Agent Based Modeling Applications for Geosciences

NASA Astrophysics Data System (ADS)

Stein, J. S.

2004-12-01

Agent-based modeling techniques have successfully been applied to systems in which complex behaviors or outcomes arise from varied interactions between individuals in the system. Each individual interacts with its environment, as well as with other individuals, by following a set of relatively simple rules. Traditionally this "bottom-up" modeling approach has been applied to problems in the fields of economics and sociology, but more recently has been introduced to various disciplines in the geosciences. This technique can help explain the origin of complex processes from a relatively simple set of rules, incorporate large and detailed datasets when they exist, and simulate the effects of extreme events on system-wide behavior. Some of the challenges associated with this modeling method include: significant computational requirements in order to keep track of thousands to millions of agents, methods and strategies of model validation are lacking, as is a formal methodology for evaluating model uncertainty. Challenges specific to the geosciences, include how to define agents that control water, contaminant fluxes, climate forcing and other physical processes and how to link these "geo-agents" into larger agent-based simulations that include social systems such as demographics economics and regulations. Effective management of limited natural resources (such as water, hydrocarbons, or land) requires an understanding of what factors influence the demand for these resources on a regional and temporal scale. Agent-based models can be used to simulate this demand across a variety of sectors under a range of conditions and determine effective and robust management policies and monitoring strategies. The recent focus on the role of biological processes in the geosciences is another example of an area that could benefit from agent-based applications. A typical approach to modeling the effect of biological processes in geologic media has been to represent these processes in a thermodynamic framework as a set of reactions that roll-up the integrated effect that diverse biological communities exert on a geological system. This approach may work well to predict the effect of certain biological communities in specific environments in which experimental data is available. However, it does not further our knowledge of how the geobiological system actually functions on a micro scale. Agent-based techniques may provide a framework to explore the fundamental interactions required to explain the system-wide behavior. This presentation will present a survey of several promising applications of agent-based modeling approaches to problems in the geosciences and describe specific contributions to some of the inherent challenges facing this approach.

A model of the demand for Islamic banks debt-based financing instrument

NASA Astrophysics Data System (ADS)

Jusoh, Mansor; Khalid, Norlin

2013-04-01

This paper presents a theoretical analysis of the demand for debt-based financing instruments of the Islamic banks. Debt-based financing, such as through baibithamanajil and al-murabahah, is by far the most prominent of the Islamic bank financing and yet it has been largely ignored in Islamic economics literature. Most studies instead have been focusing on equity-based financing of al-mudharabah and al-musyarakah. Islamic bank offers debt-based financing through various instruments derived under the principle of exchange (ukud al-mu'awadhat) or more specifically, the contract of deferred sale. Under such arrangement, Islamic debt is created when goods are purchased and the payments are deferred. Thus, unlike debt of the conventional bank which is a form of financial loan contract to facilitate demand for liquid assets, this Islamic debt is created in response to the demand to purchase goods by deferred payment. In this paper we set an analytical framework that is based on an infinitely lived representative agent model (ILRA model) to analyze the demand for goods to be purchased by deferred payment. The resulting demand will then be used to derive the demand for Islamic debt. We also investigate theoretically, factors that may have an impact on the demand for Islamic debt.

Consumer Search, Rationing Rules, and the Consequence for Competition

NASA Astrophysics Data System (ADS)

Ruebeck, Christopher S.

Firms' conjectures about demand are consequential in oligopoly games. Through agent-based modeling of consumers' search for products, we can study the rationing of demand between capacity-constrained firms offering homogeneous products and explore the robustness of analytically solvable models' results. After algorithmically formalizing short-run search behavior rather than assuming a long-run average, this study predicts stronger competition in a two-stage capacity-price game.

Pain expressiveness and altruistic behavior: an exploration using agent-based modeling.

PubMed

de C Williams, Amanda C; Gallagher, Elizabeth; Fidalgo, Antonio R; Bentley, Peter J

2016-03-01

Predictions which invoke evolutionary mechanisms are hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interactions in specific physical or social environments over many generations. The outcomes have implications for understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/nonexpresser strategies) when injured and of helping, ignoring, or exploiting another in pain (altruistic/nonaltruistic/selfish strategies). Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury interrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, contingencies that evident from experimental work with a variety of mammals, over a few interactions, were replicated in the agent-based model after selection pressure over many generations. More energy-demanding expression of pain reduced its frequency in successive generations, and increasing injury frequency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased expression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits of helping hardly changed its frequency, whereas increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent-based modeling allows simulation of complex behaviors and environmental pressures over evolutionary time.

Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility

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

Sheppard, Colin; Waraich, Rashid; Campbell, Andrew

This report summarizes the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy) and its application to simulating plug-in electric vehicle (PEV) mobility, energy consumption, and spatiotemporal charging demand. BEAM is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). We apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. We then explore the impact of a variety of common modeling assumptions in the literature regarding chargingmore » infrastructure availability and driver behavior. We find that accurately reproducing observed charging patterns requires an explicit representation of spatially disaggregated charging infrastructure as well as a more nuanced model of the decision to charge that balances tradeoffs people make with regards to time, cost, convenience, and range anxiety.« less

Travel Demand Modeling

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

Southworth, Frank; Garrow, Dr. Laurie

This chapter describes the principal types of both passenger and freight demand models in use today, providing a brief history of model development supported by references to a number of popular texts on the subject, and directing the reader to papers covering some of the more recent technical developments in the area. Over the past half century a variety of methods have been used to estimate and forecast travel demands, drawing concepts from economic/utility maximization theory, transportation system optimization and spatial interaction theory, using and often combining solution techniques as varied as Box-Jenkins methods, non-linear multivariate regression, non-linear mathematical programming,more » and agent-based microsimulation.« less

Understanding Air Transportation Market Dynamics Using a Search Algorithm for Calibrating Travel Demand and Price

NASA Technical Reports Server (NTRS)

Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.

2015-01-01

This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.

Modelling of robotic work cells using agent based-approach

NASA Astrophysics Data System (ADS)

Sękala, A.; Banaś, W.; Gwiazda, A.; Monica, Z.; Kost, G.; Hryniewicz, P.

2016-08-01

In the case of modern manufacturing systems the requirements, both according the scope and according characteristics of technical procedures are dynamically changing. This results in production system organization inability to keep up with changes in a market demand. Accordingly, there is a need for new design methods, characterized, on the one hand with a high efficiency and on the other with the adequate level of the generated organizational solutions. One of the tools that could be used for this purpose is the concept of agent systems. These systems are the tools of artificial intelligence. They allow assigning to agents the proper domains of procedures and knowledge so that they represent in a self-organizing system of an agent environment, components of a real system. The agent-based system for modelling robotic work cell should be designed taking into consideration many limitations considered with the characteristic of this production unit. It is possible to distinguish some grouped of structural components that constitute such a system. This confirms the structural complexity of a work cell as a specific production system. So it is necessary to develop agents depicting various aspects of the work cell structure. The main groups of agents that are used to model a robotic work cell should at least include next pattern representatives: machine tool agents, auxiliary equipment agents, robots agents, transport equipment agents, organizational agents as well as data and knowledge bases agents. In this way it is possible to create the holarchy of the agent-based system.

Statistical mechanics of competitive resource allocation using agent-based models

NASA Astrophysics Data System (ADS)

Chakraborti, Anirban; Challet, Damien; Chatterjee, Arnab; Marsili, Matteo; Zhang, Yi-Cheng; Chakrabarti, Bikas K.

2015-01-01

Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and others) and the methods used to understand them analytically. We emphasize the power of concepts and tools from statistical mechanics to understand and explain fully collective phenomena such as phase transitions and long memory, and the mapping between agent heterogeneity and physical disorder. As these methods can be applied to any large-scale model of competitive resource allocation made up of heterogeneous adaptive agent with non-linear interaction, they provide a prospective unifying paradigm for many scientific disciplines.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

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

Chassin, David P.; Behboodi, Sahand; Crawford, Curran

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methodsmore » presented.« less

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

DOE PAGES

Chassin, David P.; Behboodi, Sahand; Crawford, Curran; ...

2015-12-23

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methodsmore » presented.« less

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

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

Auld, Joshua; Hope, Michael; Ley, Hubert

This paper discusses the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typicallymore » done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.« less

Agent-based Modeling to Simulate the Diffusion of Water-Efficient Innovations and the Emergence of Urban Water Sustainability

NASA Astrophysics Data System (ADS)

Kanta, L.; Giacomoni, M.; Shafiee, M. E.; Berglund, E.

2014-12-01

The sustainability of water resources is threatened by urbanization, as increasing demands deplete water availability, and changes to the landscape alter runoff and the flow regime of receiving water bodies. Utility managers typically manage urban water resources through the use of centralized solutions, such as large reservoirs, which may be limited in their ability balance the needs of urbanization and ecological systems. Decentralized technologies, on the other hand, may improve the health of the water resources system and deliver urban water services. For example, low impact development technologies, such as rainwater harvesting, and water-efficient technologies, such as low-flow faucets and toilets, may be adopted by households to retain rainwater and reduce demands, offsetting the need for new centralized infrastructure. Decentralized technologies may create new complexities in infrastructure and water management, as decentralization depends on community behavior and participation beyond traditional water resources planning. Messages about water shortages and water quality from peers and the water utility managers can influence the adoption of new technologies. As a result, feedbacks between consumers and water resources emerge, creating a complex system. This research develops a framework to simulate the diffusion of water-efficient innovations and the sustainability of urban water resources, by coupling models of households in a community, hydrologic models of a water resources system, and a cellular automata model of land use change. Agent-based models are developed to simulate the land use and water demand decisions of individual households, and behavioral rules are encoded to simulate communication with other agents and adoption of decentralized technologies, using a model of the diffusion of innovation. The framework is applied for an illustrative case study to simulate water resources sustainability over a long-term planning horizon.

Demand forecasting of electricity in Indonesia with limited historical data

NASA Astrophysics Data System (ADS)

Dwi Kartikasari, Mujiati; Rohmad Prayogi, Arif

2018-03-01

Demand forecasting of electricity is an important activity for electrical agents to know the description of electricity demand in future. Prediction of demand electricity can be done using time series models. In this paper, double moving average model, Holt’s exponential smoothing model, and grey model GM(1,1) are used to predict electricity demand in Indonesia under the condition of limited historical data. The result shows that grey model GM(1,1) has the smallest value of MAE (mean absolute error), MSE (mean squared error), and MAPE (mean absolute percentage error).

Agent-Based Modelling of Agricultural Water Abstraction in Response to Climate, Policy, and Demand Changes: Results from East Anglia, UK

NASA Astrophysics Data System (ADS)

Swinscoe, T. H. A.; Knoeri, C.; Fleskens, L.; Barrett, J.

2014-12-01

Freshwater is a vital natural resource for multiple needs, such as drinking water for the public, industrial processes, hydropower for energy companies, and irrigation for agriculture. In the UK, crop production is the largest in East Anglia, while at the same time the region is also the driest, with average annual rainfall between 560 and 720 mm (1971 to 2000). Many water catchments of East Anglia are reported as over licensed or over abstracted. Therefore, freshwater available for agricultural irrigation abstraction in this region is becoming both increasingly scarce due to competing demands, and increasingly variable and uncertain due to climate and policy changes. It is vital for water users and policy makers to understand how these factors will affect individual abstractors and water resource management at the system level. We present first results of an Agent-based Model that captures the complexity of this system as individual abstractors interact, learn and adapt to these internal and external changes. The purpose of this model is to simulate what patterns of water resource management emerge on the system level based on local interactions, adaptations and behaviours, and what policies lead to a sustainable water resource management system. The model is based on an irrigation abstractor typology derived from a survey in the study area, to capture individual behavioural intentions under a range of water availability scenarios, in addition to farm attributes, and demographics. Regional climate change scenarios, current and new abstraction licence reforms by the UK regulator, such as water trading and water shares, and estimated demand increases from other sectors were used as additional input data. Findings from the integrated model provide new understanding of the patterns of water resource management likely to emerge at the system level.

Socioeconophysics:. Opinion Dynamics for Number of Transactions and Price, a Trader Based Model

NASA Astrophysics Data System (ADS)

Tuncay, Çağlar

Involving effects of media, opinion leader and other agents on the opinion of individuals of market society, a trader based model is developed and utilized to simulate price via supply and demand. Pronounced effects are considered with several weights and some personal differences between traders are taken into account. Resulting time series and probabilty distribution function involving a power law for price come out similar to the real ones.

History-Based Response Threshold Model for Division of Labor in Multi-Agent Systems

PubMed Central

Lee, Wonki; Kim, DaeEun

2017-01-01

Dynamic task allocation is a necessity in a group of robots. Each member should decide its own task such that it is most commensurate with its current state in the overall system. In this work, the response threshold model is applied to a dynamic foraging task. Each robot employs a task switching function based on the local task demand obtained from the surrounding environment, and no communication occurs between the robots. Each individual member has a constant-sized task demand history that reflects the global demand. In addition, it has response threshold values for all of the tasks and manages the task switching process depending on the stimuli of the task demands. The robot then determines the task to be executed to regulate the overall division of labor. This task selection induces a specialized tendency for performing a specific task and regulates the division of labor. In particular, maintaining a history of the task demands is very effective for the dynamic foraging task. Various experiments are performed using a simulation with multiple robots, and the results show that the proposed algorithm is more effective as compared to the conventional model. PMID:28555031

An Agent-based Modeling of Water-Food Nexus towards Sustainable Management of Urban Water Resources

NASA Astrophysics Data System (ADS)

Esmaeili, N.; Kanta, L.

2017-12-01

Growing population, urbanization, and climate change have put tremendous stress on water systems in many regions. A shortage in water system not only affects water users of a municipality but also that of food system. About 70% of global water is withdrawn for agriculture; livestock and dairy productions are also dependent on water availability. Although researchers and policy makers have identified and emphasized the water-food (WF) nexus in recent decade, most existing WF models offer strategies to reduce trade-offs and to generate benefits without considering feedback loops and adaptations between those systems. Feedback loops between water and food system can help understand long-term behavioral trends between water users of the integrated WF system which, in turn, can help manage water resources sustainably. An Agent-based modeling approach is applied here to develop a conceptual framework of WF systems. All water users in this system are modeled as agents, who are capable of making decisions and can adapt new behavior based on inputs from other agents in a shared environment through a set of logical and mathematical rules. Residential and commercial/industrial consumers are represented as municipal agents; crop, livestock, and dairy farmers are represented as food agents; and water management officials are represented as policy agent. During the period of water shortage, policy agent will propose/impose various water conservation measures, such as adapting water-efficient technologies, banning outdoor irrigation, implementing supplemental irrigation, using recycled water for livestock/dairy production, among others. Municipal and food agents may adapt conservation strategies and will update their demand accordingly. Emergent properties of the WF nexus will arise through dynamic interactions between various actors of water and food system. This model will be implemented to a case study for resource allocation and future policy development.

Micro-Level Adaptation, Macro-Level Selection, and the Dynamics of Market Partitioning

PubMed Central

García-Díaz, César; van Witteloostuijn, Arjen; Péli, Gábor

2015-01-01

This paper provides a micro-foundation for dual market structure formation through partitioning processes in marketplaces by developing a computational model of interacting economic agents. We propose an agent-based modeling approach, where firms are adaptive and profit-seeking agents entering into and exiting from the market according to their (lack of) profitability. Our firms are characterized by large and small sunk costs, respectively. They locate their offerings along a unimodal demand distribution over a one-dimensional product variety, with the distribution peak constituting the center and the tails standing for the peripheries. We found that large firms may first advance toward the most abundant demand spot, the market center, and release peripheral positions as predicted by extant dual market explanations. However, we also observed that large firms may then move back toward the market fringes to reduce competitive niche overlap in the center, triggering nonlinear resource occupation behavior. Novel results indicate that resource release dynamics depend on firm-level adaptive capabilities, and that a minimum scale of production for low sunk cost firms is key to the formation of the dual structure. PMID:26656107

Micro-Level Adaptation, Macro-Level Selection, and the Dynamics of Market Partitioning.

PubMed

García-Díaz, César; van Witteloostuijn, Arjen; Péli, Gábor

2015-01-01

This paper provides a micro-foundation for dual market structure formation through partitioning processes in marketplaces by developing a computational model of interacting economic agents. We propose an agent-based modeling approach, where firms are adaptive and profit-seeking agents entering into and exiting from the market according to their (lack of) profitability. Our firms are characterized by large and small sunk costs, respectively. They locate their offerings along a unimodal demand distribution over a one-dimensional product variety, with the distribution peak constituting the center and the tails standing for the peripheries. We found that large firms may first advance toward the most abundant demand spot, the market center, and release peripheral positions as predicted by extant dual market explanations. However, we also observed that large firms may then move back toward the market fringes to reduce competitive niche overlap in the center, triggering nonlinear resource occupation behavior. Novel results indicate that resource release dynamics depend on firm-level adaptive capabilities, and that a minimum scale of production for low sunk cost firms is key to the formation of the dual structure.

Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data

PubMed Central

Wu, Lun; Zhi, Ye; Sui, Zhengwei; Liu, Yu

2014-01-01

Most existing human mobility literature focuses on exterior characteristics of movements but neglects activities, the driving force that underlies human movements. In this research, we combine activity-based analysis with a movement-based approach to model the intra-urban human mobility observed from about 15 million check-in records during a yearlong period in Shanghai, China. The proposed model is activity-based and includes two parts: the transition of travel demands during a specific time period and the movement between locations. For the first part, we find the transition probability between activities varies over time, and then we construct a temporal transition probability matrix to represent the transition probability of travel demands during a time interval. For the second part, we suggest that the travel demands can be divided into two classes, locationally mandatory activity (LMA) and locationally stochastic activity (LSA), according to whether the demand is associated with fixed location or not. By judging the combination of predecessor activity type and successor activity type we determine three trip patterns, each associated with a different decay parameter. To validate the model, we adopt the mechanism of an agent-based model and compare the simulated results with the observed pattern from the displacement distance distribution, the spatio-temporal distribution of activities, and the temporal distribution of travel demand transitions. The results show that the simulated patterns fit the observed data well, indicating that these findings open new directions for combining activity-based analysis with a movement-based approach using social media check-in data. PMID:24824892

Using simulation modelling to examine the impact of venue lockout and last-drink policies on drinking-related harms and costs to licensees.

PubMed

Scott, Nick; Livingston, Michael; Reporter, Iyanoosh; Dietze, Paul

2017-06-01

Many variations of venue lockout and last-drink policies have been introduced in attempts to reduce drinking-related harms. We estimate the public health gains and licensee costs of these policies using a computer simulated population of young adults engaging in heavy drinking. Using an agent-based model we implemented 1 am/2 am/3 am venue lockouts in conjunction with last drinks zero/one/two hours later, or at current closing times. Outcomes included: the number of incidents of verbal aggression in public drinking venues, private venues or on the street; and changed revenue to public venues. The most effective policy in reducing verbal aggression among agents was 1 am lockouts with current closing times. All policies produced substantial reductions in street-based incidents of verbal aggression among agents (33-81%) due to the smoothing of transport demand. Direct revenue losses were 1-9% for simulated licensees, with later lockout times and longer periods between lockout and last drinks producing smaller revenue losses. Simulation models are useful for exploring consequences of policy change. Our simulation suggests that additional hours between lockout and last drinks could reduce aggression by easing transport demand, while minimising revenue loss to venue owners. Implications for public health: Direct policies to reduce late-night transport-related disputes should be considered. © 2017 The Authors.

Mechanistic origin of dragon-kings in a population of competing agents

NASA Astrophysics Data System (ADS)

Johnson, N.; Tivnan, B.

2012-05-01

We analyze the mechanistic origins of the extreme behaviors that arise in an idealized model of a population of competing agents, such as traders in a market. These extreme behaviors exhibit the defining characteristics of `dragon-kings'. Our model comprises heterogeneous agents who repeatedly compete for some limited resource, making binary choices based on the strategies that they have in their possession. It generalizes the well-known Minority Game by allowing agents whose strategies have not made accurate recent predictions, to step out of the competition until their strategies improve. This generates a complex dynamical interplay between the number V of active agents (mimicking market volume) and the imbalance D between the decisions made (mimicking excess demand). The wide spectrum of extreme behaviors which emerge, helps to explain why no unique relationship has been identified between the price and volume during real market crashes and rallies.

A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact

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

Cui, Xiaohui; Liu, Cheng; Kim, Hoe Kyoung

2011-01-01

The variation of household attributes such as income, travel distance, age, household member, and education for different residential areas may generate different market penetration rates for plug-in hybrid electric vehicle (PHEV). Residential areas with higher PHEV ownership could increase peak electric demand locally and require utilities to upgrade the electric distribution infrastructure even though the capacity of the regional power grid is under-utilized. Estimating the future PHEV ownership distribution at the residential household level can help us understand the impact of PHEV fleet on power line congestion, transformer overload and other unforeseen problems at the local residential distribution network level.more » It can also help utilities manage the timing of recharging demand to maximize load factors and utilization of existing distribution resources. This paper presents a multi agent-based simulation framework for 1) modeling spatial distribution of PHEV ownership at local residential household level, 2) discovering PHEV hot zones where PHEV ownership may quickly increase in the near future, and 3) estimating the impacts of the increasing PHEV ownership on the local electric distribution network with different charging strategies. In this paper, we use Knox County, TN as a case study to show the simulation results of the agent-based model (ABM) framework. However, the framework can be easily applied to other local areas in the US.« less

Integrated control of lateral and vertical vehicle dynamics based on multi-agent system

NASA Astrophysics Data System (ADS)

Huang, Chen; Chen, Long; Yun, Chaochun; Jiang, Haobin; Chen, Yuexia

2014-03-01

The existing research of the integrated chassis control mainly focuses on the different evaluation indexes and control strategy. Among the different evaluation indexes, the comprehensive properties are usually not considered based on the non-linear superposition principle. But, the control strategy has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, based on belief, desire and intention(BDI)-agent model framework, the TYRE agent, electric power steering(EPS) agent and active suspension system(ASS) agent are proposed. In the system(SYS) agent, the coordination mechanism is employed to manage interdependences and conflicts among other agents, so as to improve the flexibility, adaptability, and robustness of the global control system. Due to the existence of the simulation demand of dynamic performance, the vehicle multi-body dynamics model is established by SIMPACK. And then the co-simulation analysis is conducted to evaluate the proposed multi-agent system(MAS) controller. The simulation results demonstrate that the MAS has good effect on the performance of EPS and ASS. Meantime, the better road feeling for the driver is provided considering the multiple and complex driving traffic. Finally, the MAS rapid control prototyping is built to conduct the real vehicle test. The test results are consistent to the simulation results, which verifies the correctness of simulation. The proposed research ensures the driving safety, enhances the handling stability, and improves the ride comfort.

Equation-based model for the stock market

NASA Astrophysics Data System (ADS)

Xavier, Paloma O. C.; Atman, A. P. F.; de Magalhães, A. R. Bosco

2017-09-01

We propose a stock market model which is investigated in the forms of difference and differential equations whose variables correspond to the demand or supply of each agent and to the price. In the model, agents are driven by the behavior of their trust contact network as well by fundamental analysis. By means of the deterministic version of the model, the connection between such drive mechanisms and the price is analyzed: imitation behavior promotes market instability, finitude of resources is associated to stock index stability, and high sensitivity to the fair price provokes price oscillations. Long-range correlations in the price temporal series and heavy-tailed distribution of returns are observed for the version of the model which considers different proposals for stochasticity of microeconomic and macroeconomic origins.

Agents of the Father's law in a society of brothers: A philosophic and psychoanalytic perspective on legitimate use of violence.

PubMed

Even-Tzur, Efrat; Hadar, Uri

This paper explores subjective processes of "Agents of Law" - individuals who the state grants the authority to use violence - and the dissonance stemming from the contradictory demands posed on them as legitimate users of violence despite the societal taboo against violence. A conceptual model will be offered based on two theoretical legs, Lacanian psychoanalysis and political theories of legitimacy. Specifically, psychoanalytic ideas would serve to examine unconscious processes, subject position and various identifications related to the question of "self-legitimacy" of Agents of Law. A central link between psychoanalysis and political thought is found in the image of the father and in the triad ruler-God-Father, which calls for an oedipal analysis. A psychoanalytic reading of two philosophical schools that elaborated on the question of legitimacy will be presented, and yield two analytic poles of a model for the understanding of possible subject positions of agents of Law: identification with a "Living Father" vs. identification with a "Dead Father". The psychoanalytic reading will shed light on the limitations of the philosophical perspectives in reflecting on the various (im)possible psychological positions of agents of Law. Finally, then, it will be shown how psychoanalysis helps finding words to characterize different nuances in the coping of agents of Law with the contradictory demands posed on them in an age in which God is dead, the father was murdered and the king was beheaded. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Agent-Based Model for Analyzing Control Policies and the Dynamic Service-Time Performance of a Capacity-Constrained Air Traffic Management Facility

NASA Technical Reports Server (NTRS)

Conway, Sheila R.

2006-01-01

Simple agent-based models may be useful for investigating air traffic control strategies as a precursory screening for more costly, higher fidelity simulation. Of concern is the ability of the models to capture the essence of the system and provide insight into system behavior in a timely manner and without breaking the bank. The method is put to the test with the development of a model to address situations where capacity is overburdened and potential for propagation of the resultant delay though later flights is possible via flight dependencies. The resultant model includes primitive representations of principal air traffic system attributes, namely system capacity, demand, airline schedules and strategy, and aircraft capability. It affords a venue to explore their interdependence in a time-dependent, dynamic system simulation. The scope of the research question and the carefully-chosen modeling fidelity did allow for the development of an agent-based model in short order. The model predicted non-linear behavior given certain initial conditions and system control strategies. Additionally, a combination of the model and dimensionless techniques borrowed from fluid systems was demonstrated that can predict the system s dynamic behavior across a wide range of parametric settings.

On stable Pareto laws in a hierarchical model of economy

NASA Astrophysics Data System (ADS)

Chebotarev, A. M.

2007-01-01

This study considers a model of the income distribution of agents whose pairwise interaction is asymmetric and price-invariant. Asymmetric transactions are typical for chain-trading groups who arrange their business such that commodities move from senior to junior partners and money moves in the opposite direction. The price-invariance of transactions means that the probability of a pairwise interaction is a function of the ratio of incomes, which is independent of the price scale or absolute income level. These two features characterize the hierarchical model. The income distribution in this class of models is a well-defined double-Pareto function, which possesses Pareto tails for the upper and lower incomes. For gross and net upper incomes, the model predicts definite values of the Pareto exponents, agross and anet, which are stable with respect to quantitative variation of the pair-interaction. The Pareto exponents are also stable with respect to the choice of a demand function within two classes of status-dependent behavior of agents: linear demand ( agross=1, anet=2) and unlimited slowly varying demand ( agross=anet=1). For the sigmoidal demand that describes limited returns, agross=anet=1+α, with some α>0 satisfying a transcendental equation. The low-income distribution may be singular or vanishing in the neighborhood of the minimal income; in any case, it is L1-integrable and its Pareto exponent is given explicitly. The theory used in the present study is based on a simple balance equation and new results from multiplicative Markov chains and exponential moments of random geometric progressions.

A Demand-Driven Approach for a Multi-Agent System in Supply Chain Management

NASA Astrophysics Data System (ADS)

Kovalchuk, Yevgeniya; Fasli, Maria

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit.

Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX.

PubMed

Raju, Leo; Milton, R S; Mahadevan, Senthilkumaran

The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX

PubMed Central

Raju, Leo; Milton, R. S.; Mahadevan, Senthilkumaran

2016-01-01

The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations. PMID:27127802

Pricing the Services in Dynamic Environment: Agent Pricing Model

NASA Astrophysics Data System (ADS)

Žagar, Drago; Rupčić, Slavko; Rimac-Drlje, Snježana

New Internet applications and services as well as new user demands open many new issues concerning dynamic management of quality of service and price for received service, respectively. The main goals of Internet service providers are to maximize profit and maintain a negotiated quality of service. From the users' perspective the main goal is to maximize ratio of received QoS and costs of service. However, achieving these objectives could become very complex if we know that Internet service users might during the session become highly dynamic and proactive. This connotes changes in user profile or network provider/s profile caused by high level of user mobility or variable level of user demands. This paper proposes a new agent based pricing architecture for serving the highly dynamic customers in context of dynamic user/network environment. The proposed architecture comprises main aspects and basic parameters that will enable objective and transparent assessment of the costs for the service those Internet users receive while dynamically change QoS demands and cost profile.

A coupled modeling framework for sustainable watershed management in transboundary river basins

NASA Astrophysics Data System (ADS)

Furqan Khan, Hassaan; Yang, Y. C. Ethan; Xie, Hua; Ringler, Claudia

2017-12-01

There is a growing recognition among water resource managers that sustainable watershed management needs to not only account for the diverse ways humans benefit from the environment, but also incorporate the impact of human actions on the natural system. Coupled natural-human system modeling through explicit modeling of both natural and human behavior can help reveal the reciprocal interactions and co-evolution of the natural and human systems. This study develops a spatially scalable, generalized agent-based modeling (ABM) framework consisting of a process-based semi-distributed hydrologic model (SWAT) and a decentralized water system model to simulate the impacts of water resource management decisions that affect the food-water-energy-environment (FWEE) nexus at a watershed scale. Agents within a river basin are geographically delineated based on both political and watershed boundaries and represent key stakeholders of ecosystem services. Agents decide about the priority across three primary water uses: food production, hydropower generation and ecosystem health within their geographical domains. Agents interact with the environment (streamflow) through the SWAT model and interact with other agents through a parameter representing willingness to cooperate. The innovative two-way coupling between the water system model and SWAT enables this framework to fully explore the feedback of human decisions on the environmental dynamics and vice versa. To support non-technical stakeholder interactions, a web-based user interface has been developed that allows for role-play and participatory modeling. The generalized ABM framework is also tested in two key transboundary river basins, the Mekong River basin in Southeast Asia and the Niger River basin in West Africa, where water uses for ecosystem health compete with growing human demands on food and energy resources. We present modeling results for crop production, energy generation and violation of eco-hydrological indicators at both the agent and basin-wide levels to shed light on holistic FWEE management policies in these two basins.

Monopoly models with time-varying demand function

NASA Astrophysics Data System (ADS)

Cavalli, Fausto; Naimzada, Ahmad

2018-05-01

We study a family of monopoly models for markets characterized by time-varying demand functions, in which a boundedly rational agent chooses output levels on the basis of a gradient adjustment mechanism. After presenting the model for a generic framework, we analytically study the case of cyclically alternating demand functions. We show that both the perturbation size and the agent's reactivity to profitability variation signals can have counterintuitive roles on the resulting period-2 cycles and on their stability. In particular, increasing the perturbation size can have both a destabilizing and a stabilizing effect on the resulting dynamics. Moreover, in contrast with the case of time-constant demand functions, the agent's reactivity is not just destabilizing, but can improve stability, too. This means that a less cautious behavior can provide better performance, both with respect to stability and to achieved profits. We show that, even if the decision mechanism is very simple and is not able to always provide the optimal production decisions, achieved profits are very close to those optimal. Finally, we show that in agreement with the existing empirical literature, the price series obtained simulating the proposed model exhibit a significant deviation from normality and large volatility, in particular when underlying deterministic dynamics become unstable and complex.

Agent Based Model of Livestock Movements

NASA Astrophysics Data System (ADS)

Miron, D. J.; Emelyanova, I. V.; Donald, G. E.; Garner, G. M.

The modelling of livestock movements within Australia is of national importance for the purposes of the management and control of exotic disease spread, infrastructure development and the economic forecasting of livestock markets. In this paper an agent based model for the forecasting of livestock movements is presented. This models livestock movements from farm to farm through a saleyard. The decision of farmers to sell or buy cattle is often complex and involves many factors such as climate forecast, commodity prices, the type of farm enterprise, the number of animals available and associated off-shore effects. In this model the farm agent's intelligence is implemented using a fuzzy decision tree that utilises two of these factors. These two factors are the livestock price fetched at the last sale and the number of stock on the farm. On each iteration of the model farms choose either to buy, sell or abstain from the market thus creating an artificial supply and demand. The buyers and sellers then congregate at the saleyard where livestock are auctioned using a second price sealed bid. The price time series output by the model exhibits properties similar to those found in real livestock markets.

Democracy versus dictatorship in self-organized models of financial markets

NASA Astrophysics Data System (ADS)

D'Hulst, R.; Rodgers, G. J.

2000-06-01

Models to mimic the transmission of information in financial markets are introduced. As an attempt to generate the demand process, we distinguish between dictatorship associations, where groups of agents rely on one of them to make decision, and democratic associations, where each agent takes part in the group decision. In the dictatorship model, agents segregate into two distinct populations, while the democratic model is driven towards a critical state where groups of agents of all sizes exist. Hence, both models display a level of organization, but only the democratic model is self-organized. We show that the dictatorship model generates less-volatile markets than the democratic model.

Heterogeneous information-based artificial stock market

NASA Astrophysics Data System (ADS)

Pastore, S.; Ponta, L.; Cincotti, S.

2010-05-01

In this paper, an information-based artificial stock market is considered. The market is populated by heterogeneous agents that are seen as nodes of a sparsely connected graph. Agents trade a risky asset in exchange for cash. Besides the amount of cash and assets owned, each agent is characterized by a sentiment. Moreover, agents share their sentiments by means of interactions that are identified by the graph. Interactions are unidirectional and are supplied with heterogeneous weights. The agent's trading decision is based on sentiment and, consequently, the stock price process depends on the propagation of information among the interacting agents, on budget constraints and on market feedback. A central market maker (clearing house mechanism) determines the price process at the intersection of the demand and supply curves. Both closed- and open-market conditions are considered. The results point out the validity of the proposed model of information exchange among agents and are helpful for understanding the role of information in real markets. Under closed market conditions, the interaction among agents' sentiments yields a price process that reproduces the main stylized facts of real markets, e.g. the fat tails of the returns distributions and the clustering of volatility. Within open-market conditions, i.e. with an external cash inflow that results in asset price inflation, also the unitary root stylized fact is reproduced by the artificial stock market. Finally, the effects of model parameters on the properties of the artificial stock market are also addressed.

Abolishing coinsurance for oral antihyperglycemic agents: effects on social insurance budgets.

PubMed

Athanasakis, Kostas; Skroumpelos, Anastasis G; Tsiantou, Vassiliki; Milona, Katerina; Kyriopoulos, John

2011-02-01

To assess the effects of abolishing coinsurance for oral antihyperglycemic agents (OAAs) on the social insurance fund budget in Greece. A mathematical model estimating the effect of a decrease in patient coinsurance rate on demand for and adherence to OAAs and the subsequent clinical and economic outcomes. Price elasticity of demand for antidiabetic agents was used to estimate quantity demand change as a result of a coinsurance rate decrease and consequent increased adherence to OAAs. Given the inverse relationship between OAA adherence and glycated hemoglobin (A1C) level, the model calculated the mean decrease in A1C level and associated cost savings based on the cost difference between patients with controlled versus uncontrolled A1C levels. A decrease in patient coinsurance rate from 25% to 0% led to an incremental increase in OAA adherence of 30.5% and a mean decrease in A1C level of 0.6%. The A1C level decrease contributed to an 18.5% "shift" of uncontrolled patients to controlled A1C levels (<7%), which in economic terms translated into savings of 324 euro per patient over a 3-year period and an investment return rate of 122.8%. A series of 1-way and 2-way sensitivity analyses were conducted to verify the robustness and validity of the outcomes. The introduction of policies aimed at abolishing coinsurance for OAAs can result in improved patient outcomes and cost savings for the healthcare system.

The Distributed Geothermal Market Demand Model (dGeo): Documentation

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

McCabe, Kevin; Mooney, Meghan E; Sigrin, Benjamin O

The National Renewable Energy Laboratory (NREL) developed the Distributed Geothermal Market Demand Model (dGeo) as a tool to explore the potential role of geothermal distributed energy resources (DERs) in meeting thermal energy demands in the United States. The dGeo model simulates the potential for deployment of geothermal DERs in the residential and commercial sectors of the continental United States for two specific technologies: ground-source heat pumps (GHP) and geothermal direct use (DU) for district heating. To quantify the opportunity space for these technologies, dGeo leverages a highly resolved geospatial database and robust bottom-up, agent-based modeling framework. This design is consistentmore » with others in the family of Distributed Generation Market Demand models (dGen; Sigrin et al. 2016), including the Distributed Solar Market Demand (dSolar) and Distributed Wind Market Demand (dWind) models. dGeo is intended to serve as a long-term scenario-modeling tool. It has the capability to simulate the technical potential, economic potential, market potential, and technology deployment of GHP and DU through the year 2050 under a variety of user-defined input scenarios. Through these capabilities, dGeo can provide substantial analytical value to various stakeholders interested in exploring the effects of various techno-economic, macroeconomic, financial, and policy factors related to the opportunity for GHP and DU in the United States. This report documents the dGeo modeling design, methodology, assumptions, and capabilities.« less

Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration

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

Broeer, Torsten; Fuller, Jason C.; Tuffner, Francis K.

2014-01-31

Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the power system. The emergence of smart grid technologies in recent years has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This paper presents a modeling framework for an integrated electricity system where loads become an additional resource. The agent-based model represents a smart grid power system integrating generators, transmission, distribution, loads and market. The model incorporates generatormore » and load controllers, allowing suppliers and demanders to bid into a Real-Time Pricing (RTP) electricity market. The modeling framework is applied to represent a physical demonstration project conducted on the Olympic Peninsula, Washington, USA, and validation simulations are performed using actual dynamic data. Wind power is then introduced into the power generation mix illustrating the potential of demand response to mitigate the impact of wind power variability, primarily through thermostatically controlled loads. The results also indicate that effective implementation of Demand Response (DR) to assist integration of variable renewable energy resources requires a diversity of loads to ensure functionality of the overall system.« less

Occupational voice demands and their impact on the call-centre industry.

PubMed

Hazlett, D E; Duffy, O M; Moorhead, S A

2009-04-20

Within the last decade there has been a growth in the call-centre industry in the UK, with a growing awareness of the voice as an important tool for successful communication. Occupational voice problems such as occupational dysphonia, in a business which relies on healthy, effective voice as the primary professional communication tool, may threaten working ability and occupational health and safety of workers. While previous studies of telephone call-agents have reported a range of voice symptoms and functional vocal health problems, there have been no studies investigating the use and impact of vocal performance in the communication industry within the UK. This study aims to address a significant gap in the evidence-base of occupational health and safety research. The objectives of the study are: 1. to investigate the work context and vocal communication demands for call-agents; 2. to evaluate call-agents' vocal health, awareness and performance; and 3. to identify key risks and training needs for employees and employers within call-centres. This is an occupational epidemiological study, which plans to recruit call-centres throughout the UK and Ireland. Data collection will consist of three components: 1. interviews with managers from each participating call-centre to assess their communication and training needs; 2. an online biopsychosocial questionnaire will be administered to investigate the work environment and vocal demands of call-agents; and 3. voice acoustic measurements of a random sample of participants using the Multi-dimensional Voice Program (MDVP). Qualitative content analysis from the interviews will identify underlying themes and issues. A multivariate analysis approach will be adopted using Structural Equation Modelling (SEM), to develop voice measurement models in determining the construct validity of potential factors contributing to occupational dysphonia. Quantitative data will be analysed using SPSS version 15. Ethical approval is granted for this study from the School of Communication, University of Ulster. The results from this study will provide the missing element of voice-based evidence, by appraising the interactional dimensions of vocal health and communicative performance. This information will be used to inform training for call-agents and to contribute to health policies within the workplace, in order to enhance vocal health.

High performance cellular level agent-based simulation with FLAME for the GPU.

PubMed

Richmond, Paul; Walker, Dawn; Coakley, Simon; Romano, Daniela

2010-05-01

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

Self-organized criticality in a network of economic agents with finite consumption

NASA Astrophysics Data System (ADS)

da Cruz, João P.; Lind, Pedro G.

2012-02-01

We introduce a minimal agent model to explain the emergence of heavy-tailed return distributions as a result of self-organized criticality. The model assumes that agents trade their economic outputs with each other composing a complex network of agents and connections. Further, the incoming degree of an agent is proportional to the demand on its goods, while its outgoing degree is proportional to the supply. The model considers a collection of economic agents which are attracted to establish connections among them to make an exchange at a price formed by supply and demand. With our model we are able to reproduce the evolution of the return of macroscopic quantities (indices) and to correctly retrieve the non-trivial exponent value characterizing the amplitude of drops in several indices in financial markets, relating it to the underlying topology of connections. The distribution of drops in empirical data is obtained by counting the number of successive time-steps for which a decrease in the index value is observed. All eight financial indexes show an exponent m˜5/2. Finally, we present mean-field calculations of the critical exponents, and of the scaling relation m=3/2 γ-1 between the exponent m for the distribution of drops and the topological exponent γ for the degree distribution.

Modeling formalisms in Systems Biology

PubMed Central

2011-01-01

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future. PMID:22141422

Effects of vascularization on cancer nanochemotherapy outcomes

NASA Astrophysics Data System (ADS)

Paiva, L. R.; Ferreira, S. C.; Martins, M. L.

2016-08-01

Cancer therapy requires anticancer agents capable of efficient and uniform systemic delivery. One promising route to their development is nanotechnology. Here, a previous model for cancer chemotherapy based on a nanosized drug carrier (Paiva et al., 2011) is extended by including tissue vasculature and a three-dimensional growth. We study through computer simulations the therapy against tumors demanding either large or small nutrient supplies growing under different levels of tissue vascularization. Our results indicate that highly vascularized tumors demand more aggressive therapies (larger injected doses administrated at short intervals) than poorly vascularized ones. Furthermore, nanoparticle endocytic rate by tumor cells, not its selectivity, is the major factor that determines the therapeutic success. Finally, our finds indicate that therapies combining cytotoxic agents with antiangiogenic drugs that reduce the abnormal tumor vasculature, instead of angiogenic drugs that normalize it, can lead to successful treatments using feasible endocytic rates and administration intervals.

Modeling Feedbacks Between Individual Human Decisions and Hydrology Using Interconnected Physical and Social Models

NASA Astrophysics Data System (ADS)

Murphy, J.; Lammers, R. B.; Proussevitch, A. A.; Ozik, J.; Altaweel, M.; Collier, N. T.; Alessa, L.; Kliskey, A. D.

2014-12-01

The global hydrological cycle intersects with human decision making at multiple scales, from dams and irrigation works to the taps in individuals' homes. Residential water consumers are commonly encouraged to conserve; these messages are heard against a background of individual values and conceptions about water quality, uses, and availability. The degree to which these values impact the larger-hydrological dynamics, the way that changes in those values have impacts on the hydrological cycle through time, and the feedbacks by which water availability and quality in turn shape those values, are not well explored. To investigate this domain we employ a global-scale water balance model (WBM) coupled with a social-science-grounded agent-based model (ABM). The integration of a hydrological model with an agent-based model allows us to explore driving factors in the dynamics in coupled human-natural systems. From the perspective of the physical hydrologist, the ABM offers a richer means of incorporating the human decisions that drive the hydrological system; from the view of the social scientist, a physically-based hydrological model allows the decisions of the agents to play out against constraints faithful to the real world. We apply the interconnected models to a study of Tucson, Arizona, USA, and its role in the larger Colorado River system. Our core concept is Technology-Induced Environmental Distancing (TIED), which posits that layers of technology can insulate consumers from direct knowledge of a resource. In Tucson, multiple infrastructure and institutional layers have arguably increased the conceptual distance between individuals and their water supply, offering a test case of the TIED framework. Our coupled simulation allows us to show how the larger system transforms a resource with high temporal and spatial variability into a consumer constant, and the effects of this transformation on the regional system. We use this to explore how pricing, messaging, and social dynamics impact demand, how changes in demand affect the regional water system, and under what system challenges the values of the individuals are likely to change. This study is a preamble to modeling multiple regionally connected cities and larger systems with impacts on hydrology at the continental and global scales.

Agent-based human-robot interaction of a combat bulldozer

NASA Astrophysics Data System (ADS)

Granot, Reuven; Feldman, Maxim

2004-09-01

A small-scale supervised autonomous bulldozer in a remote site was developed to experience agent based human intervention. The model is based on Lego Mindstorms kit and represents combat equipment, whose job performance does not require high accuracy. The model enables evaluation of system response for different operator interventions, as well as for a small colony of semiautonomous dozers. The supervising human may better react than a fully autonomous system to unexpected contingent events, which are a major barrier to implement full autonomy. The automation is introduced as improved Man Machine Interface (MMI) by developing control agents as intelligent tools to negotiate between human requests and task level controllers as well as negotiate with other elements of the software environment. Current UGVs demand significant communication resources and constant human operation. Therefore they will be replaced by semi-autonomous human supervisory controlled systems (telerobotic). For human intervention at the low layers of the control hierarchy we suggest a task oriented control agent to take care of the fluent transition between the state in which the robot operates and the one imposed by the human. This transition should take care about the imperfections, which are responsible for the improper operation of the robot, by disconnecting or adapting them to the new situation. Preliminary conclusions from the small-scale experiments are presented.

Multi-Agent Simulation of Allocating and Routing Ambulances Under Condition of Street Blockage after Natural Disaster

NASA Astrophysics Data System (ADS)

Azimi, S.; Delavar, M. R.; Rajabifard, A.

