinical decision rules (the Ottawa Knee Rules [OKR] and Pittsburgh Decision Rules [PDR]) developed for selective use of x-rays in the evaluation of isolated knee trauma. Application of a decision rule leads to a more efficient evaluation of knee injuries and a reduction in health care costs. The diagnostic accuracy and reproducibility are compared in this study. A cross-sectional interobserver study was conducted in the emergency department of an urban teaching hospital from October 2008 to July 2009. Two observer groups collected data on standardized case-report forms: emergency medicine residents and surgical residents. Standard knee radiographs were performed in each patient. Participants were patients 18 years and older with isolated knee injuries. Pooled sensitivity and specificity were compared using χ(2) statistics, and interobserver agreement was calculated by using κ statistics. Ninety injuries were assessed. Seven injuries concerned fractures (7.8%). For the OKR, the pooled sensitivity and specificity were 0.86 (95% confidence interval [CI], 0.57-0.96) and 0.27 (95% CI, 0.21-0.35), respectively. The PDR had a pooled sensitivity and specificity of 0.86 (95% CI, 0.57-0.96) and 0.51 (95% CI, 0.44-0.59). The PDR was significantly (P = .002) more specific. The κ values for the OKR and PDR were 0.51 (95% CI, 0.32-0.71) and 0.71 (95% CI, 0.57-0.86), respectively. The PDR was found to be more specific than the OKR, with equal sensitivity. Interobserver agreement was moderate for the OKR and substantial for the PDR. Copyright © 2013 Elsevier Inc. All rights reserved.
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Dixon, Matthew L.; Christoff, Kalina
2012-01-01
Cognitive control is a fundamental skill reflecting the active use of task-rules to guide behavior and suppress inappropriate automatic responses. Prior work has traditionally used paradigms in which subjects are told when to engage cognitive control. Thus, surprisingly little is known about the factors that influence individuals' initial decision of whether or not to act in a reflective, rule-based manner. To examine this, we took three classic cognitive control tasks (Stroop, Wisconsin Card Sorting Task, Go/No-Go task) and created novel ‘free-choice’ versions in which human subjects were free to select an automatic, pre-potent action, or an action requiring rule-based cognitive control, and earned varying amounts of money based on their choices. Our findings demonstrated that subjects' decision to engage cognitive control was driven by an explicit representation of monetary rewards expected to be obtained from rule-use. Subjects rarely engaged cognitive control when the expected outcome was of equal or lesser value as compared to the value of the automatic response, but frequently engaged cognitive control when it was expected to yield a larger monetary outcome. Additionally, we exploited fMRI-adaptation to show that the lateral prefrontal cortex (LPFC) represents associations between rules and expected reward outcomes. Together, these findings suggest that individuals are more likely to act in a reflective, rule-based manner when they expect that it will result in a desired outcome. Thus, choosing to exert cognitive control is not simply a matter of reason and willpower, but rather, conforms to standard mechanisms of value-based decision making. Finally, in contrast to current models of LPFC function, our results suggest that the LPFC plays a direct role in representing motivational incentives. PMID:23284730
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Real-Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties
NASA Astrophysics Data System (ADS)
Zhu, Feilin; Zhong, Ping-An; Sun, Yimeng; Yeh, William W.-G.
2017-12-01
Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.
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NASA Astrophysics Data System (ADS)
Ma, Junhai; Xie, Lei
2018-02-01
This paper, based on the China's communications and the current situation of the mobile phone industry, focuses on the stability of a supply chain system that consists of one supplier and one bounded rational retailer. We explore the influence of the decision makers' loss sensitivity and decision adjustment speed on the stability of the supply chain. It is found that when the retailer is not sensitive to the loss or adjusts decisions cautiously, the system can be stable. The single-retailer model is extended to a multi-retailer one to study the influence of competition on the system stability. The results show that the market share of each retailer does not affect the system stability when it is fixed. The decision of each retailer does not affect that of any other retailer and the system stability. We present two decision adjustment rules (;bounded rationality expectation (BRE); and "adaptive exponential smoothing (AES)") and compare their performances on the system stability, and find that the AES rule does not affect the system stability, while the BRE rule will make the system stability be sensitive to the retailers' loss sensitivity and the decision adjustment speed. We also reveal the unstable system's negative impact on the retailers' decisions and profits, to emphasize the importance to maintain the system stability.