2017-09-01

In response to natural disasters, efficient planning for optimum allocation of the medical assistance to wounded as fast as possible and wayfinding of first responders immediately to minimize the risk of natural disasters are of prime importance. This paper aims to propose a multi-agent based modeling for optimum allocation of space to emergency centers according to the population, street network and number of ambulances in emergency centers by constraint network Voronoi diagrams, wayfinding of ambulances from emergency centers to the wounded locations and return based on the minimum ambulances travel time and path length implemented by NSGA and the use of smart city facilities to accelerate the rescue operation. Simulated annealing algorithm has been used for minimizing the difference between demands and supplies of the constrained network Voronoi diagrams. In the proposed multi-agent system, after delivering the location of the wounded and their symptoms, the constraint network Voronoi diagram for each emergency center is determined. This process was performed simultaneously for the multi-injuries in different Voronoi diagrams. In the proposed multi-agent system, the priority of the injuries for receiving medical assistance and facilities of the smart city for reporting the blocked streets was considered. Tehran Municipality District 5 was considered as the study area and during 3 minutes intervals, the volunteers reported the blocked street. The difference between the supply and the demand divided to the supply in each Voronoi diagram decreased to 0.1601. In the proposed multi-agent system, the response time of the ambulances is decreased about 36.7%.

Asset Prices and Trading Volume under Fixed Transactions Costs.

ERIC Educational Resources Information Center

Lo, Andrew W.; Mamaysky, Harry; Wang, Jiang

2004-01-01

We propose a dynamic equilibrium model of asset prices and trading volume when agents face fixed transactions costs. We show that even small fixed costs can give rise to large "no-trade" regions for each agent's optimal trading policy. The inability to trade more frequently reduces the agents' asset demand and in equilibrium gives rise to a…

The predictive power of zero intelligence in financial markets

NASA Astrophysics Data System (ADS)

Farmer, J. Doyne; Patelli, Paolo; Zovko, Ilija I.

2005-02-01

Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where constraints imposed by market institutions dominate strategic agent behavior. We use data from the London Stock Exchange to test a simple model in which minimally intelligent agents place orders to trade at random. The model treats the statistical mechanics of order placement, price formation, and the accumulation of revealed supply and demand within the context of the continuous double auction and yields simple laws relating order-arrival rates to statistical properties of the market. We test the validity of these laws in explaining cross-sectional variation for 11 stocks. The model explains 96% of the variance of the gap between the best buying and selling prices (the spread) and 76% of the variance of the price diffusion rate, with only one free parameter. We also study the market impact function, describing the response of quoted prices to the arrival of new orders. The nondimensional coordinates dictated by the model approximately collapse data from different stocks onto a single curve. This work is important from a practical point of view, because it demonstrates the existence of simple laws relating prices to order flows and, in a broader context, suggests there are circumstances where the strategic behavior of agents may be dominated by other considerations. double auction market | market microstructure | agent-based models

A Spatial-Dynamic Agent-based Model of Energy Crop Introduction in Jiangsu province, China

NASA Astrophysics Data System (ADS)

Shu, K.; Schneider, U. A.; Scheffran, J.

2012-12-01

Bioenergy, as one promising option to replace a fraction of conventional fossil fuels and lower net greenhouse gas emissions, has gained many countries', in particular developing ones' attention. Their focus is mainly on the design of efficient bioenergy utilization pathways which adapt to both local geographic features and economic conditions. The establishment of a biomass production sector would be the first and pivotal component in the whole industrial chain. Several existing studies have estimated the global biomass for energy potential but arrived at very different results. One reason for the large uncertainty of biomass potential may be ascribed to the diverse nature of biomass leading to different estimates in different circumstances. Therefore, specific research at the local level is essential. Following this thought, our research conducted in the Jiangsu province, a representative region in China, will explore the spatial distribution of biomass production. The employed methodology can also be applied to other locations both in China and similar developing countries if model parameters are adequately adjusted. In this study, we analyze the local situation in the Jiangsu province focusing on the selection of new energy crops, since the cultivation of dedicated crop for energy use is still in experimental phase. We also examine the land use conflict which is especially relevant to China with more than 1.3 billion people and a severe burden on food supply. We develop an agent-based model to find the optimal spatial distribution of biomass (SDA-SDB) in Jiangsu province. Compromising data accessibility and heterogeneity of environmental factors across the province, we resolve our model at county level and consider the aggregated farming community in one county as a single agent. The aim of SDA-SDB is to simulate farmers' decision process of allocating land to either food or energy crops facing limited resources and political targets for bioenergy development. Different to previous engineering assessments of biomass potential, SDA-SDB depicts the price of dry matter, the biomass from dedicated energy crop, as an endogenous variable. Thus, the price of dry matter will be decided by the intersection between demand and supply. The demand of biomass is established by the official development plan for bioenergy. Several alternative plans will be assessed. On the supply side, the marginal costs of bioenergy production are controlled by the aggregated behavior of all farmers. In other words, each agent's decision is influenced by other agents' decisions and will influence the final result which will continue to affect other agents' decision in a closed information feedback loop. Furthermore, SDA-SDB introduces coastal mudflat in Jiangsu province as a possible novel resource for energy crop cultivation which is believed to alleviate the conflict between food and bioenergy demand. We also introduce a carbon tax (which is, at the same time, a green-energy subsidy for bioenergy) in our model to specifically explore its effect on the penetration of biomass. Finally, we summarize our findings for efficient bioenergy utilization pathway in Jiangsu province based on our simulation results and a sensitivity analysis over the key parameters.

Collective states in social systems with interacting learning agents

NASA Astrophysics Data System (ADS)

Semeshenko, Viktoriya; Gordon, Mirta B.; Nadal, Jean-Pierre

2008-08-01

We study the implications of social interactions and individual learning features on consumer demand in a simple market model. We consider a social system of interacting heterogeneous agents with learning abilities. Given a fixed price, agents repeatedly decide whether or not to buy a unit of a good, so as to maximize their expected utilities. This model is close to Random Field Ising Models, where the random field corresponds to the idiosyncratic willingness to pay. We show that the equilibrium reached depends on the nature of the information agents use to estimate their expected utilities. It may be different from the systems’ Nash equilibria.

Study on generation investment decision-making considering multi-agent benefit for global energy internet

NASA Astrophysics Data System (ADS)

Li, Pai; Huang, Yuehui; Jia, Yanbing; Liu, Jichun; Niu, Yi

2018-02-01

Abstract . This article has studies on the generation investment decision in the background of global energy interconnection. Generation investment decision model considering the multiagent benefit is proposed. Under the back-ground of global energy Interconnection, generation investors in different clean energy base not only compete with other investors, but also facing being chosen by the power of the central area, therefor, constructing generation investment decision model considering multiagent benefit can be close to meet the interests demands. Using game theory, the complete information game model is adopted to solve the strategies of different subjects in equilibrium state.

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

Gali, Emmanuel; Eidenbenz, Stephan; Mniszewski, Sue

The United States' Department of Homeland Security aims to model, simulate, and analyze critical infrastructure and their interdependencies across multiple sectors such as electric power, telecommunications, water distribution, transportation, etc. We introduce ActivitySim, an activity simulator for a population of millions of individual agents each characterized by a set of demographic attributes that is based on US census data. ActivitySim generates daily schedules for each agent that consists of a sequence of activities, such as sleeping, shopping, working etc., each being scheduled at a geographic location, such as businesses or private residences that is appropriate for the activity type andmore » for the personal situation of the agent. ActivitySim has been developed as part of a larger effort to understand the interdependencies among national infrastructure networks and their demand profiles that emerge from the different activities of individuals in baseline scenarios as well as emergency scenarios, such as hurricane evacuations. We present the scalable software engineering principles underlying ActivitySim, the socia-technical modeling paradigms that drive the activity generation, and proof-of-principle results for a scenario in the Twin Cities, MN area of 2.6 M agents.« less

Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

PubMed Central

Schneider, Petra; Hoy, Benjamin; Wessler, Silja; Schneider, Gisbert

2011-01-01

Background The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention. Methodology/Principal Findings We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector (‘virtual ligand’) for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium. Conclusions/Significance This study demonstrates that receptor-based virtual screening with a permissive (‘fuzzy’) pharmacophore model can help identify small bioactive agents for combating bacterial infection. PMID:21483848

Large-Scale Water Resources Management within the Framework of GLOWA-Danube - Part B: The Water Supply Model

NASA Astrophysics Data System (ADS)

Nickel, D.; Barthel, R.; Schmid, C.; Braun, J.

2003-04-01

The research project GLOWA-Danube, financed by the German Federal Government, investigates long-term changes in the water cycle of the Upper Danube river basin in light of global climatic change. Its concrete aim is to build a fully integrated decision support tool that combines the competence of eleven different institutes in domains covering all major aspects governing the water cycle - from the formation of clouds to groundwater flow patterns to the behaviour of the water consumer. The research group "Water Supply" at the Institute of Hydraulic Engineering (IWS), Universitaet Stuttgart, has the central task of creating an agent-based model of the water supply sector. The Water Supply model will act as a link between the various physical models determining water quality and availability on the one hand and the actors models determining water demand on the other, which together form the tool DANUBIA. Ultimately, with the help of scenario testing, the water supply model will indicate the ability of the water supply system in the Upper Danube catchment to adapt to changing boundary conditions using different management approaches. The specific aim of the Water Supply model is the creation of a model which is not only able to simulate the present day system of water extraction, treatment and distribution but also its development and change with time. As most changes to the system are brought about by decisions made by relevant actors in the field of water management or their behaviour (in response to political and economic boundary conditions, changes in water demand or water quality, advances in technology etc.), the use of agent-based modelling was chosen, whereby an agent is seen as a computer system (in our case representing a human or group of humans) which is aware of its environment, has defined objectives and is able to act independently in order to meet these objectives. Whereas agent-based modelling has received much attention over the past decades, the use of this type of modelling for water supply systems is something very new. The initial step is the development of a conceptual water supply model (using JAVA), in which both the model boundaries and area of expertise as well as parameters to be exchanged between the Water Supply model and other models are defined. The data required to create model for such a large area is not available from the authorities, common interest organisations or in the public statistics. In order to gain access to more specific information regarding individual water supply companies, the Water Supply group is currently carrying out a wide-spread questionnaire addressed to all water supply companies in the GLOWA-Danube model area - well over 1000 in total in Bavaria, Baden-Wuerttemberg, Austria and Switzerland. The questionnaire contains questions pertaining to the two distinct fields, "economics and pricing" and "technical aspects", and aims at gathering information regarding the present day situation of the water supply system, the developments over the past 10 years as well as planned developments for the immediate future. Later, the focus will shift towards the stakeholders from the field of water resources management. A catalogue of decision-making rules will be prepared as a basis for discussion and will be debated with the relevant stakeholders. These rules will provide the basis for decision-making algorithms which will allow model agents to respond to their environment, communicate with one anther and behave in a goal-oriented manner to bring about change in the water supply system in response to changing conditions with regard to the climate, water quality, political and social boundary conditions, and changing demand.

Multi Sensor Fusion Using Fitness Adaptive Differential Evolution

NASA Astrophysics Data System (ADS)

Giri, Ritwik; Ghosh, Arnob; Chowdhury, Aritra; Das, Swagatam

The rising popularity of multi-source, multi-sensor networks supports real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on a modified version of Differential Evolution (DE), called Fitness Adaptive Differential Evolution (FiADE). FiADE treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed approach is formulated to produce good result for the problems that are high-dimensional, highly nonlinear, and random. The proposed approach gives better result in case of optimal allocation of sensors. The performance of the proposed approach is compared with an evolutionary algorithm coordination generalized particle model (C-GPM).

Coevolution in management fashion: an agent-based model of consultant-driven innovation.

PubMed

Strang, David; David, Robert J; Akhlaghpour, Saeed

2014-07-01

The rise of management consultancy has been accompanied by increasingly marked faddish cycles in management techniques, but the mechanisms that underlie this relationship are not well understood. The authors develop a simple agent-based framework that models innovation adoption and abandonment on both the supply and demand sides. In opposition to conceptions of consultants as rhetorical wizards who engineer waves of management fashion, firms and consultants are treated as boundedly rational actors who chase the secrets of success by mimicking their highest-performing peers. Computational experiments demonstrate that consultant-driven versions of this dynamic in which the outcomes of firms are strongly conditioned by their choice of consultant are robustly faddish. The invasion of boom markets by low-quality consultants undercuts popular innovations while simultaneously restarting the fashion cycle by prompting the flight of high-quality consultants into less densely occupied niches. Computational experiments also indicate conditions involving consultant mobility, aspiration levels, mimic probabilities, and client-provider matching that attenuate faddishness.

Inbound Call Centers and Emotional Dissonance in the Job Demands – Resources Model

PubMed Central

Molino, Monica; Emanuel, Federica; Zito, Margherita; Ghislieri, Chiara; Colombo, Lara; Cortese, Claudio G.

2016-01-01

Background: Emotional labor, defined as the process of regulating feelings and expressions as part of the work role, is a major characteristic in call centers. In particular, interacting with customers, agents are required to show certain emotions that are considered acceptable by the organization, even though these emotions may be different from their true feelings. This kind of experience is defined as emotional dissonance and represents a feature of the job especially for call center inbound activities. Aim: The present study was aimed at investigating whether emotional dissonance mediates the relationship between job demands (workload and customer verbal aggression) and job resources (supervisor support, colleague support, and job autonomy) on the one hand, and, on the other, affective discomfort, using the job demands-resources model as a framework. The study also observed differences between two different types of inbound activities: customer assistance service (CA) and information service. Method: The study involved agents of an Italian Telecommunication Company, 352 of whom worked in the CA and 179 in the information service. The hypothesized model was tested across the two groups through multi-group structural equation modeling. Results: Analyses showed that CA agents experience greater customer verbal aggression and emotional dissonance than information service agents. Results also showed, only for the CA group, a full mediation of emotional dissonance between workload and affective discomfort, and a partial mediation of customer verbal aggression and job autonomy, and affective discomfort. Conclusion: This study’s findings contributed both to the emotional labor literature, investigating the mediational role of emotional dissonance in the job demands-resources model, and to call center literature, considering differences between two specific kinds of inbound activities. Suggestions for organizations and practitioners emerged in order to identify practical implications useful both to support employees in coping with emotional labor and to promote well-being in inbound call centers. In detail, results showed the need to improve training programs in order to enhance employees’ emotion regulation skills, and to introduce human resource practices aimed at clarifying emotional requirements of the job. PMID:27516752

Inbound Call Centers and Emotional Dissonance in the Job Demands - Resources Model.

PubMed

Molino, Monica; Emanuel, Federica; Zito, Margherita; Ghislieri, Chiara; Colombo, Lara; Cortese, Claudio G

2016-01-01

Emotional labor, defined as the process of regulating feelings and expressions as part of the work role, is a major characteristic in call centers. In particular, interacting with customers, agents are required to show certain emotions that are considered acceptable by the organization, even though these emotions may be different from their true feelings. This kind of experience is defined as emotional dissonance and represents a feature of the job especially for call center inbound activities. The present study was aimed at investigating whether emotional dissonance mediates the relationship between job demands (workload and customer verbal aggression) and job resources (supervisor support, colleague support, and job autonomy) on the one hand, and, on the other, affective discomfort, using the job demands-resources model as a framework. The study also observed differences between two different types of inbound activities: customer assistance service (CA) and information service. The study involved agents of an Italian Telecommunication Company, 352 of whom worked in the CA and 179 in the information service. The hypothesized model was tested across the two groups through multi-group structural equation modeling. Analyses showed that CA agents experience greater customer verbal aggression and emotional dissonance than information service agents. RESULTS also showed, only for the CA group, a full mediation of emotional dissonance between workload and affective discomfort, and a partial mediation of customer verbal aggression and job autonomy, and affective discomfort. This study's findings contributed both to the emotional labor literature, investigating the mediational role of emotional dissonance in the job demands-resources model, and to call center literature, considering differences between two specific kinds of inbound activities. Suggestions for organizations and practitioners emerged in order to identify practical implications useful both to support employees in coping with emotional labor and to promote well-being in inbound call centers. In detail, results showed the need to improve training programs in order to enhance employees' emotion regulation skills, and to introduce human resource practices aimed at clarifying emotional requirements of the job.

Flexibility Support for Homecare Applications Based on Models and Multi-Agent Technology

PubMed Central

Armentia, Aintzane; Gangoiti, Unai; Priego, Rafael; Estévez, Elisabet; Marcos, Marga

2015-01-01

In developed countries, public health systems are under pressure due to the increasing percentage of population over 65. In this context, homecare based on ambient intelligence technology seems to be a suitable solution to allow elderly people to continue to enjoy the comforts of home and help optimize medical resources. Thus, current technological developments make it possible to build complex homecare applications that demand, among others, flexibility mechanisms for being able to evolve as context does (adaptability), as well as avoiding service disruptions in the case of node failure (availability). The solution proposed in this paper copes with these flexibility requirements through the whole life-cycle of the target applications: from design phase to runtime. The proposed domain modeling approach allows medical staff to design customized applications, taking into account the adaptability needs. It also guides software developers during system implementation. The application execution is managed by a multi-agent based middleware, making it possible to meet adaptation requirements, assuring at the same time the availability of the system even for stateful applications. PMID:26694416

An Agent-Based Optimization Framework for Engineered Complex Adaptive Systems with Application to Demand Response in Electricity Markets

NASA Astrophysics Data System (ADS)

Haghnevis, Moeed

The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.

Glucose supply and insulin demand dynamics of antidiabetic agents.

PubMed

Monte, Scott V; Schentag, Jerome J; Adelman, Martin H; Paladino, Joseph A

2010-03-01

For microvascular outcomes, there is compelling historical and contemporary evidence for intensive blood glucose reduction in patients with either type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM). There is also strong evidence to support macrovascular benefit with intensive blood glucose reduction in T1DM. Similar evidence remains elusive for T2DM. Because cardiovascular outcome trials utilizing conventional algorithms to attain intensive blood glucose reduction have not demonstrated superiority to less aggressive blood glucose reduction (Action to Control Cardiovascular Risk in Diabetes; Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation; and Veterans Affairs Diabetes Trial), it should be considered that the means by which the blood glucose is reduced may be as important as the actual blood glucose. By identifying quantitative differences between antidiabetic agents on carbohydrate exposure (CE), hepatic glucose uptake (HGU), hepatic gluconeogenesis (GNG), insulin resistance (IR), peripheral glucose uptake (PGU), and peripheral insulin exposure (PIE), we created a pharmacokinetic/pharmacodynamic model to characterize the effect of the agents on the glucose supply and insulin demand dynamic. Glucose supply was defined as the cumulative percentage decrease in CE, increase in HGU, decrease in GNG, and decrease in IR, while insulin demand was defined as the cumulative percentage increase in PIE and PGU. With the glucose supply and insulin demand effects of each antidiabetic agent summated, the glucose supply (numerator) was divided by the insulin demand (denominator) to create a value representative of the glucose supply and insulin demand dynamic (SD ratio). Alpha-glucosidase inhibitors (1.25), metformin (2.20), and thiazolidinediones (TZDs; 1.25-1.32) demonstrate a greater effect on glucose supply (SD ratio >1), while secretagogues (0.69-0.81), basal insulins (0.77-0.79), and bolus insulins (0.62-0.67) demonstrate a greater effect on insulin demand (SD ratio <1). Alpha-glucosidase inhibitors, metformin, and TZDs demonstrate a greater effect on glucose supply, while secretagogues, basal insulin, and bolus insulin demonstrate a greater effect on insulin demand. Because T2DM cardiovascular outcome trials have not demonstrated macrovascular benefit with more aggressive blood glucose reduction when using conventional algorithms that predominantly focus on insulin demand, it would appear logical to consider a model that incorporates both the extent of blood glucose lowering (hemoglobin A1c) and the means by which the blood glucose was reduced (SD ratio) when considering macrovascular outcomes. (c) 2010 Diabetes Technology Society.

Multi-Agent Market Modeling of Foreign Exchange Rates

NASA Astrophysics Data System (ADS)

Zimmermann, Georg; Neuneier, Ralph; Grothmann, Ralph

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The oeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach. The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market.

Evaluation of water security in Jordan using a multi-agent, hydroeconomic model: Initial model results from the Jordan Water Project

NASA Astrophysics Data System (ADS)

Yoon, J.; Klassert, C. J. A.; Lachaut, T.; Selby, P. D.; Knox, S.; Gorelick, S.; Rajsekhar, D.; Tilmant, A.; Avisse, N.; Harou, J. J.; Medellin-Azuara, J.; Gawel, E.; Klauer, B.; Mustafa, D.; Talozi, S.; Sigel, K.; Zhang, H.

2016-12-01

Our work focuses on development of a multi-agent, hydroeconomic model for water policy evaluation in Jordan. Jordan ranks among the most water-scarce countries in the world, a situation exacerbated due to a recent influx of refugees escaping the ongoing civil war in neighboring Syria. The modular, multi-agent model is used to evaluate interventions for enhancing Jordan's water security, integrating biophysical modules that simulate natural and engineered phenomena with human modules that represent behavior at multiple levels of decision making. The hydrologic modules are developed using spatially-distributed groundwater and surface water models, which are translated into compact simulators for efficient integration into the multi-agent model. For the multi-agent model, we explicitly account for human agency at multiple levels of decision making, with agents representing riparian, management, supplier, and water user groups. Human agents are implemented as autonomous entities in the model that make decisions in relation to one another and in response to hydrologic and socioeconomic conditions. The integrated model is programmed in Python using Pynsim, a generalizable, open-source object-oriented software framework for modeling network-based water resource systems. The modeling time periods include historical (2006-2014) and future (present-2050) time spans. For the historical runs, the model performance is validated against historical data for several observations that reflect the interacting dynamics of both the hydrologic and human components of the system. A historical counterfactual scenario is also constructed to isolate and identify the impacts of the recent Syrian civil war and refugee crisis on Jordan's water system. For the future period, model runs are conducted to evaluate potential supply, demand, and institutional interventions over a wide range of plausible climate and socioeconomic scenarios. In addition, model sensitivity analysis is conducted revealing the hydrologic and human aspects of the system that most strongly influence water security outcomes, providing insight into coupled human-water system dynamics as well as priority areas of focus for continued model improvement.

Towards a 3d Spatial Urban Energy Modelling Approach

NASA Astrophysics Data System (ADS)

Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.

2013-09-01

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

Business Model Evaluation for an Advanced Multimedia Service Portfolio

NASA Astrophysics Data System (ADS)

Pisciella, Paolo; Zoric, Josip; Gaivoronski, Alexei A.

In this paper we analyze quantitatively a business model for the collaborative provision of an advanced mobile data service portfolio composed of three multimedia services: Video on Demand, Internet Protocol Television and User Generated Content. We provide a description of the provision system considering the relation occurring between tecnical aspects and business aspects for each agent providing the basic multimedia service. Such a techno-business analysis is then projected into a mathematical model dealing with the problem of the definition of incentives between the different agents involved in a collaborative service provision. Through the implementation of this model we aim at shaping the behaviour of each of the contributing agents modifying the level of profitability that the Service Portfolio yields to each of them.

Analysis of a decision model in the context of equilibrium pricing and order book pricing

NASA Astrophysics Data System (ADS)

Wagner, D. C.; Schmitt, T. A.; Schäfer, R.; Guhr, T.; Wolf, D. E.

2014-12-01

An agent-based model for financial markets has to incorporate two aspects: decision making and price formation. We introduce a simple decision model and consider its implications in two different pricing schemes. First, we study its parameter dependence within a supply-demand balance setting. We find realistic behavior in a wide parameter range. Second, we embed our decision model in an order book setting. Here, we observe interesting features which are not present in the equilibrium pricing scheme. In particular, we find a nontrivial behavior of the order book volumes which reminds of a trend switching phenomenon. Thus, the decision making model alone does not realistically represent the trading and the stylized facts. The order book mechanism is crucial.

Boronic acids for fluorescence imaging of carbohydrates.

PubMed

Sun, Xiaolong; Zhai, Wenlei; Fossey, John S; James, Tony D

2016-02-28

"Fluorescence imaging" is a particularly exciting and rapidly developing area of research; the annual number of publications in the area has increased ten-fold over the last decade. The rapid increase of interest in fluorescence imaging will necessitate the development of an increasing number of molecular receptors and binding agents in order to meet the demand in this rapidly expanding area. Carbohydrate biomarkers are particularly important targets for fluorescence imaging given their pivotal role in numerous important biological events, including the development and progression of many diseases. Therefore, the development of new fluorescent receptors and binding agents for carbohydrates is and will be increasing in demand. This review highlights the development of fluorescence imaging agents based on boronic acids a particularly promising class of receptors given their strong and selective binding with carbohydrates in aqueous media.

Demands on Intranets — Viable System Model as a Foundation for Intranet Design

NASA Astrophysics Data System (ADS)

Amcoff Nyström, Christina

2006-06-01

The number of Intranets increases in organizations but their potential to support viability is not fully exploited. The cybernetic model, the Viable System Model, has not been connected to the Intranet concept before. Characteristics of the VSM, such as highlighting the importance of production, monitoring of production units through Early Warning Systems, autonomy and empowerment, are used as patterns and a base for de-signing essential parts and/or functions of an Intranet. The result is a brief description of functions vital to the operational parts of organizations. Examples are Early Warning Systems, control systems, "gate-keepers," amplifying and damping information to and from the organization and "agents" supporting search abilities on an Intranet.

Evaluating the Impacts of an Agricultural Water Market in the Guadalupe River Basin, Texas: An Agent-based Modeling Approach

NASA Astrophysics Data System (ADS)

Du, E.; Cai, X.; Minsker, B. S.

2014-12-01

Agriculture comprises about 80 percent of the total water consumption in the US. Under conditions of water shortage and fully committed water rights, market-based water allocations could be promising instruments for agricultural water redistribution from marginally profitable areas to more profitable ones. Previous studies on water market have mainly focused on theoretical or statistical analysis. However, how water users' heterogeneous physical attributes and decision rules about water use and water right trading will affect water market efficiency has been less addressed. In this study, we developed an agent-based model to evaluate the benefits of an agricultural water market in the Guadalupe River Basin during drought events. Agricultural agents with different attributes (i.e., soil type for crops, annual water diversion permit and precipitation) are defined to simulate the dynamic feedback between water availability, irrigation demand and water trading activity. Diversified crop irrigation rules and water bidding rules are tested in terms of crop yield, agricultural profit, and water-use efficiency. The model was coupled with a real-time hydrologic model and run under different water scarcity scenarios. Preliminary results indicate that an agricultural water market is capable of increasing crop yield, agricultural profit, and water-use efficiency. This capability is more significant under moderate drought scenarios than in mild and severe drought scenarios. The water market mechanism also increases agricultural resilience to climate uncertainty by reducing crop yield variance in drought events. The challenges of implementing an agricultural water market under climate uncertainty are also discussed.

Evaluation of location and number of aid post for sustainable humanitarian relief using agent based modeling (ABM) and geographic information system (GIS)

NASA Astrophysics Data System (ADS)

Khair, Fauzi; Sopha, Bertha Maya

2017-12-01

One of the crucial phases in disaster management is the response phase or the emergency response phase. It requires a sustainable system and a well-integrated management system. Any errors in the system on this phase will impact on significant increase of the victims number as well as material damage caused. Policies related to the location of aid posts are important decisions. The facts show that there are many failures in the process of providing assistance to the refugees due to lack of preparation and determination of facilities and aid post location. Therefore, this study aims to evaluate the number and location of aid posts on Merapi eruption in 2010. This study uses an integration between Agent Based Modeling (ABM) and Geographic Information System (GIS) about evaluation of the number and location of the aid post using some scenarios. The ABM approach aims to describe the agents behaviour (refugees and volunteers) in the event of a disaster with their respective characteristics. While the spatial data, GIS useful to describe real condition of the Sleman regency road. Based on the simulation result, it shows alternative scenarios that combine DERU UGM post, Maguwoharjo Stadium, Tagana Post and Pakem Main Post has better result in handling and distributing aid to evacuation barrack compared to initial scenario. Alternative scenarios indicates the unmet demands are less than the initial scenario.

Counter-terrorism threat prediction architecture

NASA Astrophysics Data System (ADS)

Lehman, Lynn A.; Krause, Lee S.

2004-09-01

This paper will evaluate the feasibility of constructing a system to support intelligence analysts engaged in counter-terrorism. It will discuss the use of emerging techniques to evaluate a large-scale threat data repository (or Infosphere) and comparing analyst developed models to identify and discover potential threat-related activity with a uncertainty metric used to evaluate the threat. This system will also employ the use of psychological (or intent) modeling to incorporate combatant (i.e. terrorist) beliefs and intent. The paper will explore the feasibility of constructing a hetero-hierarchical (a hierarchy of more than one kind or type characterized by loose connection/feedback among elements of the hierarchy) agent based framework or "family of agents" to support "evidence retrieval" defined as combing, or searching the threat data repository and returning information with an uncertainty metric. The counter-terrorism threat prediction architecture will be guided by a series of models, constructed to represent threat operational objectives, potential targets, or terrorist objectives. The approach would compare model representations against information retrieved by the agent family to isolate or identify patterns that match within reasonable measures of proximity. The central areas of discussion will be the construction of an agent framework to search the available threat related information repository, evaluation of results against models that will represent the cultural foundations, mindset, sociology and emotional drive of typical threat combatants (i.e. the mind and objectives of a terrorist), and the development of evaluation techniques to compare result sets with the models representing threat behavior and threat targets. The applicability of concepts surrounding Modeling Field Theory (MFT) will be discussed as the basis of this research into development of proximity measures between the models and result sets and to provide feedback in support of model adaptation (learning). The increasingly complex demands facing analysts evaluating activity threatening to the security of the United States make the family of agent-based data collection (fusion) a promising area. This paper will discuss a system to support the collection and evaluation of potential threat activity as well as an approach fro presentation of the information.

An agent-based approach to modelling the effects of extreme events on global food prices

NASA Astrophysics Data System (ADS)

Schewe, Jacob; Otto, Christian; Frieler, Katja

2015-04-01

Extreme climate events such as droughts or heat waves affect agricultural production in major food producing regions and therefore can influence the price of staple foods on the world market. There is evidence that recent dramatic spikes in grain prices were at least partly triggered by actual and/or expected supply shortages. The reaction of the market to supply changes is however highly nonlinear and depends on complex and interlinked processes such as warehousing, speculation, and export restrictions. Here we present for the first time an agent-based modelling framework that accounts, in simplified terms, for these processes and allows to estimate the reaction of world food prices to supply shocks on a short (monthly) timescale. We test the basic model using observed historical supply, demand, and price data of wheat as a major food grain. Further, we illustrate how the model can be used in conjunction with biophysical crop models to assess the effect of future changes in extreme event regimes on the volatility of food prices. In particular, the explicit representation of storage dynamics makes it possible to investigate the potentially nonlinear interaction between simultaneous extreme events in different food producing regions, or between several consecutive events in the same region, which may both occur more frequently under future global warming.

Competitive game theoretic optimal routing in optical networks

NASA Astrophysics Data System (ADS)

Yassine, Abdulsalam; Kabranov, Ognian; Makrakis, Dimitrios

2002-09-01

Optical transport service providers need control and optimization strategies for wavelength management, network provisioning, restoration and protection, allowing them to define and deploy new services and maintain competitiveness. In this paper, we investigate a game theory based model for wavelength and flow assignment in multi wavelength optical networks, consisting of several backbone long-haul optical network transport service providers (TSPs) who are offering their services -in terms of bandwidth- to Internet service providers (ISPs). The ISPs act as brokers or agents between the TSP and end user. The agent (ISP) buys services (bandwidth) from the TSP. The TSPs compete among themselves to sell their services and maintain profitability. We present a case study, demonstrating the impact of different bandwidth broker demands on the supplier's profit and the price paid by the network broker.

Group decisions in biodiversity conservation: implications from game theory.

PubMed

Frank, David M; Sarkar, Sahotra

2010-05-27

Decision analysis and game theory have proved useful tools in various biodiversity conservation planning and modeling contexts. This paper shows how game theory may be used to inform group decisions in biodiversity conservation scenarios by modeling conflicts between stakeholders to identify Pareto-inefficient Nash equilibria. These are cases in which each agent pursuing individual self-interest leads to a worse outcome for all, relative to other feasible outcomes. Three case studies from biodiversity conservation contexts showing this feature are modeled to demonstrate how game-theoretical representation can inform group decision-making. The mathematical theory of games is used to model three biodiversity conservation scenarios with Pareto-inefficient Nash equilibria: (i) a two-agent case involving wild dogs in South Africa; (ii) a three-agent raptor and grouse conservation scenario from the United Kingdom; and (iii) an n-agent fish and coral conservation scenario from the Philippines. In each case there is reason to believe that traditional mechanism-design solutions that appeal to material incentives may be inadequate, and the game-theoretical analysis recommends a resumption of further deliberation between agents and the initiation of trust--and confidence--building measures. Game theory can and should be used as a normative tool in biodiversity conservation contexts: identifying scenarios with Pareto-inefficient Nash equilibria enables constructive action in order to achieve (closer to) optimal conservation outcomes, whether by policy solutions based on mechanism design or otherwise. However, there is mounting evidence that formal mechanism-design solutions may backfire in certain cases. Such scenarios demand a return to group deliberation and the creation of reciprocal relationships of trust.

Efficient Agent-Based Cluster Ensembles

NASA Technical Reports Server (NTRS)

Agogino, Adrian; Tumer, Kagan

2006-01-01

Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified clustering. Unfortunately current non-agent-based cluster combining methods do not work in a distributed environment, are not robust to corrupted clusterings and require centralized access to all original clusterings. Overcoming these issues will allow cluster ensembles to be used in fundamentally distributed and failure-prone domains such as data acquisition from satellite constellations, in addition to domains demanding confidentiality such as combining clusterings of user profiles. This paper proposes an efficient, distributed, agent-based clustering ensemble method that addresses these issues. In this approach each agent is assigned a small subset of the data and votes on which final cluster its data points should belong to. The final clustering is then evaluated by a global utility, computed in a distributed way. This clustering is also evaluated using an agent-specific utility that is shown to be easier for the agents to maximize. Results show that agents using the agent-specific utility can achieve better performance than traditional non-agent based methods and are effective even when up to 50% of the agents fail.

Behavioral determinants of occupational exposure to chemical agents.

PubMed

Meijman, T F; Ulenbelt, P; Lumens, M E; Herber, R F

1996-01-01

In the demand-control model (see T. Theorell & R. A. Karasek, 1996), it is hypothesized that workers in active jobs (high demands-high decision latitude) can exert effective coping strategies when confronted with environmental stessors. Thus, when exposed to similar levels of a chemical agent, lower concentrations of this agent in blood could be expected in these workers in comparison with workers in passive jobs. This theory was tested in 2 studies of lead-exposed workers: 18 male Caucasian workers from an electric accumulatory factory and 18 male Caucasian workers from a lead smelting factory. The results did not follow the hypothesized outcomes. In the work environment of the workers in active jobs, lower concentrations of lead in air were measured, but higher levels of lead in blood were observed in these workers. The opposite was true of workers in passive jobs. Differences in hygienic behavior at work may explain these unexpected results.

Simulating partially illegal markets of private tanker water providers on the country level: A multi-agent, hydroeconomic case-study of Jordan

NASA Astrophysics Data System (ADS)

Klassert, C. J. A.; Yoon, J.; Gawel, E.; Klauer, B.; Sigel, K.; Talozi, S.; Lachaut, T.; Selby, P. D.; Knox, S.; Gorelick, S.; Tilmant, A.; Harou, J. J.; Mustafa, D.; Medellin-Azuara, J.; Rajsekhar, D.; Avisse, N.; Zhang, H.

2016-12-01

In arid countries around the world, markets of private small-scale water providers, mostly delivering water via tanker trucks, have emerged to balance the shortcomings of public water supply systems. While these markets can provide substantial contributions to meeting customers' water demands, they often partially rely on illegal water abstractions, thus imposing an unregulated and unmonitored strain on ground and surface water resources. Despite their important impacts on water users' welfare and resource sustainability, these markets are still poorly understood. We use a multi-agent, hydroeconomic simulation model, developed as part of the Jordan Water Project, to investigate the role of these markets in a country-wide case-study of Jordan. Jordan's water sector is characterized by a severe and growing scarcity of water resources, high intermittency in the public water network, and a strongly increasing demand due to an unprecedented refugee crisis. The tanker water market serves an important role in providing water from rural wells to households and commercial enterprises, especially during supply interruptions. In order to overcome the lack of direct data about this partially illegal market, we simulate demand and supply for tanker water. The demand for tanker water is conceptualized as a residual demand, remaining after a water user has depleted all available cheap and qualitatively reliable piped water. It is derived from residential and commercial demand functions on the basis of survey data. Tanker water supply is determined by farm simulation models calculating the groundwater pumping cost and the agricultural opportunity cost of tanker water. A market algorithm is then used to match rural supplies with users' demands, accounting for survey data on tanker operators' transport costs and profit expectations. The model is used to gain insights into the size of the tanker markets in all 89 subdistricts of Jordan and their responsiveness to various policy interventions. A dynamic coupling of the model with a country-wide groundwater model allows for projections of the spatial development of the tanker market over time. Accounting for this important supply source will be essential for the formulation of any policy aiming to reconcile the interests of water users with resource sustainability.

A fractional reaction-diffusion description of supply and demand

NASA Astrophysics Data System (ADS)

Benzaquen, Michael; Bouchaud, Jean-Philippe

2018-02-01

We suggest that the broad distribution of time scales in financial markets could be a crucial ingredient to reproduce realistic price dynamics in stylised Agent-Based Models. We propose a fractional reaction-diffusion model for the dynamics of latent liquidity in financial markets, where agents are very heterogeneous in terms of their characteristic frequencies. Several features of our model are amenable to an exact analytical treatment. We find in particular that the impact is a concave function of the transacted volume (aka the "square-root impact law"), as in the normal diffusion limit. However, the impact kernel decays as t-β with β = 1/2 in the diffusive case, which is inconsistent with market efficiency. In the sub-diffusive case the decay exponent β takes any value in [0, 1/2], and can be tuned to match the empirical value β ≈ 1/4. Numerical simulations confirm our theoretical results. Several extensions of the model are suggested. Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver.

Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling

PubMed Central

Aguilera, Miguel; Bedia, Manuel G.; Barandiaran, Xabier E.

2016-01-01

The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of “internalist neuroscience.” A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We conclude with a reflection about how our results contribute in a more general way to current progress in neuroscientific research. PMID:27721746

Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling.

PubMed

Aguilera, Miguel; Bedia, Manuel G; Barandiaran, Xabier E

2016-01-01

The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of "internalist neuroscience." A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We conclude with a reflection about how our results contribute in a more general way to current progress in neuroscientific research.

Spatial analysis of private tanker water markets in Jordan: Using a hydroeconomic multi-agent model to simulate non-observed water transfers

NASA Astrophysics Data System (ADS)

Klassert, Christian; Yoon, Jim; Gawel, Erik; Sigel, Katja; Klauer, Bernd; Talozi, Samer; Lachaut, Thibaut; Selby, Philip; Knox, Stephen; Gorelick, Steven; Tilmant, Amaury; Harou, Julien; Mustafa, Daanish; Medellin-Azuara, Josue; Rajsekhar, Deepthi; Avisse, Nicolas; Zhang, Hua

2017-04-01

The country of Jordan is characterized by severe water scarcity and deficient public water supply networks. To address these issues, Jordan's water sector authorities have adopted a water rationing scheme implemented by interrupting piped water supply for several days per week. As in many arid countries around the world, this has led to the emergence of private markets of small-scale providers, delivering water via tanker trucks. On the one hand, these markets play a crucial role in meeting residential and commercial water demands by balancing the shortcomings of the public supply system. On the other hand, providers partially rely on illegal abstractions from rural ground and surface water sources, thereby circumventing regulatory efforts to conserve these resources. Private tanker water markets, therefore, provide a substantial contribution to consumer welfare while jeopardizing freshwater resource sustainability. Thus, a better understanding of these markets is of great importance for the formulation of policy interventions pursuing freshwater sustainability in a socially acceptable manner. Direct assessments of the size of these markets or their responses to policy interventions are, however, impeded by their partially illegal nature and the resulting lack of available information. To overcome this data collection challenge, we use a hydroeconomic multi-agent model developed in the Jordan Water Project to indirectly simulate country-wide tanker water market activities on the basis of demand and supply estimates. The demand for tanker water is conceptualized as a residual demand, remaining after a water user has depleted all available cheap and qualitatively reliable piped water. It is derived from residential and commercial demand functions on the basis of survey data. Tanker water supply is determined by farm simulation models calculating the groundwater pumping cost and the agricultural opportunity cost of tanker water. Finally, a spatial market algorithm matches rural supplies with users' demands across the 89 subdistricts of Jordan. This algorithm is parameterized with survey data we collected on tanker operators' transport costs and profit expectations. The model is successfully validated with available data on tanker truck registrations and tanker water prices. Model results reveal the spatial distribution of the private tanker markets' freshwater extractions, sales quantities, and economic impacts on different water user groups across all of Jordan. The results confirm the quantitative importance of these markets for consumer welfare. A dynamic coupling of farm agents with a country-wide groundwater model allows us to capture feedbacks between tanker water markets and groundwater levels. This enables us to assess policy impacts over time. Model analyses show that policies aiming to mitigate the negative sustainability impacts of private tanker water markets need to simultaneously address the shortcomings of the piped water supply system in order to avoid undue burdens on water users.

Evolution of fairness in the one-shot anonymous Ultimatum Game

PubMed Central

Rand, David G.; Tarnita, Corina E.; Ohtsuki, Hisashi; Nowak, Martin A.

2013-01-01

Classical economic models assume that people are fully rational and selfish, while experiments often point to different conclusions. A canonical example is the Ultimatum Game: one player proposes a division of a sum of money between herself and a second player, who either accepts or rejects. Based on rational self-interest, responders should accept any nonzero offer and proposers should offer the smallest possible amount. Traditional, deterministic models of evolutionary game theory agree: in the one-shot anonymous Ultimatum Game, natural selection favors low offers and demands. Experiments instead show a preference for fairness: often responders reject low offers and proposers make higher offers than needed to avoid rejection. Here we show that using stochastic evolutionary game theory, where agents make mistakes when judging the payoffs and strategies of others, natural selection favors fairness. Across a range of parameters, the average strategy matches the observed behavior: proposers offer between 30% and 50%, and responders demand between 25% and 40%. Rejecting low offers increases relative payoff in pairwise competition between two strategies and is favored when selection is sufficiently weak. Offering more than you demand increases payoff when many strategies are present simultaneously and is favored when mutation is sufficiently high. We also perform a behavioral experiment and find empirical support for these theoretical findings: uncertainty about the success of others is associated with higher demands and offers; and inconsistency in the behavior of others is associated with higher offers but not predictive of demands. In an uncertain world, fairness finishes first. PMID:23341593

Evolution of fairness in the one-shot anonymous Ultimatum Game.

PubMed

Rand, David G; Tarnita, Corina E; Ohtsuki, Hisashi; Nowak, Martin A

2013-02-12

Classical economic models assume that people are fully rational and selfish, while experiments often point to different conclusions. A canonical example is the Ultimatum Game: one player proposes a division of a sum of money between herself and a second player, who either accepts or rejects. Based on rational self-interest, responders should accept any nonzero offer and proposers should offer the smallest possible amount. Traditional, deterministic models of evolutionary game theory agree: in the one-shot anonymous Ultimatum Game, natural selection favors low offers and demands. Experiments instead show a preference for fairness: often responders reject low offers and proposers make higher offers than needed to avoid rejection. Here we show that using stochastic evolutionary game theory, where agents make mistakes when judging the payoffs and strategies of others, natural selection favors fairness. Across a range of parameters, the average strategy matches the observed behavior: proposers offer between 30% and 50%, and responders demand between 25% and 40%. Rejecting low offers increases relative payoff in pairwise competition between two strategies and is favored when selection is sufficiently weak. Offering more than you demand increases payoff when many strategies are present simultaneously and is favored when mutation is sufficiently high. We also perform a behavioral experiment and find empirical support for these theoretical findings: uncertainty about the success of others is associated with higher demands and offers; and inconsistency in the behavior of others is associated with higher offers but not predictive of demands. In an uncertain world, fairness finishes first.

New approaches in agent-based modeling of complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity.

PubMed

Dalmasso, Giovanni; Marin Zapata, Paula Andrea; Brady, Nathan Ryan; Hamacher-Brady, Anne

2017-01-01

Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity.

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity

PubMed Central

Dalmasso, Giovanni; Marin Zapata, Paula Andrea; Brady, Nathan Ryan; Hamacher-Brady, Anne

2017-01-01

Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity. PMID:28060865

Effects of competition and cooperation interaction between agents on networks in the presence of a market capacity

NASA Astrophysics Data System (ADS)

Sonubi, A.; Arcagni, A.; Stefani, S.; Ausloos, M.

2016-08-01

A network effect is introduced taking into account competition, cooperation, and mixed-type interaction among agents along a generalized Verhulst-Lotka-Volterra model. It is also argued that the presence of a market capacity undoubtedly enforces a definite limit on the agent's size growth. The state stability of triadic agents, i.e., the most basic network plaquette, is investigated analytically for possible scenarios, through a fixed-point analysis. It is discovered that: (i) market demand is only satisfied for full competition when one agent monopolizes the market; (ii) growth of agent size is encouraged in full cooperation; (iii) collaboration among agents to compete against one single agent may result in the disappearance of this single agent out of the market; and (iv) cooperating with two rivals may become a growth strategy for an intelligent agent.

Effects of competition and cooperation interaction between agents on networks in the presence of a market capacity.

PubMed

Sonubi, A; Arcagni, A; Stefani, S; Ausloos, M

2016-08-01

A network effect is introduced taking into account competition, cooperation, and mixed-type interaction among agents along a generalized Verhulst-Lotka-Volterra model. It is also argued that the presence of a market capacity undoubtedly enforces a definite limit on the agent's size growth. The state stability of triadic agents, i.e., the most basic network plaquette, is investigated analytically for possible scenarios, through a fixed-point analysis. It is discovered that: (i) market demand is only satisfied for full competition when one agent monopolizes the market; (ii) growth of agent size is encouraged in full cooperation; (iii) collaboration among agents to compete against one single agent may result in the disappearance of this single agent out of the market; and (iv) cooperating with two rivals may become a growth strategy for an intelligent agent.

Simulating Residential Demand in Singapore through Five Decades of Demographic Change

NASA Astrophysics Data System (ADS)

Davis, N. R.; Fernández, J.

2011-12-01

Singapore's rapid and well-documented development over the last half-century provides an ideal case for studying urban metabolism. Extensive data [1, 2] facilitate the modeling of historical dynamics of population and resource consumption. This paper presents an agent-based population model that simulates key demographic factors - number, size, and relative income of households - through fifty years of development in Singapore. This is the first step in a broader study linking demographic factors to residential demand for urban land, materials, water, and energy. Previous studies of the resource demands of housing stock have accounted for demographics by modifying the important population driver with a single, aggregated "lifestyle" term [3, 4]. However, demographic changes that result from development can influence the nature of the residential sector, and warrant a closer look. Increasing levels of education and affluence coupled with decreasing birth rates have yielded an aging population and changing family structures in Singapore [5]. These factors all contribute to an increasingly resource-intense residential sector. Singaporeans' elevated per capita income and life expectancy have created demand for larger household area, which means a growing percentage of available land must be dedicated to residential use [6]. While the majority of Singapore's housing is public - a strategy designed to maximize land use efficiency - residents are increasingly seeking private alternatives [7]. In the private sector, lower density housing puts even greater pressure on the finite supply of undeveloped land. Agent-based modeling is used to study the selected aspects of demography. The population is disaggregated into historical time-series distributions of age, family size, education, and income. We propose a simplified methodology correlating average education level with birth rate, and income to categorize households and establish housing unit demand. Aggregated lifestyle variables have proven useful for simulating past resource consumption in some cases, but demographic shifts are important causal factors in future demand that would not be captured by these simple terms. For this reason disaggregated population modeling provides better insight into the size and income distributions of households that ultimately drive residential resource consumption. References [1] Yearbook of Statistics Singapore. Dept. of Statistics, Ministry of Trade & Industry, 1960-2011. [2] HDB Annual Report. Housing & Development Board, Ministry of National Development, 1960-2011. [3] B. Muller, "Stock dynamics for forecasting material flows-case study for housing in the Netherlands," Ecol Econ, vol. 59, no. 1, pp. 142-156, 2006. [4] H. Bergsdal, et al., "Dynamic material flow analysis for Norway's dwelling stock," Build Res Inf, vol. 35, no. 5, pp. 557-570, 2007. [5] D. Phillips and H. Bartlett, "Aging trends-Singapore," J Cross Cult Gerontol, vol. 10, no. 4, pp. 349-356, 1995. [6] T. Wong and A. Yap, Four decades of transformation: Land use in Singapore, 1960-2000. Eastern University Press, 2004. [7] -, "From universal public housing to meeting the increasing aspiration for private housing in Singapore," Habitat Int, vol. 27, no. 3, pp. 361-380, 2003.

A Budget Impact Model of Hemophilia Bypassing Agent Prophylaxis Relative to Recombinant Factor VIIa On-Demand.

PubMed

Mehta, Darshan A; Oladapo, Abiola O; Epstein, Joshua D; Novack, Aaron R; Neufeld, Ellis J; Hay, Joel W

2016-02-01

Hemophilia patients use factor-clotting concentrates (factor VIII for hemophilia A and factor IX for hemophilia B) for improved blood clotting. These products are used to prevent or stop bleeding episodes. However, some hemophilia patients develop inhibitors (i.e., the patient's immune system develops antibodies against these factor concentrates). Hence, these patients do not respond well to the factor concentrates. A majority of hemophilia patients with inhibitors are managed on-demand with the following bypassing agents: recombinant factor VIIa (rFVIIa) and activated prothrombin complex concentrate (aPCC). The recently published U.S. registries Dosing Observational Study in Hemophilia (DOSE) and Hemostasis and Thrombosis Research Society (HTRS) reported higher rFVIIa on-demand use for bleed management than previously described. To estimate aPCC and rFVIIa prophylaxis costs relative to rFVIIa on-demand treatment cost based on rFVIIa doses reported in U.S. registries. A literature-based cost model was developed assuming a base case on-demand annual bleed rate (ABR) of 28.7 per inhibitor patient, which was taken from a randomized phase 3 clinical trial. The doses for rFVIIa on-demand were taken from the median dose per bleed reported by the DOSE and HTRS registries. Model inputs for aPCC and rFVIIa prophylaxis (i.e., dosing and efficacy) were derived from respective randomized clinical trials. Cost analysis was from the U.S. payer perspective, and only direct drug costs were considered. The drug cost was based on the Medicare Part B 2014 average sale price (ASP). Two-way sensitivity and threshold analyses were performed by simultaneously varying on-demand ABR, prophylaxis efficacy, and unit drug cost. In addition to studying relative costs associated with on-demand and prophylaxis treatments, relative cost per bleeding episode avoided were also calculated for aPCC and rFVIIa prophylaxis treatments. The prophylaxis efficacy reported in the trials were used to determine the number of bleeding episodes avoided. Based on the median on-demand dose of 695 mcg per kg per bleed, reported by the DOSE registry, the annual rFVIIa on-demand cost was $34,009 per kg of body weight. The annual rFVIIa on-demand cost was $22,020 per kg of body weight when the median dose of 450 mcg per kg per bleed reported by the HTRS registry was considered. The annual cost rose to $38,461 per kg of body weight when the rFVIIa on-demand dose of 786 mcg per kg per bleed among patients infusing an initial dose ≥ 250 mcg per kg was considered. The aPCC (85 units per kg per every other day) and rFVIIa (90 mcg per kg per every day) annual prophylaxis costs were $26,536 and $60,700, respectively. Also, aPCC and rFVIIa prophyaxis treatments were estimated to prevent a total of 20.8 and 12.9 annual bleeding episodes, respectively. When compared with the on-demand dose of 695 mcg per kg per bleed (DOSE registry), the annual aPCC and rFVIIa prophylaxis costs were 21.9% lower and 78.4% higher, respectively. Additionally, aPCC prophylaxis saved $360 per kg for each bleeding episode avoided. rFVIIa prophylaxis cost $2,066 per kg for each bleeding episode avoided. Compared with the on-demand dose of 450 mcg per kg per bleed (HTRS registry), aPCC and rFVIIa prophylaxis costs were 20.5% and 174.9% higher, respectively. In this case, aPCC and rFVIIa prophylaxis treatment costs were $217 per kg and $2,995 per kg, respectively, for each bleeding episode avoided. aPCC and rFVIIa prophylaxis costs were 31.0% lower and 57.8% higher, respectively, when compared with the rFVIIa on-demand dose of 786 mcg per kg per bleed, among patients infusing an initial dose ≥ 250 mcg per kg (HTRS registry). In this case, aPCC prophylaxis saved $573 per kg for each bleeding episode avoided, while rFVIIa prophylaxis costs $1,724 per kg for each bleeding episode avoided. Results of the 2-way sensitivity analyses were robust in the majority of the scenarios considered. aPCC prophylaxis may be cost saving for managing hemophilia patients with inhibitors who bleed frequently and infuse significant quantities of rFVIIa on-demand.

Capturing well-being in activity pattern models within activity-based travel demand models.

DOT National Transportation Integrated Search

2013-03-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Capturing well-being in activity pattern models within activity-based travel demand models.

DOT National Transportation Integrated Search

2013-04-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Recent Advances in the Development of Chromophore-Based Chemosensors for Nerve Agents and Phosgene.

PubMed

Chen, Liyan; Wu, Di; Yoon, Juyoung

2018-01-26

The extreme toxicity and ready accessibility of nerve agents and phosgene has caused an increase in the demand to develop effective systems for the detection of these substances. Among the traditional platforms utilized for this purpose, chemosensors including surface acoustic wave (SAW) sensors, enzymes, carbon nanotubes, nanoparticles, and chromophore based sensors have attracted increasing attention. In this review, we describe in a comprehensive manner recent progress that has been made on the development of chromophore-based chemosensors for detecting nerve agents (mimic) and phosgene. This review comprises two sections focusing on studies of the development of chemosensors for nerve agents (mimic) and phosgene. In each of the sections, the discussion follows a format which concentrates on different reaction sites/mechanisms involved in the sensing processes. Finally, chemosensors uncovered in these efforts are compared with those based on other sensing methods and challenges facing the design of more effective chemosensors for the detection of nerve agents (mimic) and phosgene are discussed.

Studying strategic interaction under environmental and economic uncertainties among water users in the Zambezi River Basin - From descriptive analysis to institutional design for better transboundary management

NASA Astrophysics Data System (ADS)

Beck, L.; Siegfried, T. U.; Bernauer, T.

2009-12-01

The Zambezi River Basin (ZRB) is one of the largest freshwater catchments in Africa and worldwide. Consumptive water use in the ZRB is currently estimated at 15 - 20 percent of total runoff. This suggests many development possibilities, particularly for irrigated agriculture and hydropower production. The key drivers in the basin are population development on the demand side as well as uncertain impacts from climate change for supply. Development plans of the riparian countries suggest that consumptive water use might increase up to 40 percent of total runoff by 2025. This suggests that expanding water use in the Zambezi basin could become a source of disputes among the eight riparian countries. We study the surface water allocation in the basin by means of a couple hydrological-economic modeling approach. A conceptual lumped-parameter rainfall-runoff model for the ZRB was constructed and calibrated on the best available runoff data for the basin. Water users are modeled based on an agent-based framework and implemented as distributed sequential decision makers that act in an uncertain environment. Given the current non-cooperative status quo, we use the stochastic optimization technique of reinforcement learning to model the individual agents’ behavior. Their goals include the maximization of a) their long-term reward as conditioned on the state of the multi-agent system and b) the immediate reward obtained from operational decisions of reservoirs and water diversions under their control. We feed a wide range of water demand drivers as well as climate change predictions into the model and assess agents’ responses and the resulting implications for runoff at key points in the water catchment, including Victoria Falls, Kariba reservoir, Kafue Gorge, and Cahora Bassa reservoir in the downstream. It will be shown that considerable benefits exist if the current non-cooperative regime is replaced by a basin-wide, coordinated allocation strategy that regulates water storage and allocation in this complex multi-reservoir river basin. Benefits increase along the river towards the downstream, which suggests the establishment of an upstream-downstream compensation approach. The latter considers tradeoffs from water and hydropower exchanges during respective seasons and locations of peak demand.

Light responsive hybrid nanofibres for on-demand therapeutic drug and cell delivery.

PubMed

Li, Yan-Fang; Slemming-Adamsen, Peter; Wang, Jing; Song, Jie; Wang, Xueqin; Yu, Ying; Dong, Mingdong; Chen, Chunying; Besenbacher, Flemming; Chen, Menglin

2017-08-01

Smart materials for on-demand delivery of therapeutically active agents are challenging in pharmaceutical and biomaterials science. In the present study, we report hybrid nanofibres capable of being reversibly controlled to pulsatile deliver both therapeutic drugs and cells on-demand of near-infrared (NIR) light. The nanofibres, fabricated by co-electrospinning of poly (N-isopropylacrylamide), silica-coated gold nanorods and polyhedral oligomeric silsesquinoxanes have, for the first time, demonstrated rapid, reversible large-volume changes of 83% on-demand with NIR stimulation, with retained nanofibrous morphology. Combining with the extracellular matrix-mimicking fibrillary properties, the nanofibres achieved accelerated release of model drug or cells on demand with NIR triggering. The release of the model drug doxorubicin demonstrated normal anti-cancer efficacy by reducing the viability of human cervical cancer HeLa cells by 97% in 48 h. In parallel, the fibres allowed model cell NIH3T3 fibroblast entrapment, adhesion, proliferation, differentiation and, upon NIR irradiation, cell release with undisturbed cellular function. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Validation Of The Airspace Concept Evaluation System Using Real World Data

NASA Technical Reports Server (NTRS)

Zelinski, Shannon

2005-01-01

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.

A hybrid agent-based approach for modeling microbiological systems.

PubMed

Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

2008-11-21

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.

Price dynamics and market power in an agent-based power exchange

NASA Astrophysics Data System (ADS)

Cincotti, Silvano; Guerci, Eric; Raberto, Marco

2005-05-01

This paper presents an agent-based model of a power exchange. Supply of electric power is provided by competing generating companies, whereas demand is assumed to be inelastic with respect to price and is constant over time. The transmission network topology is assumed to be a fully connected graph and no transmission constraints are taken into account. The price formation process follows a common scheme for real power exchanges: a clearing house mechanism with uniform price, i.e., with price set equal across all matched buyer-seller pairs. A single class of generating companies is considered, characterized by linear cost function for each technology. Generating companies compete for the sale of electricity through repeated rounds of the uniform auction and determine their supply functions according to production costs. However, an individual reinforcement learning algorithm characterizes generating companies behaviors in order to attain the expected maximum possible profit in each auction round. The paper investigates how the market competitive equilibrium is affected by market microstructure and production costs.

The LUE data model for representation of agents and fields

NASA Astrophysics Data System (ADS)

de Jong, Kor; Schmitz, Oliver; Karssenberg, Derek

2017-04-01

Traditionally, agents-based and field-based modelling environments use different data models to represent the state of information they manipulate. In agent-based modelling, involving the representation of phenomena as objects bounded in space and time, agents are often represented by classes, each of which represents a particular kind of agent and all its properties. Such classes can be used to represent entities like people, birds, cars and countries. In field-based modelling, involving the representation of the environment as continuous fields, fields are often represented by a discretization of space, using multidimensional arrays, each storing mostly a single attribute. Such arrays can be used to represent the elevation of the land-surface, the pH of the soil, or the population density in an area, for example. Representing a population of agents by class instances grouped in collections is an intuitive way of organizing information. A drawback, though, is that models in which class instances grouping properties are stored in collections are less efficient (execute slower) than models in which collections of properties are grouped. The field representation, on the other hand, is convenient for the efficient execution of models. Another drawback is that, because the data models used are so different, integrating agent-based and field-based models becomes difficult, since the model builder has to deal with multiple concepts, and often multiple modelling environments. With the development of the LUE data model [1] we aim at representing agents and fields within a single paradigm, by combining the advantages of the data models used in agent-based and field-based data modelling. This removes the barrier for writing integrated agent-based and field-based models. The resulting data model is intuitive to use and allows for efficient execution of models. LUE is both a high-level conceptual data model and a low-level physical data model. The LUE conceptual data model is a generalization of the data models used in agent-based and field-based modelling. The LUE physical data model [2] is an implementation of the LUE conceptual data model in HDF5. In our presentation we will provide details of our approach to organizing information about agents and fields. We will show examples of agent and field data represented by the conceptual and physical data model. References: [1] de Bakker, M.P., de Jong, K., Schmitz, O., Karssenberg, D., 2016. Design and demonstration of a data model to integrate agent-based and field-based modelling. Environmental Modelling and Software. http://dx.doi.org/10.1016/j.envsoft.2016.11.016 [2] de Jong, K., 2017. LUE source code. https://github.com/pcraster/lue

Natural Gas Value-Chain and Network Assessments

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

Kobos, Peter H.; Outkin, Alexander V.; Beyeler, Walter E.

2015-09-01

The current expansion of natural gas (NG) development in the United States requires an understanding of how this change will affect the natural gas industry, downstream consumers, and economic growth in order to promote effective planning and policy development. The impact of this expansion may propagate through the NG system and US economy via changes in manufacturing, electric power generation, transportation, commerce, and increased exports of liquefied natural gas. We conceptualize this problem as supply shock propagation that pushes the NG system and the economy away from its current state of infrastructure development and level of natural gas use. Tomore » illustrate this, the project developed two core modeling approaches. The first is an Agent-Based Modeling (ABM) approach which addresses shock propagation throughout the existing natural gas distribution system. The second approach uses a System Dynamics-based model to illustrate the feedback mechanisms related to finding new supplies of natural gas - notably shale gas - and how those mechanisms affect exploration investments in the natural gas market with respect to proven reserves. The ABM illustrates several stylized scenarios of large liquefied natural gas (LNG) exports from the U.S. The ABM preliminary results demonstrate that such scenario is likely to have substantial effects on NG prices and on pipeline capacity utilization. Our preliminary results indicate that the price of natural gas in the U.S. may rise by about 50% when the LNG exports represent 15% of the system-wide demand. The main findings of the System Dynamics model indicate that proven reserves for coalbed methane, conventional gas and now shale gas can be adequately modeled based on a combination of geologic, economic and technology-based variables. A base case scenario matches historical proven reserves data for these three types of natural gas. An environmental scenario, based on implementing a $50/tonne CO 2 tax results in less proven reserves being developed in the coming years while demand may decrease in the absence of acceptable substitutes, incentives or changes in consumer behavior. An increase in demand of 25% increases proven reserves being developed by a very small amount by the end of the forecast period of 2025.« less

A novel approach to multihazard modeling and simulation.

PubMed

Smith, Silas W; Portelli, Ian; Narzisi, Giuseppe; Nelson, Lewis S; Menges, Fabian; Rekow, E Dianne; Mincer, Joshua S; Mishra, Bhubaneswar; Goldfrank, Lewis R

2009-06-01

To develop and apply a novel modeling approach to support medical and public health disaster planning and response using a sarin release scenario in a metropolitan environment. An agent-based disaster simulation model was developed incorporating the principles of dose response, surge response, and psychosocial characteristics superimposed on topographically accurate geographic information system architecture. The modeling scenarios involved passive and active releases of sarin in multiple transportation hubs in a metropolitan city. Parameters evaluated included emergency medical services, hospital surge capacity (including implementation of disaster plan), and behavioral and psychosocial characteristics of the victims. In passive sarin release scenarios of 5 to 15 L, mortality increased nonlinearly from 0.13% to 8.69%, reaching 55.4% with active dispersion, reflecting higher initial doses. Cumulative mortality rates from releases in 1 to 3 major transportation hubs similarly increased nonlinearly as a function of dose and systemic stress. The increase in mortality rate was most pronounced in the 80% to 100% emergency department occupancy range, analogous to the previously observed queuing phenomenon. Effective implementation of hospital disaster plans decreased mortality and injury severity. Decreasing ambulance response time and increasing available responding units reduced mortality among potentially salvageable patients. Adverse psychosocial characteristics (excess worry and low compliance) increased demands on health care resources. Transfer to alternative urban sites was possible. An agent-based modeling approach provides a mechanism to assess complex individual and systemwide effects in rare events.

Evolution and anti-evolution in a minimal stock market model

NASA Astrophysics Data System (ADS)

Rothenstein, R.; Pawelzik, K.

2003-08-01

We present a novel microscopic stock market model consisting of a large number of random agents modeling traders in a market. Each agent is characterized by a set of parameters that serve to make iterated predictions of two successive returns. The future price is determined according to the offer and the demand of all agents. The system evolves by redistributing the capital among the agents in each trading cycle. Without noise the dynamics of this system is nearly regular and thereby fails to reproduce the stochastic return fluctuations observed in real markets. However, when in each cycle a small amount of noise is introduced we find the typical features of real financial time series like fat-tails of the return distribution and large temporal correlations in the volatility without significant correlations in the price returns. Introducing the noise by an evolutionary process leads to different scalings of the return distributions that depend on the definition of fitness. Because our realistic model has only very few parameters, and the results appear to be robust with respect to the noise level and the number of agents we expect that our framework may serve as new paradigm for modeling self-generated return fluctuations in markets.

0-6759 : developing a business process and logical model to support a tour-based travel demand model design for TxDOT.

DOT National Transportation Integrated Search

2013-08-01

The Texas Department of Transportation : (TxDOT) created a standardized trip-based : modeling approach for travel demand modeling : called the Texas Package Suite of Travel Demand : Models (referred to as the Texas Package) to : oversee the travel de...

A market-based optimization approach to sensor and resource management

NASA Astrophysics Data System (ADS)

Schrage, Dan; Farnham, Christopher; Gonsalves, Paul G.

2006-05-01

Dynamic resource allocation for sensor management is a problem that demands solutions beyond traditional approaches to optimization. Market-based optimization applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents' bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. This paper makes two contributions to the field of market-based optimization: First, we develop a market protocol to handle heterogeneous goods in a dynamic setting. Second, we develop arbitrage agents to improve the efficiency in the market in light of its dynamic nature.

A Systematic Review of Agent-Based Modelling and Simulation Applications in the Higher Education Domain

ERIC Educational Resources Information Center

Gu, X.; Blackmore, K. L.

2015-01-01

This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a "bottom-up" modelling paradigm in which system-level behaviour (macro) is modelled through the behaviour of individual local-level agent interactions (micro).…

Agent autonomy approach to probabilistic physics-of-failure modeling of complex dynamic systems with interacting failure mechanisms

NASA Astrophysics Data System (ADS)

Gromek, Katherine Emily

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007.

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

PubMed Central

Lawley, Mark A.; Siscovick, David S.; Zhang, Donglan; Pagán, José A.

2016-01-01

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions. PMID:27236380

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions.

PubMed

Li, Yan; Lawley, Mark A; Siscovick, David S; Zhang, Donglan; Pagán, José A

2016-05-26

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.

Meeting Rural Demand: A Case for Combining Community-Based Distribution and Social Marketing of Injectable Contraceptives in Tigray, Ethiopia

PubMed Central

Prata, Ndola; Weidert, Karen; Fraser, Ashley; Gessessew, Amanuel

2013-01-01

Background In Sub-Saharan Africa, policy changes have begun to pave the way for community distribution of injectable contraceptives but sustaining such efforts remains challenging. Combining social marketing with community-based distribution provides an opportunity to recover some program costs and compensate workers with proceeds from contraceptive sales. This paper proposes a model for increasing access to injectable contraceptives in rural settings by using community-based distributers as social marketing agents and incorporating financing systems to improve sustainability. Methods This intervention was implemented in three districts of the Central Zone of Tigray, Ethiopia and program data has been collected from November 2011 through October 2012. A total of 137 Community Based Reproductive Health Agents (CBRHAs) were trained to provide injectable contraceptives and were provided with a loan of 25 injectable contraceptives from a drug revolving fund, created with project funds. The price of a single dose credited to a CBRHA was 3 birr ($0.17) and they provide injections to women for 5 birr ($0.29), determined with willingness-to-pay data. Social marketing was used to create awareness and generate demand. Both quantitative and qualitative methods were used to examine important feasibility aspects of the intervention. Results Forty-four percent of CBRHAs were providing family planning methods at the time of the training and 96% believed providing injectable contraceptives would improve their services. By October 2012, 137 CBRHAs had successfully completed training and provided 2541 injections. Of total injections, 47% were provided to new users of injectable contraceptives. Approximately 31% of injections were given for free to the poorest women, including adolescents. Conclusions Insights gained from the first year of implementation of the model provide a framework for further expansion in Tigray, Ethiopia. Our experience highlights how program planners can tailor interventions to match family planning preferences and create more sustainable contraceptive service provision with greater impact. PMID:23874767

Meeting rural demand: a case for combining community-based distribution and social marketing of injectable contraceptives in Tigray, Ethiopia.

PubMed

Prata, Ndola; Weidert, Karen; Fraser, Ashley; Gessessew, Amanuel

2013-01-01

In Sub-Saharan Africa, policy changes have begun to pave the way for community distribution of injectable contraceptives but sustaining such efforts remains challenging. Combining social marketing with community-based distribution provides an opportunity to recover some program costs and compensate workers with proceeds from contraceptive sales. This paper proposes a model for increasing access to injectable contraceptives in rural settings by using community-based distributers as social marketing agents and incorporating financing systems to improve sustainability. This intervention was implemented in three districts of the Central Zone of Tigray, Ethiopia and program data has been collected from November 2011 through October 2012. A total of 137 Community Based Reproductive Health Agents (CBRHAs) were trained to provide injectable contraceptives and were provided with a loan of 25 injectable contraceptives from a drug revolving fund, created with project funds. The price of a single dose credited to a CBRHA was 3 birr ($0.17) and they provide injections to women for 5 birr ($0.29), determined with willingness-to-pay data. Social marketing was used to create awareness and generate demand. Both quantitative and qualitative methods were used to examine important feasibility aspects of the intervention. Forty-four percent of CBRHAs were providing family planning methods at the time of the training and 96% believed providing injectable contraceptives would improve their services. By October 2012, 137 CBRHAs had successfully completed training and provided 2541 injections. Of total injections, 47% were provided to new users of injectable contraceptives. Approximately 31% of injections were given for free to the poorest women, including adolescents. Insights gained from the first year of implementation of the model provide a framework for further expansion in Tigray, Ethiopia. Our experience highlights how program planners can tailor interventions to match family planning preferences and create more sustainable contraceptive service provision with greater impact.

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

ERIC Educational Resources Information Center

Snijders, Tom A. B.; Steglich, Christian E. G.

2015-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of…

Neo-classical theory of competition or Adam Smith's hand as mathematized ideology

NASA Astrophysics Data System (ADS)

McCauley, Joseph L.

2001-10-01

Orthodox economic theory (utility maximization, rational agents, efficient markets in equilibrium) is based on arbitrarily postulated, nonempiric notions. The disagreement between economic reality and a key feature of neo-classical economic theory was criticized empirically by Osborne. I show that the orthodox theory is internally self-inconsistent for the very reason suggested by Osborne: lack of invertibility of demand and supply as functions of price to obtain price as functions of supply and demand. The reason for the noninvertibililty arises from nonintegrable excess demand dynamics, a feature of their theory completely ignored by economists.

AGENT-BASED MODELS IN EMPIRICAL SOCIAL RESEARCH*

PubMed Central

Bruch, Elizabeth; Atwell, Jon

2014-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research. PMID:25983351

Comparing microscopic activity-based and traditional models of travel demand : an Austin area case study

DOT National Transportation Integrated Search

2007-09-01

Two competing approaches to travel demand modeling exist today. The more traditional 4-step travel demand models rely on aggregate demographic data at a traffic analysis zone (TAZ) level. Activity-based microsimulation methods employ more robus...

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Physics of Traffic Congestion and Road Pricing in Transportation Planning

NASA Astrophysics Data System (ADS)

Levinson, David

2010-03-01

This presentation develops congestion theory and congestion pricing theory from its micro- foundations, the interaction of two or more vehicles. Using game theory, with a two- player game it is shown that the emergence of congestion depends on the players' relative valuations of early arrival, late arrival, and journey delay. Congestion pricing can be used as a cooperation mechanism to minimize total costs (if returned to the players). The analysis is then extended to the case of the three- player game, which illustrates congestion as a negative externality imposed on players who do not themselves contribute to it. A multi-agent model of travelers competing to utilize a roadway in time and space is presented. To realize the spillover effect among travelers, N-player games are constructed in which the strategy set includes N+1 strategies. We solve the N-player game (for N = 7) and find Nash equilibria if they exist. This model is compared to the bottleneck model. The results of numerical simulation show that the two models yield identical results in terms of lowest total costs and marginal costs when a social optimum exists. Moving from temporal dynamics to spatial complexity, using consistent agent- based techniques, we model the decision-making processes of users and infrastructure owner/operators to explore the welfare consequence of price competition, capacity choice, and product differentiation on congested transportation networks. Component models include: (1) An agent-based travel demand model wherein each traveler has learning capabilities and unique characteristics (e.g. value of time); (2) Econometric facility provision cost models; and (3) Representations of road authorities making pricing and capacity decisions. Different from small-network equilibrium models in prior literature, this agent- based model is applicable to pricing and investment analyses on large complex networks. The subsequent economic analysis focuses on the source, evolution, measurement, and impact of product differentiation with heterogeneous users on a mixed ownership network (with tolled and untolled roads). Two types of product differentiation in the presence of toll roads, path differentiation and space differentiation, are defined and measured for a base case and several variants with different types of price and capacity competition and with various degrees of user heterogeneity. The findings favor a fixed-rate road pricing policy compared to complete pricing freedom on toll roads. It is also shown that the relationship between net social benefit and user heterogeneity is not monotonic on a complex network with toll roads.

An Active Learning Exercise for Introducing Agent-Based Modeling

ERIC Educational Resources Information Center

Pinder, Jonathan P.

2013-01-01

Recent developments in agent-based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent-based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent-based…

Nanostructured thermites based on iodine pentoxide for bio agent defeat systems.

NASA Astrophysics Data System (ADS)

Hobosyan, Mkhitar; Kazansky, Alexander; Martirosyan, Karen

2011-10-01

The risk for bioterrorist events involving the intentional airborne release of contagious agents has led to development of new approaches for bio agent defeat technologies both indoors and outdoors. Novel approaches to defeat harmful biological agents have generated a strong demand for new active materials. The preferred solutions are to neutralize the biological agents within the immediate target area by using aerosolized biocidal substances released in situ by high energetic reactions. By using nano-thermite reactions, with energy release up to 25 kJ/cc, based on I2O5/Al nanoparticles we intend to generate high quantity of vaporized iodine for spatial deposition onto harmful bacteria for their destruction. In this report, the effect of reaction product on growth and survival of Escherichia coli (E-coli) expressing GFP (Green Fluorescent Protein) was investigated. Moreover, we developed an approach to increase sensitivity of the detection. The study has shown that I2O5/Al nanosystem is extremely effective to disinfect harmful biological agents such (E-coli) bacteria in seconds.

Rational expectations, psychology and inductive learning via moving thresholds

NASA Astrophysics Data System (ADS)

Lamba, H.; Seaman, T.

2008-06-01

This paper modifies a previously introduced class of heterogeneous agent models in a way that allows for the inclusion of different types of agent motivations and behaviours in a consistent manner. The agents operate within a highly simplified environment where they are only able to be long or short one unit of the asset. The price of the asset is influenced by both an external information stream and the demand of the agents. The current strategy of each agent is defined by a pair of moving thresholds straddling the current price. When the price crosses either of the thresholds for a particular agent, that agent switches position and a new pair of thresholds is generated. The threshold dynamics can mimic different sources of investor motivation, running the gamut from purely rational information-processing, through rational (but often undesirable) behaviour induced by perverse incentives and moral hazards, to purely psychological effects. The simplest model of this kind precisely conforms to the Efficient Market Hypothesis (EMH) and this allows causal relationships to be established between actions at the agent level and violations of EMH price statistics at the global level. In particular, the effects of herding behaviour and perverse incentives are examined.

Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

NASA Astrophysics Data System (ADS)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future face of post-industrial cities are revealed. Finally, the advantages and limitations of linking pixels and people by combining AI and machine learning techniques in a multi-scale geosimulation approach are to be discussed.

Coevolution of Epidemics, Social Networks, and Individual Behavior: A Case Study

NASA Astrophysics Data System (ADS)

Chen, Jiangzhuo; Marathe, Achla; Marathe, Madhav

This research shows how a limited supply of antivirals can be distributed optimally between the hospitals and the market so that the attack rate is minimized and enough revenue is generated to recover the cost of the antivirals. Results using an individual based model find that prevalence elastic demand behavior delays the epidemic and change in the social contact network induced by isolation reduces the peak of the epidemic significantly. A microeconomic analysis methodology combining behavioral economics and agent-based simulation is a major contribution of this work. In this paper we apply this methodology to analyze the fairness of the stockpile distribution, and the response of human behavior to disease prevalence level and its interaction with the market.

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

PubMed

Solovyev, Alexey; Mikheev, Maxim; Zhou, Leming; Dutta-Moscato, Joyeeta; Ziraldo, Cordelia; An, Gary; Vodovotz, Yoram; Mi, Qi

2010-01-01

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

Dynamic Task Assignment of Autonomous Distributed AGV in an Intelligent FMS Environment

NASA Astrophysics Data System (ADS)

Fauadi, Muhammad Hafidz Fazli Bin Md; Lin, Hao Wen; Murata, Tomohiro

The need of implementing distributed system is growing significantly as it is proven to be effective for organization to be flexible against a highly demanding market. Nevertheless, there are still large technical gaps need to be addressed to gain significant achievement. We propose a distributed architecture to control Automated Guided Vehicle (AGV) operation based on multi-agent architecture. System architectures and agents' functions have been designed to support distributed control of AGV. Furthermore, enhanced agent communication protocol has been configured to accommodate dynamic attributes of AGV task assignment procedure. Result proved that the technique successfully provides a better solution.

Consentaneous Agent-Based and Stochastic Model of the Financial Markets

PubMed Central

Gontis, Vygintas; Kononovicius, Aleksejus

2014-01-01

We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous emphasizes the exceptional value of well defined analytically tractable agent systems. Herding as one of the behavior peculiarities considered in the behavioral finance is the main property of the agent interactions we deal with in this contribution. Looking for the consentaneous agent-based and macroscopic approach we combine two origins of the noise: exogenous one, related to the information flow, and endogenous one, arising form the complex stochastic dynamics of agents. As a result we propose a three state agent-based herding model of the financial markets. From this agent-based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of agent population and log price in the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation. PMID:25029364

Spatial effects, sampling errors, and task specialization in the honey bee.

PubMed

Johnson, B R

2010-05-01

Task allocation patterns should depend on the spatial distribution of work within the nest, variation in task demand, and the movement patterns of workers, however, relatively little research has focused on these topics. This study uses a spatially explicit agent based model to determine whether such factors alone can generate biases in task performance at the individual level in the honey bees, Apis mellifera. Specialization (bias in task performance) is shown to result from strong sampling error due to localized task demand, relatively slow moving workers relative to nest size, and strong spatial variation in task demand. To date, specialization has been primarily interpreted with the response threshold concept, which is focused on intrinsic (typically genotypic) differences between workers. Response threshold variation and sampling error due to spatial effects are not mutually exclusive, however, and this study suggests that both contribute to patterns of task bias at the individual level. While spatial effects are strong enough to explain some documented cases of specialization; they are relatively short term and not explanatory for long term cases of specialization. In general, this study suggests that the spatial layout of tasks and fluctuations in their demand must be explicitly controlled for in studies focused on identifying genotypic specialists.

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and NSGA2-BASED Multiobjective Optimization Approaches

NASA Astrophysics Data System (ADS)

Sahelgozin, M.; Alimohammadi, A.

2015-12-01

Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

The highly intelligent virtual agents for modeling financial markets

NASA Astrophysics Data System (ADS)

Yang, G.; Chen, Y.; Huang, J. P.

2016-02-01

Researchers have borrowed many theories from statistical physics, like ensemble, Ising model, etc., to study complex adaptive systems through agent-based modeling. However, one fundamental difference between entities (such as spins) in physics and micro-units in complex adaptive systems is that the latter are usually with high intelligence, such as investors in financial markets. Although highly intelligent virtual agents are essential for agent-based modeling to play a full role in the study of complex adaptive systems, how to create such agents is still an open question. Hence, we propose three principles for designing high artificial intelligence in financial markets and then build a specific class of agents called iAgents based on these three principles. Finally, we evaluate the intelligence of iAgents through virtual index trading in two different stock markets. For comparison, we also include three other types of agents in this contest, namely, random traders, agents from the wealth game (modified on the famous minority game), and agents from an upgraded wealth game. As a result, iAgents perform the best, which gives a well support for the three principles. This work offers a general framework for the further development of agent-based modeling for various kinds of complex adaptive systems.

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

DOE PAGES

Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

2015-10-30

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

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

Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less

Pattern-oriented modeling of agent-based complex systems: Lessons from ecology

USGS Publications Warehouse

Grimm, Volker; Revilla, Eloy; Berger, Uta; Jeltsch, Florian; Mooij, Wolf M.; Railsback, Steven F.; Thulke, Hans-Hermann; Weiner, Jacob; Wiegand, Thorsten; DeAngelis, Donald L.

2005-01-01

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

NASA Astrophysics Data System (ADS)

Grimm, Volker; Revilla, Eloy; Berger, Uta; Jeltsch, Florian; Mooij, Wolf M.; Railsback, Steven F.; Thulke, Hans-Hermann; Weiner, Jacob; Wiegand, Thorsten; DeAngelis, Donald L.

2005-11-01

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

An ambient agent model for analyzing managers' performance during stress

NASA Astrophysics Data System (ADS)

ChePa, Noraziah; Aziz, Azizi Ab; Gratim, Haned

2016-08-01

Stress at work have been reported everywhere. Work related performance during stress is a pattern of reactions that occurs when managers are presented with work demands that are not matched with their knowledge, skills, or abilities, and which challenge their ability to cope. Although there are many prior findings pertaining to explain the development of manager performance during stress, less attention has been given to explain the same concept through computational models. In such, a descriptive nature in psychological theories about managers' performance during stress can be transformed into a causal-mechanistic stage that explains the relationship between a series of observed phenomena. This paper proposed an ambient agent model for analyzing managers' performance during stress. Set of properties and variables are identified through past literatures to construct the model. Differential equations have been used in formalizing the model. Set of equations reflecting relations involved in the proposed model are presented. The proposed model is essential and can be encapsulated within an intelligent agent or robots that can be used to support managers during stress.

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

A CSP-Based Agent Modeling Framework for the Cougaar Agent-Based Architecture

NASA Technical Reports Server (NTRS)

Gracanin, Denis; Singh, H. Lally; Eltoweissy, Mohamed; Hinchey, Michael G.; Bohner, Shawn A.

2005-01-01

Cognitive Agent Architecture (Cougaar) is a Java-based architecture for large-scale distributed agent-based applications. A Cougaar agent is an autonomous software entity with behaviors that represent a real-world entity (e.g., a business process). A Cougaar-based Model Driven Architecture approach, currently under development, uses a description of system's functionality (requirements) to automatically implement the system in Cougaar. The Communicating Sequential Processes (CSP) formalism is used for the formal validation of the generated system. Two main agent components, a blackboard and a plugin, are modeled as CSP processes. A set of channels represents communications between the blackboard and individual plugins. The blackboard is represented as a CSP process that communicates with every agent in the collection. The developed CSP-based Cougaar modeling framework provides a starting point for a more complete formal verification of the automatically generated Cougaar code. Currently it is used to verify the behavior of an individual agent in terms of CSP properties and to analyze the corresponding Cougaar society.

Expert knowledge elicitation using computer simulation: the organization of frail elderly case management as an illustration.

PubMed

Chiêm, Jean-Christophe; Van Durme, Thérèse; Vandendorpe, Florence; Schmitz, Olivier; Speybroeck, Niko; Cès, Sophie; Macq, Jean

2014-08-01

Various elderly case management projects have been implemented in Belgium. This type of long-term health care intervention involves contextual factors and human interactions. These underlying complex mechanisms can be usefully informed with field experts' knowledge, which are hard to make explicit. However, computer simulation has been suggested as one possible method of overcoming the difficulty of articulating such elicited qualitative views. A simulation model of case management was designed using an agent-based methodology, based on the initial qualitative research material. Variables and rules of interaction were formulated into a simple conceptual framework. This model has been implemented and was used as a support for a structured discussion with experts in case management. The rigorous formulation provided by the agent-based methodology clarified the descriptions of the interventions and the problems encountered regarding: the diverse network topologies of health care actors in the project; the adaptation time required by the intervention; the communication between the health care actors; the institutional context; the organization of the care; and the role of the case manager and his or hers personal ability to interpret the informal demands of the frail older person. The simulation model should be seen primarily as a tool for thinking and learning. A number of insights were gained as part of a valuable cognitive process. Computer simulation supporting field experts' elicitation can lead to better-informed decisions in the organization of complex health care interventions. © 2013 John Wiley & Sons, Ltd.

A Collective Case Study of Secondary Students' Model-Based Inquiry on Natural Selection through Programming in an Agent-Based Modeling Environment

ERIC Educational Resources Information Center

Xiang, Lin

2011-01-01

This is a collective case study seeking to develop detailed descriptions of how programming an agent-based simulation influences a group of 8th grade students' model-based inquiry (MBI) by examining students' agent-based programmable modeling (ABPM) processes and the learning outcomes. The context of the present study was a biology unit on…

Biological therapies in Crohn's disease: are they cost-effective? A critical appraisal of model-based analyses.

PubMed

Marchetti, Monia; Liberato, Nicola Lucio

2014-12-01

In refractory Crohn's disease, anti-TNF and anti-α 4 integrin agents are used for ameliorating disease activity but impose high costs to health-care systems. The authors systematically reviewed cost-effectiveness analyses based on decision models: most of the studies were judged to have a good quality, but a large portion assessed health and costs in a short time horizon, usually disregarding fistulizing disease and not considering safety. Infliximab induction followed by on-demand retreatment consistently proved to have a good cost per quality-adjusted life year, while maintenance treatment never satisfied commonly accepted cost-utility thresholds. Challenges in cost-effectiveness analysis include the lack of a standard model structure, a large variability in the costs of surgery and poor data on indirect costs. As clinical practice is moving to mucosal healing as a robust response marker, personalized schedules of anti-TNF therapies might prove cost-effective even in the perspective of the health-care system in the near future.

Data-driven agent-based modeling, with application to rooftop solar adoption

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

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends andmore » provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.« less

Data-driven agent-based modeling, with application to rooftop solar adoption

DOE PAGES

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua; ...

2016-01-25

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends andmore » provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.« less

Disaggregation and Refinement of System Dynamics Models via Agent-based Modeling

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

Nutaro, James J; Ozmen, Ozgur; Schryver, Jack C

System dynamics models are usually used to investigate aggregate level behavior, but these models can be decomposed into agents that have more realistic individual behaviors. Here we develop a simple model of the STEM workforce to illuminate the impacts that arise from the disaggregation and refinement of system dynamics models via agent-based modeling. Particularly, alteration of Poisson assumptions, adding heterogeneity to decision-making processes of agents, and discrete-time formulation are investigated and their impacts are illustrated. The goal is to demonstrate both the promise and danger of agent-based modeling in the context of a relatively simple model and to delineate themore » importance of modeling decisions that are often overlooked.« less

Applications of agent-based modeling to nutrient movement Lake Michigan

EPA Science Inventory

As part of an ongoing project aiming to provide useful information for nearshore management (harmful algal blooms, nutrient loading), we explore the value of agent-based models in Lake Michigan. Agent-based models follow many individual “agents” moving through a simul...

The Agent-based Approach: A New Direction for Computational Models of Development.

ERIC Educational Resources Information Center

Schlesinger, Matthew; Parisi, Domenico

2001-01-01

Introduces the concepts of online and offline sampling and highlights the role of online sampling in agent-based models of learning and development. Compares the strengths of each approach for modeling particular developmental phenomena and research questions. Describes a recent agent-based model of infant causal perception. Discusses limitations…

Demeter, persephone, and the search for emergence in agent-based models.

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

North, M. J.; Howe, T. R.; Collier, N. T.

2006-01-01

In Greek mythology, the earth goddess Demeter was unable to find her daughter Persephone after Persephone was abducted by Hades, the god of the underworld. Demeter is said to have embarked on a long and frustrating, but ultimately successful, search to find her daughter. Unfortunately, long and frustrating searches are not confined to Greek mythology. In modern times, agent-based modelers often face similar troubles when searching for agents that are to be to be connected to one another and when seeking appropriate target agents while defining agent behaviors. The result is a 'search for emergence' in that many emergent ormore » potentially emergent behaviors in agent-based models of complex adaptive systems either implicitly or explicitly require search functions. This paper considers a new nested querying approach to simplifying such agent-based modeling and multi-agent simulation search problems.« less

Developing a Conceptual Architecture for a Generalized Agent-based Modeling Environment (GAME)

DTIC Science & Technology

2008-03-01

4. REPAST (Java, Python , C#, Open Source) ........28 5. MASON: Multi-Agent Modeling Language (Swarm Extension... Python , C#, Open Source) Repast (Recursive Porous Agent Simulation Toolkit) was designed for building agent-based models and simulations in the...Repast makes it easy for inexperienced users to build models by including a built-in simple model and provide interfaces through which menus and Python

Noninvasive visualization of in vivo release and intratumoral distribution of surrogate MR contrast agent using the dual MR contrast technique.