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NASA Astrophysics Data System (ADS)
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto optimality of the found solutions can be made. Identification of the leading particle traditionally requires a costly combination of ranking and niching techniques. In our approach, we use a decision rule under uncertainty to identify the currently leading particle of the swarm. In doing so, we consider the different objectives of our optimization problem as competing agents with partially conflicting interests. Analysis of the maximin fitness function allows for robust and cheap identification of the currently leading particle. The final optimization result comprises a set of possible models spread along the Pareto front. For convex Pareto fronts, solution density is expected to be maximal in the region ideally compromising all objectives, i.e. the region of highest curvature.
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Code of Federal Regulations, 2010 CFR
2010-04-01
... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false What are the rules for reopening a decision by an administrative law judge of the Office of Medicare Hearings and Appeals (OMHA) or by the Medicare Appeals Council (MAC)? 418.1355 Section 418.1355 Employees' Benefits SOCIAL SECURITY ADMINISTRATION MEDICARE SUBSIDIES Medicare Part B...
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40 CFR 164.90 - Initial decision.
Code of Federal Regulations, 2010 CFR
2010-07-01
....90 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Environmental Appeals Board. The initial decision shall become the decision of the Environmental Appeals Board...
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40 CFR 164.90 - Initial decision.
Code of Federal Regulations, 2011 CFR
2011-07-01
....90 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Environmental Appeals Board. The initial decision shall become the decision of the Environmental Appeals Board...
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DesAutels, Spencer J; Fox, Zachary E; Giuse, Dario A; Williams, Annette M; Kou, Qing-Hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia
2016-01-01
Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems.
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Separating Decision and Encoding Noise in Signal Detection Tasks
Cabrera, Carlos Alexander; Lu, Zhong-Lin; Dosher, Barbara Anne
2015-01-01
In this paper we develop an extension to the Signal Detection Theory (SDT) framework to separately estimate internal noise arising from representational and decision processes. Our approach constrains SDT models with decision noise by combining a multi-pass external noise paradigm with confidence rating responses. In a simulation study we present evidence that representation and decision noise can be separately estimated over a range of representative underlying representational and decision noise level configurations. These results also hold across a number of decision rules and show resilience to rule miss-specification. The new theoretical framework is applied to a visual detection confidence-rating task with three and five response categories. This study compliments and extends the recent efforts of researchers (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008; Rosner & Kochanski, 2009, Kellen, Klauer, & Singmann, 2012) to separate and quantify underlying sources of response variability in signal detection tasks. PMID:26120907
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Johnson, M M
1990-03-01
This study explored the use of process tracing techniques in examining the decision-making processes of older and younger adults. Thirty-six college-age and thirty-six retirement-age participants decided which one of six cars they would purchase on the basis of computer-accessed data. They provided information search protocols. Results indicate that total time to reach a decision did not differ according to age. However, retirement-age participants used less information, spent more time viewing, and re-viewed fewer bits of information than college-age participants. Information search patterns differed markedly between age groups. Patterns of retirement-age adults indicated their use of noncompensatory decision rules which, according to decision-making literature (Payne, 1976), reduce cognitive processing demands. The patterns of the college-age adults indicated their use of compensatory decision rules, which have higher processing demands.
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16 CFR 5.65 - Review of initial decision.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Review of initial decision. 5.65 Section 5.65 Commercial Practices FEDERAL TRADE COMMISSION ORGANIZATION, PROCEDURES AND RULES OF PRACTICE... initial decision. Appeals from the initial decision of the Administrative Law Judge or review by the...
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Code of Federal Regulations, 2010 CFR
2010-04-01
..., permit, favorable decision, ruling, status, or loan gurarantee. (e) Claim means any request, demand, or.... (k) Initial decision means the written decision of the ALJ required by § 35.10 or § 35.37, and includes a revised initial decision issued following a remand or a motion for reconsideration. (l...
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Cairns, Andrew W; Bond, Raymond R; Finlay, Dewar D; Guldenring, Daniel; Badilini, Fabio; Libretti, Guido; Peace, Aaron J; Leslie, Stephen J
The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named 'Interactive Progressive based Interpretation' (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. To improve interpretation accuracy and reduce missed co-abnormalities. The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach. A total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size. Copyright © 2017 Elsevier Inc. All rights reserved.