PubMed

Onuki, Yoshinori; Jacobs, Igor; Artemov, Dmitri; Kato, Yoshinori

2010-09-01

A direct evaluation of the in vivo release profile of drugs from carriers is a clinical demand in drug delivery systems, because drug release characterized in vitro correlates poorly with in vivo release. The purpose of this study is to demonstrate the in vivo applicability of the dual MR contrast technique as a useful tool for noninvasive monitoring of the stability and the release profile of drug carriers, by visualizing in vivo release of the encapsulated surrogate MR contrast agent from carriers and its subsequent intratumoral distribution profile. The important aspect of this technique is that it incorporates both positive and negative contrast agents within a single carrier. GdDTPA, superparamagnetic iron oxide nanoparticles, and 5-fluorouracil were encapsulated in nano- and microspheres composed of poly(D,L-lactide-co-glycolide), which was used as a model carrier. In vivo studies were performed with orthotopic xenograft of human breast cancer. The MR-based technique demonstrated here has enabled visualization of the delivery of carriers, and release and intratumoral distribution of the encapsulated positive contrast agent. This study demonstrated proof-of-principle results for the noninvasive monitoring of in vivo release and distribution profiles of MR contrast agents, and thus, this technique will make a great contribution to the field. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Simulating Cancer Growth with Multiscale Agent-Based Modeling

PubMed Central

Wang, Zhihui; Butner, Joseph D.; Kerketta, Romica; Cristini, Vittorio; Deisboeck, Thomas S.

2014-01-01

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models. PMID:24793698

Implicit Social Scaling from an Institutional Perspective

ERIC Educational Resources Information Center

D'Epifanio, Giulio

2009-01-01

The methodological question concerns constructing a cardinal social index, in order to assess performances of social agents, taking into account implicit political judgments. Based on the formal structure of a Choquet's expected utility, index construction demands quantification of levels of a meaningful ordinal indicator of overall performance.…

Building occupancy simulation and data assimilation using a graph-based agent-oriented model

NASA Astrophysics Data System (ADS)

Rai, Sanish; Hu, Xiaolin

2018-07-01

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates their real-time spatial distribution in a building. It requires a simulation model and an algorithm for data assimilation that assimilates real-time sensor data into the simulation model. Existing building occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating large numbers of occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of existing models, this paper presents a new graph-based agent-oriented model which can efficiently simulate large numbers of occupants in various kinds of building structures. To support real-time occupancy dynamics estimation, a data assimilation framework based on Sequential Monte Carlo Methods is also developed and applied to the graph-based agent-oriented model to assimilate real-time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this work are to provide an efficient model for building occupancy simulation that can accommodate large numbers of occupants and an effective data assimilation framework that can provide real-time estimations of building occupancy from sensor data.

Agent-based modeling of the spread of the 1918-1919 flu in three Canadian fur trading communities.

PubMed

O'Neil, Caroline A; Sattenspiel, Lisa

2010-01-01

Previous attempts to study the 1918-1919 flu in three small communities in central Manitoba have used both three-community population-based and single-community agent-based models. These studies identified critical factors influencing epidemic spread, but they also left important questions unanswered. The objective of this project was to design a more realistic agent-based model that would overcome limitations of earlier models and provide new insights into these outstanding questions. The new model extends the previous agent-based model to three communities so that results can be compared to those from the population-based model. Sensitivity testing was conducted, and the new model was used to investigate the influence of seasonal settlement and mobility patterns, the geographic heterogeneity of the observed 1918-1919 epidemic in Manitoba, and other questions addressed previously. Results confirm outcomes from the population-based model that suggest that (a) social organization and mobility strongly influence the timing and severity of epidemics and (b) the impact of the epidemic would have been greater if it had arrived in the summer rather than the winter. New insights from the model suggest that the observed heterogeneity among communities in epidemic impact was not unusual and would have been the expected outcome given settlement structure and levels of interaction among communities. Application of an agent-based computer simulation has helped to better explain observed patterns of spread of the 1918-1919 flu epidemic in central Manitoba. Contrasts between agent-based and population-based models illustrate the advantages of agent-based models for the study of small populations. © 2010 Wiley-Liss, Inc.

Development of an Agent-based Model to Analyze Contemporary Helium Markets

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

Riddle, Matthew E.; Uckun, Canan; Conzelmann, Guenter

Although U.S. helium demand has remained relatively flat since 2009, exports of helium have increased significantly since then, driven primarily by demand for electronic and semiconductor manufacturing in Asia. In the midst of this global demand shift, the Helium Act dictates a new procedure for pricing and distributing the gas through a reserve that historically functioned as a loose “oligarchy.” The new procedure requires prices to be determined by the open market through auctions and a survey of market prices, as opposed to increasing prices according to the consumer price index. Response to these changes has caused temporary shortages, pricemore » increases, and a significant increase in the development of the helium extraction technologies used to produce helium from formerly marginal sources. Technologies are being developed and refined to extract helium from formerly low-yielding natural gas fields containing much lower amounts of helium than the previously considered economic threshold of 0.3%. Combining these transformative policies with the potential for new and significant global supplies from Qatar, Algeria, and Russia could lead to new and unforeseen market behaviors and reactions from global helium markets. The objective of the project is to analyze the global helium markets.« less

School Principals as Mediating Agents in Education Reforms

ERIC Educational Resources Information Center

Shaked, Haim; Schechter, Chen

2017-01-01

School principals may be seen as mediating agents, standing at the school doorstep, between the extra-school and intra-school worlds. The principals' mediating role becomes more crucial during a time of education reform, which involves external demands on the one hand, and teachers' resistance to these demands on the other. This study explores how…

Unsilencing Critical Conversations in Social-Studies Teacher Education Using Agent-Based Modeling

ERIC Educational Resources Information Center

Hostetler, Andrew; Sengupta, Pratim; Hollett, Ty

2018-01-01

In this article, we argue that when complex sociopolitical issues such as ethnocentrism and racial segregation are represented as complex, emergent systems using agent-based computational models (in short agent-based models or ABMs), discourse about these representations can disrupt social studies teacher candidates' dispositions of teaching…

Brief introductory guide to agent-based modeling and an illustration from urban health research.

PubMed

Auchincloss, Amy H; Garcia, Leandro Martin Totaro

2015-11-01

There is growing interest among urban health researchers in addressing complex problems using conceptual and computation models from the field of complex systems. Agent-based modeling (ABM) is one computational modeling tool that has received a lot of interest. However, many researchers remain unfamiliar with developing and carrying out an ABM, hindering the understanding and application of it. This paper first presents a brief introductory guide to carrying out a simple agent-based model. Then, the method is illustrated by discussing a previously developed agent-based model, which explored inequalities in diet in the context of urban residential segregation.

Brief introductory guide to agent-based modeling and an illustration from urban health research

PubMed Central

Auchincloss, Amy H.; Garcia, Leandro Martin Totaro

2017-01-01

There is growing interest among urban health researchers in addressing complex problems using conceptual and computation models from the field of complex systems. Agent-based modeling (ABM) is one computational modeling tool that has received a lot of interest. However, many researchers remain unfamiliar with developing and carrying out an ABM, hindering the understanding and application of it. This paper first presents a brief introductory guide to carrying out a simple agent-based model. Then, the method is illustrated by discussing a previously developed agent-based model, which explored inequalities in diet in the context of urban residential segregation. PMID:26648364

A technology path to tactical agent-based modeling

NASA Astrophysics Data System (ADS)

James, Alex; Hanratty, Timothy P.

2017-05-01

Wargaming is a process of thinking through and visualizing events that could occur during a possible course of action. Over the past 200 years, wargaming has matured into a set of formalized processes. One area of growing interest is the application of agent-based modeling. Agent-based modeling and its additional supporting technologies has potential to introduce a third-generation wargaming capability to the Army, creating a positive overmatch decision-making capability. In its simplest form, agent-based modeling is a computational technique that helps the modeler understand and simulate how the "whole of a system" responds to change over time. It provides a decentralized method of looking at situations where individual agents are instantiated within an environment, interact with each other, and empowered to make their own decisions. However, this technology is not without its own risks and limitations. This paper explores a technology roadmap, identifying research topics that could realize agent-based modeling within a tactical wargaming context.

A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission

PubMed Central

Parker, Jon; Epstein, Joshua M.

2013-01-01

The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM’s speed and scalability. PMID:24465120

An extensible simulation environment and movement metrics for testing walking behavior in agent-based models

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

Paul M. Torrens; Atsushi Nara; Xun Li

2012-01-01

Human movement is a significant ingredient of many social, environmental, and technical systems, yet the importance of movement is often discounted in considering systems complexity. Movement is commonly abstracted in agent-based modeling (which is perhaps the methodological vehicle for modeling complex systems), despite the influence of movement upon information exchange and adaptation in a system. In particular, agent-based models of urban pedestrians often treat movement in proxy form at the expense of faithfully treating movement behavior with realistic agency. There exists little consensus about which method is appropriate for representing movement in agent-based schemes. In this paper, we examine popularly-usedmore » methods to drive movement in agent-based models, first by introducing a methodology that can flexibly handle many representations of movement at many different scales and second, introducing a suite of tools to benchmark agent movement between models and against real-world trajectory data. We find that most popular movement schemes do a relatively poor job of representing movement, but that some schemes may well be 'good enough' for some applications. We also discuss potential avenues for improving the representation of movement in agent-based frameworks.« less

Agent-Based Modeling in Public Health: Current Applications and Future Directions.

PubMed

Tracy, Melissa; Cerdá, Magdalena; Keyes, Katherine M

2018-04-01

Agent-based modeling is a computational approach in which agents with a specified set of characteristics interact with each other and with their environment according to predefined rules. We review key areas in public health where agent-based modeling has been adopted, including both communicable and noncommunicable disease, health behaviors, and social epidemiology. We also describe the main strengths and limitations of this approach for questions with public health relevance. Finally, we describe both methodologic and substantive future directions that we believe will enhance the value of agent-based modeling for public health. In particular, advances in model validation, comparisons with other causal modeling procedures, and the expansion of the models to consider comorbidity and joint influences more systematically will improve the utility of this approach to inform public health research, practice, and policy.

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model.

PubMed

Riaz, Faisal; Niazi, Muaz A

2017-01-01

This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson's arms race model has also been presented. The performance of the proposed social agent has been validated at two levels-firstly it has been simulated using NetLogo, a standard agent-based modeling tool and also, at a practical level using a prototype AV. The simulation results have confirmed that the proposed social agent-based collision avoidance strategy is 78.52% more efficient than Random walk based collision avoidance strategy in congested flock-like topologies. Whereas practical results have confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme.

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson’s arms race model

PubMed Central

Niazi, Muaz A.

2017-01-01

This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson’s arms race model has also been presented. The performance of the proposed social agent has been validated at two levels–firstly it has been simulated using NetLogo, a standard agent-based modeling tool and also, at a practical level using a prototype AV. The simulation results have confirmed that the proposed social agent-based collision avoidance strategy is 78.52% more efficient than Random walk based collision avoidance strategy in congested flock-like topologies. Whereas practical results have confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme. PMID:29040294

Elderly demand for family-based care and support: evidence from a social intervention strategy.

PubMed

Aboagye, Emmanuel; Agyemang, Otuo Serebour; Tjerbo, Trond

2013-12-06

This paper examines the influence of the national health insurance scheme on elderly demand for family-based care and support. It contributes to the growing concern on the rapid increase in the elderly population globally using micro-level social theory to examine the influence the health insurance has on elderly demand for family support. A qualitative case study approach is applied to construct a comprehensive and thick description of how the national health insurance scheme influences the elderly in their demand for family support.Through focused interviews and direct observation of six selected cases, in-depth information on primary carers, living arrangement and the interaction between the health insurance as structure and elders as agents are analyzed. The study highlights that the interaction between the elderly and the national health insurance scheme has produced a new stratum of relationship between the elderly and their primary carers. Consequently, this has created equilibrium between the elderly demand for support and support made available by their primary carers. As the demand of the elderly for support is declining, supply of support by family members for the elderly is also on the decline.

Elderly Demand for Family-based Care and Support: Evidence from a Social Intervention Strategy

PubMed Central

Aboagye, Emmanuel; Agyemang, Otuo Serebour; Tjerbo, Trond

2014-01-01

This paper examines the influence of the national health insurance scheme on elderly demand for family-based care and support. It contributes to the growing concern on the rapid increase in the elderly population globally using micro-level social theory to examine the influence the health insurance has on elderly demand for family support. A qualitative case study approach is applied to construct a comprehensive and thick description of how the national health insurance scheme influences the elderly in their demand for family support. Through focused interviews and direct observation of six selected cases, in-depth information on primary carers, living arrangement and the interaction between the health insurance as structure and elders as agents are analyzed. The study highlights that the interaction between the elderly and the national health insurance scheme has produced a new stratum of relationship between the elderly and their primary carers. Consequently, this has created equilibrium between the elderly demand for support and support made available by their primary carers. As the demand of the elderly for support is declining, supply of support by family members for the elderly is also on the decline. PMID:24576369

A Small Aircraft Transportation System (SATS) Demand Model

NASA Technical Reports Server (NTRS)

Long, Dou; Lee, David; Johnson, Jesse; Kostiuk, Peter; Yackovetsky, Robert (Technical Monitor)

2001-01-01

The Small Aircraft Transportation System (SATS) demand modeling is a tool that will be useful for decision-makers to analyze SATS demands in both airport and airspace. We constructed a series of models following the general top-down, modular principles in systems engineering. There are three principal models, SATS Airport Demand Model (SATS-ADM), SATS Flight Demand Model (SATS-FDM), and LMINET-SATS. SATS-ADM models SATS operations, by aircraft type, from the forecasts in fleet, configuration and performance, utilization, and traffic mixture. Given the SATS airport operations such as the ones generated by SATS-ADM, SATS-FDM constructs the SATS origin and destination (O&D) traffic flow based on the solution of the gravity model, from which it then generates SATS flights using the Monte Carlo simulation based on the departure time-of-day profile. LMINET-SATS, an extension of LMINET, models SATS demands at airspace and airport by all aircraft operations in US The models use parameters to provide the user with flexibility and ease of use to generate SATS demand for different scenarios. Several case studies are included to illustrate the use of the models, which are useful to identify the need for a new air traffic management system to cope with SATS.

Understanding Islamist political violence through computational social simulation

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

Watkins, Jennifer H; Mackerrow, Edward P; Patelli, Paolo G

Understanding the process that enables political violence is of great value in reducing the future demand for and support of violent opposition groups. Methods are needed that allow alternative scenarios and counterfactuals to be scientifically researched. Computational social simulation shows promise in developing 'computer experiments' that would be unfeasible or unethical in the real world. Additionally, the process of modeling and simulation reveals and challenges assumptions that may not be noted in theories, exposes areas where data is not available, and provides a rigorous, repeatable, and transparent framework for analyzing the complex dynamics of political violence. This paper demonstrates themore » computational modeling process using two simulation techniques: system dynamics and agent-based modeling. The benefits and drawbacks of both techniques are discussed. In developing these social simulations, we discovered that the social science concepts and theories needed to accurately simulate the associated psychological and social phenomena were lacking.« less

An Economic Evalution of Demand-side Energy Storage Systems by using a Multi-agent based Electricity Market

NASA Astrophysics Data System (ADS)

Furusawa, Ken; Sugihara, Hideharu; Tsuji, Kiichiro

Opened wholesale electric power market in April 2005, deregulation of electric power industry in Japan has faced a new competitive environment. In the new environment, Independent Power Producer (: IPP), Power Producer and Supplier (: PPS), Load Service Entity (: LSE) and electric utility can trade electric energy through both bilateral contracts and single-price auction at the electricity market. In general, the market clearing price (: MCP) is largely changed by amount of total load demand in the market. The influence may cause price spike, and consequently the volatility of MCP will make LSEs and their customers to face a risk of revenue and cost. DSM is attracted as a means of load leveling, and has effect on decreasing MCP at peak load period. Introducing Energy Storage systems (: ES) is one of DSM in order to change demand profile at customer-side. In case that customers decrease their own demand at jumped MCP, a bidding strategy of generating companies may be changed their strategy. As a result, MCP is changed through such complex mechanism. In this paper the authors evaluate MCP by multi-agent. It is considered that customer-side ES has an effect on MCP fluctuation. Through numerical examples, this paper evaluates the influence on MCP by controlling customer-side ES corresponding to variation of MCP.

The practice of agent-based model visualization.

PubMed

Dorin, Alan; Geard, Nicholas

2014-01-01

We discuss approaches to agent-based model visualization. Agent-based modeling has its own requirements for visualization, some shared with other forms of simulation software, and some unique to this approach. In particular, agent-based models are typified by complexity, dynamism, nonequilibrium and transient behavior, heterogeneity, and a researcher's interest in both individual- and aggregate-level behavior. These are all traits requiring careful consideration in the design, experimentation, and communication of results. In the case of all but final communication for dissemination, researchers may not make their visualizations public. Hence, the knowledge of how to visualize during these earlier stages is unavailable to the research community in a readily accessible form. Here we explore means by which all phases of agent-based modeling can benefit from visualization, and we provide examples from the available literature and online sources to illustrate key stages and techniques.

Mindfulness as a personal resource to reduce work stress in the job demands-resources model.

PubMed

Grover, Steven L; Teo, Stephen T T; Pick, David; Roche, Maree

2017-10-01

Based on the job demands-resources (JD-R) model, this study examines the different ways that the personal resource of mindfulness reduces stress. Structural equation modeling based on data from 415 Australian nurses shows that mindfulness relates directly and negatively to work stress and perceptions of emotional demands as well as buffering the relation of emotional demands on psychological stress. This study contributes to the literature by employing empirical analysis to the task of unravelling how personal resources function within the JD-R model. It also introduces mindfulness as a personal resource in the JD-R model. Copyright © 2016 John Wiley & Sons, Ltd.

A Cybernetic Approach to the Modeling of Agent Communities

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Karlin, Jay

2000-01-01

In an earlier paper [1] examples of agent technology in a NASA context were presented. Both groundbased and space-based applications were addressed. This paper continues the discussion of one aspect of the Goddard Space Flight Center's continuing efforts to develop a community of agents that can support both ground-based and space-based systems autonomy. The paper focuses on an approach to agent-community modeling based on the theory of viable systems developed by Stafford Beer. It gives the status of an initial attempt to capture some of the agent-community behaviors in a viable system context. This paper is expository in nature and focuses on a discussion of the modeling of some of the underlying concepts and infrastructure that will serve as the basis of more detailed investigative work into the behavior of agent communities. The paper is organized as follows. First, a general introduction to agent community requirements is presented. Secondly, a brief introduction to the cybernetic concept of a viable system is given. This concept forms the foundation of the modeling approach. Then the concept of an agent community is modeled in the cybernetic context.

Simulating cancer growth with multiscale agent-based modeling.

PubMed

Wang, Zhihui; Butner, Joseph D; Kerketta, Romica; Cristini, Vittorio; Deisboeck, Thomas S

2015-02-01

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models. Copyright © 2014 Elsevier Ltd. All rights reserved.

The predictive power of zero intelligence in financial markets.

PubMed

Farmer, J Doyne; Patelli, Paolo; Zovko, Ilija I

2005-02-08

Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where constraints imposed by market institutions dominate strategic agent behavior. We use data from the London Stock Exchange to test a simple model in which minimally intelligent agents place orders to trade at random. The model treats the statistical mechanics of order placement, price formation, and the accumulation of revealed supply and demand within the context of the continuous double auction and yields simple laws relating order-arrival rates to statistical properties of the market. We test the validity of these laws in explaining cross-sectional variation for 11 stocks. The model explains 96% of the variance of the gap between the best buying and selling prices (the spread) and 76% of the variance of the price diffusion rate, with only one free parameter. We also study the market impact function, describing the response of quoted prices to the arrival of new orders. The nondimensional coordinates dictated by the model approximately collapse data from different stocks onto a single curve. This work is important from a practical point of view, because it demonstrates the existence of simple laws relating prices to order flows and, in a broader context, suggests there are circumstances where the strategic behavior of agents may be dominated by other considerations.

Tour-based model development for TxDOT : implementation steps for the tour-based model design option and the data needs.

DOT National Transportation Integrated Search

2009-10-01

Travel demand modeling, in recent years, has seen a paradigm shift with an emphasis on analyzing travel at the : individual level rather than using direct statistical projections of aggregate travel demand as in the trip-based : approach. Specificall...

Intracellular CXCR4+ cell targeting with T22-empowered protein-only nanoparticles

PubMed Central

Unzueta, Ugutz; Céspedes, María Virtudes; Ferrer-Miralles, Neus; Casanova, Isolda; Cedano, Juan; Corchero, José Luis; Domingo-Espín, Joan; Villaverde, Antonio; Mangues, Ramón; Vázquez, Esther

2012-01-01

Background Cell-targeting peptides or proteins are appealing tools in nanomedicine and innovative medicines because they increase the local drug concentration and reduce potential side effects. CXC chemokine receptor 4 (CXCR4) is a cell surface marker associated with several severe human pathologies, including colorectal cancer, for which intracellular targeting agents are currently missing. Results Four different peptides that bind CXCR4 were tested for their ability to internalize a green fluorescent protein-based reporter nanoparticle into CXCR4+ cells. Among them, only the 18 mer peptide T22, an engineered segment derivative of polyphemusin II from the horseshoe crab, efficiently penetrated target cells via a rapid, receptor-specific endosomal route. This resulted in accumulation of the reporter nanoparticle in a fully fluorescent and stable form in the perinuclear region of the target cells, without toxicity either in cell culture or in an in vivo model of metastatic colorectal cancer. Conclusion Given the urgent demand for targeting agents in the research, diagnosis, and treatment of CXCR4-linked diseases, including colorectal cancer and human immunodeficiency virus infection, T22 appears to be a promising tag for the intracellular delivery of protein drugs, nanoparticles, and imaging agents. PMID:22923991

Evaluating Processes and Platforms for Potential ePortfolio Use: The Role of the Middle Agent

ERIC Educational Resources Information Center

Slade, Christine; Murfin, Keith; Readman, Kylie

2013-01-01

With the changing face of higher education comes a demand to include new technological tools. Universities need to build their capacity to respond to new technology-related challenges. The introduction of ePortfolios is a significant strategy in this response. A number of organizational change management models are used to analyze the…

Price elasticity and medication use: cost sharing across multiple clinical conditions.

PubMed

Gatwood, Justin; Gibson, Teresa B; Chernew, Michael E; Farr, Amanda M; Vogtmann, Emily; Fendrick, A Mark

2014-11-01

To address the impact that out-of-pocket prices may have on medication use, it is vital to understand how the demand for medications may be affected when patients are faced with changes in the price to acquire treatment and how price responsiveness differs across medication classes.  To examine the impact of cost-sharing changes on the demand for 8 classes of prescription medications. This was a retrospective database analysis of 11,550,363 commercially insured enrollees within the 2005-2009 MarketScan Database. Patient cost sharing, expressed as a price index for each medication class, was the main explanatory variable to examine the price elasticity of demand. Negative binomial fixed effect models were estimated to examine medication fills. The elasticity estimates reflect how use changes over time as a function of changes in copayments. Model estimates revealed that price elasticity of demand ranged from -0.015 to -0.157 within the 8 categories of medications (P  less than  0.01 for 7 of 8 categories). The price elasticity of demand for smoking deterrents was largest (-0.157, P  less than  0.0001), while demand for antiplatelet agents was not responsive to price (P  greater than 0.05). The price elasticity of demand varied considerably by medication class, suggesting that the influence of cost sharing on medication use may be related to characteristics inherent to each medication class or underlying condition.

Agent-based model for rural-urban migration: A dynamic consideration

NASA Astrophysics Data System (ADS)

Cai, Ning; Ma, Hai-Ying; Khan, M. Junaid

2015-10-01

This paper develops a dynamic agent-based model for rural-urban migration, based on the previous relevant works. The model conforms to the typical dynamic linear multi-agent systems model concerned extensively in systems science, in which the communication network is formulated as a digraph. Simulations reveal that consensus of certain variable could be harmful to the overall stability and should be avoided.

Stimuli-responsive chitosan-based nanocarriers for cancer therapy

PubMed Central

Fathi, Marziyeh; Sahandi Zangabad, Parham; Majidi, Sima; Barar, Jaleh; Erfan-Niya, Hamid

2017-01-01

Introduction: Stimuli-responsive nanocarriers offer unique advantages over the traditional drug delivery systems (DDSs) in terms of targeted drug delivery and on-demand release of cargo drug molecules. Of these, chitosan (CS)-based DDSs offer several advantages such as high compatibility with biological settings. Methods: In this study, we surveyed the literature in terms of the stimuli-responsive nanocarriers and discussed the most recent advancements in terms of CS-based nanosystems and their applications in cancer therapy and diagnosis. Results: These advanced DDSs are able to release the entrapped drugs in response to a specific endogenous stimulus (e.g., pH, glutathione concentration or certain enzymes) or exogenous stimulus (e.g., temperature, light, ultrasound, and magnetic field) at the desired time and target site. Dual-responsive nanocarriers by the combination of different stimuli have also been developed as efficient and improved DDSs. Among the stimuli-responsive nanocarriers, CS-based DDSs offer several advantages, including biocompatibility and biodegradability, antibacterial activity, ease of modification and functionalization, and non-immunogenicity. They are as one of the most ideal smart multifunction DDSs. Conclusion: The CS-based stimuli-responsive multifunctional nanosystems (NSs) offer unique potential for the targeted delivery of anticancer agents and provide great potential for on-demand and controlled-release of anticancer agents in response to diverse external/internal stimuli. PMID:29435435

Stimuli-responsive chitosan-based nanocarriers for cancer therapy.

PubMed

Fathi, Marziyeh; Sahandi Zangabad, Parham; Majidi, Sima; Barar, Jaleh; Erfan-Niya, Hamid; Omidi, Yadollah

2017-01-01

Introduction: Stimuli-responsive nanocarriers offer unique advantages over the traditional drug delivery systems (DDSs) in terms of targeted drug delivery and on-demand release of cargo drug molecules. Of these, chitosan (CS)-based DDSs offer several advantages such as high compatibility with biological settings. Methods: In this study, we surveyed the literature in terms of the stimuli-responsive nanocarriers and discussed the most recent advancements in terms of CS-based nanosystems and their applications in cancer therapy and diagnosis. Results: These advanced DDSs are able to release the entrapped drugs in response to a specific endogenous stimulus (e.g., pH, glutathione concentration or certain enzymes) or exogenous stimulus (e.g., temperature, light, ultrasound, and magnetic field) at the desired time and target site. Dual-responsive nanocarriers by the combination of different stimuli have also been developed as efficient and improved DDSs. Among the stimuli-responsive nanocarriers, CS-based DDSs offer several advantages, including biocompatibility and biodegradability, antibacterial activity, ease of modification and functionalization, and non-immunogenicity. They are as one of the most ideal smart multifunction DDSs. Conclusion: The CS-based stimuli-responsive multifunctional nanosystems (NSs) offer unique potential for the targeted delivery of anticancer agents and provide great potential for on-demand and controlled-release of anticancer agents in response to diverse external/internal stimuli.

Development of Mechanistic Reasoning and Multilevel Explanations of Ecology in Third Grade Using Agent-Based Models

ERIC Educational Resources Information Center

Dickes, Amanda Catherine; Sengupta, Pratim; Farris, Amy Voss; Satabdi, Basu

2016-01-01

In this paper, we present a third-grade ecology learning environment that integrates two forms of modeling--embodied modeling and agent-based modeling (ABMs)--through the generation of mathematical representations that are common to both forms of modeling. The term "agent" in the context of ABMs indicates individual computational objects…

Agent based reasoning for the non-linear stochastic models of long-range memory

NASA Astrophysics Data System (ADS)

Kononovicius, A.; Gontis, V.

2012-02-01

We extend Kirman's model by introducing variable event time scale. The proposed flexible time scale is equivalent to the variable trading activity observed in financial markets. Stochastic version of the extended Kirman's agent based model is compared to the non-linear stochastic models of long-range memory in financial markets. The agent based model providing matching macroscopic description serves as a microscopic reasoning of the earlier proposed stochastic model exhibiting power law statistics.

Going beyond the unitary curve: incorporating richer cognition into agent-based water resources models

NASA Astrophysics Data System (ADS)

Kock, B. E.

2008-12-01

The increased availability and understanding of agent-based modeling technology and techniques provides a unique opportunity for water resources modelers, allowing them to go beyond traditional behavioral approaches from neoclassical economics, and add rich cognition to social-hydrological models. Agent-based models provide for an individual focus, and the easier and more realistic incorporation of learning, memory and other mechanisms for increased cognitive sophistication. We are in an age of global change impacting complex water resources systems, and social responses are increasingly recognized as fundamentally adaptive and emergent. In consideration of this, water resources models and modelers need to better address social dynamics in a manner beyond the capabilities of neoclassical economics theory and practice. However, going beyond the unitary curve requires unique levels of engagement with stakeholders, both to elicit the richer knowledge necessary for structuring and parameterizing agent-based models, but also to make sure such models are appropriately used. With the aim of encouraging epistemological and methodological convergence in the agent-based modeling of water resources, we have developed a water resources-specific cognitive model and an associated collaborative modeling process. Our cognitive model emphasizes efficiency in architecture and operation, and capacity to adapt to different application contexts. We describe a current application of this cognitive model and modeling process in the Arkansas Basin of Colorado. In particular, we highlight the potential benefits of, and challenges to, using more sophisticated cognitive models in agent-based water resources models.

Multi-stimuli responsive Cu2S nanocrystals as trimodal imaging and synergistic chemo-photothermal therapy agents

NASA Astrophysics Data System (ADS)

Poulose, Aby Cheruvathoor; Veeranarayanan, Srivani; Mohamed, M. Sheikh; Nagaoka, Yutaka; Romero Aburto, Rebeca; Mitcham, Trevor; Ajayan, Pulickel M.; Bouchard, Richard R.; Sakamoto, Yasushi; Yoshida, Yasuhiko; Maekawa, Toru; Sakthi Kumar, D.

2015-04-01

A size and shape tuned, multifunctional metal chalcogenide, Cu2S-based nanotheranostic agent is developed for trimodal imaging and multimodal therapeutics against brain cancer cells. This theranostic agent was highly efficient in optical, photoacoustic and X-ray contrast imaging systems. The folate targeted NIR-responsive photothermal ablation in synergism with the chemotherapeutic action of doxorubicin proved to be a rapid precision guided cancer-killing module. The multi-stimuli, i.e., pH-, thermo- and photo-responsive drug release behavior of the nanoconjugates opens up a wider corridor for on-demand triggered drug administration. The simple synthesis protocol, combined with the multitudes of interesting features packed into a single nanoformulation, clearly demonstrates the competing role of this Cu2S nanosystem in future cancer treatment strategies.A size and shape tuned, multifunctional metal chalcogenide, Cu2S-based nanotheranostic agent is developed for trimodal imaging and multimodal therapeutics against brain cancer cells. This theranostic agent was highly efficient in optical, photoacoustic and X-ray contrast imaging systems. The folate targeted NIR-responsive photothermal ablation in synergism with the chemotherapeutic action of doxorubicin proved to be a rapid precision guided cancer-killing module. The multi-stimuli, i.e., pH-, thermo- and photo-responsive drug release behavior of the nanoconjugates opens up a wider corridor for on-demand triggered drug administration. The simple synthesis protocol, combined with the multitudes of interesting features packed into a single nanoformulation, clearly demonstrates the competing role of this Cu2S nanosystem in future cancer treatment strategies. Electronic supplementary information (ESI) available: Methodology and additional experimental results. See DOI: 10.1039/c4nr07139e

Multi-issue Agent Negotiation Based on Fairness

NASA Astrophysics Data System (ADS)

Zuo, Baohe; Zheng, Sue; Wu, Hong

Agent-based e-commerce service has become a hotspot now. How to make the agent negotiation process quickly and high-efficiently is the main research direction of this area. In the multi-issue model, MAUT(Multi-attribute Utility Theory) or its derived theory usually consider little about the fairness of both negotiators. This work presents a general model of agent negotiation which considered the satisfaction of both negotiators via autonomous learning. The model can evaluate offers from the opponent agent based on the satisfaction degree, learn online to get the opponent's knowledge from interactive instances of history and negotiation of this time, make concessions dynamically based on fair object. Through building the optimal negotiation model, the bilateral negotiation achieved a higher efficiency and fairer deal.

Agent Model Development for Assessing Climate-Induced Geopolitical Instability.

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

Boslough, Mark B.; Backus, George A.

2005-12-01

We present the initial stages of development of new agent-based computational methods to generate and test hypotheses about linkages between environmental change and international instability. This report summarizes the first year's effort of an originally proposed three-year Laboratory Directed Research and Development (LDRD) project. The preliminary work focused on a set of simple agent-based models and benefited from lessons learned in previous related projects and case studies of human response to climate change and environmental scarcity. Our approach was to define a qualitative model using extremely simple cellular agent models akin to Lovelock's Daisyworld and Schelling's segregation model. Such modelsmore » do not require significant computing resources, and users can modify behavior rules to gain insights. One of the difficulties in agent-based modeling is finding the right balance between model simplicity and real-world representation. Our approach was to keep agent behaviors as simple as possible during the development stage (described herein) and to ground them with a realistic geospatial Earth system model in subsequent years. This work is directed toward incorporating projected climate data--including various C02 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report--and ultimately toward coupling a useful agent-based model to a general circulation model.3« less

Patterns of Use of an Agent-Based Model and a System Dynamics Model: The Application of Patterns of Use and the Impacts on Learning Outcomes

ERIC Educational Resources Information Center

Thompson, Kate; Reimann, Peter

2010-01-01

A classification system that was developed for the use of agent-based models was applied to strategies used by school-aged students to interrogate an agent-based model and a system dynamics model. These were compared, and relationships between learning outcomes and the strategies used were also analysed. It was found that the classification system…

Companion diagnostics for the targeted therapy of gastric cancer.

PubMed

Yoo, Changhoon; Park, Young Soo

2015-10-21

Gastric cancer is the fourth most common type of cancer and represents a major cause of cancer-related deaths worldwide. With recent biomedical advances in our understanding of the molecular characteristics of gastric cancer, many genetic alterations have been identified as potential targets for its treatment. Multiple novel agents are currently under development as the demand for active agents that improve the survival of gastric cancer patients constantly increases. Based on lessons from previous trials of targeted agents, it is now widely accepted that the establishment of an optimal diagnostic test to select molecularly defined patients is of equal importance to the development of active agents against targetable genetic alterations. Herein, we highlight the current status and future perspectives of companion diagnostics in the treatment of gastric cancer.

Modeling and simulation of consumer response to dynamic pricing.

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

Valenzuela, J.; Thimmapuram, P.; Kim, J

2012-08-01

Assessing the impacts of dynamic-pricing under the smart grid concept is becoming extremely important for deciding its full deployment. In this paper, we develop a model that represents the response of consumers to dynamic pricing. In the model, consumers use forecasted day-ahead prices to shift daily energy consumption from hours when the price is expected to be high to hours when the price is expected to be low while maintaining the total energy consumption as unchanged. We integrate the consumer response model into the Electricity Market Complex Adaptive System (EMCAS). EMCAS is an agent-based model that simulates restructured electricity markets.more » We explore the impacts of dynamic-pricing on price spikes, peak demand, consumer energy bills, power supplier profits, and congestion costs. A simulation of an 11-node test network that includes eight generation companies and five aggregated consumers is performed for a period of 1 month. In addition, we simulate the Korean power system.« less

Evaluation of PersianCAT Agent's Accepting Policy in Continuous Double Auction, Participant in CAT 2007 Competition

NASA Astrophysics Data System (ADS)

Honari, Sina; Fos-Hati, Amin; Ebadi, Mojtaba; Gomrokchi, Maziar

As there is a growing tendency towards online auctions, online Multi Agent Systems for economic activities like stock exchanges is more in demand. CAT (CATallactics) competition has produced a great opportunity for researchers since 2007 to put their theories into practice in a real-time economic-based competition, combining traders and brokers. As one of participants, we evaluated our new accepting policy by putting it to challenge. In this paper we give a general overview of one of our policies in the market.

Acquisition of Autonomous Behaviors by Robotic Assistants

NASA Technical Reports Server (NTRS)

Peters, R. A., II; Sarkar, N.; Bodenheimer, R. E.; Brown, E.; Campbell, C.; Hambuchen, K.; Johnson, C.; Koku, A. B.; Nilas, P.; Peng, J.

2005-01-01

Our research achievements under the NASA-JSC grant contributed significantly in the following areas. Multi-agent based robot control architecture called the Intelligent Machine Architecture (IMA) : The Vanderbilt team received a Space Act Award for this research from NASA JSC in October 2004. Cognitive Control and the Self Agent : Cognitive control in human is the ability to consciously manipulate thoughts and behaviors using attention to deal with conflicting goals and demands. We have been updating the IMA Self Agent towards this goal. If opportunity arises, we would like to work with NASA to empower Robonaut to do cognitive control. Applications 1. SES for Robonaut, 2. Robonaut Fault Diagnostic System, 3. ISAC Behavior Generation and Learning, 4. Segway Research.

A Coupled Simulation Architecture for Agent-Based/Geohydrological Modelling

NASA Astrophysics Data System (ADS)

Jaxa-Rozen, M.

2016-12-01

The quantitative modelling of social-ecological systems can provide useful insights into the interplay between social and environmental processes, and their impact on emergent system dynamics. However, such models should acknowledge the complexity and uncertainty of both of the underlying subsystems. For instance, the agent-based models which are increasingly popular for groundwater management studies can be made more useful by directly accounting for the hydrological processes which drive environmental outcomes. Conversely, conventional environmental models can benefit from an agent-based depiction of the feedbacks and heuristics which influence the decisions of groundwater users. From this perspective, this work describes a Python-based software architecture which couples the popular NetLogo agent-based platform with the MODFLOW/SEAWAT geohydrological modelling environment. This approach enables users to implement agent-based models in NetLogo's user-friendly platform, while benefiting from the full capabilities of MODFLOW/SEAWAT packages or reusing existing geohydrological models. The software architecture is based on the pyNetLogo connector, which provides an interface between the NetLogo agent-based modelling software and the Python programming language. This functionality is then extended and combined with Python's object-oriented features, to design a simulation architecture which couples NetLogo with MODFLOW/SEAWAT through the FloPy library (Bakker et al., 2016). The Python programming language also provides access to a range of external packages which can be used for testing and analysing the coupled models, which is illustrated for an application of Aquifer Thermal Energy Storage (ATES).

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach.

PubMed

Laghari, Samreen; Niazi, Muaz A

2016-01-01

Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.

The Science of Transportation Analysis and Simulation

NASA Astrophysics Data System (ADS)

Gleibe, John

2010-03-01

Transportation Science focuses on methods developed to model and analyze the interaction between human behavior and transportation systems. From the human behavioral, or demand, perspective, we are interested in how person and households organize their activities across space and time, with travel viewed as an enabling activity. We have a particular interest in how to model the range of responses to public policy and transportation system changes, which leads to the consideration of both short- and long-term decision-making, interpersonal dependencies, and non-transportation-related opportunities and constraints, including household budgets, land use systems and economic systems. This has led to the development of complex structural econometric modeling systems as well as agent-based simulations. From the transportation systems, or supply, perspective we are interested in the level of service provide by transportation facilities, be it auto, transit or multi-modal systems. This has led to the development of network models and equilibrium concepts as well as hybrid simulation systems based on concepts borrowed from physics, such as fluid flow models, and cellular automata-type models. In this presentation, we review a representative sample of these methods and their use in transportation planning and public policy analysis.

Derivation of Continuum Models from An Agent-based Cancer Model: Optimization and Sensitivity Analysis.

PubMed

Voulgarelis, Dimitrios; Velayudhan, Ajoy; Smith, Frank

2017-01-01

Agent-based models provide a formidable tool for exploring complex and emergent behaviour of biological systems as well as accurate results but with the drawback of needing a lot of computational power and time for subsequent analysis. On the other hand, equation-based models can more easily be used for complex analysis in a much shorter timescale. This paper formulates an ordinary differential equations and stochastic differential equations model to capture the behaviour of an existing agent-based model of tumour cell reprogramming and applies it to optimization of possible treatment as well as dosage sensitivity analysis. For certain values of the parameter space a close match between the equation-based and agent-based models is achieved. The need for division of labour between the two approaches is explored. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

a Simulation-As Framework Facilitating Webgis Based Installation Planning

NASA Astrophysics Data System (ADS)

Zheng, Z.; Chang, Z. Y.; Fei, Y. F.

2017-09-01

Installation Planning is constrained by both natural and social conditions, especially for spatially sparse but functionally connected facilities. Simulation is important for proper deploy in space and configuration in function of facilities to make them a cohesive and supportive system to meet users' operation needs. Based on requirement analysis, we propose a framework to combine GIS and Agent simulation to overcome the shortness in temporal analysis and task simulation of traditional GIS. In this framework, Agent based simulation runs as a service on the server, exposes basic simulation functions, such as scenario configuration, simulation control, and simulation data retrieval to installation planners. At the same time, the simulation service is able to utilize various kinds of geoprocessing services in Agents' process logic to make sophisticated spatial inferences and analysis. This simulation-as-a-service framework has many potential benefits, such as easy-to-use, on-demand, shared understanding, and boosted performances. At the end, we present a preliminary implement of this concept using ArcGIS javascript api 4.0 and ArcGIS for server, showing how trip planning and driving can be carried out by agents.

The demand control model and circadian saliva cortisol variations in a Swedish population based sample (The PART study)

PubMed Central

Alderling, Magnus; Theorell, Töres; de la Torre, Bartolomé; Lundberg, Ingvar

2006-01-01

Background Previous studies of the relationship between job strain and blood or saliva cortisol levels have been small and based on selected occupational groups. Our aim was to examine the association between job strain and saliva cortisol levels in a population-based study in which a number of potential confounders could be adjusted for. Methods The material derives from a population-based study in Stockholm on mental health and its potential determinants. Two data collections were performed three years apart with more than 8500 subjects responding to a questionnaire in both waves. In this paper our analyses are based on 529 individuals who held a job, participated in both waves as well as in an interview linked to the second wave. They gave saliva samples at awakening, half an hour later, at lunchtime and before going to bed on a weekday in close connection with the interview. Job control and job demands were assessed from the questionnaire in the second wave. Mixed models were used to analyse the association between the demand control model and saliva cortisol. Results Women in low strain jobs (high control and low demands) had significantly lower cortisol levels half an hour after awakening than women in high strain (low control and high demands), active (high control and high demands) or passive jobs (low control and low demands). There were no significant differences between the groups during other parts of the day and furthermore there was no difference between the job strain, active and passive groups. For men, no differences were found between demand control groups. Conclusion This population-based study, on a relatively large sample, weakly support the hypothesis that the demand control model is associated with saliva cortisol concentrations. PMID:17129377

Little by Little Does the Trick: Design and Construction of a Discrete Event Agent-Based Simulation Framework

DTIC Science & Technology

2007-12-01

model. Finally, we build a small agent-based model using the component architecture to demonstrate the library’s functionality. 15. NUMBER OF...and a Behavioral model. Finally, we build a small agent-based model using the component architecture to demonstrate the library’s functionality...prototypes an architectural design which is generalizable, reusable, and extensible. We have created an initial set of model elements that demonstrate

The fractional volatility model: An agent-based interpretation

NASA Astrophysics Data System (ADS)

Vilela Mendes, R.

2008-06-01

Based on the criteria of mathematical simplicity and consistency with empirical market data, a model with volatility driven by fractional noise has been constructed which provides a fairly accurate mathematical parametrization of the data. Here, some features of the model are reviewed and extended to account for leverage effects. Using agent-based models, one tries to find which agent strategies and (or) properties of the financial institutions might be responsible for the features of the fractional volatility model.

A place for agent-based models. Comment on "Statistical physics of crime: A review" by M.R. D'Orsogna and M. Perc

NASA Astrophysics Data System (ADS)

Barbaro, Alethea

2015-03-01

Agent-based models have been widely applied in theoretical ecology to explain migrations and other collective animal movements [2,5,8]. As D'Orsogna and Perc have expertly highlighted in [6], the recent emergence of crime modeling has opened another interesting avenue for mathematical investigation. The area of crime modeling is particularly suited to agent-based models, because these models offer a great deal of flexibility within the model and also ease of communication among criminologist, law enforcement and modelers.

Acting to gain information

NASA Technical Reports Server (NTRS)

Rosenchein, Stanley J.; Burns, J. Brian; Chapman, David; Kaelbling, Leslie P.; Kahn, Philip; Nishihara, H. Keith; Turk, Matthew

1993-01-01

This report is concerned with agents that act to gain information. In previous work, we developed agent models combining qualitative modeling with real-time control. That work, however, focused primarily on actions that affect physical states of the environment. The current study extends that work by explicitly considering problems of active information-gathering and by exploring specialized aspects of information-gathering in computational perception, learning, and language. In our theoretical investigations, we analyzed agents into their perceptual and action components and identified these with elements of a state-machine model of control. The mathematical properties of each was developed in isolation and interactions were then studied. We considered the complexity dimension and the uncertainty dimension and related these to intelligent-agent design issues. We also explored active information gathering in visual processing. Working within the active vision paradigm, we developed a concept of 'minimal meaningful measurements' suitable for demand-driven vision. We then developed and tested an architecture for ongoing recognition and interpretation of visual information. In the area of information gathering through learning, we explored techniques for coping with combinatorial complexity. We also explored information gathering through explicit linguistic action by considering the nature of conversational rules, coordination, and situated communication behavior.

Examination of simplified travel demand model. [Internal volume forecasting model

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

Smith, R.L. Jr.; McFarlane, W.J.

1978-01-01

A simplified travel demand model, the Internal Volume Forecasting (IVF) model, proposed by Low in 1972 is evaluated as an alternative to the conventional urban travel demand modeling process. The calibration of the IVF model for a county-level study area in Central Wisconsin results in what appears to be a reasonable model; however, analysis of the structure of the model reveals two primary mis-specifications. Correction of the mis-specifications leads to a simplified gravity model version of the conventional urban travel demand models. Application of the original IVF model to ''forecast'' 1960 traffic volumes based on the model calibrated for 1970more » produces accurate estimates. Shortcut and ad hoc models may appear to provide reasonable results in both the base and horizon years; however, as shown by the IVF mode, such models will not always provide a reliable basis for transportation planning and investment decisions.« less

Body Size as a Driver of Scavenging in Theropod Dinosaurs.

PubMed

Kane, Adam; Healy, Kevin; Ruxton, Graeme D; Jackson, Andrew L

2016-06-01

Theropod dinosaurs dominated Earth's terrestrial ecosystem as a diverse group of predators for more than 160 million years, yet little is known about their foraging ecology. Maintaining a balanced energy budget presented a major challenge for therapods, which ranged from the chicken-sized Microraptor up to the whale-sized Giganotosaurus, in the face of intense competition and the demands of ontogenetic growth. Facultative scavenging, a behavior present in almost all modern predators, may have been important in supplementing energetically expensive lifestyles. By using agent-based models based on the allometric relationship between size and foraging behaviors, we show that theropods between 27 and 1,044 kg would have gained a significant energetic advantage over individuals at both the small and large extremes of theropod body mass through their scavenging efficiency. These results were robust to rate of competition, primary productivity, and detection distance. Our models demonstrate the potential importance of facultative scavenging in theropods and the role of body size in defining its prevalence in Mesozoic terrestrial systems.

Multiscale Modeling of Angiogenesis and Predictive Capacity

NASA Astrophysics Data System (ADS)

Pillay, Samara; Byrne, Helen; Maini, Philip

Tumors induce the growth of new blood vessels from existing vasculature through angiogenesis. Using an agent-based approach, we model the behavior of individual endothelial cells during angiogenesis. We incorporate crowding effects through volume exclusion, motility of cells through biased random walks, and include birth and death-like processes. We use the transition probabilities associated with the discrete model and a discrete conservation equation for cell occupancy to determine collective cell behavior, in terms of partial differential equations (PDEs). We derive three PDE models incorporating single, multi-species and no volume exclusion. By fitting the parameters in our PDE models and other well-established continuum models to agent-based simulations during a specific time period, and then comparing the outputs from the PDE models and agent-based model at later times, we aim to determine how well the PDE models predict the future behavior of the agent-based model. We also determine whether predictions differ across PDE models and the significance of those differences. This may impact drug development strategies based on PDE models.

Development and verification of an agent-based model of opinion leadership.

PubMed

Anderson, Christine A; Titler, Marita G

2014-09-27

The use of opinion leaders is a strategy used to speed the process of translating research into practice. Much is still unknown about opinion leader attributes and activities and the context in which they are most effective. Agent-based modeling is a methodological tool that enables demonstration of the interactive and dynamic effects of individuals and their behaviors on other individuals in the environment. The purpose of this study was to develop and test an agent-based model of opinion leadership. The details of the design and verification of the model are presented. The agent-based model was developed by using a software development platform to translate an underlying conceptual model of opinion leadership into a computer model. Individual agent attributes (for example, motives and credibility) and behaviors (seeking or providing an opinion) were specified as variables in the model in the context of a fictitious patient care unit. The verification process was designed to test whether or not the agent-based model was capable of reproducing the conditions of the preliminary conceptual model. The verification methods included iterative programmatic testing ('debugging') and exploratory analysis of simulated data obtained from execution of the model. The simulation tests included a parameter sweep, in which the model input variables were adjusted systematically followed by an individual time series experiment. Statistical analysis of model output for the 288 possible simulation scenarios in the parameter sweep revealed that the agent-based model was performing, consistent with the posited relationships in the underlying model. Nurse opinion leaders act on the strength of their beliefs and as a result, become an opinion resource for their uncertain colleagues, depending on their perceived credibility. Over time, some nurses consistently act as this type of resource and have the potential to emerge as opinion leaders in a context where uncertainty exists. The development and testing of agent-based models is an iterative process. The opinion leader model presented here provides a basic structure for continued model development, ongoing verification, and the establishment of validation procedures, including empirical data collection.

Tissue oxygen demand in regulation of the behavior of the cells in the vasculature.

PubMed

Barvitenko, Nadezhda N; Aslam, Muhammad; Filosa, Jessica; Matteucci, Elena; Nikinmaa, Mikko; Pantaleo, Antonella; Saldanha, Carlota; Baskurt, Oguz K

2013-08-01

The control of arteriolar diameters in microvasculature has been in the focus of studies on mechanisms matching oxygen demand and supply at the tissue level. Functionally, important vascular elements include EC, VSMC, and RBC. Integration of these different cell types into functional units aimed at matching tissue oxygen supply with tissue oxygen demand is only achieved when all these cells can respond to the signals of tissue oxygen demand. Many vasoactive agents that serve as signals of tissue oxygen demand have their receptors on all these types of cells (VSMC, EC, and RBC) implying that there can be a coordinated regulation of their behavior by the tissue oxygen demand. Such functions of RBC as oxygen carrying by Hb, rheology, and release of vasoactive agents are considered. Several common extra- and intracellular signaling pathways that link tissue oxygen demand with control of VSMC contractility, EC permeability, and RBC functioning are discussed. © 2013 John Wiley & Sons Ltd.

The Effect of the Demand Control and Effort Reward Imbalance Models on the Academic Burnout of Korean Adolescents

ERIC Educational Resources Information Center

Lee, Jayoung; Puig, Ana; Lee, Sang Min

2012-01-01

The purpose of this study was to examine the effects of the Demand Control Model (DCM) and the Effort Reward Imbalance Model (ERIM) on academic burnout for Korean students. Specifically, this study identified the effects of the predictor variables based on DCM and ERIM (i.e., demand, control, effort, reward, Demand Control Ratio, Effort Reward…

Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study

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

Sukumar, Sreenivas R; Nutaro, James J

This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigmmore » to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.« less

Demand forecast model based on CRM

NASA Astrophysics Data System (ADS)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

Situation awareness-based agent transparency for human-autonomy teaming effectiveness

NASA Astrophysics Data System (ADS)

Chen, Jessie Y. C.; Barnes, Michael J.; Wright, Julia L.; Stowers, Kimberly; Lakhmani, Shan G.

2017-05-01

We developed the Situation awareness-based Agent Transparency (SAT) model to support human operators' situation awareness of the mission environment through teaming with intelligent agents. The model includes the agent's current actions and plans (Level 1), its reasoning process (Level 2), and its projection of future outcomes (Level 3). Human-inthe-loop simulation experiments have been conducted (Autonomous Squad Member and IMPACT) to illustrate the utility of the model for human-autonomy team interface designs. Across studies, the results consistently showed that human operators' task performance improved as the agents became more transparent. They also perceived transparent agents as more trustworthy.

Materials Chemistry of Nanoultrasonic Biomedicine.

PubMed

Tang, Hailin; Zheng, Yuanyi; Chen, Yu

2017-03-01

As a special cross-disciplinary research frontier, nanoultrasonic biomedicine refers to the design and synthesis of nanomaterials to solve some critical issues of ultrasound (US)-based biomedicine. The concept of nanoultrasonic biomedicine can also overcome the drawbacks of traditional microbubbles and promote the generation of novel US-based contrast agents or synergistic agents for US theranostics. Here, we discuss the recent developments of material chemistry in advancing the nanoultrasonic biomedicine for diverse US-based bio-applications. We initially introduce the design principles of novel nanoplatforms for serving the nanoultrasonic biomedicine, from the viewpoint of synthetic material chemistry. Based on these principles and diverse US-based bio-application backgrounds, the representative proof-of-concept paradigms on this topic are clarified in detail, including nanodroplet vaporization for intelligent/responsive US imaging, multifunctional nano-contrast agents for US-based multi-modality imaging, activatable synergistic agents for US-based therapy, US-triggered on-demand drug releasing, US-enhanced gene transfection, US-based synergistic therapy on combating the cancer and potential toxicity issue of screening various nanosystems suitable for nanoultrasonic biomedicine. It is highly expected that this novel nanoultrasonic biomedicine and corresponding high performance in US imaging and therapy can significantly promote the generation of new sub-discipline of US-based biomedicine by rationally integrating material chemistry and theranostic nanomedicine with clinical US-based biomedicine. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Agent-based modeling as a tool for program design and evaluation.

PubMed

Lawlor, Jennifer A; McGirr, Sara

2017-12-01

Recently, systems thinking and systems science approaches have gained popularity in the field of evaluation; however, there has been relatively little exploration of how evaluators could use quantitative tools to assist in the implementation of systems approaches therein. The purpose of this paper is to explore potential uses of one such quantitative tool, agent-based modeling, in evaluation practice. To this end, we define agent-based modeling and offer potential uses for it in typical evaluation activities, including: engaging stakeholders, selecting an intervention, modeling program theory, setting performance targets, and interpreting evaluation results. We provide demonstrative examples from published agent-based modeling efforts both inside and outside the field of evaluation for each of the evaluative activities discussed. We further describe potential pitfalls of this tool and offer cautions for evaluators who may chose to implement it in their practice. Finally, the article concludes with a discussion of the future of agent-based modeling in evaluation practice and a call for more formal exploration of this tool as well as other approaches to simulation modeling in the field. Copyright © 2017 Elsevier Ltd. All rights reserved.

Agent Based Modeling of Collaboration and Work Practices Onboard the International Space Station

NASA Technical Reports Server (NTRS)

Acquisti, Alessandro; Sierhuis, Maarten; Clancey, William J.; Bradshaw, Jeffrey M.; Shaffo, Mike (Technical Monitor)

2002-01-01

The International Space Station is one the most complex projects ever, with numerous interdependent constraints affecting productivity and crew safety. This requires planning years before crew expeditions, and the use of sophisticated scheduling tools. Human work practices, however, are difficult to study and represent within traditional planning tools. We present an agent-based model and simulation of the activities and work practices of astronauts onboard the ISS based on an agent-oriented approach. The model represents 'a day in the life' of the ISS crew and is developed in Brahms, an agent-oriented, activity-based language used to model knowledge in situated action and learning in human activities.

Ensuring congruency in multiscale modeling: towards linking agent based and continuum biomechanical models of arterial adaptation.

PubMed

Hayenga, Heather N; Thorne, Bryan C; Peirce, Shayn M; Humphrey, Jay D

2011-11-01

There is a need to develop multiscale models of vascular adaptations to understand tissue-level manifestations of cellular level mechanisms. Continuum-based biomechanical models are well suited for relating blood pressures and flows to stress-mediated changes in geometry and properties, but less so for describing underlying mechanobiological processes. Discrete stochastic agent-based models are well suited for representing biological processes at a cellular level, but not for describing tissue-level mechanical changes. We present here a conceptually new approach to facilitate the coupling of continuum and agent-based models. Because of ubiquitous limitations in both the tissue- and cell-level data from which one derives constitutive relations for continuum models and rule-sets for agent-based models, we suggest that model verification should enforce congruency across scales. That is, multiscale model parameters initially determined from data sets representing different scales should be refined, when possible, to ensure that common outputs are consistent. Potential advantages of this approach are illustrated by comparing simulated aortic responses to a sustained increase in blood pressure predicted by continuum and agent-based models both before and after instituting a genetic algorithm to refine 16 objectively bounded model parameters. We show that congruency-based parameter refinement not only yielded increased consistency across scales, it also yielded predictions that are closer to in vivo observations.

Agent-Based Model Approach to Complex Phenomena in Real Economy

NASA Astrophysics Data System (ADS)

Iyetomi, H.; Aoyama, H.; Fujiwara, Y.; Ikeda, Y.; Souma, W.

An agent-based model for firms' dynamics is developed. The model consists of firm agents with identical characteristic parameters and a bank agent. Dynamics of those agents are described by their balance sheets. Each firm tries to maximize its expected profit with possible risks in market. Infinite growth of a firm directed by the ``profit maximization" principle is suppressed by a concept of ``going concern". Possibility of bankruptcy of firms is also introduced by incorporating a retardation effect of information on firms' decision. The firms, mutually interacting through the monopolistic bank, become heterogeneous in the course of temporal evolution. Statistical properties of firms' dynamics obtained by simulations based on the model are discussed in light of observations in the real economy.

Integrating adaptive behaviour in large-scale flood risk assessments: an Agent-Based Modelling approach

NASA Astrophysics Data System (ADS)

Haer, Toon; Aerts, Jeroen

2015-04-01

Between 1998 and 2009, Europe suffered over 213 major damaging floods, causing 1126 deaths, displacing around half a million people. In this period, floods caused at least 52 billion euro in insured economic losses making floods the most costly natural hazard faced in Europe. In many low-lying areas, the main strategy to cope with floods is to reduce the risk of the hazard through flood defence structures, like dikes and levees. However, it is suggested that part of the responsibility for flood protection needs to shift to households and businesses in areas at risk, and that governments and insurers can effectively stimulate the implementation of individual protective measures. However, adaptive behaviour towards flood risk reduction and the interaction between the government, insurers, and individuals has hardly been studied in large-scale flood risk assessments. In this study, an European Agent-Based Model is developed including agent representatives for the administrative stakeholders of European Member states, insurers and reinsurers markets, and individuals following complex behaviour models. The Agent-Based Modelling approach allows for an in-depth analysis of the interaction between heterogeneous autonomous agents and the resulting (non-)adaptive behaviour. Existing flood damage models are part of the European Agent-Based Model to allow for a dynamic response of both the agents and the environment to changing flood risk and protective efforts. By following an Agent-Based Modelling approach this study is a first contribution to overcome the limitations of traditional large-scale flood risk models in which the influence of individual adaptive behaviour towards flood risk reduction is often lacking.

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

PubMed Central

2016-01-01

Background Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach. PMID:26812235

DAMS: A Model to Assess Domino Effects by Using Agent-Based Modeling and Simulation.

PubMed

Zhang, Laobing; Landucci, Gabriele; Reniers, Genserik; Khakzad, Nima; Zhou, Jianfeng

2017-12-19

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases. © 2017 Society for Risk Analysis.

A physical data model for fields and agents

NASA Astrophysics Data System (ADS)

de Jong, Kor; de Bakker, Merijn; Karssenberg, Derek

2016-04-01

Two approaches exist in simulation modeling: agent-based and field-based modeling. In agent-based (or individual-based) simulation modeling, the entities representing the system's state are represented by objects, which are bounded in space and time. Individual objects, like an animal, a house, or a more abstract entity like a country's economy, have properties representing their state. In an agent-based model this state is manipulated. In field-based modeling, the entities representing the system's state are represented by fields. Fields capture the state of a continuous property within a spatial extent, examples of which are elevation, atmospheric pressure, and water flow velocity. With respect to the technology used to create these models, the domains of agent-based and field-based modeling have often been separate worlds. In environmental modeling, widely used logical data models include feature data models for point, line and polygon objects, and the raster data model for fields. Simulation models are often either agent-based or field-based, even though the modeled system might contain both entities that are better represented by individuals and entities that are better represented by fields. We think that the reason for this dichotomy in kinds of models might be that the traditional object and field data models underlying those models are relatively low level. We have developed a higher level conceptual data model for representing both non-spatial and spatial objects, and spatial fields (De Bakker et al. 2016). Based on this conceptual data model we designed a logical and physical data model for representing many kinds of data, including the kinds used in earth system modeling (e.g. hydrological and ecological models). The goal of this work is to be able to create high level code and tools for the creation of models in which entities are representable by both objects and fields. Our conceptual data model is capable of representing the traditional feature data models and the raster data model, among many other data models. Our physical data model is capable of storing a first set of kinds of data, like omnipresent scalars, mobile spatio-temporal points and property values, and spatio-temporal rasters. With our poster we will provide an overview of the physical data model expressed in HDF5 and show examples of how it can be used to capture both object- and field-based information. References De Bakker, M, K. de Jong, D. Karssenberg. 2016. A conceptual data model and language for fields and agents. European Geosciences Union, EGU General Assembly, 2016, Vienna.

When Does Model-Based Control Pay Off?

PubMed

Kool, Wouter; Cushman, Fiery A; Gershman, Samuel J

2016-08-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

NASA Technical Reports Server (NTRS)

Viken, Jeff; Dollyhigh, Samuel; Smith, Jeremy; Trani, Antonio; Baik, Hojong; Hinze, Nicholas; Ashiabor, Senanu

2006-01-01

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS

NASA Technical Reports Server (NTRS)

Viken, Jeff; Dollyhigh, Samuel; Smith, Jeremy; Trani, Antonio; Baik, Hojong; Hinze, Nicholas; Ashiabor, Senanu

2006-01-01

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth.

PubMed

Poleszczuk, Jan; Macklin, Paul; Enderling, Heiko

2016-01-01

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

The predictive power of zero intelligence in financial markets

PubMed Central

Farmer, J. Doyne; Patelli, Paolo; Zovko, Ilija I.

2005-01-01

Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where constraints imposed by market institutions dominate strategic agent behavior. We use data from the London Stock Exchange to test a simple model in which minimally intelligent agents place orders to trade at random. The model treats the statistical mechanics of order placement, price formation, and the accumulation of revealed supply and demand within the context of the continuous double auction and yields simple laws relating order-arrival rates to statistical properties of the market. We test the validity of these laws in explaining cross-sectional variation for 11 stocks. The model explains 96% of the variance of the gap between the best buying and selling prices (the spread) and 76% of the variance of the price diffusion rate, with only one free parameter. We also study the market impact function, describing the response of quoted prices to the arrival of new orders. The nondimensional coordinates dictated by the model approximately collapse data from different stocks onto a single curve. This work is important from a practical point of view, because it demonstrates the existence of simple laws relating prices to order flows and, in a broader context, suggests there are circumstances where the strategic behavior of agents may be dominated by other considerations. PMID:15687505

Let's Move! School Psychologists as Change Agents in the Domain of School-Based Physical Activity

ERIC Educational Resources Information Center

Fedewa, Alicia L.; Clark, Teresa P.

2010-01-01

Many of the students the authors have worked with see recess as a refuge from the multiple demands of school. Yet, how many school psychologists truly understand the multidimensional benefit of physical activity in schools? Increased physical activity has been associated with better physical health, improved mental health, and higher academic…

Model reduction for agent-based social simulation: coarse-graining a civil violence model.

PubMed

Zou, Yu; Fonoberov, Vladimir A; Fonoberova, Maria; Mezic, Igor; Kevrekidis, Ioannis G

2012-06-01

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).

Model reduction for agent-based social simulation: Coarse-graining a civil violence model

NASA Astrophysics Data System (ADS)

Zou, Yu; Fonoberov, Vladimir A.; Fonoberova, Maria; Mezic, Igor; Kevrekidis, Ioannis G.

2012-06-01

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).

Whose fault is it? Fraud scandal in the milk industry and its impact on product image and consumption - The case of Brazil.

PubMed

Breitenbach, Raquel; Rodrigues, Heber; Brandão, Janaína Balk

2018-06-01

Food safety is a crucial thematic to any nation and the frauds in the food chain are an increasingly phenomena nowadays. The general goal of the present work was to identify the perception and behavior of Brazilian fresh milk consumers' in relation to the fraud in the milk production and sell chain. A total of 1015 consumers answered an online questionnaire on the variables that shape decision-making (individual differences, environmental influences and psychological stimuli or processes) and the behavior related to this decision process. The results were discussed based on the theoretical model proposed by Blackwell, Miniard, and Engel (2005) and their 7 stages. The results of this qualitative study showed that the impacts were declared, specially, in the reduction of milk consumption; losses to the image of the sector and the warming of the informal milk market, due to the increase in demand in this trade. The reduction of demand and the depreciation of the agents' image, have a negative impact on the entire chain, since it reduces revenues for all agents and, to a greater degree, for processors and related suppliers/farmers directly with the brands identified in the investigation actions and disclosed in the media. The female consumers were more affected, impacting on a greater reduction of the consumption. Unlike young consumers, they have not changed their behavior in relation to the chain. Copyright © 2018 Elsevier Ltd. All rights reserved.

Persuasion Model and Its Evaluation Based on Positive Change Degree of Agent Emotion

NASA Astrophysics Data System (ADS)

Jinghua, Wu; Wenguang, Lu; Hailiang, Meng

For it can meet needs of negotiation among organizations take place in different time and place, and for it can make its course more rationality and result more ideal, persuasion based on agent can improve cooperation among organizations well. Integrated emotion change in agent persuasion can further bring agent advantage of artificial intelligence into play. Emotion of agent persuasion is classified, and the concept of positive change degree is given. Based on this, persuasion model based on positive change degree of agent emotion is constructed, which is explained clearly through an example. Finally, the method of relative evaluation is given, which is also verified through a calculation example.

Habits, action sequences, and reinforcement learning

PubMed Central

Dezfouli, Amir; Balleine, Bernard W.

2012-01-01

It is now widely accepted that instrumental actions can be either goal-directed or habitual; whereas the former are rapidly acquire and regulated by their outcome, the latter are reflexive, elicited by antecedent stimuli rather than their consequences. Model-based reinforcement learning (RL) provides an elegant description of goal-directed action. Through exposure to states, actions and rewards, the agent rapidly constructs a model of the world and can choose an appropriate action based on quite abstract changes in environmental and evaluative demands. This model is powerful but has a problem explaining the development of habitual actions. To account for habits, theorists have argued that another action controller is required, called model-free RL, that does not form a model of the world but rather caches action values within states allowing a state to select an action based on its reward history rather than its consequences. Nevertheless, there are persistent problems with important predictions from the model; most notably the failure of model-free RL correctly to predict the insensitivity of habitual actions to changes in the action-reward contingency. Here, we suggest that introducing model-free RL in instrumental conditioning is unnecessary and demonstrate that reconceptualizing habits as action sequences allows model-based RL to be applied to both goal-directed and habitual actions in a manner consistent with what real animals do. This approach has significant implications for the way habits are currently investigated and generates new experimental predictions. PMID:22487034

Using Personalized Education to Take the Place of Standardized Education

ERIC Educational Resources Information Center

Gao, Pengyu

2014-01-01

Economic model has been greatly shifted from labor demanding to innovation demanding, which requires education system has to produce creative people. This paper illustrates how traditional education model accrued and developed based on satisfying the old economic model for labor demanding but did not meet the new social requirement for innovation…

Transactive Control of Commercial Buildings for Demand Response

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

Hao, He; Corbin, Charles D.; Kalsi, Karanjit

Transactive control is a type of distributed control strategy that uses market mechanism to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building Heating, Ventilation, and Air- Conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from Systems Engineering Building (SEB) located on our Pacific Northwest National Laboratory (PNNL) campus. We next present a transactive control market structure for commercial building HVAC system, and describe its agent bidding and market clearing strategies. Severalmore » case studies are performed in a simulation environment using Building Control Virtual Test Bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak clipping, load shifting, and strategic conservation for commercial building HVAC systems.« less

Advancement in integrin facilitated drug delivery.

PubMed

Arosio, Daniela; Casagrande, Cesare

2016-02-01

The research of integrin-targeted anticancer agents has recorded important advancements in ingenious design of delivery systems, based either on the prodrug approach, or on nanoparticle carriers, but for now, none of these has reached a clinical stage of development. Past work in this area has been extensively reviewed by us and others. Thus, the purpose and scope of the present review is to survey the advancement reported in the last 3years, with focus on innovative delivery systems that appear to afford openings for future developments. These systems exploit the labelling with conventional and novel integrin ligands for targeting the interface of cancer cells and of endothelial cells involved in cancer angiogenesis, with the proteins of the extracellular matrix, in the circulation, in tissues, and in tumour stroma, as the site of progression and metastatic evolution of the disease. Furthermore, these systems implement the expertise in the development of nanomedicines to the purpose of achieving preferential biodistribution and uptake in cancer tissues, internalisation in cancer cells, and release of the transported drugs at intracellular sites. The assessment of the value of controlling these factors, and their combination, for future developments requires support of biological testing in appropriate mechanistic models, but also imperatively demand confirmation in therapeutically relevant in vivo models for biodistribution, efficacy, and lack of off-target effects. Thus, among many studies, we have tried to point out the results supported by relevant in vivo studies, and we have emphasised in specific sections those addressing the medical needs of drug delivery to brain tumours, as well as the delivery of oligonucleotides modulating gene-dependent pathological mechanism. The latter could constitute the basis of a promising third branch in the therapeutic armamentarium against cancer, in addition to antibody-based agents and to cytotoxic agents. Copyright © 2015 Elsevier B.V. All rights reserved.

Integration agent-based models and GIS as a virtual urban dynamic laboratory

NASA Astrophysics Data System (ADS)

Chen, Peng; Liu, Miaolong

2007-06-01

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

NASA Astrophysics Data System (ADS)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets

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

Behboodi, Sahand; Chassin, David P.; Djilali, Ned

Coordinated operation of distributed thermostatic loads such as heat pumps and air conditioners can reduce energy costs and prevents grid congestion, while maintaining room temperatures in the comfort range set by consumers. This paper furthers efforts towards enabling thermostatically controlled loads (TCLs) to participate in real-time retail electricity markets under a transactive control paradigm. An agent-based approach is used to develop an effective and low complexity demand response control scheme for TCLs. The proposed scheme adjusts aggregated thermostatic loads according to real-time grid conditions under both heating and cooling modes. Here, a case study is presented showing the method reducesmore » consumer electricity costs by over 10% compared to uncoordinated operation.« less

Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets

DOE PAGES

Behboodi, Sahand; Chassin, David P.; Djilali, Ned; ...

2017-07-29

Coordinated operation of distributed thermostatic loads such as heat pumps and air conditioners can reduce energy costs and prevents grid congestion, while maintaining room temperatures in the comfort range set by consumers. This paper furthers efforts towards enabling thermostatically controlled loads (TCLs) to participate in real-time retail electricity markets under a transactive control paradigm. An agent-based approach is used to develop an effective and low complexity demand response control scheme for TCLs. The proposed scheme adjusts aggregated thermostatic loads according to real-time grid conditions under both heating and cooling modes. Here, a case study is presented showing the method reducesmore » consumer electricity costs by over 10% compared to uncoordinated operation.« less

Enhanced Collaboration for Space Situational Awareness via Proxy Agents

NASA Astrophysics Data System (ADS)

Picciano, P.; Schurr, N.

2012-09-01

The call for dynamic partnerships demanded in the US. Space Policy confronts two formidable challenges. The first is evident in the lack of the adoption of technical innovations that could substantially enhance collaboration. The second category, and perhaps a greater impediment, involves organizational and social constraints that minimize information sharing. Compounding the technical challenges, the organizational barriers to collaboration present a different problem set. There is a culture in the space domain that predisposes most stakeholders to guard their information. Most owner/operators are reluctant to share asset data, whether experiencing an anomaly or just providing status updates. This is unfortunate, because the owner/operators generally have the most accurate and timely data pertaining to their satellite. Comprehensive Space Situational Awareness (SSA) requires the marshaling of disparate mission critical elements. The mission threads reliant on SSA are complex and often require analysis from a diverse team of experts with sophisticated systems and tools that may be dispersed across multiple entities including military, commercial, and public interests. Two significant trends are likely to further perpetuate this state of affairs: 1) the space environment continues to be more congested, contested, and competitive, and 2) further pressures to increase SSA Sharing with a greater number of stakeholders throughout the world. The challenge of delivering the right information to the right people, while protecting national security and privacy interests, is in need of an innovative solution. Our approach, entitled Space Collaboration via an Agent Network (SCAN), enables proxy software agents to represent stakeholders (as individuals and organizations) to enhance collaboration among various agency producers and consumers of space information The SCAN agent network will facilitate collaboration by identifying opportunities to collaborate, as well as optimize the processes given the mission context. The agent-based approach is uniquely capable of addressing the collaboration challenges from both the technical and organizational perspectives. To achieve these objectives, we are employing a modeling approach based on a Markov decision process (MDP). MDPs are very general models for optimizing decisions under uncertainty. The model was chosen because it is able to represent the uncertainty in outcomes (such as loss of satellite communication or inability for a colleague to finish a task on time) as well as the associated values of decisions made and their resulting outcomes. The key aspect of MDPs is that it is a sequential model, not only does it represent uncertainty for single decisions and outcomes, but estimates the state of the world in the future and optimizes decisions based on these future expectations. The SCAN prototype will be used to assess modeling parameters and assumptions as well as collect user feedback.

Travel demand forecasting models: a comparison of EMME/2 and QUR II using a real-world network.

DOT National Transportation Integrated Search

2000-10-01

In order to automate the travel demand forecasting process in urban transportation planning, a number of : commercial computer based travel demand forecasting models have been developed, which have provided : transportation planners with powerful and...

An Agent-Based Model for Studying Child Maltreatment and Child Maltreatment Prevention

NASA Astrophysics Data System (ADS)

Hu, Xiaolin; Puddy, Richard W.

This paper presents an agent-based model that simulates the dynamics of child maltreatment and child maltreatment prevention. The developed model follows the principles of complex systems science and explicitly models a community and its families with multi-level factors and interconnections across the social ecology. This makes it possible to experiment how different factors and prevention strategies can affect the rate of child maltreatment. We present the background of this work and give an overview of the agent-based model and show some simulation results.

Agent-based models of cellular systems.

PubMed

Cannata, Nicola; Corradini, Flavio; Merelli, Emanuela; Tesei, Luca

2013-01-01

Software agents are particularly suitable for engineering models and simulations of cellular systems. In a very natural and intuitive manner, individual software components are therein delegated to reproduce "in silico" the behavior of individual components of alive systems at a given level of resolution. Individuals' actions and interactions among individuals allow complex collective behavior to emerge. In this chapter we first introduce the readers to software agents and multi-agent systems, reviewing the evolution of agent-based modeling of biomolecular systems in the last decade. We then describe the main tools, platforms, and methodologies available for programming societies of agents, possibly profiting also of toolkits that do not require advanced programming skills.

When Does Model-Based Control Pay Off?

PubMed Central

2016-01-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand. PMID:27564094

B-tree search reinforcement learning for model based intelligent agent

NASA Astrophysics Data System (ADS)

Bhuvaneswari, S.; Vignashwaran, R.

2013-03-01

Agents trained by learning techniques provide a powerful approximation of active solutions for naive approaches. In this study using B - Trees implying reinforced learning the data search for information retrieval is moderated to achieve accuracy with minimum search time. The impact of variables and tactics applied in training are determined using reinforcement learning. Agents based on these techniques perform satisfactory baseline and act as finite agents based on the predetermined model against competitors from the course.

Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents

NASA Astrophysics Data System (ADS)

Rienow, Andreas; Stenger, Dirk

2014-07-01

The Ruhr is an "old acquaintance" in the discourse of urban decline in old industrialized cities. The agglomeration has to struggle with archetypical problems of former monofunctional manufacturing cities. Surprisingly, the image of a shrinking city has to be refuted if you shift the focus from socioeconomic wealth to its morphological extension. Thus, it is the objective of this study to meet the challenge of modeling urban sprawl and demographic decline by combining two artificial intelligent solutions: The popular urban cellular automaton SLEUTH simulates urban growth using four simple but effective growth rules. In order to improve its performance, SLEUTH has been modified among others by combining it with a robust probability map based on support vector machines. Additionally, a complex multi-agent system is developed to simulate residential mobility in a shrinking city agglomeration: residential mobility and the housing market of shrinking city systems focuses on the dynamic of interregional housing markets implying the development of potential dwelling areas. The multi-agent system comprises the simulation of population patterns, housing prices, and housing demand in shrinking city agglomerations. Both models are calibrated and validated regarding their localization and quantification performance. Subsequently, the urban landscape configuration and composition of the Ruhr 2025 are simulated. A simple spatial join is used to combine the results serving as valuable inputs for future regional planning in the context of multifarious demographic change and preceding urban growth.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications.

PubMed

Dinh, Thanh; Kim, Younghan; Lee, Hyukjoon

2017-03-01

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications †

PubMed Central

Dinh, Thanh; Kim, Younghan; Lee, Hyukjoon

2017-01-01

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model. PMID:28257067

Understanding Group/Party Affiliation Using Social Networks and Agent-Based Modeling

NASA Technical Reports Server (NTRS)

Campbell, Kenyth

2012-01-01

The dynamics of group affiliation and group dispersion is a concept that is most often studied in order for political candidates to better understand the most efficient way to conduct their campaigns. While political campaigning in the United States is a very hot topic that most politicians analyze and study, the concept of group/party affiliation presents its own area of study that producers very interesting results. One tool for examining party affiliation on a large scale is agent-based modeling (ABM), a paradigm in the modeling and simulation (M&S) field perfectly suited for aggregating individual behaviors to observe large swaths of a population. For this study agent based modeling was used in order to look at a community of agents and determine what factors can affect the group/party affiliation patterns that are present. In the agent-based model that was used for this experiment many factors were present but two main factors were used to determine the results. The results of this study show that it is possible to use agent-based modeling to explore group/party affiliation and construct a model that can mimic real world events. More importantly, the model in the study allows for the results found in a smaller community to be translated into larger experiments to determine if the results will remain present on a much larger scale.

Preparing Teachers for Schooling in the Digital Age: A Meta-Perspective on Existing Strategies and Future Challenges

ERIC Educational Resources Information Center

Erstad, Ola; Eickelmann, Birgit; Eichhorn, Koos

2015-01-01

Recent developments in educational innovation and new technologies have made tensions between old and new models of schooling more apparent, creating new demands upon teachers as agents of change. Looking back at the last 20 years, it is clear that important steps in development have tried to find a good balance between technology- and…

Emerging and investigational drugs for premature ejaculation

PubMed Central

2016-01-01

Over the past 20−30 years, the premature ejaculation (PE) treatment paradigm, previously limited to behavioural psychotherapy, has expanded to include drug treatment. Pharmacotherapy for PE predominantly targets the multiple neurotransmitters and receptors involved in the control of ejaculation which include serotonin, dopamine, oxytocin, norepinephrine, gamma amino-butyric acid (GABA) and nitric oxide (NO). The objective of this article is to review emerging PE interventions contemporary data on the treatment of PE was reviewed and critiqued using the principles of evidence-based medicine. Multiple well-controlled evidence-based studies have demonstrated the efficacy and safety of selective serotonin reuptake inhibitors (SSRIs) in delaying ejaculation, confirming their role as first-line agents for the medical treatment of lifelong and acquired PE. Daily dosing of SSRIs is likely to be associated with superior fold increases in IELT compared to on-demand SSRIs. On-demand SSRIs are less effective but may fulfill the treatment goals of many patients. Integrated pharmacotherapy and CBT may achieve superior treatment outcomes in some patients. PDE-5 inhibitors alone or in combination with SSRIs should be limited to men with acquired PE secondary to co-morbid ED. New on-demand rapid acting SSRIs, oxytocin receptor antagonists, or single agents that target multiple receptors may form the foundation of more effective future on-demand medication. Current evidence confirms the efficacy and safety of dapoxetine, off-label SSRI drugs, tramadol and topical anaesthetics drugs. Treatment with α1-adrenoceptor antagonists cannot be recommended until the results of large well-designed RCTs are published in major international peer-reviewed medical journals. As our understanding of the neurochemical control of ejaculation improves, new therapeutic targets and candidate molecules will be identified which may increase our pharmacotherapeutic armamentarium. PMID:27652222

Review of the systems biology of the immune system using agent-based models.

PubMed

Shinde, Snehal B; Kurhekar, Manish P

2018-06-01

The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.

LED and low level laser therapy association in tooth bleaching using a novel low concentration H2O2/N-doped TiO2 bleaching agent

NASA Astrophysics Data System (ADS)

Bezerra Dias, Hércules; Teixeira Carrera, Emanuelle; Freitas Bortolatto, Janaína; Ferrarezi de Andrade, Marcelo; Nara de Souza Rastelli, Alessandra

2016-01-01

Since low concentration bleaching agents containing N-doped TiO2 nanoparticles have been introduced as an alternative to conventional agents, it is important to verify their efficacy and the hypersensitivity effect in clinical practice. Six volunteer patients were evaluated for color change and hypersensitivity after bleaching using 35% H2O2 (one session of two 12 min applications) and 6% H2O2/N-doped TiO2 (one session of three 12 min applications) and after low level laser therapy application (LLLT) (780 nm, 40 mW, 10 J.cm-2, 10 s). Based on this case study, the nanobleaching agent provided better or similar aesthetic results than the conventional agent under high concentration, and its association with LLLT satisfactorily decreased the hypersensitivity. The 6% H2O2/N-doped TiO2 agent could be used instead of conventional in-office bleaching agents under high concentrations to fulfill the rising patient demand for aesthetics.

Health economics in haemophilia: a review from the clinician's perspective.

PubMed

Escobar, M A

2010-05-01

Health economic evaluations provide valuable information for healthcare providers, facilitating the treatment decision-making process in a climate where demand for healthcare exceeds the supply. Although an uncommon disease, haemophilia is a life-long condition that places a considerable burden on patients, healthcare systems and society. This burden is particularly large for patients with haemophilia with inhibitors, who can develop serious bleeding complications unresponsive to standard factor replacement therapies. Hence, bleeding episodes in these patients are treated with bypassing agents such as recombinant activated FVII (rFVIIa) and plasma-derived activated prothrombin complex concentrates (pd-APCC). With the efficacy of these agents now well established, a number of health economic studies have been conducted to compare their cost-effectiveness for the on-demand treatment of bleeding episodes in haemophiliacs with inhibitors. In a cost-utility analysis, which assesses the effects of treatment on quality of life (QoL) and quantity of life, the incremental cost per quality-adjusted life-year (QALY) gained (US $44,834) indicated that rFVIIa was cost-effective. Similarly, eight of 11 other economic modelling evaluations found that rFVIIa was more cost-effective than pd-APCC in the on-demand treatment of bleeding episodes. These findings indicate that treating patients with haemophilia promptly and with the most effective therapy available may result in cost savings.

Chronic Heart Failure Follow-up Management Based on Agent Technology.

PubMed

Mohammadzadeh, Niloofar; Safdari, Reza

2015-10-01

Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. This research was performed in 2013-2014 to determine appropriate scenarios and the data required to monitor and follow-up CHF patients, and then an agent-based model was designed. Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making.

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

PubMed Central

Snijders, Tom A.B.; Steglich, Christian E.G.

2014-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro-macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by micro-specifications of actor-based models. PMID:25960578

Scoping Planning Agents With Shared Models

NASA Technical Reports Server (NTRS)

Bedrax-Weiss, Tania; Frank, Jeremy D.; Jonsson, Ari K.; McGann, Conor

2003-01-01

In this paper we provide a formal framework to define the scope of planning agents based on a single declarative model. Having multiple agents sharing a single model provides numerous advantages that lead to reduced development costs and increase reliability of the system. We formally define planning in terms of extensions of an initial partial plan, and a set of flaws that make the plan unacceptable. A Flaw Filter (FF) allows us to identify those flaws relevant to an agent. Flaw filters motivate the Plan Identification Function (PIF), which specifies when an agent is is ready hand control to another agent for further work. PIFs define a set of plan extensions that can be generated from a model and a plan request. FFs and PIFs can be used to define the scope of agents without changing the model. We describe an implementation of PIFsand FFswithin the context of EUROPA, a constraint-based planning architecture, and show how it can be used to easily design many different agents.

An Agent-Based Data Mining System for Ontology Evolution

NASA Astrophysics Data System (ADS)

Hadzic, Maja; Dillon, Darshan

We have developed an evidence-based mental health ontological model that represents mental health in multiple dimensions. The ongoing addition of new mental health knowledge requires a continual update of the Mental Health Ontology. In this paper, we describe how the ontology evolution can be realized using a multi-agent system in combination with data mining algorithms. We use the TICSA methodology to design this multi-agent system which is composed of four different types of agents: Information agent, Data Warehouse agent, Data Mining agents and Ontology agent. We use UML 2.1 sequence diagrams to model the collaborative nature of the agents and a UML 2.1 composite structure diagram to model the structure of individual agents. The Mental Heath Ontology has the potential to underpin various mental health research experiments of a collaborative nature which are greatly needed in times of increasing mental distress and illness.

A novel medical information management and decision model for uncertain demand optimization.

PubMed

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.

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models.

PubMed

Ajelli, Marco; Gonçalves, Bruno; Balcan, Duygu; Colizza, Vittoria; Hu, Hao; Ramasco, José J; Merler, Stefano; Vespignani, Alessandro

2010-06-29

In recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used. We provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels. The results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, R0, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are obtained for the younger age classes. The good agreement between the two modeling approaches is very important for defining the tradeoff between data availability and the information provided by the models. The results we present define the possibility of hybrid models combining the agent-based and the metapopulation approaches according to the available data and computational resources.

Strategies to Enhance Online Learning Teams. Team Assessment and Diagnostics Instrument and Agent-based Modeling

DTIC Science & Technology

2010-08-12

Strategies to Enhance Online Learning Teams Team Assessment and Diagnostics Instrument and Agent-based Modeling Tristan E. Johnson, Ph.D. Learning ...REPORT DATE AUG 2010 2. REPORT TYPE 3. DATES COVERED 00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Strategies to Enhance Online Learning ...TeamsTeam Strategies to Enhance Online Learning Teams: Team Assessment and Diagnostics Instrument and Agent-based Modeling 5a. CONTRACT NUMBER 5b. GRANT

Agent-based modeling: Methods and techniques for simulating human systems

PubMed Central

Bonabeau, Eric

2002-01-01

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed. PMID:12011407

Agent based modeling of the coevolution of hostility and pacifism

NASA Astrophysics Data System (ADS)

Dalmagro, Fermin; Jimenez, Juan

2015-01-01

We propose a model based on a population of agents whose states represent either hostile or peaceful behavior. Randomly selected pairs of agents interact according to a variation of the Prisoners Dilemma game, and the probabilities that the agents behave aggressively or not are constantly updated by the model so that the agents that remain in the game are those with the highest fitness. We show that the population of agents oscillate between generalized conflict and global peace, without either reaching a stable state. We then use this model to explain some of the emergent behaviors in collective conflicts, by comparing the simulated results with empirical data obtained from social systems. In particular, using public data reports we show how the model precisely reproduces interesting quantitative characteristics of diverse types of armed conflicts, public protests, riots and strikes.

Agent-based Modeling with MATSim for Hazards Evacuation Planning

NASA Astrophysics Data System (ADS)

Jones, J. M.; Ng, P.; Henry, K.; Peters, J.; Wood, N. J.

2015-12-01

Hazard evacuation planning requires robust modeling tools and techniques, such as least cost distance or agent-based modeling, to gain an understanding of a community's potential to reach safety before event (e.g. tsunami) arrival. Least cost distance modeling provides a static view of the evacuation landscape with an estimate of travel times to safety from each location in the hazard space. With this information, practitioners can assess a community's overall ability for timely evacuation. More information may be needed if evacuee congestion creates bottlenecks in the flow patterns. Dynamic movement patterns are best explored with agent-based models that simulate movement of and interaction between individual agents as evacuees through the hazard space, reacting to potential congestion areas along the evacuation route. The multi-agent transport simulation model MATSim is an agent-based modeling framework that can be applied to hazard evacuation planning. Developed jointly by universities in Switzerland and Germany, MATSim is open-source software written in Java and freely available for modification or enhancement. We successfully used MATSim to illustrate tsunami evacuation challenges in two island communities in California, USA, that are impacted by limited escape routes. However, working with MATSim's data preparation, simulation, and visualization modules in an integrated development environment requires a significant investment of time to develop the software expertise to link the modules and run a simulation. To facilitate our evacuation research, we packaged the MATSim modules into a single application tailored to the needs of the hazards community. By exposing the modeling parameters of interest to researchers in an intuitive user interface and hiding the software complexities, we bring agent-based modeling closer to practitioners and provide access to the powerful visual and analytic information that this modeling can provide.

A new proposal for greenhouse gas emissions responsibility allocation: best available technologies approach.

PubMed

Berzosa, Álvaro; Barandica, Jesús M; Fernández-Sánchez, Gonzalo

2014-01-01

In recent years, several methodologies have been developed for the quantification of greenhouse gas (GHG) emissions. However, determining who is responsible for these emissions is also quite challenging. The most common approach is to assign emissions to the producer (based on the Kyoto Protocol), but proposals also exist for its allocation to the consumer (based on an ecological footprint perspective) and for a hybrid approach called shared responsibility. In this study, the existing proposals and standards regarding the allocation of GHG emissions responsibilities are analyzed, focusing on their main advantages and problems. A new model of shared responsibility that overcomes some of the existing problems is also proposed. This model is based on applying the best available technologies (BATs). This new approach allocates the responsibility between the producers and the final consumers based on the real capacity of each agent to reduce emissions. The proposed approach is demonstrated using a simple case study of a 4-step life cycle of ammonia nitrate (AN) fertilizer production. The proposed model has the characteristics that the standards and publications for assignment of GHG emissions responsibilities demand. This study presents a new way to assign responsibilities that pushes all the actors in the production chain, including consumers, to reduce pollution. © 2013 SETAC.