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Federal Register 2010, 2011, 2012, 2013, 2014
2010-10-12
... information collection requirements contained in its Used Motor Vehicle Trade Regulation Rule (``Used Car Rule...-5634. SUPPLEMENTARY INFORMATION: The Used Car Rule facilitates informed purchasing decisions by requiring used car dealers to disclose information about warranty coverage, if any, and the mechanical...
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77 FR 52676 - Submission for OMB Review; Comment Request
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2012-08-30
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78 FR 62296 - Combined Notice of Filings #1
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76 FR 6596 - Submission for OMB Review; Comment Request
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78 FR 76100 - Black Hills National Forest Advisory Board
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2013-12-16
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77 FR 53168 - Submission for OMB Review; Comment Request
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78 FR 17920 - Submission for OMB Review; Comment Request
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76 FR 62336 - Submission for OMB Review; Comment Request
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2011-10-07
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77 FR 56606 - Glenn/Colusa County Resource Advisory Committee
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75 FR 71411 - Radio Broadcasting Services; Silverpeak, NV
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2010-11-23
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34 CFR 668.98 - Interlocutory appeals to the Secretary from rulings of a hearing official.
Code of Federal Regulations, 2010 CFR
2010-07-01
... a hearing official. 668.98 Section 668.98 Education Regulations of the Offices of the Department of... Secretary from rulings of a hearing official. (a) A ruling by a hearing official may not be appealed to the... issuance of the initial decision, grant a review of a ruling upon either a certification by a hearing...
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NASA Astrophysics Data System (ADS)
Ai, Xueshan; Dong, Zuo; Mo, Mingzhu
2017-04-01
The optimal reservoir operation is in generally a multi-objective problem. In real life, most of the reservoir operation optimization problems involve conflicting objectives, for which there is no single optimal solution which can simultaneously gain an optimal result of all the purposes, but rather a set of well distributed non-inferior solutions or Pareto frontier exists. On the other hand, most of the reservoirs operation rules is to gain greater social and economic benefits at the expense of ecological environment, resulting to the destruction of riverine ecology and reduction of aquatic biodiversity. To overcome these drawbacks, this study developed a multi-objective model for the reservoir operating with the conflicting functions of hydroelectric energy generation, irrigation and ecological protection. To solve the model with the objectives of maximize energy production, maximize the water demand satisfaction rate of irrigation and ecology, we proposed a multi-objective optimization method of variable penalty coefficient (VPC), which was based on integrate dynamic programming (DP) with discrete differential dynamic programming (DDDP), to generate a well distributed non-inferior along the Pareto front by changing the penalties coefficient of different objectives. This method was applied to an existing China reservoir named Donggu, through a course of a year, which is a multi-annual storage reservoir with multiple purposes. The case study results showed a good relationship between any two of the objectives and a good Pareto optimal solutions, which provide a reference for the reservoir decision makers.
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Scalable software architectures for decision support.
Musen, M A
1999-12-01
Interest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods--domain-independent algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.
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Koontz, Lynne; Hoag, Dana L.
2005-01-01
Many programs and tools have been developed by different disciplines to facilitate group negotiation and decision making. Three examples are relevant here. First, decision analysis models such as the Analytical Hierarchy Process (AHP) are commonly used to prioritize the goals and objectives of stakeholders’ preferences for resource planning by formally structuring conflicts and assisting decision makers in developing a compromised solution (Forman, 1998). Second, institutional models such as the Legal Institutional Analysis Model (LIAM) have been used to describe the organizational rules of behavior and the institutional boundaries constraining management decisions (Lamb and others, 1998). Finally, public choice models have been used to predict the potential success of rent-seeking activity (spending additional time and money to exert political pressure) to change the political rules (Becker, 1983). While these tools have been successful at addressing various pieces of the natural resource decision making process, their use in isolation is not enough to fully depict the complexities of the physical and biological systems with the rules and constraints of the underlying economic and political systems. An approach is needed that combines natural sciences, economics, and politics.
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Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi
2013-02-01
The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.