Operation of hydropower generation systems in the Alps under future climate and socio-economic drivers

NASA Astrophysics Data System (ADS)

Anghileri, Daniela; Castelletti, Andrea; Burlando, Paolo

2015-04-01

Alpine hydropower systems are an important source of renewable energy for many countries in Europe. In Switzerland, for instance, they represent the most important domestic source of renewable energy (around 55%). However, future hydropower production may be threatened by unprecedented challenges, such as a decreasing water availability, due to climate change (CC) and associated glacier retreat, and uncertain operating conditions, such as future power needs and highly fluctuating demand on the energy market. This second aspect has gained increasingly relevance since the massive introduction of solar and wind generating systems in the portfolios of many European countries. Because hydropower systems have the potential to provide backup storage of energy to compensate for fluctuations that are typical, for instance, of solar and wind generation systems, it is important to investigate how the increased demand for flexible operation, together with climate change challenge and fluctuating markets, can impact their operating policies. The Swiss Competence Center on Supply of Electricity (www.sccer-soe.ch) has been recently established to explore new potential paths for the development of future power generation systems. In this context, we develop modelling and optimization tools to design and assess new operation strategies for hydropower systems to increase their reliability, flexibility, and robustness to future operation conditions. In particular, we develop an advanced modelling framework for the integrated simulation of the operation of hydropower plants, which accounts for CC-altered streamflow regimes, new demand and market conditions, as well as new boundary conditions for operation (e.g., aquatic ecosystem conservation). The model construction consists of two primary components: a physically based and spatially distributed hydrological model, which describes the relevant hydrological processes at the basin scale, and an agent based decision model, which describes the behavior of hydropower operators. This integrated model allows to quantitatively explore possible trajectories of future evolution of the hydropower systems under the combined effect of climate and socio-economic drivers. In a multi-objective perspective, the model can test how different hydropower operation strategies perform in terms of power production, reliability and flexibility of supply, profitability of operation, and ecosystem conservation. This contribution presents the methodological framework designed to formulate the integrated model, its expected outcomes, and some preliminary results on a pilot study.

Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology

PubMed Central

Marshall, Brandon D. L.; Galea, Sandro

2015-01-01

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry. PMID:25480821

Peptide-based antibody alternatives for biological sensing in austere environments

NASA Astrophysics Data System (ADS)

Coppock, Matthew B.; Sarkes, Deborah A.; Hurley, Margaret M.; Stratis-Cullum, Dimitra N.

2017-02-01

The most critical component of a biosensor, the biorecognition element, must exhibit high selectivity and strong affinity for a target of interest in operational sensing. Monoclonal antibodies are the current standard reagents for such devices, but their adaptability, manufacturability, and stability greatly limit their effectiveness in fieldable sensors. Peptides have emerged as potential antibody replacements in such applications due to their similar binding performance, extreme chemical and thermal stabilities, and on-demand scalability. In conjunction with modeling capabilities, work at the Army Research Lab focuses on protein catalyzed capture (PCC) agent technology and bacterial display for the discovery of these novel peptide binding reagents. The synthetic, bottom-up PCC agent technology uses an iterative, in situ "click chemistry" approach to produce high performing peptides against specific epitopes translatable to the protein target. Bacterial display allows rapid reagent discovery due to the combination of fast bacterial growth and effective peptide sequence enrichment through multiple rounds of biopanning. Recent advances in both methods are highlighted in regards to the discovery of reagents against Army high priority protein targets for soldier safety, performance, and diagnostics.

Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities

PubMed Central

2009-01-01

Background This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. Methods As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. Results An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. Conclusion This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it. PMID:19922684

Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities.

PubMed

Borkowski, Maciej; Podaima, Blake W; McLeod, Robert D

2009-11-18

This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it.

Essays in the California electricity reserves markets

NASA Astrophysics Data System (ADS)

Metaxoglou, Konstantinos

This dissertation examines inefficiencies in the California electricity reserves markets. In Chapter 1, I use the information released during the investigation of the state's electricity crisis of 2000 and 2001 by the Federal Energy Regulatory Commission to diagnose allocative inefficiencies. Building upon the work of Wolak (2000), I calculate a lower bound for the sellers' price-cost margins using the inverse elasticities of their residual demand curves. The downward bias in my estimates stems from the fact that I don't account for the hierarchical substitutability of the reserve types. The margins averaged at least 20 percent for the two highest quality types of reserves, regulation and spinning, generating millions of dollars in transfers to a handful of sellers. I provide evidence that the deviations from marginal cost pricing were due to the markets' high concentration and a principal-agent relationship that emerged from their design. In Chapter 2, I document systematic differences between the markets' day- and hour-ahead prices. I use a high-dimensional vector moving average model to estimate the premia and conduct correct inferences. To obtain exact maximum likelihood estimates of the model, I employ the EM algorithm that I develop in Chapter 3. I uncover significant day-ahead premia, which I attribute to market design characteristics too. On the demand side, the market design established a principal-agent relationship between the markets' buyers (principal) and their supervisory authority (agent). The agent had very limited incentives to shift reserve purchases to the lower priced hour-ahead markets. On the supply side, the market design raised substantial entry barriers by precluding purely speculative trading and by introducing a complicated code of conduct that induced uncertainty about which actions were subject to regulatory scrutiny. In Chapter 3, I introduce a state-space representation for vector autoregressive moving average models that enables exact maximum likelihood estimation using the EM algorithm. Moreover, my algorithm uses only analytical expressions; it requires the Kalman filter and a fixed-interval smoother in the E step and least squares-type regression in the M step. In contrast, existing maximum likelihood estimation methods require numerical differentiation, both for univariate and multivariate models.

A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)

DTIC Science & Technology

Information Theory (IIT) of Consciousness , which proposes that the fundamental structural elements of consciousness are qualia. By modeling the...This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories

Critical capacity, travel time delays and travel time distribution of rapid mass transit systems

NASA Astrophysics Data System (ADS)

Legara, Erika Fille; Monterola, Christopher; Lee, Kee Khoon; Hung, Gih Guang

2014-07-01

We set up a mechanistic agent-based model of a rapid mass transit system. Using empirical data from Singapore's unidentifiable smart fare card, we validate our model by reconstructing actual travel demand and duration of travel statistics. We subsequently use this model to investigate two phenomena that are known to significantly affect the dynamics within the RTS: (1) overloading in trains and (2) overcrowding in the RTS platform. We demonstrate that by varying the loading capacity of trains, a tipping point emerges at which an exponential increase in the duration of travel time delays is observed. We also probe the impact on the rail system dynamics of three types of passenger growth distribution across stations: (i) Dirac delta, (ii) uniform and (iii) geometric, which is reminiscent of the effect of land use on transport. Under the assumption of a fixed loading capacity, we demonstrate the dependence of a given origin-destination (OD) pair on the flow volume of commuters in station platforms.

Modelling the perennial energy crop market: the role of spatial diffusion

PubMed Central

Alexander, Peter; Moran, Dominic; Rounsevell, Mark D. A.; Smith, Pete

2013-01-01

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies. PMID:24026474

Modelling the perennial energy crop market: the role of spatial diffusion.

PubMed

Alexander, Peter; Moran, Dominic; Rounsevell, Mark D A; Smith, Pete

2013-11-06

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies.

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review).

PubMed

Vizirianakis, Ioannis S; Mystridis, George A; Avgoustakis, Konstantinos; Fatouros, Dimitrios G; Spanakis, Marios

2016-04-01

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

A Multiagent Based Model for Tactical Planning

DTIC Science & Technology

2002-10-01

Pub. Co. 1985. [10] Castillo, J.M. Aproximación mediante procedimientos de Inteligencia Artificial al planeamiento táctico. Doctoral Thesis...been developed under the same conceptual model and using similar Artificial Intelligence Tools. We use four different stimulus/response agents in...The conceptual model is built on base of the Agents theory. To implement the different agents we have used Artificial Intelligence techniques such

Lapse of time effects on tax evasion in an agent-based econophysics model

NASA Astrophysics Data System (ADS)

Seibold, Götz; Pickhardt, Michael

2013-05-01

We investigate an inhomogeneous Ising model in the context of tax evasion dynamics where different types of agents are parameterized via local temperatures and magnetic fields. In particular, we analyze the impact of lapse of time effects (i.e. backauditing) and endogenously determined penalty rates on tax compliance. Both features contribute to a microfoundation of agent-based econophysics models of tax evasion.

A Multi-Level Approach to Modeling Rapidly Growing Mega-Regions as a Coupled Human-Natural System

NASA Astrophysics Data System (ADS)

Koch, J. A.; Tang, W.; Meentemeyer, R. K.

2013-12-01

The FUTure Urban-Regional Environment Simulation (FUTURES) integrates information on nonstationary drivers of land change (per capita land area demand, site suitability, and spatial structure of conversion events) into spatial-temporal projections of changes in landscape patterns (Meentemeyer et al., 2013). One striking feature of FUTURES is its patch-growth algorithm that includes feedback effects of former development events across several temporal and spatial scales: cell-level transition events are aggregated into patches of land change and their further growth is based on empirically derived parameters controlling its size, shape, and dispersion. Here, we augment the FUTURES modeling framework by expanding its multilevel structure and its representation of human decision making. The new modeling framework is hierarchically organized as nested subsystems including the latest theory on telecouplings in coupled human-natural systems (Liu et al., 2013). Each subsystem represents a specific level of spatial scale and embraces agents that have decision making authority at a particular level. The subsystems are characterized with regard to their spatial representation and are connected via flows of information (e.g. regulations and policies) or material (e.g. population migration). To provide a modeling framework that is applicable to a wide range of settings and geographical regions and to keep it computationally manageable, we implement a 'zooming factor' that allows to enable or disable subsystems (and hence the represented processes), based on the extent of the study region. The implementation of the FUTURES modeling framework for a specific case study follows the observational modeling approach described in Grimm et al. (2005), starting from the analysis of empirical data in order to capture the processes relevant for specific scales and to allow a rigorous calibration and validation of the model application. In this paper, we give an introduction to the basic concept of our modeling approach and describe its strengths and weaknesses. We furthermore use empirical data for the states of North and South Carolina to demonstrate how the modeling framework can be applied to a large, heterogeneous study system with diverse decision-making agents. Grimm et al. (2005) Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science 310, 987-991. Liu et al. (2013) Framing Sustainability in a Telecoupled World. Ecology and Society 18(2), 26. Meentemeyer et al. (2013) FUTURES: Multilevel Simulations of Merging Urban-Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. Annals of the Association of American Geographers 103(4), 785-807.

Agent-Based Modeling of Growth Processes

ERIC Educational Resources Information Center

Abraham, Ralph

2014-01-01

Growth processes abound in nature, and are frequently the target of modeling exercises in the sciences. In this article we illustrate an agent-based approach to modeling, in the case of a single example from the social sciences: bullying.

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting.

PubMed

Hu, Yi-Chung

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

PubMed Central

2017-01-01

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants. PMID:28981548

From Agents to Continuous Change via Aesthetics: Learning Mechanics with Visual Agent-Based Computational Modeling

ERIC Educational Resources Information Center

Sengupta, Pratim; Farris, Amy Voss; Wright, Mason

2012-01-01

Novice learners find motion as a continuous process of change challenging to understand. In this paper, we present a pedagogical approach based on agent-based, visual programming to address this issue. Integrating agent-based programming, in particular, Logo programming, with curricular science has been shown to be challenging in previous research…

Multiple-Use Site Demand Analysis: An Application to the Boundary Waters Canoe Area Wilderness.

ERIC Educational Resources Information Center

Peterson, George L.; And Others

1982-01-01

A single-site, multiple-use model for analyzing trip demand is derived from a multiple site regional model based on utility maximizing choice theory. The model is used to analyze and compare trips to the Boundary Waters Canoe Area Wilderness for several types of use. Travel cost elasticities of demand are compared and discussed. (Authors/JN)

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors

NASA Astrophysics Data System (ADS)

Cenek, Martin; Dahl, Spencer K.

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors.

PubMed

Cenek, Martin; Dahl, Spencer K

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Access Control for Cooperation Systems Based on Group Situation

NASA Astrophysics Data System (ADS)

Kim, Minsoo; Joshi, James B. D.; Kim, Minkoo

Cooperation systems characterize many emerging environments such as ubiquitous and pervasive systems. Agent based cooperation systems have been proposed in the literature to address challenges of such emerging application environments. A key aspect of such agent based cooperation system is the group situation that changes dynamically and governs the requirements of the cooperation. While individual agent context is important, the overall cooperation behavior is more driven by the group context because of relationships and interactions between agents. Dynamic access control based on group situation is a crucial challenge in such cooperation systems. In this paper we propose a dynamic role based access control model for cooperation systems based on group situation. The model emphasizes capability based agent to role mapping and group situation based permission assignment to allow capturing dynamic access policies that evolve continuously.

TORC3: Token-ring clearing heuristic for currency circulation

NASA Astrophysics Data System (ADS)

Humes, Carlos, Jr.; Lauretto, Marcelo S.; Nakano, Fábio; Pereira, Carlos A. B.; Rafare, Guilherme F. G.; Stern, Julio Michael

2012-10-01

Clearing algorithms are at the core of modern payment systems, facilitating the settling of multilateral credit messages with (near) minimum transfers of currency. Traditional clearing procedures use batch processing based on MILP - mixed-integer linear programming algorithms. The MILP approach demands intensive computational resources; moreover, it is also vulnerable to operational risks generated by possible defaults during the inter-batch period. This paper presents TORC3 - the Token-Ring Clearing Algorithm for Currency Circulation. In contrast to the MILP approach, TORC3 is a real time heuristic procedure, demanding modest computational resources, and able to completely shield the clearing operation against the participating agents' risk of default.

A MODEL FOR THE DEMAND FOR HIGHER EDUCATION IN THE UNITED STATES, 1919-64.

ERIC Educational Resources Information Center

CAMPBELL, ROBERT; SIEGEL, BARRY N.

STATISTICAL DEMAND ANALYSIS, WHICH EMPHASIZES THE INFLUENCE OF RELATIVE PRICES AND REAL INCOME UPON THE DEMAND FOR A COMMODITY, WAS USED TO DEVELOP A MODEL OF THE DEMAND FOR HIGHER EDUCATION. THE STUDY IS BASED ON THE FACT THAT COLLEGE ENROLLMENT REPRESENTS THE PURCHASE OF BOTH A PRODUCER AND CONSUMER DURABLE, AND IS AN ACT OF INVESTMENT.…

Autonomous Mission Operations for Sensor Webs

NASA Astrophysics Data System (ADS)

Underbrink, A.; Witt, K.; Stanley, J.; Mandl, D.

2008-12-01

We present interim results of a 2005 ROSES AIST project entitled, "Using Intelligent Agents to Form a Sensor Web for Autonomous Mission Operations", or SWAMO. The goal of the SWAMO project is to shift the control of spacecraft missions from a ground-based, centrally controlled architecture to a collaborative, distributed set of intelligent agents. The network of intelligent agents intends to reduce management requirements by utilizing model-based system prediction and autonomic model/agent collaboration. SWAMO agents are distributed throughout the Sensor Web environment, which may include multiple spacecraft, aircraft, ground systems, and ocean systems, as well as manned operations centers. The agents monitor and manage sensor platforms, Earth sensing systems, and Earth sensing models and processes. The SWAMO agents form a Sensor Web of agents via peer-to-peer coordination. Some of the intelligent agents are mobile and able to traverse between on-orbit and ground-based systems. Other agents in the network are responsible for encapsulating system models to perform prediction of future behavior of the modeled subsystems and components to which they are assigned. The software agents use semantic web technologies to enable improved information sharing among the operational entities of the Sensor Web. The semantics include ontological conceptualizations of the Sensor Web environment, plus conceptualizations of the SWAMO agents themselves. By conceptualizations of the agents, we mean knowledge of their state, operational capabilities, current operational capacities, Web Service search and discovery results, agent collaboration rules, etc. The need for ontological conceptualizations over the agents is to enable autonomous and autonomic operations of the Sensor Web. The SWAMO ontology enables automated decision making and responses to the dynamic Sensor Web environment and to end user science requests. The current ontology is compatible with Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) Sensor Model Language (SensorML) concepts and structures. The agents are currently deployed on the U.S. Naval Academy MidSTAR-1 satellite and are actively managing the power subsystem on-orbit without the need for human intervention.

Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models

ERIC Educational Resources Information Center

Dickes, Amanda Catherine; Sengupta, Pratim

2013-01-01

In this paper, we investigate how elementary school students develop multi-level explanations of population dynamics in a simple predator-prey ecosystem, through scaffolded interactions with a multi-agent-based computational model (MABM). The term "agent" in an MABM indicates individual computational objects or actors (e.g., cars), and these…

Recent progress in econophysics: Chaos, leverage, and business cycles as revealed by agent-based modeling and human experiments

NASA Astrophysics Data System (ADS)

Xin, Chen; Huang, Ji-Ping

2017-12-01

Agent-based modeling and controlled human experiments serve as two fundamental research methods in the field of econophysics. Agent-based modeling has been in development for over 20 years, but how to design virtual agents with high levels of human-like "intelligence" remains a challenge. On the other hand, experimental econophysics is an emerging field; however, there is a lack of experience and paradigms related to the field. Here, we review some of the most recent research results obtained through the use of these two methods concerning financial problems such as chaos, leverage, and business cycles. We also review the principles behind assessments of agents' intelligence levels, and some relevant designs for human experiments. The main theme of this review is to show that by combining theory, agent-based modeling, and controlled human experiments, one can garner more reliable and credible results on account of a better verification of theory; accordingly, this way, a wider range of economic and financial problems and phenomena can be studied.

Kinetic theory of situated agents applied to pedestrian flow in a corridor

NASA Astrophysics Data System (ADS)

Rangel-Huerta, A.; Muñoz-Meléndez, A.

2010-03-01

A situated agent-based model for simulation of pedestrian flow in a corridor is presented. In this model, pedestrians choose their paths freely and make decisions based on local criteria for solving collision conflicts. The crowd consists of multiple walking agents equipped with a function of perception as well as a competitive rule-based strategy that enables pedestrians to reach free access areas. Pedestrians in our model are autonomous entities capable of perceiving and making decisions. They apply socially accepted conventions, such as avoidance rules, as well as individual preferences such as the use of specific exit points, or the execution of eventual comfort turns resulting in spontaneous changes of walking speed. Periodic boundary conditions were considered in order to determine the density-average walking speed, and the density-average activity with respect to specific parameters: comfort angle turn and frequency of angle turn of walking agents. The main contribution of this work is an agent-based model where each pedestrian is represented as an autonomous agent. At the same time the pedestrian crowd dynamics is framed by the kinetic theory of biological systems.

An agent-based computational model for tuberculosis spreading on age-structured populations

NASA Astrophysics Data System (ADS)

Graciani Rodrigues, C. C.; Espíndola, Aquino L.; Penna, T. J. P.

2015-06-01

In this work we present an agent-based computational model to study the spreading of the tuberculosis (TB) disease on age-structured populations. The model proposed is a merge of two previous models: an agent-based computational model for the spreading of tuberculosis and a bit-string model for biological aging. The combination of TB with the population aging, reproduces the coexistence of health states, as seen in real populations. In addition, the universal exponential behavior of mortalities curves is still preserved. Finally, the population distribution as function of age shows the prevalence of TB mostly in elders, for high efficacy treatments.

Intelligent judgements over health risks in a spatial agent-based model.

PubMed

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

Essays on Mathematical Optimization for Residential Demand Response in the Energy Sector

NASA Astrophysics Data System (ADS)

Palaparambil Dinesh, Lakshmi

In the electric utility industry, it could be challenging to adjust supply to match demand due to large generator ramp up times, high generation costs and insufficient in-house generation capacity. Demand response (DR) is a technique for adjusting the demand for electric power instead of the supply. Direct Load Control (DLC) is one of the ways to implement DR. DLC program participants sign up for power interruption contracts and are given financial incentives for curtailing electricity usage during peak demand time periods. This dissertation studies a DLC program for residential air conditioners using mathematical optimization models. First, we develop a model that determines what contract parameters to use in designing contracts between the provider and residential customers, when to turn which power unit on or off and how much power to cut during peak demand hours. The model uses information on customer preferences for choice of contract parameters such as DLC financial incentives and energy usage curtailment. In numerical experiments, the proposed model leads to projected cost savings of the order of 20%, compared to a current benchmark model used in practice. We also quantify the impact of factors leading to cost savings and study characteristics of customers picked by different contracts. Second, we study a DLC program in a macro economic environment using a Computable General Equilibrium (CGE) model. A CGE model is used to study the impact of external factors such as policy and technology changes on different economic sectors. Here we differentiate customers based on their preference for DLC programs by using different values for price elasticity of demand for electricity commodity. Consequently, DLC program customers could substitute demand for electricity commodity with other commodities such as transportation sector. Price elasticity of demand is calculated using a novel methodology that incorporates customer preferences for DLC contracts from the first model. The calculation of elasticity based on our methodology is useful since the prices of commodities are not only determined by aggregate demand and supply but also by customers' relative preferences for commodities. In addition to this we quantify the indirect substitution and rebound effects on sectoral activity levels, incomes and prices based on customer differences, when DLC is implemented.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

Development of speed models for improving travel forecasting and highway performance evaluation : [technical summary].

DOT National Transportation Integrated Search

2013-12-01

Travel forecasting models predict travel demand based on the present transportation system and its use. Transportation modelers must develop, validate, and calibrate models to ensure that predicted travel demand is as close to reality as possible. Mo...

Efficacy of peroxisome proliferator activated receptor agonist in the treatment of virus-associated haemophagocytic syndrome in a rabbit model.

PubMed

Hsieh, Wen-Chuan; Lan, Bau-Shin; Chen, Yi-Ling; Chang, Yao; Chuang, Huai-Chia; Su, Ih-Jen

2010-01-01

Virus-associated haemophagocytic syndrome (VAHS) is a fatal complication of viral infections, such as Epstein-Barr virus and H5N1 influenza, that results from macrophage activation and pro inflammatory cytokine injuries. The high comorbidity and mortality of current therapy urgently demands an ideal agent based on VAHS pathogenesis. Peroxisome proliferator activated receptor (PPAR) agonists, regulators of metabolic syndrome, can exhibit immunomodulatory effects on macrophage activation and cytokine secretion. In this study, we adopted rosiglitazone, a PPAR-gamma agonist, for VAHS control in a Herpesvirus papio (HVP)-infected rabbit model. Various doses of rosiglitazone were orally administered to rabbits on day 7 or day 20 after intravenous challenge with 5 x 10(7) copies of HVP. The rabbits that received 4 mg/day rosiglitazone had significantly increased survival when treated at an early stage of infection (P<0.01), whereas a higher dose (8 mg/day) was required at the advanced stage of the disease (P<0.05). All rosiglitazone-treated rabbits had significantly improved laboratory parameters and plasma tumour necrosis factor-alpha levels. Importantly, rosiglitazone could also inhibit viral replication in vitro and in vivo. PPAR agonists could represent a potentially new agent for the therapy of VAHS.

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

Residential water demand model under block rate pricing: A case study of Beijing, China

NASA Astrophysics Data System (ADS)

Chen, H.; Yang, Z. F.

2009-05-01

In many cities, the inconsistency between water supply and water demand has become a critical problem because of deteriorating water shortage and increasing water demand. Uniform price of residential water cannot promote the efficient water allocation. In China, block water price will be put into practice in the future, but the outcome of such regulation measure is unpredictable without theory support. In this paper, the residential water is classified by the volume of water usage based on economic rules and block water is considered as different kinds of goods. A model based on extended linear expenditure system (ELES) is constructed to simulate the relationship between block water price and water demand, which provide theoretical support for the decision-makers. Finally, the proposed model is used to simulate residential water demand under block rate pricing in Beijing.

Agent-Based Models in Social Physics

NASA Astrophysics Data System (ADS)

Quang, Le Anh; Jung, Nam; Cho, Eun Sung; Choi, Jae Han; Lee, Jae Woo

2018-06-01

We review the agent-based models (ABM) on social physics including econophysics. The ABM consists of agent, system space, and external environment. The agent is autonomous and decides his/her behavior by interacting with the neighbors or the external environment with the rules of behavior. Agents are irrational because they have only limited information when they make decisions. They adapt using learning from past memories. Agents have various attributes and are heterogeneous. ABM is a non-equilibrium complex system that exhibits various emergence phenomena. The social complexity ABM describes human behavioral characteristics. In ABMs of econophysics, we introduce the Sugarscape model and the artificial market models. We review minority games and majority games in ABMs of game theory. Social flow ABM introduces crowding, evacuation, traffic congestion, and pedestrian dynamics. We also review ABM for opinion dynamics and voter model. We discuss features and advantages and disadvantages of Netlogo, Repast, Swarm, and Mason, which are representative platforms for implementing ABM.

Breeds of risk-adjusted fundamentalist strategies in an order-driven market

NASA Astrophysics Data System (ADS)

LiCalzi, Marco; Pellizzari, Paolo

2006-01-01

This paper studies an order-driven stock market where agents have heterogeneous estimates of the fundamental value of the risky asset. The agents are budget-constrained and follow a value-based trading strategy which buys or sells depending on whether the price of the asset is below or above its risk-adjusted fundamental value. This environment generates returns that are remarkably leptokurtic and fat-tailed. By extending the study over a grid of different parameters for the fundamentalist trading strategy, we exhibit the existence of monotone relationships between the bid-ask spread demanded by the agents and several statistics of the returns. We conjecture that this effect, coupled with positive dependence of the risk premium on the volatility, generates positive feedbacks that might explain volatility bursts.

Reverse engineering a social agent-based hidden markov model--visage.

PubMed

Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A

2008-12-01

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

Proceedings 3rd NASA/IEEE Workshop on Formal Approaches to Agent-Based Systems (FAABS-III)

NASA Technical Reports Server (NTRS)

Hinchey, Michael (Editor); Rash, James (Editor); Truszkowski, Walt (Editor); Rouff, Christopher (Editor)

2004-01-01

These preceedings contain 18 papers and 4 poster presentation, covering topics such as: multi-agent systems, agent-based control, formalism, norms, as well as physical and biological models of agent-based systems. Some applications presented in the proceedings include systems analysis, software engineering, computer networks and robot control.

Chronic Heart Failure Follow-up Management Based on Agent Technology

PubMed Central

Safdari, Reza

2015-01-01

Objectives Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. Methods This research was performed in 2013-2014 to determine appropriate scenarios and the data required to monitor and follow-up CHF patients, and then an agent-based model was designed. Results Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. Conclusions The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making. PMID:26618038

Foundations of “new” social science: Institutional legitimacy from philosophy, complexity science, postmodernism, and agent-based modeling

PubMed Central

Henrickson, Leslie; McKelvey, Bill

2002-01-01

Since the death of positivism in the 1970s, philosophers have turned their attention to scientific realism, evolutionary epistemology, and the Semantic Conception of Theories. Building on these trends, Campbellian Realism allows social scientists to accept real-world phenomena as criterion variables against which theories may be tested without denying the reality of individual interpretation and social construction. The Semantic Conception reduces the importance of axioms, but reaffirms the role of models and experiments. Philosophers now see models as “autonomous agents” that exert independent influence on the development of a science, in addition to theory and data. The inappropriate molding effects of math models on social behavior modeling are noted. Complexity science offers a “new” normal science epistemology focusing on order creation by self-organizing heterogeneous agents and agent-based models. The more responsible core of postmodernism builds on the idea that agents operate in a constantly changing web of interconnections among other agents. The connectionist agent-based models of complexity science draw on the same conception of social ontology as do postmodernists. These recent developments combine to provide foundations for a “new” social science centered on formal modeling not requiring the mathematical assumptions of agent homogeneity and equilibrium conditions. They give this “new” social science legitimacy in scientific circles that current social science approaches lack. PMID:12011408

Estimating Oxygen Needs for Childhood Pneumonia in Developing Country Health Systems: A New Model for Expecting the Unexpected

PubMed Central

Bradley, Beverly D.; Howie, Stephen R. C.; Chan, Timothy C. Y.; Cheng, Yu-Ling

2014-01-01

Background Planning for the reliable and cost-effective supply of a health service commodity such as medical oxygen requires an understanding of the dynamic need or ‘demand’ for the commodity over time. In developing country health systems, however, collecting longitudinal clinical data for forecasting purposes is very difficult. Furthermore, approaches to estimating demand for supplies based on annual averages can underestimate demand some of the time by missing temporal variability. Methods A discrete event simulation model was developed to estimate variable demand for a health service commodity using the important example of medical oxygen for childhood pneumonia. The model is based on five key factors affecting oxygen demand: annual pneumonia admission rate, hypoxaemia prevalence, degree of seasonality, treatment duration, and oxygen flow rate. These parameters were varied over a wide range of values to generate simulation results for different settings. Total oxygen volume, peak patient load, and hours spent above average-based demand estimates were computed for both low and high seasons. Findings Oxygen demand estimates based on annual average values of demand factors can often severely underestimate actual demand. For scenarios with high hypoxaemia prevalence and degree of seasonality, demand can exceed average levels up to 68% of the time. Even for typical scenarios, demand may exceed three times the average level for several hours per day. Peak patient load is sensitive to hypoxaemia prevalence, whereas time spent at such peak loads is strongly influenced by degree of seasonality. Conclusion A theoretical study is presented whereby a simulation approach to estimating oxygen demand is used to better capture temporal variability compared to standard average-based approaches. This approach provides better grounds for health service planning, including decision-making around technologies for oxygen delivery. Beyond oxygen, this approach is widely applicable to other areas of resource and technology planning in developing country health systems. PMID:24587089

Processing power limits social group size: computational evidence for the cognitive costs of sociality

PubMed Central

Dávid-Barrett, T.; Dunbar, R. I. M.

2013-01-01

Sociality is primarily a coordination problem. However, the social (or communication) complexity hypothesis suggests that the kinds of information that can be acquired and processed may limit the size and/or complexity of social groups that a species can maintain. We use an agent-based model to test the hypothesis that the complexity of information processed influences the computational demands involved. We show that successive increases in the kinds of information processed allow organisms to break through the glass ceilings that otherwise limit the size of social groups: larger groups can only be achieved at the cost of more sophisticated kinds of information processing that are disadvantageous when optimal group size is small. These results simultaneously support both the social brain and the social complexity hypotheses. PMID:23804623

Market-oriented Programming Using Small-world Networks for Controlling Building Environments

NASA Astrophysics Data System (ADS)

Shigei, Noritaka; Miyajima, Hiromi; Osako, Tsukasa

The market model, which is one of the economic activity models, is modeled as an agent system, and applying the model to the resource allocation problem has been studied. For air conditioning control of building, which is one of the resource allocation problems, an effective method based on the agent system using auction has been proposed for traditional PID controller. On the other hand, it has been considered that this method is performed by decentralized control. However, its decentralization is not perfect, and its performace is not enough. In this paper, firstly, we propose a perfectly decentralized agent model and show its performance. Secondly, in order to improve the model, we propose the agent model based on small-world model. The effectiveness of the proposed model is shown by simulation.

A Two-Stage Multi-Agent Based Assessment Approach to Enhance Students' Learning Motivation through Negotiated Skills Assessment

ERIC Educational Resources Information Center

Chadli, Abdelhafid; Bendella, Fatima; Tranvouez, Erwan

2015-01-01

In this paper we present an Agent-based evaluation approach in a context of Multi-agent simulation learning systems. Our evaluation model is based on a two stage assessment approach: (1) a Distributed skill evaluation combining agents and fuzzy sets theory; and (2) a Negotiation based evaluation of students' performance during a training…

Mathematical modeling of malaria infection with innate and adaptive immunity in individuals and agent-based communities.

PubMed

Gurarie, David; Karl, Stephan; Zimmerman, Peter A; King, Charles H; St Pierre, Timothy G; Davis, Timothy M E

2012-01-01

Agent-based modeling of Plasmodium falciparum infection offers an attractive alternative to the conventional Ross-Macdonald methodology, as it allows simulation of heterogeneous communities subjected to realistic transmission (inoculation patterns). We developed a new, agent based model that accounts for the essential in-host processes: parasite replication and its regulation by innate and adaptive immunity. The model also incorporates a simplified version of antigenic variation by Plasmodium falciparum. We calibrated the model using data from malaria-therapy (MT) studies, and developed a novel calibration procedure that accounts for a deterministic and a pseudo-random component in the observed parasite density patterns. Using the parasite density patterns of 122 MT patients, we generated a large number of calibrated parameters. The resulting data set served as a basis for constructing and simulating heterogeneous agent-based (AB) communities of MT-like hosts. We conducted several numerical experiments subjecting AB communities to realistic inoculation patterns reported from previous field studies, and compared the model output to the observed malaria prevalence in the field. There was overall consistency, supporting the potential of this agent-based methodology to represent transmission in realistic communities. Our approach represents a novel, convenient and versatile method to model Plasmodium falciparum infection.

Bubble Jet agent release cartridge for chemical single cell stimulation.

PubMed

Wangler, N; Welsche, M; Blazek, M; Blessing, M; Vervliet-Scheebaum, M; Reski, R; Müller, C; Reinecke, H; Steigert, J; Roth, G; Zengerle, R; Paust, N

2013-02-01

We present a new method for the distinct specific chemical stimulation of single cells and small cell clusters within their natural environment. By single-drop release of chemical agents with droplets in size of typical cell diameters (d <30 μm) on-demand micro gradients can be generated for the specific manipulation of single cells. A single channel and a double channel agent release cartridge with integrated fluidic structures and integrated agent reservoirs are shown, tested, and compared in this publication. The single channel setup features a fluidic structure fabricated by anisotropic etching of silicon. To allow for simultaneous release of different agents even though maintaining the same device size, the second type comprises a double channel fluidic structure, fabricated by photolithographic patterning of TMMF. Dispensed droplet volumes are V = 15 pl and V = 10 pl for the silicon and the TMMF based setups, respectively. Utilizing the agent release cartridges, the application in biological assays was demonstrated by hormone-stimulated premature bud formation in Physcomitrella patens and the individual staining of one single L 929 cell within a confluent grown cell culture.

Estimating habitat carrying capacity for migrating and wintering waterfowl: Considerations, pitfalls and improvements

USGS Publications Warehouse

Williams, Christopher; Dugger, Bruce D.; Brasher, Michael G.; Coluccy, John M.; Cramer, Dane M.; Eadie, John M.; Gray, Matthew J.; Hagy, Heath M.; Livolsi, Mark; McWilliams, Scott R.; Petrie, Matthew; Soulliere, Gregory J.; Tirpak, John M.; Webb, Elisabeth B.

2014-01-01

Population-based habitat conservation planning for migrating and wintering waterfowl in North America is carried out by habitat Joint Venture (JV) initiatives and is based on the premise that food can limit demography (i.e. food limitation hypothesis). Consequently, planners use bioenergetic models to estimate food (energy) availability and population-level energy demands at appropriate spatial and temporal scales, and translate these values into regional habitat objectives. While simple in principle, there are both empirical and theoretical challenges associated with calculating energy supply and demand including: 1) estimating food availability, 2) estimating the energy content of specific foods, 3) extrapolating site-specific estimates of food availability to landscapes for focal species, 4) applicability of estimates from a single species to other species, 5) estimating resting metabolic rate, 6) estimating cost of daily behaviours, and 7) estimating costs of thermoregulation or tissue synthesis. Most models being used are daily ration models (DRMs) whose set of simplifying assumptions are well established and whose use is widely accepted and feasible given the empirical data available to populate such models. However, DRMs do not link habitat objectives to metrics of ultimate ecological importance such as individual body condition or survival, and largely only consider food-producing habitats. Agent-based models (ABMs) provide a possible alternative for creating more biologically realistic models under some conditions; however, ABMs require different types of empirical inputs, many of which have yet to be estimated for key North American waterfowl. Decisions about how JVs can best proceed with habitat conservation would benefit from the use of sensitivity analyses that could identify the empirical and theoretical uncertainties that have the greatest influence on efforts to estimate habitat carrying capacity. Development of ABMs at restricted, yet biologically relevant spatial scales, followed by comparisons of their outputs to those generated from more simplistic, deterministic models can provide a means of assessing degrees of dissimilarity in how alternative models describe desired landscape conditions for migrating and wintering waterfowl.

Examining the Impact of the Walking School Bus With an Agent-Based Model

PubMed Central

Diez-Roux, Ana; Evenson, Kelly R.; Colabianchi, Natalie

2014-01-01

We used an agent-based model to examine the impact of the walking school bus (WSB) on children’s active travel to school. We identified a synergistic effect of the WSB with other intervention components such as an educational campaign designed to improve attitudes toward active travel to school. Results suggest that to maximize active travel to school, children should arrive on time at “bus stops” to allow faster WSB walking speeds. We also illustrate how an agent-based model can be used to identify the location of routes maximizing the effects of the WSB on active travel. Agent-based models can be used to examine plausible effects of the WSB on active travel to school under various conditions and to identify ways of implementing the WSB that maximize its effectiveness. PMID:24832410

Psychosocial risks, burnout and intention to quit following the introduction of new software at work.

PubMed

Knani, Mouna; Fournier, Pierre-Sébastien; Biron, Caroline

2018-05-01

Despite a rich literature on association between psychosocial factors, the demand-control-support (DCS) model and burnout, there are few integrated frameworks encompassing the DCS model, burnout and intention to quit, particularly in a technological context. This manuscript examines the relationships between psychosocial risks, the demand-control-support (DCS) model, burnout syndrome and intention to quit following the introduction of new software at work. Data was collected from agents and advisors working at a Canadian university and using newstudy management software. An online questionnaire was sent via the university's internal mail. Finally, 112 people completed the online survey for a response rate of 60.9% . The results of structural equation modeling show that psychological demands, decision latitude and social support are associated with burnout. It is also clear that burnout, in particular depersonalization and emotional exhaustion, is positively associated with intention to quit. The few studies that raise the negative consequences of technology on quality of life in the workplace, and particularly on health, have not succeeded in establishing a direct link between a deterioration of health and the use of technology. This is due to the fact that there are few epidemiological studies on the direct consequences of the use of ITC on health.

Large-scale multi-agent transportation simulations

NASA Astrophysics Data System (ADS)

Cetin, Nurhan; Nagel, Kai; Raney, Bryan; Voellmy, Andreas

2002-08-01

It is now possible to microsimulate the traffic of whole metropolitan areas with 10 million travelers or more, "micro" meaning that each traveler is resolved individually as a particle. In contrast to physics or chemistry, these particles have internal intelligence; for example, they know where they are going. This means that a transportation simulation project will have, besides the traffic microsimulation, modules which model this intelligent behavior. The most important modules are for route generation and for demand generation. Demand is generated by each individual in the simulation making a plan of activities such as sleeping, eating, working, shopping, etc. If activities are planned at different locations, they obviously generate demand for transportation. This however is not enough since those plans are influenced by congestion which initially is not known. This is solved via a relaxation method, which means iterating back and forth between the activities/routes generation and the traffic simulation.

Comparing supply and demand models for future photovoltaic power generation in the USA

DOE PAGES

Basore, Paul A.; Cole, Wesley J.

2018-02-22

We explore the plausible range of future deployment of photovoltaic generation capacity in the USA using a supply-focused model based on supply-chain growth constraints and a demand-focused model based on minimizing the overall cost of the electricity system. Both approaches require assumptions based on previous experience and anticipated trends. For each of the models, we assign plausible ranges for the key assumptions and then compare the resulting PV deployment over time. Each model was applied to 2 different future scenarios: one in which PV market penetration is ultimately constrained by the uncontrolled variability of solar power and one in whichmore » low-cost energy storage or some equivalent measure largely alleviates this constraint. The supply-focused and demand-focused models are in substantial agreement, not just in the long term, where deployment is largely determined by the assumed market penetration constraints, but also in the interim years. For the future scenario without low-cost energy storage or equivalent measures, the 2 models give an average plausible range of PV generation capacity in the USA of 150 to 530 GWdc in 2030 and 260 to 810 GWdc in 2040. With low-cost energy storage or equivalent measures, the corresponding ranges are 160 to 630 GWdc in 2030 and 280 to 1200 GWdc in 2040. The latter range is enough to supply 10% to 40% of US electricity demand in 2040, based on current demand growth.« less

Comparing supply and demand models for future photovoltaic power generation in the USA

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

Basore, Paul A.; Cole, Wesley J.

We explore the plausible range of future deployment of photovoltaic generation capacity in the USA using a supply-focused model based on supply-chain growth constraints and a demand-focused model based on minimizing the overall cost of the electricity system. Both approaches require assumptions based on previous experience and anticipated trends. For each of the models, we assign plausible ranges for the key assumptions and then compare the resulting PV deployment over time. Each model was applied to 2 different future scenarios: one in which PV market penetration is ultimately constrained by the uncontrolled variability of solar power and one in whichmore » low-cost energy storage or some equivalent measure largely alleviates this constraint. The supply-focused and demand-focused models are in substantial agreement, not just in the long term, where deployment is largely determined by the assumed market penetration constraints, but also in the interim years. For the future scenario without low-cost energy storage or equivalent measures, the 2 models give an average plausible range of PV generation capacity in the USA of 150 to 530 GWdc in 2030 and 260 to 810 GWdc in 2040. With low-cost energy storage or equivalent measures, the corresponding ranges are 160 to 630 GWdc in 2030 and 280 to 1200 GWdc in 2040. The latter range is enough to supply 10% to 40% of US electricity demand in 2040, based on current demand growth.« less

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

NASA Technical Reports Server (NTRS)

Shameldin, Ramez Ahmed; Bowling, Shannon R.

2010-01-01

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Multi-agent Reinforcement Learning Model for Effective Action Selection

NASA Astrophysics Data System (ADS)

Youk, Sang Jo; Lee, Bong Keun

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

Using Model Replication to Improve the Reliability of Agent-Based Models

NASA Astrophysics Data System (ADS)

Zhong, Wei; Kim, Yushim

The basic presupposition of model replication activities for a computational model such as an agent-based model (ABM) is that, as a robust and reliable tool, it must be replicable in other computing settings. This assumption has recently gained attention in the community of artificial society and simulation due to the challenges of model verification and validation. Illustrating the replication of an ABM representing fraudulent behavior in a public service delivery system originally developed in the Java-based MASON toolkit for NetLogo by a different author, this paper exemplifies how model replication exercises provide unique opportunities for model verification and validation process. At the same time, it helps accumulate best practices and patterns of model replication and contributes to the agenda of developing a standard methodological protocol for agent-based social simulation.

An Agent Based Collaborative Simplification of 3D Mesh Model

NASA Astrophysics Data System (ADS)

Wang, Li-Rong; Yu, Bo; Hagiwara, Ichiro

Large-volume mesh model faces the challenge in fast rendering and transmission by Internet. The current mesh models obtained by using three-dimensional (3D) scanning technology are usually very large in data volume. This paper develops a mobile agent based collaborative environment on the development platform of mobile-C. Communication among distributed agents includes grasping image of visualized mesh model, annotation to grasped image and instant message. Remote and collaborative simplification can be efficiently conducted by Internet.

By Ounce or By Calorie: The Differential Effects of Alternative Sugar-Sweetened Beverage Tax Strategies

PubMed Central

Zhen, Chen; Brissette, Ian F.; Ruff, Ryan R.

2014-01-01

The obesity epidemic and excessive consumption of sugar-sweetened beverages have led to proposals of economics-based interventions to promote healthy eating in the United States. Targeted food and beverage taxes and subsidies are prominent examples of such potential intervention strategies. This paper examines the differential effects of taxing sugar-sweetened beverages by calories and by ounces on beverage demand. To properly measure the extent of substitution and complementarity between beverage products, we developed a fully modified distance metric model of differentiated product demand that endogenizes the cross-price effects. We illustrated the proposed methodology in a linear approximate almost ideal demand system, although other flexible demand systems can also be used. In the empirical application using supermarket scanner data, the product-level demand model consists of 178 beverage products with combined market share of over 90%. The novel demand model outperformed the conventional distance metric model in non-nested model comparison tests and in terms of the economic significance of model predictions. In the fully modified model, a calorie-based beverage tax was estimated to cost $1.40 less in compensating variation than an ounce-based tax per 3,500 beverage calories reduced. This difference in welfare cost estimates between two tax strategies is more than three times as much as the difference estimated by the conventional distance metric model. If applied to products purchased from all sources, a 0.04-cent per kcal tax on sugar-sweetened beverages is predicted to reduce annual per capita beverage intake by 5,800 kcal. PMID:25414517

Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

PubMed Central

Zhao, Xiuli; Yiranbon, Ethel

2014-01-01

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor. PMID:24511292

Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

PubMed

Zhao, Xiuli; Asante Antwi, Henry; Yiranbon, Ethel

2014-01-01

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, "least-cost," and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

Enabling private and public sector organizations as agents of homeland security

NASA Astrophysics Data System (ADS)

Glassco, David H. J.; Glassco, Jordan C.

2006-05-01

Homeland security and defense applications seek to reduce the risk of undesirable eventualities across physical space in real-time. With that functional requirement in mind, our work focused on the development of IP based agent telecommunication solutions for heterogeneous sensor / robotic intelligent "Things" that could be deployed across the internet. This paper explains how multi-organization information and device sharing alliances may be formed to enable organizations to act as agents of homeland security (in addition to other uses). Topics include: (i) using location-aware, agent based, real-time information sharing systems to integrate business systems, mobile devices, sensor and actuator based devices and embedded devices used in physical infrastructure assets, equipment and other man-made "Things"; (ii) organization-centric real-time information sharing spaces using on-demand XML schema formatted networks; (iii) object-oriented XML serialization as a methodology for heterogeneous device glue code; (iv) how complex requirements for inter / intra organization information and device ownership and sharing, security and access control, mobility and remote communication service, tailored solution life cycle management, service QoS, service and geographic scalability and the projection of remote physical presence (through sensing and robotics) and remote informational presence (knowledge of what is going elsewhere) can be more easily supported through feature inheritance with a rapid agent system development methodology; (v) how remote object identification and tracking can be supported across large areas; (vi) how agent synergy may be leveraged with analytics to complement heterogeneous device networks.

Integrated Mode Choice, Small Aircraft Demand, and Airport Operations Model User's Guide

NASA Technical Reports Server (NTRS)

Yackovetsky, Robert E. (Technical Monitor); Dollyhigh, Samuel M.

2004-01-01

A mode choice model that generates on-demand air travel forecasts at a set of GA airports based on changes in economic characteristics, vehicle performance characteristics such as speed and cost, and demographic trends has been integrated with a model to generate itinerate aircraft operations by airplane category at a set of 3227 airports. Numerous intermediate outputs can be generated, such as the number of additional trips diverted from automobiles and schedule air by the improved performance and cost of on-demand air vehicles. The total number of transported passenger miles that are diverted is also available. From these results the number of new aircraft to service the increased demand can be calculated. Output from the models discussed is in the format to generate the origin and destination traffic flow between the 3227 airports based on solutions to a gravity model.

Analysis of Foreign Exchange Interventions by Intervention Agent with an Artificial Market Approach

NASA Astrophysics Data System (ADS)

Matsui, Hiroki; Tojo, Satoshi

We propose a multi-agent system which learns intervention policies and evaluates the effect of interventions in an artificial foreign exchange market. Izumi et al. had presented a system called AGEDASI TOF to simulate artificial market, together with a support system for the government to decide foreign exchange policies. However, the system needed to fix the amount of governmental intervention prior to the simulation, and was not realistic. In addition, the interventions in the system did not affect supply and demand of currencies; thus we could not discuss the effect of intervention correctly. First, we improve the system so as to make much of the weights of influential factors. Thereafter, we introduce an intervention agent that has the role of the central bank to stabilize the market. We could show that the agent learned the effective intervention policies through the reinforcement learning, and that the exchange rate converged to a certain extent in the expected range. We could also estimate the amount of intervention, showing the efficacy of signaling. In this model, in order to investigate the aliasing of the perception of the intervention agent, we introduced a pseudo-agent who was supposed to be able to observe all the behaviors of dealer agents; with this super-agent, we discussed the adequate granularity for a market state description.

A framework for the use of agent based modeling to simulate ...

EPA Pesticide Factsheets

Simulation of human behavior in exposure modeling is a complex task. Traditionally, inter-individual variation in human activity has been modeled by drawing from a pool of single day time-activity diaries such as the US EPA Consolidated Human Activity Database (CHAD). Here, an agent-based model (ABM) is used to simulate population distributions of longitudinal patterns of four macro activities (sleeping, eating, working, and commuting) in populations of adults over a period of one year. In this ABM, an individual is modeled as an agent whose movement through time and space is determined by a set of decision rules. The rules are based on the agent having time-varying “needs” that are satisfied by performing actions. Needs are modeled as increasing over time, and taking an action reduces the need. Need-satisfying actions include sleeping (meeting the need for rest), eating (meeting the need for food), and commuting/working (meeting the need for income). Every time an action is completed, the model determines the next action the agent will take based on the magnitude of each of the agent’s needs at that point in time. Different activities advertise their ability to satisfy various needs of the agent (such as food to eat or sleeping in a bed or on a couch). The model then chooses the activity that satisfies the greatest of the agent’s needs. When multiple actions could address a need, the model will choose the most effective of the actions (bed over the couc

Hybrid evolutionary computing model for mobile agents of wireless Internet multimedia

NASA Astrophysics Data System (ADS)

Hortos, William S.

2001-03-01

The ecosystem is used as an evolutionary paradigm of natural laws for the distributed information retrieval via mobile agents to allow the computational load to be added to server nodes of wireless networks, while reducing the traffic on communication links. Based on the Food Web model, a set of computational rules of natural balance form the outer stage to control the evolution of mobile agents providing multimedia services with a wireless Internet protocol WIP. The evolutionary model shows how mobile agents should behave with the WIP, in particular, how mobile agents can cooperate, compete and learn from each other, based on an underlying competition for radio network resources to establish the wireless connections to support the quality of service QoS of user requests. Mobile agents are also allowed to clone themselves, propagate and communicate with other agents. A two-layer model is proposed for agent evolution: the outer layer is based on the law of natural balancing, the inner layer is based on a discrete version of a Kohonen self-organizing feature map SOFM to distribute network resources to meet QoS requirements. The former is embedded in the higher OSI layers of the WIP, while the latter is used in the resource management procedures of Layer 2 and 3 of the protocol. Algorithms for the distributed computation of mobile agent evolutionary behavior are developed by adding a learning state to the agent evolution state diagram. When an agent is in an indeterminate state, it can communicate to other agents. Computing models can be replicated from other agents. Then the agents transitions to the mutating state to wait for a new information-retrieval goal. When a wireless terminal or station lacks a network resource, an agent in the suspending state can change its policy to submit to the environment before it transitions to the searching state. The agents learn the facts of agent state information entered into an external database. In the cloning process, two agents on a host station sharing a common goal can be merged or married to compose a new agent. Application of the two-layer set of algorithms for mobile agent evolution, performed in a distributed processing environment, is made to the QoS management functions of the IP multimedia IM sub-network of the third generation 3G Wideband Code-division Multiple Access W-CDMA wireless network.

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

PubMed

Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

2013-01-01

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

A spatial web/agent-based model to support stakeholders' negotiation regarding land development.

PubMed

Pooyandeh, Majeed; Marceau, Danielle J

2013-11-15

Decision making in land management can be greatly enhanced if the perspectives of concerned stakeholders are taken into consideration. This often implies negotiation in order to reach an agreement based on the examination of multiple alternatives. This paper describes a spatial web/agent-based modeling system that was developed to support the negotiation process of stakeholders regarding land development in southern Alberta, Canada. This system integrates a fuzzy analytic hierarchy procedure within an agent-based model in an interactive visualization environment provided through a web interface to facilitate the learning and negotiation of the stakeholders. In the pre-negotiation phase, the stakeholders compare their evaluation criteria using linguistic expressions. Due to the uncertainty and fuzzy nature of such comparisons, a fuzzy Analytic Hierarchy Process is then used to prioritize the criteria. The negotiation starts by a development plan being submitted by a user (stakeholder) through the web interface. An agent called the proposer, which represents the proposer of the plan, receives this plan and starts negotiating with all other agents. The negotiation is conducted in a step-wise manner where the agents change their attitudes by assigning a new set of weights to their criteria. If an agreement is not achieved, a new location for development is proposed by the proposer agent. This process is repeated until a location is found that satisfies all agents to a certain predefined degree. To evaluate the performance of the model, the negotiation was simulated with four agents, one of which being the proposer agent, using two hypothetical development plans. The first plan was selected randomly; the other one was chosen in an area that is of high importance to one of the agents. While the agents managed to achieve an agreement about the location of the land development after three rounds of negotiation in the first scenario, seven rounds were required in the second scenario. The proposed web/agent-based model facilitates the interaction and learning among stakeholders when facing multiple alternatives. Copyright © 2013 Elsevier Ltd. All rights reserved.

A knowledge base architecture for distributed knowledge agents

NASA Technical Reports Server (NTRS)

Riedesel, Joel; Walls, Bryan

1990-01-01

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

Brahms Mobile Agents: Architecture and Field Tests

NASA Technical Reports Server (NTRS)

Clancey, William J.; Sierhuis, Maarten; Kaskiris, Charis; vanHoof, Ron

2002-01-01

We have developed a model-based, distributed architecture that integrates diverse components in a system designed for lunar and planetary surface operations: an astronaut's space suit, cameras, rover/All-Terrain Vehicle (ATV), robotic assistant, other personnel in a local habitat, and a remote mission support team (with time delay). Software processes, called agents, implemented in the Brahms language, run on multiple, mobile platforms. These mobile agents interpret and transform available data to help people and robotic systems coordinate their actions to make operations more safe and efficient. The Brahms-based mobile agent architecture (MAA) uses a novel combination of agent types so the software agents may understand and facilitate communications between people and between system components. A state-of-the-art spoken dialogue interface is integrated with Brahms models, supporting a speech-driven field observation record and rover command system (e.g., return here later and bring this back to the habitat ). This combination of agents, rover, and model-based spoken dialogue interface constitutes a personal assistant. An important aspect of the methodology involves first simulating the entire system in Brahms, then configuring the agents into a run-time system.

Development of Antibacterials Targeting the MEP Pathway of Select Agents

DTIC Science & Technology

2013-02-01

based assays for lead inhibitor discovery, evaluation of lead inhibitors in microbial growth assays, determining X- ray crystal structures of MEP pathway...inhibitors. • On-demand production and delivery of recombinant proteins to WRAIR for X- ray crystallography. Reportable Outcomes...characterization and phosphoregulation. PLoS ONE 6: e20884. doi:10.1371/journal.pone.0020884. 3. Zhang JH, Chung TD, Oldenburg KR (1999) A Simple

An equation-free approach to agent-based computation: Bifurcation analysis and control of stationary states

NASA Astrophysics Data System (ADS)

Siettos, C. I.; Gear, C. W.; Kevrekidis, I. G.

2012-08-01

We show how the equation-free approach can be exploited to enable agent-based simulators to perform system-level computations such as bifurcation, stability analysis and controller design. We illustrate these tasks through an event-driven agent-based model describing the dynamic behaviour of many interacting investors in the presence of mimesis. Using short bursts of appropriately initialized runs of the detailed, agent-based simulator, we construct the coarse-grained bifurcation diagram of the (expected) density of agents and investigate the stability of its multiple solution branches. When the mimetic coupling between agents becomes strong enough, the stable stationary state loses its stability at a coarse turning point bifurcation. We also demonstrate how the framework can be used to design a wash-out dynamic controller that stabilizes open-loop unstable stationary states even under model uncertainty.

IPA (v1): a framework for agent-based modelling of soil water movement

NASA Astrophysics Data System (ADS)

Mewes, Benjamin; Schumann, Andreas H.

2018-06-01

In the last decade, agent-based modelling (ABM) became a popular modelling technique in social sciences, medicine, biology, and ecology. ABM was designed to simulate systems that are highly dynamic and sensitive to small variations in their composition and their state. As hydrological systems, and natural systems in general, often show dynamic and non-linear behaviour, ABM can be an appropriate way to model these systems. Nevertheless, only a few studies have utilized the ABM method for process-based modelling in hydrology. The percolation of water through the unsaturated soil is highly responsive to the current state of the soil system; small variations in composition lead to major changes in the transport system. Hence, we present a new approach for modelling the movement of water through a soil column: autonomous water agents that transport water through the soil while interacting with their environment as well as with other agents under physical laws.

An Immune Agent for Web-Based AI Course

ERIC Educational Resources Information Center

Gong, Tao; Cai, Zixing

2006-01-01

To overcome weakness and faults of a web-based e-learning course such as Artificial Intelligence (AI), an immune agent was proposed, simulating a natural immune mechanism against a virus. The immune agent was built on the multi-dimension education agent model and immune algorithm. The web-based AI course was comprised of many files, such as HTML…

Comparison of an Agent-based Model of Disease Propagation with the Generalised SIR Epidemic Model

DTIC Science & Technology

2009-08-01

has become a practical method for conducting Epidemiological Modelling. In the agent- based approach the whole township can be modelled as a system of...SIR system was initially developed based on a very simplified model of social interaction. For instance an assumption of uniform population mixing was...simulating the progress of a disease within a host and of transmission between hosts is based upon Transportation Analysis and Simulation System

From Walras’ auctioneer to continuous time double auctions: a general dynamic theory of supply and demand

NASA Astrophysics Data System (ADS)

Donier, J.; Bouchaud, J.-P.

2016-12-01

In standard Walrasian auctions, the price of a good is defined as the point where the supply and demand curves intersect. Since both curves are generically regular, the response to small perturbations is linearly small. However, a crucial ingredient is absent of the theory, namely transactions themselves. What happens after they occur? To answer the question, we develop a dynamic theory for supply and demand based on agents with heterogeneous beliefs. When the inter-auction time is infinitely long, the Walrasian mechanism is recovered. When transactions are allowed to happen in continuous time, a peculiar property emerges: close to the price, supply and demand vanish quadratically, which we empirically confirm on the Bitcoin. This explains why price impact in financial markets is universally observed to behave as the square root of the excess volume. The consequences are important, as they imply that the very fact of clearing the market makes prices hypersensitive to small fluctuations.

Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

PubMed Central

Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

2015-01-01

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

Diversity and Community: The Role of Agent-Based Modeling.

PubMed

Stivala, Alex

2017-06-01

Community psychology involves several dialectics between potentially opposing ideals, such as theory and practice, rights and needs, and respect for human diversity and sense of community. Some recent papers in the American Journal of Community Psychology have examined the diversity-community dialectic, some with the aid of agent-based modeling and concepts from network science. This paper further elucidates these concepts and suggests that research in community psychology can benefit from a useful dialectic between agent-based modeling and the real-world concerns of community psychology. © Society for Community Research and Action 2017.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

2008-06-01

postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the

Stability of subsystem solutions in agent-based models

NASA Astrophysics Data System (ADS)

Perc, Matjaž

2018-01-01

The fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behaviour when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modelling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behaviour of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: when can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterised by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.

New spatial clustering-based models for optimal urban facility location considering geographical obstacles

NASA Astrophysics Data System (ADS)

Javadi, Maryam; Shahrabi, Jamal

2014-03-01

The problems of facility location and the allocation of demand points to facilities are crucial research issues in spatial data analysis and urban planning. It is very important for an organization or governments to best locate its resources and facilities and efficiently manage resources to ensure that all demand points are covered and all the needs are met. Most of the recent studies, which focused on solving facility location problems by performing spatial clustering, have used the Euclidean distance between two points as the dissimilarity function. Natural obstacles, such as mountains and rivers, can have drastic impacts on the distance that needs to be traveled between two geographical locations. While calculating the distance between various supply chain entities (including facilities and demand points), it is necessary to take such obstacles into account to obtain better and more realistic results regarding location-allocation. In this article, new models were presented for location of urban facilities while considering geographical obstacles at the same time. In these models, three new distance functions were proposed. The first function was based on the analysis of shortest path in linear network, which was called SPD function. The other two functions, namely PD and P2D, were based on the algorithms that deal with robot geometry and route-based robot navigation in the presence of obstacles. The models were implemented in ArcGIS Desktop 9.2 software using the visual basic programming language. These models were evaluated using synthetic and real data sets. The overall performance was evaluated based on the sum of distance from demand points to their corresponding facilities. Because of the distance between the demand points and facilities becoming more realistic in the proposed functions, results indicated desired quality of the proposed models in terms of quality of allocating points to centers and logistic cost. Obtained results show promising improvements of the allocation, the logistics costs and the response time. It can also be inferred from this study that the P2D-based model and the SPD-based model yield similar results in terms of the facility location and the demand allocation. It is noted that the P2D-based model showed better execution time than the SPD-based model. Considering logistic costs, facility location and response time, the P2D-based model was appropriate choice for urban facility location problem considering the geographical obstacles.

Combining Human and Machine Intelligence to Derive Agents' Behavioral Rules for Groundwater Irrigation

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.

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

Kim, Joyce Jihyun; Yin, Rongxin; Kiliccote, Sila

Open Automated Demand Response (OpenADR), an XML-based information exchange model, is used to facilitate continuous price-responsive operation and demand response participation for large commercial buildings in New York who are subject to the default day-ahead hourly pricing. We summarize the existing demand response programs in New York and discuss OpenADR communication, prioritization of demand response signals, and control methods. Building energy simulation models are developed and field tests are conducted to evaluate continuous energy management and demand response capabilities of two commercial buildings in New York City. Preliminary results reveal that providing machine-readable prices to commercial buildings can facilitate bothmore » demand response participation and continuous energy cost savings. Hence, efforts should be made to develop more sophisticated algorithms for building control systems to minimize customer's utility bill based on price and reliability information from the electricity grid.« less

[Emotional well-being and discomfort at work in call center].

PubMed

Emanuel, Federica; Colombo, Lara; Ghislieri, Chiara

2014-01-01

The theme of well-being and discomfort at work has attracted increasing interest in recent years. The present study, according to Job Demands-Resources model (JD-R), inquires the effects of personal (optimism, internal locus of control) and organizational resources (job autonomy, supervisors and colleagues support) and general (work-to-family conflict, workload) and context specific demands (emotional dissonance) on emotional well-being and discomfort at work in call centre employees. This research was conducted through an online questionnaire, composed by measures present in scientific literature, filled out individually by call center agents (N = 507) of the same telecommunication firm. Data analysis (PASW 18) provides: descriptive statistics, correlations and multiple regressions. Personal and organizational resources improve emotional well-being at work, except for colleagues support. Optimism and supervisors support reduce emotional discomfort at work. Among organizational demands, work-family conflict and emotional dissonance increase emotional discomfort at work and, to a lesser extent, reduce the emotional well-being at work. The results, according to theoretical model, highlight the different role of demands and resources on emotional well-being and discomfort at work. The results suggest organizational politics and investments to promote emotional well-being at work, in particular training program to support emotional skills, training for supervisors, increasing job autonomy and support to work-family balance.

Development of agent-based on-line adaptive signal control (ASC) framework using connected vehicle (CV) technology.

DOT National Transportation Integrated Search

2016-04-01

In this study, we developed an adaptive signal control (ASC) framework for connected vehicles (CVs) using agent-based modeling technique. : The proposed framework consists of two types of agents: 1) vehicle agents (VAs); and 2) signal controller agen...

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

NASA Astrophysics Data System (ADS)

Alexandridis, Konstantinos T.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research.

The Relationship between Job Demands and Employees' Counterproductive Work Behaviors: The Mediating Effect of Psychological Detachment and Job Anxiety.

PubMed

Chen, Yang; Li, Shuang; Xia, Qing; He, Chao

2017-01-01

This study aims to explore the relation between job demands and counterproductive work behaviors (CWBs). A cross-sectional sample of 439 coal miners completed a self-report questionnaire that assessed their job demands, psychological detachment, job anxiety, and CWBs in a Chinese context. The conceptual model, based on the stressor-detachment model, was examined using structural equation modeling. The results suggest that psychological detachment mediates not only the relation between job demands and job anxiety but also that between job demands and CWBs. Furthermore, the relation between job demands and CWBs is sequentially mediated by psychological detachment and job anxiety. Our findings validate the effectiveness of the stressor-detachment model. Moreover, we demonstrate that the underlying mechanism of the relation between job demands and CWBs can be explained by psychological detachment and job anxiety.

The Relationship between Job Demands and Employees’ Counterproductive Work Behaviors: The Mediating Effect of Psychological Detachment and Job Anxiety

PubMed Central

Chen, Yang; Li, Shuang; Xia, Qing; He, Chao

2017-01-01

This study aims to explore the relation between job demands and counterproductive work behaviors (CWBs). A cross-sectional sample of 439 coal miners completed a self-report questionnaire that assessed their job demands, psychological detachment, job anxiety, and CWBs in a Chinese context. The conceptual model, based on the stressor-detachment model, was examined using structural equation modeling. The results suggest that psychological detachment mediates not only the relation between job demands and job anxiety but also that between job demands and CWBs. Furthermore, the relation between job demands and CWBs is sequentially mediated by psychological detachment and job anxiety. Our findings validate the effectiveness of the stressor-detachment model. Moreover, we demonstrate that the underlying mechanism of the relation between job demands and CWBs can be explained by psychological detachment and job anxiety. PMID:29163274

The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

NASA Astrophysics Data System (ADS)

Ivanin, O. A.; Direktor, L. B.

2018-05-01

The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

Supply based on demand dynamical model

NASA Astrophysics Data System (ADS)

Levi, Asaf; Sabuco, Juan; Sanjuán, Miguel A. F.

2018-04-01

We propose and numerically analyze a simple dynamical model that describes the firm behaviors under uncertainty of demand. Iterating this simple model and varying some parameter values, we observe a wide variety of market dynamics such as equilibria, periodic, and chaotic behaviors. Interestingly, the model is also able to reproduce market collapses.

Organization-based Model-driven Development of High-assurance Multiagent Systems

DTIC Science & Technology

2009-02-27

based Model -driven Development of High-assurance Multiagent Systems " performed by Dr. Scott A . DeLoach and Dr Robby at Kansas State University... A Capabilities Based Model for Artificial Organizations. Journal of Autonomous Agents and Multiagent Systems . Volume 16, no. 1, February 2008, pp...Matson, E . T. (2007). A capabilities based theory of artificial organizations. Journal of Autonomous Agents and Multiagent Systems

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

NASA Astrophysics Data System (ADS)

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

2009-12-01

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

Characterization of chemical agent transport in paints.

PubMed

Willis, Matthew P; Gordon, Wesley; Lalain, Teri; Mantooth, Brent

2013-09-15

A combination of vacuum-based vapor emission measurements with a mass transport model was employed to determine the interaction of chemical warfare agents with various materials, including transport parameters of agents in paints. Accurate determination of mass transport parameters enables the simulation of the chemical agent distribution in a material for decontaminant performance modeling. The evaluation was performed with the chemical warfare agents bis(2-chloroethyl) sulfide (distilled mustard, known as the chemical warfare blister agent HD) and O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate (VX), an organophosphate nerve agent, deposited on to two different types of polyurethane paint coatings. The results demonstrated alignment between the experimentally measured vapor emission flux and the predicted vapor flux. Mass transport modeling demonstrated rapid transport of VX into the coatings; VX penetrated through the aliphatic polyurethane-based coating (100 μm) within approximately 107 min. By comparison, while HD was more soluble in the coatings, the penetration depth in the coatings was approximately 2× lower than VX. Applications of mass transport parameters include the ability to predict agent uptake, and subsequent long-term vapor emission or contact transfer where the agent could present exposure risks. Additionally, these parameters and model enable the ability to perform decontamination modeling to predict how decontaminants remove agent from these materials. Published by Elsevier B.V.

Driving-forces model on individual behavior in scenarios considering moving threat agents

NASA Astrophysics Data System (ADS)

Li, Shuying; Zhuang, Jun; Shen, Shifei; Wang, Jia

2017-09-01

The individual behavior model is a contributory factor to improve the accuracy of agent-based simulation in different scenarios. However, few studies have considered moving threat agents, which often occur in terrorist attacks caused by attackers with close-range weapons (e.g., sword, stick). At the same time, many existing behavior models lack validation from cases or experiments. This paper builds a new individual behavior model based on seven behavioral hypotheses. The driving-forces model is an extension of the classical social force model considering scenarios including moving threat agents. An experiment was conducted to validate the key components of the model. Then the model is compared with an advanced Elliptical Specification II social force model, by calculating the fitting errors between the simulated and experimental trajectories, and being applied to simulate a specific circumstance. Our results show that the driving-forces model reduced the fitting error by an average of 33.9% and the standard deviation by an average of 44.5%, which indicates the accuracy and stability of the model in the studied situation. The new driving-forces model could be used to simulate individual behavior when analyzing the risk of specific scenarios using agent-based simulation methods, such as risk analysis of close-range terrorist attacks in public places.

TRANPLAN and GIS support for agencies in Alabama

DOT National Transportation Integrated Search

2001-08-06

Travel demand models are computerized programs intended to forecast future roadway traffic volumes for a community based on selected socioeconomic variables and travel behavior algorithms. Software to operate these travel demand models is currently a...

A Novel Framework for Characterizing Exposure-Related Behaviors Using Agent-Based Models Embedded with Needs-Based Artificial Intelligence (CSSSA2016)

EPA Science Inventory

Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that is able to simulate longitudinal patterns in behaviors. By basing o...

Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

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

Chen, Qifang; Wang, Fei; Hodge, Bri-Mathias

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and uppermore » bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.« less

Evaluating the effect of human activity patterns on air pollution exposure using an integrated field-based and agent-based modelling framework

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; Beelen, Rob M. J.; de Bakker, Merijn P.; Karssenberg, Derek

2015-04-01

Constructing spatio-temporal numerical models to support risk assessment, such as assessing the exposure of humans to air pollution, often requires the integration of field-based and agent-based modelling approaches. Continuous environmental variables such as air pollution are best represented using the field-based approach which considers phenomena as continuous fields having attribute values at all locations. When calculating human exposure to such pollutants it is, however, preferable to consider the population as a set of individuals each with a particular activity pattern. This would allow to account for the spatio-temporal variation in a pollutant along the space-time paths travelled by individuals, determined, for example, by home and work locations, road network, and travel times. Modelling this activity pattern requires an agent-based or individual based modelling approach. In general, field- and agent-based models are constructed with the help of separate software tools, while both approaches should play together in an interacting way and preferably should be combined into one modelling framework, which would allow for efficient and effective implementation of models by domain specialists. To overcome this lack in integrated modelling frameworks, we aim at the development of concepts and software for an integrated field-based and agent-based modelling framework. Concepts merging field- and agent-based modelling were implemented by extending PCRaster (http://www.pcraster.eu), a field-based modelling library implemented in C++, with components for 1) representation of discrete, mobile, agents, 2) spatial networks and algorithms by integrating the NetworkX library (http://networkx.github.io), allowing therefore to calculate e.g. shortest routes or total transport costs between locations, and 3) functions for field-network interactions, allowing to assign field-based attribute values to networks (i.e. as edge weights), such as aggregated or averaged concentration values. We demonstrate the approach by using six land use regression (LUR) models developed in the ESCAPE (European Study of Cohorts for Air Pollution Effects) project. These models calculate several air pollutants (e.g. NO2, NOx, PM2.5) for the entire Netherlands at a high (5 m) resolution. Using these air pollution maps, we compare exposure of individuals calculated at their x, y location of their home, their work place, and aggregated over the close surroundings of these locations. In addition, total exposure is accumulated over daily activity patterns, summing exposure at home, at the work place, and while travelling between home and workplace, by routing individuals over the Dutch road network, using the shortest route. Finally, we illustrate how routes can be calculated with the minimum total exposure (instead of shortest distance).

Petri Nets as Modeling Tool for Emergent Agents

NASA Technical Reports Server (NTRS)

Bergman, Marto

2004-01-01

Emergent agents, those agents whose local interactions can cause unexpected global results, require a method of modeling that is both dynamic and structured Petri Nets, a modeling tool developed for dynamic discrete event system of mainly functional agents, provide this, and have the benefit of being an established tool. We present here the details of the modeling method here and discuss how to implement its use for modeling agent-based systems. Petri Nets have been used extensively in the modeling of functional agents, those agents who have defined purposes and whose actions should result in a know outcome. However, emergent agents, those agents who have a defined structure but whose interaction causes outcomes that are unpredictable, have not yet found a modeling style that suits them. A problem with formally modeling emergent agents that any formal modeling style usually expects to show the results of a problem and the results of problems studied using emergent agents are not apparent from the initial construction. However, the study of emergent agents still requires a method to analyze the agents themselves, and have sensible conversation about the differences and similarities between types of emergent agents. We attempt to correct this problem by applying Petri Nets to the characterization of emergent agents. In doing so, the emergent properties of these agents can be highlighted, and conversation about the nature and compatibility of the differing methods of agent creation can begin.

Agent-Based Models in Empirical Social Research

ERIC Educational Resources Information Center

Bruch, Elizabeth; Atwell, Jon

2015-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first…

Multimodal Transportation Analysis Process (MTAP): A Travel Demand Forecasting Model

DOT National Transportation Integrated Search

1990-01-01

In 1986, the North Central Texas Council of Governments (NCTCOG) undertook the revision of its travel demand forecasting model. The outcome was a model which was developed based on travel patterns in the Dallas-Forth Worth area and used jointly by th...

Commercial Demand Module - NEMS Documentation

EIA Publications

2017-01-01

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

The recovery model and complex health needs: what health psychology can learn from mental health and substance misuse service provision.

PubMed

Webb, Lucy

2012-07-01

This article reviews key arguments around evidence-based practice and outlines the methodological demands for effective adoption of recovery model principles. The recovery model is outlined and demonstrated as compatible with current needs in substance misuse service provision. However, the concepts of evidence-based practice and the recovery model are currently incompatible unless the current value system of evidence-based practice changes to accommodate the methodologies demanded by the recovery model. It is suggested that critical health psychology has an important role to play in widening the scope of evidence-based practice to better accommodate complex social health needs.

Model-based Utility Functions

NASA Astrophysics Data System (ADS)

Hibbard, Bill

2012-05-01

Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.

Remote Agent Demonstration

NASA Technical Reports Server (NTRS)

Dorais, Gregory A.; Kurien, James; Rajan, Kanna

1999-01-01

We describe the computer demonstration of the Remote Agent Experiment (RAX). The Remote Agent is a high-level, model-based, autonomous control agent being validated on the NASA Deep Space 1 spacecraft.

Spreading of nonmotile bacteria on a hard agar plate: Comparison between agent-based and stochastic simulations

NASA Astrophysics Data System (ADS)

Rana, Navdeep; Ghosh, Pushpita; Perlekar, Prasad

2017-11-01

We study spreading of a nonmotile bacteria colony on a hard agar plate by using agent-based and continuum models. We show that the spreading dynamics depends on the initial nutrient concentration, the motility, and the inherent demographic noise. Population fluctuations are inherent in an agent-based model, whereas for the continuum model we model them by using a stochastic Langevin equation. We show that the intrinsic population fluctuations coupled with nonlinear diffusivity lead to a transition from a diffusion limited aggregation type of morphology to an Eden-like morphology on decreasing the initial nutrient concentration.

Toward Agent-Based Models of the Development And Evolution of Business Relations and Networks

NASA Astrophysics Data System (ADS)

Wilkinson, Ian F.; Marks, Robert E.; Young, Louise

Firms achieve competitive advantage in part through the development of cooperative relations with other firms and organisations. We describe a program of research designed to map and model the development of cooperative inter-firm relations, including the processes and paths by which firms may evolve from adversarial to more cooperative relations. Narrative-event-history methods will be used to develop stylised histories of the emergence of business relations in various contexts and to identify relevant causal mechanisms to be included in the agent-based models of relationship and network evolution. The relationship histories will provide the means of assuring the agent-based models developed.

Optimal inverse functions created via population-based optimization.

PubMed

Jennings, Alan L; Ordóñez, Raúl

2014-06-01

Finding optimal inputs for a multiple-input, single-output system is taxing for a system operator. Population-based optimization is used to create sets of functions that produce a locally optimal input based on a desired output. An operator or higher level planner could use one of the functions in real time. For the optimization, each agent in the population uses the cost and output gradients to take steps lowering the cost while maintaining their current output. When an agent reaches an optimal input for its current output, additional agents are generated in the output gradient directions. The new agents then settle to the local optima for the new output values. The set of associated optimal points forms an inverse function, via spline interpolation, from a desired output to an optimal input. In this manner, multiple locally optimal functions can be created. These functions are naturally clustered in input and output spaces allowing for a continuous inverse function. The operator selects the best cluster over the anticipated range of desired outputs and adjusts the set point (desired output) while maintaining optimality. This reduces the demand from controlling multiple inputs, to controlling a single set point with no loss in performance. Results are demonstrated on a sample set of functions and on a robot control problem.

Agent-based game theory modeling for driverless vehicles at intersections.

DOT National Transportation Integrated Search

2013-02-01

This report presents three research efforts that were published in various journals. The first research effort presents a reactive-driving agent based algorithm for modeling driver left turn gap acceptance behavior at signalized intersections. This m...

Constructing Agent Model for Virtual Training Systems

NASA Astrophysics Data System (ADS)

Murakami, Yohei; Sugimoto, Yuki; Ishida, Toru

Constructing highly realistic agents is essential if agents are to be employed in virtual training systems. In training for collaboration based on face-to-face interaction, the generation of emotional expressions is one key. In training for guidance based on one-to-many interaction such as direction giving for evacuations, emotional expressions must be supplemented by diverse agent behaviors to make the training realistic. To reproduce diverse behavior, we characterize agents by using a various combinations of operation rules instantiated by the user operating the agent. To accomplish this goal, we introduce a user modeling method based on participatory simulations. These simulations enable us to acquire information observed by each user in the simulation and the operating history. Using these data and the domain knowledge including known operation rules, we can generate an explanation for each behavior. Moreover, the application of hypothetical reasoning, which offers consistent selection of hypotheses, to the generation of explanations allows us to use otherwise incompatible operation rules as domain knowledge. In order to validate the proposed modeling method, we apply it to the acquisition of an evacuee's model in a fire-drill experiment. We successfully acquire a subject's model corresponding to the results of an interview with the subject.

iMuseumA: an agent-based context-aware intelligent museum system.

PubMed

Ayala, Inmaculada; Amor, Mercedes; Pinto, Mónica; Fuentes, Lidia; Gámez, Nadia

2014-11-10

Currently, museums provide their visitors with interactive tour guide applications that can be installed in mobile devices and provide timely tailor-made multimedia information about exhibits on display. In this paper, we argue that mobile devices not only could provide help to visitors, but also to museum staff. Our goal is to integrate, within the same system, multimedia tour guides with the management facilities required by museums. In this paper, we present iMuseumA (intelligent museum with agents), a mobile-based solution to customize visits and perform context-aware management tasks. iMuseumA follows an agent-based approach, which makes it possible to interact easily with the museum environment and make decisions based on its current status. This system is currently deployed in the Museum of Informatics at the Informatics School of the University of Málaga, and its main contributions are: (i) a mobile application that provides management facilities to museum staff by means of sensing and processing environmental data; (ii) providing an integrated solution for visitors, tour guides and museum staff that allows coordination and communication enrichment among different groups of users; (iii) using and benefiting from group communication for heterogeneous groups of users that can be created on demand.

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

PubMed

Mostafa, Salama A; Mustapha, Aida; Mohammed, Mazin Abed; Ahmad, Mohd Sharifuddin; Mahmoud, Moamin A

2018-04-01

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls. Copyright © 2018 Elsevier B.V. All rights reserved.

Joint Planning Of Energy Storage and Transmission Considering Wind-Storage Combined System and Demand Side Response

NASA Astrophysics Data System (ADS)

Huang, Y.; Liu, B. Z.; Wang, K. Y.; Ai, X.

2017-12-01

In response to the new requirements of the operation mode of wind-storage combined system and demand side response for transmission network planning, this paper presents a joint planning of energy storage and transmission considering wind-storage combined system and demand side response. Firstly, the charge-discharge strategy of energy storage system equipped at the outlet of wind farm and demand side response strategy are analysed to achieve the best comprehensive benefits through the coordination of the two. Secondly, in the general transmission network planning model with wind power, both energy storage cost and demand side response cost are added to the objective function. Not only energy storage operation constraints and but also demand side response constraints are introduced into the constraint condition. Based on the classical formulation of TEP, a new formulation is developed considering the simultaneous addition of the charge-discharge strategy of energy storage system equipped at the outlet of the wind farm and demand side response strategy, which belongs to a typical mixed integer linear programming model that can be solved by mature optimization software. The case study based on the Garver-6 bus system shows that the validity of the proposed model is verified by comparison with general transmission network planning model. Furthermore, the results demonstrate that the joint planning model can gain more economic benefits through setting up different cases.

Distributed Cooperation Solution Method of Complex System Based on MAS

NASA Astrophysics Data System (ADS)

Weijin, Jiang; Yuhui, Xu

To adapt the model in reconfiguring fault diagnosing to dynamic environment and the needs of solving the tasks of complex system fully, the paper introduced multi-Agent and related technology to the complicated fault diagnosis, an integrated intelligent control system is studied in this paper. Based on the thought of the structure of diagnostic decision and hierarchy in modeling, based on multi-layer decomposition strategy of diagnosis task, a multi-agent synchronous diagnosis federation integrated different knowledge expression modes and inference mechanisms are presented, the functions of management agent, diagnosis agent and decision agent are analyzed, the organization and evolution of agents in the system are proposed, and the corresponding conflict resolution algorithm in given, Layered structure of abstract agent with public attributes is build. System architecture is realized based on MAS distributed layered blackboard. The real world application shows that the proposed control structure successfully solves the fault diagnose problem of the complex plant, and the special advantage in the distributed domain.

Stochastic Technology Choice Model for Consequential Life Cycle Assessment.

PubMed

Kätelhön, Arne; Bardow, André; Suh, Sangwon

2016-12-06

Discussions on Consequential Life Cycle Assessment (CLCA) have relied largely on partial or general equilibrium models. Such models are useful for integrating market effects into CLCA, but also have well-recognized limitations such as the poor granularity of the sectoral definition and the assumption of perfect oversight by all economic agents. Building on the Rectangular-Choice-of-Technology (RCOT) model, this study proposes a new modeling approach for CLCA, the Technology Choice Model (TCM). In this approach, the RCOT model is adapted for its use in CLCA and extended to incorporate parameter uncertainties and suboptimal decisions due to market imperfections and information asymmetry in a stochastic setting. In a case study on rice production, we demonstrate that the proposed approach allows modeling of complex production technology mixes and their expected environmental outcomes under uncertainty, at a high level of detail. Incorporating the effect of production constraints, uncertainty, and suboptimal decisions by economic agents significantly affects technology mixes and associated greenhouse gas (GHG) emissions of the system under study. The case study also shows the model's ability to determine both the average and marginal environmental impacts of a product in response to changes in the quantity of final demand.

Learning dynamics in social dilemmas

PubMed Central

Macy, Michael W.; Flache, Andreas

2002-01-01

The Nash equilibrium, the main solution concept in analytical game theory, cannot make precise predictions about the outcome of repeated mixed-motive games. Nor can it tell us much about the dynamics by which a population of players moves from one equilibrium to another. These limitations, along with concerns about the cognitive demands of forward-looking rationality, have motivated efforts to explore backward-looking alternatives to analytical game theory. Most of the effort has been invested in evolutionary models of population dynamics. We shift attention to a learning-theoretic alternative. Computational experiments with adaptive agents identify a fundamental solution concept for social dilemmas–−stochastic collusion–−based on a random walk from a self-limiting noncooperative equilibrium into a self-reinforcing cooperative equilibrium. However, we show that this solution is viable only within a narrow range of aspiration levels. Below the lower threshold, agents are pulled into a deficient equilibrium that is a stronger attractor than mutual cooperation. Above the upper threshold, agents are dissatisfied with mutual cooperation. Aspirations that adapt with experience (producing habituation to stimuli) do not gravitate into the window of viability; rather, they are the worst of both worlds. Habituation destabilizes cooperation and stabilizes defection. Results from the two-person problem suggest that applications to multiplex and embedded relationships will yield unexpected insights into the global dynamics of cooperation in social dilemmas. PMID:12011402

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.

Cultural Geography Modeling and Analysis in Helmand Province

DTIC Science & Technology

2010-10-01

the application of an agent-based model called “Cultural Geography” to represent the civilian populace. This project uses a multi-agent system ...represent the civilian populace. This project uses a multi-agent system consisting of an environment, agents, objects (things), operations that can be...environments[1]. The model is patterned after the conflict eco- system described by Kilcullen[2] in an attempt to capture the complexities of irregular

Formalizing the role of agent-based modeling in causal inference and epidemiology.

PubMed

Marshall, Brandon D L; Galea, Sandro

2015-01-15

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Design of a multi-agent hydroeconomic model to simulate a complex human-water system: Early insights from the Jordan Water Project

NASA Astrophysics Data System (ADS)

Yoon, J.; Klassert, C. J. A.; Lachaut, T.; Selby, P. D.; Knox, S.; Gorelick, S.; Rajsekhar, D.; Tilmant, A.; Avisse, N.; Harou, J. J.; Gawel, E.; Klauer, B.; Mustafa, D.; Talozi, S.; Sigel, K.

2015-12-01

Our work focuses on development of a multi-agent, hydroeconomic model for purposes of water policy evaluation in Jordan. The model adopts a modular approach, integrating biophysical modules that simulate natural and engineered phenomena with human modules that represent behavior at multiple levels of decision making. The hydrologic modules are developed using spatially-distributed groundwater and surface water models, which are translated into compact simulators for efficient integration into the multi-agent model. For the groundwater model, we adopt a response matrix method approach in which a 3-dimensional MODFLOW model of a complex regional groundwater system is converted into a linear simulator of groundwater response by pre-processing drawdown results from several hundred numerical simulation runs. Surface water models for each major surface water basin in the country are developed in SWAT and similarly translated into simple rainfall-runoff functions for integration with the multi-agent model. The approach balances physically-based, spatially-explicit representation of hydrologic systems with the efficiency required for integration into a complex multi-agent model that is computationally amenable to robust scenario analysis. For the multi-agent model, we explicitly represent human agency at multiple levels of decision making, with agents representing riparian, management, supplier, and water user groups. The agents' decision making models incorporate both rule-based heuristics as well as economic optimization. The model is programmed in Python using Pynsim, a generalizable, open-source object-oriented code framework for modeling network-based water resource systems. The Jordan model is one of the first applications of Pynsim to a real-world water management case study. Preliminary results from a tanker market scenario run through year 2050 are presented in which several salient features of the water system are investigated: competition between urban and private farmer agents, the emergence of a private tanker market, disparities in economic wellbeing to different user groups caused by unique supply conditions, and response of the complex system to various policy interventions.

Economic communication model set

NASA Astrophysics Data System (ADS)

Zvereva, Olga M.; Berg, Dmitry B.

2017-06-01

This paper details findings from the research work targeted at economic communications investigation with agent-based models usage. The agent-based model set was engineered to simulate economic communications. Money in the form of internal and external currencies was introduced into the models to support exchanges in communications. Every model, being based on the general concept, has its own peculiarities in algorithm and input data set since it was engineered to solve the specific problem. Several and different origin data sets were used in experiments: theoretic sets were estimated on the basis of static Leontief's equilibrium equation and the real set was constructed on the basis of statistical data. While simulation experiments, communication process was observed in dynamics, and system macroparameters were estimated. This research approved that combination of an agent-based and mathematical model can cause a synergetic effect.

How should we build a generic open-source water management simulator?

NASA Astrophysics Data System (ADS)

Khadem, M.; Meier, P.; Rheinheimer, D. E.; Padula, S.; Matrosov, E.; Selby, P. D.; Knox, S.; Harou, J. J.

2014-12-01

Increasing water needs for agriculture, industry and cities mean effective and flexible water resource system management tools will remain in high demand. Currently many regions or countries use simulators that have been adapted over time to their unique system properties and water management rules and realities. Most regions operate with a preferred short-list of water management and planning decision support systems. Is there scope for a simulator, shared within the water management community, that could be adapted to different contexts, integrate community contributions, and connect to generic data and model management software? What role could open-source play in such a project? How could a genericuser-interface and data/model management software sustainably be attached to this model or suite of models? Finally, how could such a system effectively leverage existing model formulations, modeling technologies and software? These questions are addressed by the initial work presented here. We introduce a generic water resource simulation formulation that enables and integrates both rule-based and optimization driven technologies. We suggest how it could be linked to other sub-models allowing for detailed agent-based simulation of water management behaviours. An early formulation is applied as an example to the Thames water resource system in the UK. The model uses centralised optimisation to calculate allocations but allows for rule-based operations as well in an effort to represent observed behaviours and rules with fidelity. The model is linked through import/export commands to a generic network model platform named Hydra. Benefits and limitations of the approach are discussed and planned work and potential use cases are outlined.

[Determinants of health care utilization in Costa Rica].

PubMed

Morera Salas, Melvin; Aparicio Llanos, Amada

2010-01-01

To analyze the determinants of health care utilization (visits to the doctor) in Costa Rica using an econometric approach. Data were drawn from the National Survey of Health for Costa Rica 2006. We modeled the Grossman approach to the demand for health services by using a standard negative binomial regression, and used a hurdle model for the principal-agent specification. The factors determining healthcare utilization were level of education, self-assessed health, number of declared chronic diseases and geographic region of residence. The number of outpatient visits to the doctor depends on the proxies for medical need, but we found no multivariate association between the use of outpatient visits and income or insurance status. This result suggests that there is no problem with access in the public - almost universal - Costa Rican health system. No conclusive results were obtained on the influence of the physician on the frequency of use of health care services, as postulated by the principal-agent model. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

Quantitative Agent Based Model of User Behavior in an Internet Discussion Forum

PubMed Central

Sobkowicz, Pawel

2013-01-01

The paper presents an agent based simulation of opinion evolution, based on a nonlinear emotion/information/opinion (E/I/O) individual dynamics, to an actual Internet discussion forum. The goal is to reproduce the results of two-year long observations and analyses of the user communication behavior and of the expressed opinions and emotions, via simulations using an agent based model. The model allowed to derive various characteristics of the forum, including the distribution of user activity and popularity (outdegree and indegree), the distribution of length of dialogs between the participants, their political sympathies and the emotional content and purpose of the comments. The parameters used in the model have intuitive meanings, and can be translated into psychological observables. PMID:24324606

Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study.

PubMed

Laskowski, Marek; Demianyk, Bryan C P; Witt, Julia; Mukhi, Shamir N; Friesen, Marcia R; McLeod, Robert D

2011-11-01

The objective of this paper was to develop an agent-based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent-based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C++ using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that became infected in one day within the simulation was the dependent variable. The results suggest that within the given instance context, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

Model-Drive Architecture for Agent-Based Systems

NASA Technical Reports Server (NTRS)

Gradanin, Denis; Singh, H. Lally; Bohner, Shawn A.; Hinchey, Michael G.

2004-01-01

The Model Driven Architecture (MDA) approach uses a platform-independent model to define system functionality, or requirements, using some specification language. The requirements are then translated to a platform-specific model for implementation. An agent architecture based on the human cognitive model of planning, the Cognitive Agent Architecture (Cougaar) is selected for the implementation platform. The resulting Cougaar MDA prescribes certain kinds of models to be used, how those models may be prepared and the relationships of the different kinds of models. Using the existing Cougaar architecture, the level of application composition is elevated from individual components to domain level model specifications in order to generate software artifacts. The software artifacts generation is based on a metamodel. Each component maps to a UML structured component which is then converted into multiple artifacts: Cougaar/Java code, documentation, and test cases.

[Advances in peroxide-based decontaminating technologies].

PubMed

Xi, Hai-ling; Zhao, San-ping; Zhou, Wen

2013-05-01

With the boosting demand for eco-friendly decontaminants, great achievements in peroxide-based decontaminating technologies have been made in recent years. These technologies have been applied in countering chemical/biological terrorist attacks, dealing with chemical/biological disasters and destructing environmental pollutants. Recent research advances in alpha-nucleophilic/oxidative reaction mechanisms of peroxide-based decontamination against chemical warfare agents were reviewed, and some classical peroxide-based decontaminants such as aqueous decontaminating solution, decontaminating foam, decontaminating emulsions, decontaminating gels, decontaminating vapors, and some newly developed decontaminating media (e.g., peroxide-based self-decontaminating materials and heterogeneous nano-catalytic decontamination systems) were introduced. However, currently available peroxide-based decontaminants still have some deficiencies. For example, their decontamination efficiencies are not as high as those of chlorine-containing decontaminants, and some peroxide-based decontaminants show relatively poor effect against certain agents. More study on the mechanisms of peroxide-based decontaminants and the interfacial interactions in heterogeneous decontamination media is suggested. New catalysts, multifunctional surfactants, self-decontaminating materials and corrosion preventing technologies should be developed before peroxide-based decontaminants really become true "green" decontaminants.

Development of Ensemble Model Based Water Demand Forecasting Model

NASA Astrophysics Data System (ADS)

Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

2014-05-01

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

Consensus statement: appropriate consumer education and communication programs for weight- loss agents in Asia.

PubMed

Chan, Siew Pheng; Chui, William C; Lo, Kwok Wing; Huang, Kuo-Chin; Leyesa, Normita D; Lin, Wen-Yuan; Mirasol, Roberto C; Robles, Yolanda R; Tey, Beng Hea; Paraidathathu, Thomas

2012-07-01

The increasing prevalence of overweight and obesity worldwide demands increased efforts in the prevention and management of obesity. This article aims to present consensus statements promoting appropriate consumer education and communication programs for weight-loss agents in Asia. Panel members from various disciplines developed consensus statements based on an expert meeting on the benefits of consumer education and communication programs for over-the-counter weight-loss agents. Key opinion leaders discussed relevant data that served as the basis of the recommendations. Obesity is a growing epidemic in Asia, turning the region into a potential market for weight-loss products and services. Current trends in direct-to-consumer advertising demonstrate the pervasiveness of false representations lacking adequate substantiation. Relevant issues and recommendations were established. Public education on weight management is a shared responsibility; there is a need to raise public awareness of obesity and its health-related consequences. Advertising guidelines should ensure responsible direct-to-consumer advertising of weight-loss agents.

Power system voltage stability and agent based distribution automation in smart grid

NASA Astrophysics Data System (ADS)

Nguyen, Cuong Phuc

2011-12-01

Our interconnected electric power system is presently facing many challenges that it was not originally designed and engineered to handle. The increased inter-area power transfers, aging infrastructure, and old technologies, have caused many problems including voltage instability, widespread blackouts, slow control response, among others. These problems have created an urgent need to transform the present electric power system to a highly stable, reliable, efficient, and self-healing electric power system of the future, which has been termed "smart grid". This dissertation begins with an investigation of voltage stability in bulk transmission networks. A new continuation power flow tool for studying the impacts of generator merit order based dispatch on inter-area transfer capability and static voltage stability is presented. The load demands are represented by lumped load models on the transmission system. While this representation is acceptable in traditional power system analysis, it may not be valid in the future smart grid where the distribution system will be integrated with intelligent and quick control capabilities to mitigate voltage problems before they propagate into the entire system. Therefore, before analyzing the operation of the whole smart grid, it is important to understand the distribution system first. The second part of this dissertation presents a new platform for studying and testing emerging technologies in advanced Distribution Automation (DA) within smart grids. Due to the key benefits over the traditional centralized approach, namely flexible deployment, scalability, and avoidance of single-point-of-failure, a new distributed approach is employed to design and develop all elements of the platform. A multi-agent system (MAS), which has the three key characteristics of autonomy, local view, and decentralization, is selected to implement the advanced DA functions. The intelligent agents utilize a communication network for cooperation and negotiation. Communication latency is modeled using a user-defined probability density function. Failure-tolerant communication strategies are developed for agent communications. Major elements of advanced DA are developed in a completely distributed way and successfully tested for several IEEE standard systems, including: Fault Detection, Location, Isolation, and Service Restoration (FLISR); Coordination of Distributed Energy Storage Systems (DES); Distributed Power Flow (DPF); Volt-VAR Control (VVC); and Loss Reduction (LR).

What Do We Know about the Demand for Evaluation? Insights from the Parliamentary Arena

ERIC Educational Resources Information Center

Bundi, Pirmin

2016-01-01

Research on evaluation has mainly focused on the use of evaluation and has given little attention to the origins of evaluation demand. In this article, I consider the question of why parliamentarians demand evaluations with parliamentary requests. Building on the literature of delegation, I use a principal-agent framework to explain the origins of…

Balancing Work and Family Responsibilities as an Extension 4-H Agent

ERIC Educational Resources Information Center

Rhea, Joseph Richard, Jr.

2009-01-01

A career with Extension can be very rewarding, but also very demanding, as employees have to balance job stress and time demands with family goals and demands. The very nature of Extension work brings some tension between the job and family, and employees need to be equipped to make decisions about personal and work time. If the Extension System…

Safe motion planning for mobile agents: A model of reactive planning for multiple mobile agents

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

Fujimura, Kikuo.

1990-01-01

The problem of motion planning for multiple mobile agents is studied. Each planning agent independently plans its own action based on its map which contains a limited information about the environment. In an environment where more than one mobile agent interacts, the motions of the robots are uncertain and dynamic. A model for reactive agents is described and simulation results are presented to show their behavior patterns. 18 refs., 2 figs.

An Agent-Based Model of New Venture Creation: Conceptual Design for Simulating Entrepreneurship

NASA Technical Reports Server (NTRS)

Provance, Mike; Collins, Andrew; Carayannis, Elias

2012-01-01

There is a growing debate over the means by which regions can foster the growth of entrepreneurial activity in order to stimulate recovery and growth of their economies. On one side, agglomeration theory suggests the regions grow because of strong clusters that foster knowledge spillover locally; on the other side, the entrepreneurial action camp argues that innovative business models are generated by entrepreneurs with unique market perspectives who draw on knowledge from more distant domains. We will show you the design for a novel agent-based model of new venture creation that will demonstrate the relationship between agglomeration and action. The primary focus of this model is information exchange as the medium for these agent interactions. Our modeling and simulation study proposes to reveal interesting relationships in these perspectives, offer a foundation on which these disparate theories from economics and sociology can find common ground, and expand the use of agent-based modeling into entrepreneurship research.

Modeling relief demands in an emergency supply chain system under large-scale disasters based on a queuing network.

PubMed

He, Xinhua; Hu, Wenfa

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network

PubMed Central

He, Xinhua

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model. PMID:24688367

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

A Primer for Agent-Based Simulation and Modeling in Transportation Applications

DOT National Transportation Integrated Search

2013-11-01

Agent-based modeling and simulation (ABMS) methods have been applied in a spectrum of research domains. This primer focuses on ABMS in the transportation interdisciplinary domain, describes the basic concepts of ABMS and the recent progress of ABMS i...

Modeling Temporal and Spatial Flows of Ecosystem Services in Chittenden County, VT

NASA Astrophysics Data System (ADS)

Voigt, B. G.; Bagstad, K.; Johnson, G.; Villa, F.

2010-12-01

This paper documents the integration of ARIES (ARtificial Intelligence for Ecosystem Services) with the land use change model UrbanSim to explore the impacts of current and future land use patterns on flood protection and water provision services in Chittenden County, VT. ARIES, an open source modeling platform, is particularly well-suited for measuring, mapping, and modeling the temporal and spatial flows of ecosystem services across the landscape, linking the areas of provision (sources) with human beneficiaries (users) through a spatially explicit agent-based modeling approach. UrbanSim is an open source agent-based land use model designed to facilitate a wide-range of scenarios based on user-specified behavioral assumptions, zoning regulations, and demographic, economic, and infrastructure (e.g. transportation, water, sewer, etc.) parameters. Ecosystem services travel through time and space and are susceptible to disruption and destruction from both natural and anthropogenic perturbations. The conversion of forested or agricultural land to urbanizing uses is replete with a long history of hydrologic impairment, habitat fragmentation, and the degradation of sensitive landscapes. Development decisions are predicated on the presence of landscape characteristics that meet the needs of developers and satisfy the desires of consumers, with minimal consideration of access to or effect on the provision of ecosystem services. The County houses nearly 25% of the state’s population and several employment centers that draw labor from throughout the region. Additionally, the County is expected to maintain modest residential and employment growth over the next 30 years, and will continue to serve as the state’s population and employment center. Expected future growth is likely to adversely affect the remaining farm and forest land in the County in the absence of policies to support sustainable development. We demonstrate how ARIES can be used to quantify changes in ecosystem service provision based on the outcomes of alternative land use change model scenarios. Stakeholder workshops were hosted to develop scenarios relevant to planning for future growth in the County, including alternative zoning regulations, road network improvements, and a range of future population projections. The results of the land use change simulations were passed to ARIES to model flood protection and water provision services for each of the alternative scenarios. We present Bayesian models of the ecosystem services as individual source, sink, and use components coupled with models of temporal and spatial flows of services across the landscape. Specific beneficiaries include homeowners, farmers, and other business property owners. The location choice decisions of residential and non-residential agents under the alternative scenarios resulted in varying access to ecosystem services depending on development density, habitat fragmentation, and the degree of hydrological impairment, among other factors. Modeled outputs include maps depicting flow paths (linking sources to beneficiaries), changes in land use, hotspot locations that are critical to sustain the flow of services across the landscape, and the demand for and supply of the modeled services.

Interacting with an artificial partner: modeling the role of emotional aspects.

PubMed

Cattinelli, Isabella; Goldwurm, Massimiliano; Borghese, N Alberto

2008-12-01

In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent's personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner's behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand.

PubMed

Gupta, Saurabh; Black-Schaffer, W Stephen; Crawford, James M; Gross, David; Karcher, Donald S; Kaufman, Jill; Knapman, Doug; Prystowsky, Michael B; Wheeler, Thomas M; Bean, Sarah; Kumar, Paramhans; Sharma, Raghav; Chamoli, Vaibhav; Ghai, Vikrant; Gogia, Vineet; Weintraub, Sally; Cohen, Michael B; Robboy, Stanley J

2015-01-01

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand

PubMed Central

Gupta, Saurabh; Black-Schaffer, W. Stephen; Crawford, James M.; Gross, David; Karcher, Donald S.; Kaufman, Jill; Knapman, Doug; Prystowsky, Michael B.; Wheeler, Thomas M.; Bean, Sarah; Kumar, Paramhans; Sharma, Raghav; Chamoli, Vaibhav; Ghai, Vikrant; Gogia, Vineet; Weintraub, Sally; Cohen, Michael B.

2015-01-01

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models. PMID:28725751

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

Impact of different policies on unhealthy dietary behaviors in an urban adult population: an agent-based simulation model.

PubMed

Zhang, Donglan; Giabbanelli, Philippe J; Arah, Onyebuchi A; Zimmerman, Frederick J

2014-07-01

Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems.

Adapting Price Predictions in TAC SCM

NASA Astrophysics Data System (ADS)

Pardoe, David; Stone, Peter

In agent-based markets, adapting to the behavior of other agents is often necessary for success. When it is not possible to directly model individual competitors, an agent may instead model and adapt to the market conditions that result from competitor behavior. Such an agent could still benefit from reasoning about specific competitor strategies by considering how various combinations of these strategies would impact the conditions being modeled. We present an application of such an approach to a specific prediction problem faced by the agent TacTex-06 in the Trading Agent Competition's Supply Chain Management scenario (TAC SCM).

Modeling persistence of motion in a crowded environment: The diffusive limit of excluding velocity-jump processes

NASA Astrophysics Data System (ADS)

Gavagnin, Enrico; Yates, Christian A.

2018-03-01

Persistence of motion is the tendency of an object to maintain motion in a direction for short time scales without necessarily being biased in any direction in the long term. One of the most appropriate mathematical tools to study this behavior is an agent-based velocity-jump process. In the absence of agent-agent interaction, the mean-field continuum limit of the agent-based model (ABM) gives rise to the well known hyperbolic telegraph equation. When agent-agent interaction is included in the ABM, a strictly advective system of partial differential equations (PDEs) can be derived at the population level. However, no diffusive limit of the ABM has been obtained from such a model. Connecting the microscopic behavior of the ABM to a diffusive macroscopic description is desirable, since it allows the exploration of a wider range of scenarios and establishes a direct connection with commonly used statistical tools of movement analysis. In order to connect the ABM at the population level to a diffusive PDE at the population level, we consider a generalization of the agent-based velocity-jump process on a two-dimensional lattice with three forms of agent interaction. This generalization allows us to take a diffusive limit and obtain a faithful population-level description. We investigate the properties of the model at both the individual and population levels and we elucidate some of the models' key characteristic features. In particular, we show an intrinsic anisotropy inherent to the models and we find evidence of a spontaneous form of aggregation at both the micro- and macroscales.

Performance of a system of reservoirs on futuristic front

NASA Astrophysics Data System (ADS)

Saha, Satabdi; Roy, Debasri; Mazumdar, Asis

2017-10-01

Application of simulation model HEC-5 to analyze the performance of the DVC Reservoir System (a multipurpose system with a network of five reservoirs and one barrage) on the river Damodar in Eastern India in meeting projected future demand as well as controlling flood for synthetically generated future scenario is addressed here with a view to develop an appropriate strategy for its operation. Thomas-Fiering model (based on Markov autoregressive model) has been adopted for generation of synthetic scenario (monthly streamflow series) and subsequently downscaling of modeled monthly streamflow to daily values was carried out. The performance of the system (analysed on seasonal basis) in terms of `Performance Indices' (viz., both quantity based reliability and time based reliability, mean daily deficit, average failure period, resilience and maximum vulnerability indices) for the projected scenario with enhanced demand turned out to be poor compared to that for historical scenario. However, judicious adoption of resource enhancement (marginal reallocation of reservoir storage capacity) and demand management strategy (curtailment of projected high water requirements and trading off between demands) was found to be a viable option for improvement of the performance of the reservoir system appreciably [improvement being (1-51 %), (2-35 %), (16-96 %), (25-50 %), (8-36 %) and (12-30 %) for the indices viz., quantity based reliability, time based reliability, mean daily deficit, average failure period, resilience and maximum vulnerability, respectively] compared to that with normal storage and projected demand. Again, 100 % reliability for flood control for current as well as future synthetically generated scenarios was noted. The results from the study would assist concerned authority in successful operation of reservoirs in the context of growing demand and dwindling resource.

Minority game and anomalies in financial markets

NASA Astrophysics Data System (ADS)

Liu, Xinghua; Liang, Xiaobei; Tang, Bingyong

2004-02-01

The minority game (MG), which is intrinsically associated with financial markets, is an agent-based model of a competing population with limited resources. We find that the fluctuation features of MG in crowded region are more similar to real market than that of in perfect cooperation region. So we propose and study a modified model based on the MG in which agents accumulate virtual points for their strategies from the last H steps instead of from the beginning of the game. The results of numerical simulations on our new model show that agents will be more intelligent, and the types of features of fluctuations are the same in real-world market. We also give a numerical explanation of the high adaptability of agents in new model.

Nanomaterials incorporated ultrasound contrast agents for cancer theranostics

PubMed Central

Fu, Lei; Ke, Heng-Te

2016-01-01

Nanotechnology provides various nanomaterials with tremendous functionalities for cancer diagnostics and therapeutics. Recently, theranostics has been developed as an alternative strategy for efficient cancer treatment through combination of imaging diagnosis and therapeutic interventions under the guidance of diagnostic results. Ultrasound (US) imaging shows unique advantages with excellent features of real-time imaging, low cost, high safety and portability, making US contrast agents (UCAs) an ideal platform for construction of cancer theranostic agents. This review focuses on the development of nanomaterials incorporated multifunctional UCAs serving as theranostic agents for cancer diagnostics and therapeutics, via conjugation of superparamagnetic iron oxide nanoparticles (SPIOs), CuS nanoparticles, DNA, siRNA, gold nanoparticles (GNPs), gold nanorods (GNRs), gold nanoshell (GNS), graphene oxides (GOs), polypyrrole (PPy) nanocapsules, Prussian blue (PB) nanoparticles and so on to different types of UCAs. The cancer treatment could be more effectively and accurately carried out under the guidance and monitoring with the help of the achieved theranostic agents. Furthermore, nanomaterials incorporated theranostic agents based on UCAs can be designed and constructed by demand for personalized and accurate treatment of cancer, demonstrating their great potential to address the challenges of cancer heterogeneity and adaptation, which can provide alternative strategies for cancer diagnosis and therapeutics. PMID:27807499

Applications research in ultrasonic testing of carbon fiber composite based on an optical fiber F-p sensor

NASA Astrophysics Data System (ADS)

Shan, Ning

2016-10-01

Carbon fiber composite is widely applied to the field of aerospace engineering because of its excellent performance. But it will be able to form more defects in the process of manufacturing inevitably on account of unique manufacturing process. Meanwhile it has sophisticated structure and services in the bad environment long time. The existence of defects will be able to cause the sharp decline in component's performance when the defect accumulates to a certain degree. So the reliability and safety test demand of carbon fiber composite is higher and higher. Ultrasonic testing technology is the important means used for characteristics of component inspection of composite materials. Ultrasonic information detection uses acoustic transducer generally. It need coupling agent and is higher demand for the surface of sample. It has narrow frequency band and low test precision. The extrinsic type optical fiber F-P interference cavity structure is designed to this problem. Its optical interference model is studied. The initial length of F-P cavity is designed. The realtime online detection system of carbon fiber composite is established based on optical fiber F-P Ultrasound sensing technology. Finally, the testing experiment study is conducted. The results show that the system can realize real-time online detection of carbon fiber composite's defect effectively. It operates simply and realizes easily. It has low cost and is easy to practical engineering.

Study of an intraurban travel demand model incorporating commuter preference variables

NASA Technical Reports Server (NTRS)

Holligan, P. E.; Coote, M. A.; Rushmer, C. R.; Fanning, M. L.

1971-01-01

The model is based on the substantial travel data base for the nine-county San Francisco Bay Area, provided by the Metropolitan Transportation Commission. The model is of the abstract type, and makes use of commuter attitudes towards modes and simple demographic characteristics of zones in a region to predict interzonal travel by mode for the region. A characterization of the STOL/VTOL mode was extrapolated by means of a subjective comparison of its expected characteristics with those of modes characterized by the survey. Predictions of STOL demand were made for the Bay Area and an aircraft network was developed to serve this demand. When this aircraft system is compared to the base case system, the demand for STOL service has increased five fold and the resulting economics show considerable benefit from the increased scale of operations. In the previous study all systems required subsidy in varying amounts. The new system shows a substantial profit at an average fare of $3.55 per trip.

Efficient Agent-Based Models for Non-Genomic Evolution

NASA Technical Reports Server (NTRS)

Gupta, Nachi; Agogino, Adrian; Tumer, Kagan

2006-01-01

Modeling dynamical systems composed of aggregations of primitive proteins is critical to the field of astrobiological science involving early evolutionary structures and the origins of life. Unfortunately traditional non-multi-agent methods either require oversimplified models or are slow to converge to adequate solutions. This paper shows how to address these deficiencies by modeling the protein aggregations through a utility based multi-agent system. In this method each agent controls the properties of a set of proteins assigned to that agent. Some of these properties determine the dynamics of the system, such as the ability for some proteins to join or split other proteins, while additional properties determine the aggregation s fitness as a viable primitive cell. We show that over a wide range of starting conditions, there are mechanisins that allow protein aggregations to achieve high values of overall fitness. In addition through the use of agent-specific utilities that remain aligned with the overall global utility, we are able to reach these conclusions with 50 times fewer learning steps.

Dual Rationality and Deliberative Agents

NASA Astrophysics Data System (ADS)

Debenham, John; Sierra, Carles

Human agents deliberate using models based on reason for only a minute proportion of the decisions that they make. In stark contrast, the deliberation of artificial agents is heavily dominated by formal models based on reason such as game theory, decision theory and logic—despite that fact that formal reasoning will not necessarily lead to superior real-world decisions. Further the Nobel Laureate Friedrich Hayek warns us of the ‘fatal conceit’ in controlling deliberative systems using models based on reason as the particular model chosen will then shape the system’s future and either impede, or eventually destroy, the subtle evolutionary processes that are an integral part of human systems and institutions, and are crucial to their evolution and long-term survival. We describe an architecture for artificial agents that is founded on Hayek’s two rationalities and supports the two forms of deliberation used by mankind.

Transition from Exponential to Power Law Income Distributions in a Chaotic Market

NASA Astrophysics Data System (ADS)

Pellicer-Lostao, Carmen; Lopez-Ruiz, Ricardo

Economy is demanding new models, able to understand and predict the evolution of markets. To this respect, Econophysics offers models of markets as complex systems, that try to comprehend macro-, system-wide states of the economy from the interaction of many agents at micro-level. One of these models is the gas-like model for trading markets. This tries to predict money distributions in closed economies and quite simply, obtains the ones observed in real economies. However, it reveals technical hitches to explain the power law distribution, observed in individuals with high incomes. In this work, nonlinear dynamics is introduced in the gas-like model in an effort to overcomes these flaws. A particular chaotic dynamics is used to break the pairing symmetry of agents (i, j) ⇔ (j, i). The results demonstrate that a "chaotic gas-like model" can reproduce the Exponential and Power law distributions observed in real economies. Moreover, it controls the transition between them. This may give some insight of the micro-level causes that originate unfair distributions of money in a global society. Ultimately, the chaotic model makes obvious the inherent instability of asymmetric scenarios, where sinks of wealth appear and doom the market to extreme inequality.

Incorporating home demands into models of job strain: findings from the work, family, and health network.

PubMed

Ertel, Karen A; Koenen, Karestan C; Berkman, Lisa F

2008-11-01

The purpose of this article was to integrate home demands with the demand-control-support model to test if home demands interact with job strain to increase depressive symptoms. Data were from 431 employees in four extended care facilities. Presence of a child younger than 18 years in the household signified home demands. The outcome was depressive symptoms based on a shortened version of the Center for Epidemiologic Studies Depression Scale. The association between job strain and depressive symptoms was moderated by social support (SS) and presence of a child in the household (child). There was no association among participants with high SS and no child, but a positive one among participants with low SS and a child. Job strain may be a particularly important determinant of depressive symptoms among employees with family demands. Models of job strain should expand to incorporate family demands.

Agent-Based Phytoplankton Models of Cellular and Population Processes: Fostering Individual-Based Learning in Undergraduate Research

NASA Astrophysics Data System (ADS)

Berges, J. A.; Raphael, T.; Rafa Todd, C. S.; Bate, T. C.; Hellweger, F. L.

2016-02-01

Engaging undergraduate students in research projects that require expertise in multiple disciplines (e.g. cell biology, population ecology, and mathematical modeling) can be challenging because they have often not developed the expertise that allows them to participate at a satisfying level. Use of agent-based modeling can allow exploration of concepts at more intuitive levels, and encourage experimentation that emphasizes processes over computational skills. Over the past several years, we have involved undergraduate students in projects examining both ecological and cell biological aspects of aquatic microbial biology, using the freely-downloadable, agent-based modeling environment NetLogo (https://ccl.northwestern.edu/netlogo/). In Netlogo, actions of large numbers of individuals can be simulated, leading to complex systems with emergent behavior. The interface features appealing graphics, monitors, and control structures. In one example, a group of sophomores in a BioMathematics program developed an agent-based model of phytoplankton population dynamics in a pond ecosystem, motivated by observed macroscopic changes in cell numbers (due to growth and death), and driven by responses to irradiance, temperature and a limiting nutrient. In a second example, junior and senior undergraduates conducting Independent Studies created a model of the intracellular processes governing stress and cell death for individual phytoplankton cells (based on parameters derived from experiments using single-cell culturing and flow cytometry), and then this model was embedded in the agents in the pond ecosystem model. In our experience, students with a range of mathematical abilities learned to code quickly and could use the software with varying degrees of sophistication, for example, creation of spatially-explicit two and three-dimensional models. Skills developed quickly and transferred readily to other platforms (e.g. Matlab).

A Coupled Snow Operations-Skier Demand Model for the Ontario (Canada) Ski Region

NASA Astrophysics Data System (ADS)

Pons, Marc; Scott, Daniel; Steiger, Robert; Rutty, Michelle; Johnson, Peter; Vilella, Marc

2016-04-01

The multi-billion dollar global ski industry is one of the tourism subsectors most directly impacted by climate variability and change. In the decades ahead, the scholarly literature consistently projects decreased reliability of natural snow cover, shortened and more variable ski seasons, as well as increased reliance on snowmaking with associated increases in operational costs. In order to develop the coupled snow, ski operations and demand model for the Ontario ski region (which represents approximately 18% of Canada's ski market), the research utilized multiple methods, including: a in situ survey of over 2400 skiers, daily operations data from ski resorts over the last 10 years, climate station data (1981-2013), climate change scenario ensemble (AR5 - RCP 8.5), an updated SkiSim model (building on Scott et al. 2003; Steiger 2010), and an agent-based model (building on Pons et al. 2014). Daily snow and ski operations for all ski areas in southern Ontario were modeled with the updated SkiSim model, which utilized current differential snowmaking capacity of individual resorts, as determined from daily ski area operations data. Snowmaking capacities and decision rules were informed by interviews with ski area managers and daily operations data. Model outputs were validated with local climate station and ski operations data. The coupled SkiSim-ABM model was run with historical weather data for seasons representative of an average winter for the 1981-2010 period, as well as an anomalously cold winter (2012-13) and the record warm winter in the region (2011-12). The impact on total skier visits and revenues, and the geographic and temporal distribution of skier visits were compared. The implications of further climate adaptation (i.e., improving the snowmaking capacity of all ski areas to the level of leading resorts in the region) were also explored. This research advances system modelling, especially improving the integration of snow and ski operations models with demand and socioeconomic implications. This innovative integrated systems model approach can be exported to other major ski tourism markets (e.g., Canada, USA, Western and Eastern Europe, Australia, Japan) to facilitate global comparative assessments of ski tourism vulnerability to climate change, establishing the standard for ski tourism vulnerability assessments and advancing scholarly work on sustainable tourism and climate-compatible development in mountain communities.

Can agent based models effectively reduce fisheries management implementation uncertainty?

NASA Astrophysics Data System (ADS)

Drexler, M.

2016-02-01

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

Systems, methods and apparatus for modeling, specifying and deploying policies in autonomous and autonomic systems using agent-oriented software engineering

NASA Technical Reports Server (NTRS)

Sterritt, Roy (Inventor); Hinchey, Michael G. (Inventor); Penn, Joaquin (Inventor)

2011-01-01

Systems, methods and apparatus are provided through which in some embodiments, an agent-oriented specification modeled with MaCMAS, is analyzed, flaws in the agent-oriented specification modeled with MaCMAS are corrected, and an implementation is derived from the corrected agent-oriented specification. Described herein are systems, method and apparatus that produce fully (mathematically) tractable development of agent-oriented specification(s) modeled with methodology fragment for analyzing complex multiagent systems (MaCMAS) and policies for autonomic systems from requirements through to code generation. The systems, method and apparatus described herein are illustrated through an example showing how user formulated policies can be translated into a formal mode which can then be converted to code. The requirements-based programming systems, method and apparatus described herein may provide faster, higher quality development and maintenance of autonomic systems based on user formulation of policies.

When push comes to shove: Exclusion processes with nonlocal consequences

NASA Astrophysics Data System (ADS)

Almet, Axel A.; Pan, Michael; Hughes, Barry D.; Landman, Kerry A.

2015-11-01

Stochastic agent-based models are useful for modelling collective movement of biological cells. Lattice-based random walk models of interacting agents where each site can be occupied by at most one agent are called simple exclusion processes. An alternative motility mechanism to simple exclusion is formulated, in which agents are granted more freedom to move under the compromise that interactions are no longer necessarily local. This mechanism is termed shoving. A nonlinear diffusion equation is derived for a single population of shoving agents using mean-field continuum approximations. A continuum model is also derived for a multispecies problem with interacting subpopulations, which either obey the shoving rules or the simple exclusion rules. Numerical solutions of the derived partial differential equations compare well with averaged simulation results for both the single species and multispecies processes in two dimensions, while some issues arise in one dimension for the multispecies case.

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models.

PubMed

Elsawah, Sondoss; Guillaume, Joseph H A; Filatova, Tatiana; Rook, Josefine; Jakeman, Anthony J

2015-03-15

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Agent-Based Modeling Template for a Cohort of Veterans with Diabetic Retinopathy.

PubMed

Day, Theodore Eugene; Ravi, Nathan; Xian, Hong; Brugh, Ann

2013-01-01

Agent-based models are valuable for examining systems where large numbers of discrete individuals interact with each other, or with some environment. Diabetic Veterans seeking eye care at a Veterans Administration hospital represent one such cohort. The objective of this study was to develop an agent-based template to be used as a model for a patient with diabetic retinopathy (DR). This template may be replicated arbitrarily many times in order to generate a large cohort which is representative of a real-world population, upon which in-silico experimentation may be conducted. Agent-based template development was performed in java-based computer simulation suite AnyLogic Professional 6.6. The model was informed by medical data abstracted from 535 patient records representing a retrospective cohort of current patients of the VA St. Louis Healthcare System Eye clinic. Logistic regression was performed to determine the predictors associated with advancing stages of DR. Predicted probabilities obtained from logistic regression were used to generate the stage of DR in the simulated cohort. The simulated cohort of DR patients exhibited no significant deviation from the test population of real-world patients in proportion of stage of DR, duration of diabetes mellitus (DM), or the other abstracted predictors. Simulated patients after 10 years were significantly more likely to exhibit proliferative DR (P<0.001). Agent-based modeling is an emerging platform, capable of simulating large cohorts of individuals based on manageable data abstraction efforts. The modeling method described may be useful in simulating many different conditions where course of disease is described in categorical stages.

Simulating the conversion of rural settlements to town land based on multi-agent systems and cellular automata.

PubMed

Liu, Yaolin; Kong, Xuesong; Liu, Yanfang; Chen, Yiyun

2013-01-01

Rapid urbanization in China has triggered the conversion of land from rural to urban use, particularly the conversion of rural settlements to town land. This conversion is the result of the joint effects of the geographic environment and agents involving the government, investors, and farmers. To understand the dynamic interaction dominated by agents and to predict the future landscape of town expansion, a small town land-planning model is proposed based on the integration of multi-agent systems (MAS) and cellular automata (CA). The MAS-CA model links the decision-making behaviors of agents with the neighbor effect of CA. The interaction rules are projected by analyzing the preference conflicts among agents. To better illustrate the effects of the geographic environment, neighborhood, and agent behavior, a comparative analysis between the CA and MAS-CA models in three different towns is presented, revealing interesting patterns in terms of quantity, spatial characteristics, and the coordinating process. The simulation of rural settlements conversion to town land through modeling agent decision and human-environment interaction is very useful for understanding the mechanisms of rural-urban land-use change in developing countries. This process can assist town planners in formulating appropriate development plans.

Enabling Microscopic Simulators to Perform System Level Tasks: A System-Identification Based, Closure-on-Demand Toolkit for Multiscale Simulation Stability/Bifurcation Analysis, Optimization and Control

DTIC Science & Technology

2006-10-01

The objective was to construct a bridge between existing and future microscopic simulation codes ( kMC , MD, MC, BD, LB etc.) and traditional, continuum...kinetic Monte Carlo, kMC , equilibrium MC, Lattice-Boltzmann, LB, Brownian Dynamics, BD, or general agent-based, AB) simulators. It also, fortuitously...cond-mat/0310460 at arXiv.org. 27. Coarse Projective kMC Integration: Forward/Reverse Initial and Boundary Value Problems", R. Rico-Martinez, C. W

Disaggregating residential water demand for improved forecasts and decision making

NASA Astrophysics Data System (ADS)

Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

2003-04-01

Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water-using fixtures, and ranges from hedonism (installing pools and whirlpool tubs) to satisficing (adjusting irrigation timers only twice per year) to acting on deeply-held conservation ethics in ways that not only fail any benefit-cost test, but are discouraged, or even illegal (reuse of gray water and black water). Research findings are being captured in dynamic simulation models that integrate social and natural science to create tools to assist water resource managers in providing sustainable water supplies and improving residential water demand forecasts. These models feature simple, graphical user interfaces and output screens that provide decision makers with visual, easy-to-understand information at the basin level. The models reveal connections between various supply and demand components, and highlight direct impacts and feedback mechanisms associated with various policy options.

Multi-period equilibrium/near-equilibrium in electricity markets based on locational marginal prices

NASA Astrophysics Data System (ADS)

Garcia Bertrand, Raquel

In this dissertation we propose an equilibrium procedure that coordinates the point of view of every market agent resulting in an equilibrium that simultaneously maximizes the independent objective of every market agent and satisfies network constraints. Therefore, the activities of the generating companies, consumers and an independent system operator are modeled: (1) The generating companies seek to maximize profits by specifying hourly step functions of productions and minimum selling prices, and bounds on productions. (2) The goals of the consumers are to maximize their economic utilities by specifying hourly step functions of demands and maximum buying prices, and bounds on demands. (3) The independent system operator then clears the market taking into account consistency conditions as well as capacity and line losses so as to achieve maximum social welfare. Then, we approach this equilibrium problem using complementarity theory in order to have the capability of imposing constraints on dual variables, i.e., on prices, such as minimum profit conditions for the generating units or maximum cost conditions for the consumers. In this way, given the form of the individual optimization problems, the Karush-Kuhn-Tucker conditions for the generating companies, the consumers and the independent system operator are both necessary and sufficient. The simultaneous solution to all these conditions constitutes a mixed linear complementarity problem. We include minimum profit constraints imposed by the units in the market equilibrium model. These constraints are added as additional constraints to the equivalent quadratic programming problem of the mixed linear complementarity problem previously described. For the sake of clarity, the proposed equilibrium or near-equilibrium is first developed for the particular case considering only one time period. Afterwards, we consider an equilibrium or near-equilibrium applied to a multi-period framework. This model embodies binary decisions, i.e., on/off status for the units, and therefore optimality conditions cannot be directly applied. To avoid limitations provoked by binary variables, while retaining the advantages of using optimality conditions, we define the multi-period market equilibrium using Benders decomposition, which allows computing binary variables through the master problem and continuous variables through the subproblem. Finally, we illustrate these market equilibrium concepts through several case studies.

Developing Passenger Demand Models for International Aviation from/to Egypt: A Case Study of Cairo Airport and Egyptair

NASA Technical Reports Server (NTRS)

Abbas, Khaled A.; Fattah, Nabil Abdel; Reda, Hala R.

2003-01-01

This research is concerned with developing passenger demand models for international aviation from/to Egypt. In this context, aviation sector in Egypt is represented by the biggest and main airport namely Cairo airport as well as by the main Egyptian international air carrier namely Egyptair. The developed models utilize two variables to represent aviation demand, namely total number of international flights originating from and attracted to Cairo airport as well as total number of passengers using Egyptair international flights originating from and attracted to Cairo airport. Such demand variables were related, using different functional forms, to several explanatory variables including population, GDP and number of foreign tourists. Finally, two models were selected based on their logical acceptability, best fit and statistical significance. To demonstrate usefulness of developed models, these were used to forecast future demand patterns.

Adding ecosystem function to agent-based land use models

USDA-ARS?s Scientific Manuscript database

The objective of this paper is to examine issues in the inclusion of simulations of ecosystem functions in agent-based models of land use decision-making. The reasons for incorporating these simulations include local interests in land fertility and global interests in carbon sequestration. Biogeoche...

Transition to parenthood: the role of social interaction and endogenous networks.

PubMed

Diaz, Belinda Aparicio; Fent, Thomas; Prskawetz, Alexia; Bernardi, Laura

2011-05-01

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdependencies across individuals' transition to parenthood and its timing, we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics: age, intended education, and parity. Agents endogenously form their network based on social closeness. Network members may then influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social interactions can explain the shift of first-birth probabilities in Austria during the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Reaching unanimous agreements within agent-based negotiation teams with linear and monotonic utility functions.

PubMed

Sanchez-Anguix, Victor; Julian, Vicente; Botti, Vicente; García-Fornes, Ana

2012-06-01

In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.

An Application of Artificial Intelligence to the Implementation of Electronic Commerce

NASA Astrophysics Data System (ADS)

Srivastava, Anoop Kumar

In this paper, we present an application of Artificial Intelligence (AI) to the implementation of Electronic Commerce. We provide a multi autonomous agent based framework. Our agent based architecture leads to flexible design of a spectrum of multiagent system (MAS) by distributing computation and by providing a unified interface to data and programs. Autonomous agents are intelligent enough and provide autonomy, simplicity of communication, computation, and a well developed semantics. The steps of design and implementation are discussed in depth, structure of Electronic Marketplace, an ontology, the agent model, and interaction pattern between agents is given. We have developed mechanisms for coordination between agents using a language, which is called Virtual Enterprise Modeling Language (VEML). VEML is a integration of Java and Knowledge Query and Manipulation Language (KQML). VEML provides application programmers with potential to globally develop different kinds of MAS based on their requirements and applications. We have implemented a multi autonomous agent based system called VE System. We demonstrate efficacy of our system by discussing experimental results and its salient features.

A Multi-Scale Energy Food Systems Modeling Framework For Climate Adaptation

NASA Astrophysics Data System (ADS)

Siddiqui, S.; Bakker, C.; Zaitchik, B. F.; Hobbs, B. F.; Broaddus, E.; Neff, R.; Haskett, J.; Parker, C.

2016-12-01

Our goal is to understand coupled system dynamics across scales in a manner that allows us to quantify the sensitivity of critical human outcomes (nutritional satisfaction, household economic well-being) to development strategies and to climate or market induced shocks in sub-Saharan Africa. We adopt both bottom-up and top-down multi-scale modeling approaches focusing our efforts on food, energy, water (FEW) dynamics to define, parameterize, and evaluate modeled processes nationally as well as across climate zones and communities. Our framework comprises three complementary modeling techniques spanning local, sub-national and national scales to capture interdependencies between sectors, across time scales, and on multiple levels of geographic aggregation. At the center is a multi-player micro-economic (MME) partial equilibrium model for the production, consumption, storage, and transportation of food, energy, and fuels, which is the focus of this presentation. We show why such models can be very useful for linking and integrating across time and spatial scales, as well as a wide variety of models including an agent-based model applied to rural villages and larger population centers, an optimization-based electricity infrastructure model at a regional scale, and a computable general equilibrium model, which is applied to understand FEW resources and economic patterns at national scale. The MME is based on aggregating individual optimization problems for relevant players in an energy, electricity, or food market and captures important food supply chain components of trade and food distribution accounting for infrastructure and geography. Second, our model considers food access and utilization by modeling food waste and disaggregating consumption by income and age. Third, the model is set up to evaluate the effects of seasonality and system shocks on supply, demand, infrastructure, and transportation in both energy and food.

The comparison of the use of holonic and agent-based methods in modelling of manufacturing systems

NASA Astrophysics Data System (ADS)

Foit, K.; Banaś, W.; Gwiazda, A.; Hryniewicz, P.

2017-08-01

The rapid evolution in the field of industrial automation and manufacturing is often called the 4th Industry Revolution. Worldwide availability of the internet access contributes to the competition between manufacturers, gives the opportunity for buying materials, parts and for creating the partnership networks, like cloud manufacturing, grid manufacturing (MGrid), virtual enterprises etc. The effect of the industry evolution is the need to search for new solutions in the field of manufacturing systems modelling and simulation. During the last decade researchers have developed the agent-based approach of modelling. This methodology have been taken from the computer science, but was adapted to the philosophy of industrial automation and robotization. The operation of the agent-based system depends on the simultaneous acting of different agents that may have different roles. On the other hand, there is the holon-based approach that uses the structures created by holons. It differs from the agent-based structure in some aspects, while the other ones are quite similar in both methodologies. The aim of this paper is to present the both methodologies and discuss the similarities and the differences. This may could help to select the optimal method of modelling, according to the considered problem and software resources.

CulSim: A simulator of emergence and resilience of cultural diversity

NASA Astrophysics Data System (ADS)

Ulloa, Roberto

CulSim is an agent-based computer simulation software that allows further exploration of influential and recent models of emergence of cultural groups grounded in sociological theories. CulSim provides a collection of tools to analyze resilience of cultural diversity when events affect agents, institutions or global parameters of the simulations; upon combination, events can be used to approximate historical circumstances. The software provides a graphical and text-based user interface, and so makes this agent-based modeling methodology accessible to a variety of users from different research fields.

An Analysis on a Negotiation Model Based on Multiagent Systems with Symbiotic Learning and Evolution

NASA Astrophysics Data System (ADS)

Hossain, Md. Tofazzal

This study explores an evolutionary analysis on a negotiation model based on Masbiole (Multiagent Systems with Symbiotic Learning and Evolution) which has been proposed as a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. In Masbiole, agents evolve in consideration of not only their own benefits and losses, but also the benefits and losses of opponent agents. To aid effective application of Masbiole, we develop a competitive negotiation model where rigorous and advanced intelligent decision-making mechanisms are required for agents to achieve solutions. A Negotiation Protocol is devised aiming at developing a set of rules for agents' behavior during evolution. Simulations use a newly developed evolutionary computing technique, called Genetic Network Programming (GNP) which has the directed graph-type gene structure that can develop and design the required intelligent mechanisms for agents. In a typical scenario, competitive negotiation solutions are reached by concessions that are usually predetermined in the conventional MAS. In this model, however, not only concession is determined automatically by symbiotic evolution (making the system intelligent, automated, and efficient) but the solution also achieves Pareto optimal automatically.

Combating Rhino Horn Trafficking: The Need to Disrupt Criminal Networks.

PubMed

Haas, Timothy C; Ferreira, Sam M

2016-01-01

The onslaught on the World's wildlife continues despite numerous initiatives aimed at curbing it. We build a model that integrates rhino horn trade with rhino population dynamics in order to evaluate the impact of various management policies on rhino sustainability. In our model, an agent-based sub-model of horn trade from the poaching event up through a purchase of rhino horn in Asia impacts rhino abundance. A data-validated, individual-based sub-model of the rhino population of South Africa provides these abundance values. We evaluate policies that consist of different combinations of legal trade initiatives, demand reduction marketing campaigns, increased anti-poaching measures within protected areas, and transnational policing initiatives aimed at disrupting those criminal syndicates engaged in horn trafficking. Simulation runs of our model over the next 35 years produces a sustainable rhino population under only one management policy. This policy includes both a transnational policing effort aimed at dismantling those criminal networks engaged in rhino horn trafficking-coupled with increases in legal economic opportunities for people living next to protected areas where rhinos live. This multi-faceted approach should be the focus of the international debate on strategies to combat the current slaughter of rhino rather than the binary debate about whether rhino horn trade should be legalized. This approach to the evaluation of wildlife management policies may be useful to apply to other species threatened by wildlife trafficking.

Combating Rhino Horn Trafficking: The Need to Disrupt Criminal Networks

PubMed Central

Haas, Timothy C.; Ferreira, Sam M.

2016-01-01

The onslaught on the World’s wildlife continues despite numerous initiatives aimed at curbing it. We build a model that integrates rhino horn trade with rhino population dynamics in order to evaluate the impact of various management policies on rhino sustainability. In our model, an agent-based sub-model of horn trade from the poaching event up through a purchase of rhino horn in Asia impacts rhino abundance. A data-validated, individual-based sub-model of the rhino population of South Africa provides these abundance values. We evaluate policies that consist of different combinations of legal trade initiatives, demand reduction marketing campaigns, increased anti-poaching measures within protected areas, and transnational policing initiatives aimed at disrupting those criminal syndicates engaged in horn trafficking. Simulation runs of our model over the next 35 years produces a sustainable rhino population under only one management policy. This policy includes both a transnational policing effort aimed at dismantling those criminal networks engaged in rhino horn trafficking—coupled with increases in legal economic opportunities for people living next to protected areas where rhinos live. This multi-faceted approach should be the focus of the international debate on strategies to combat the current slaughter of rhino rather than the binary debate about whether rhino horn trade should be legalized. This approach to the evaluation of wildlife management policies may be useful to apply to other species threatened by wildlife trafficking. PMID:27870917

A research on personal credit rating under big-data

NASA Astrophysics Data System (ADS)

Chen, Yong; Nie, Erbao; Chen, Shaozhen

2018-04-01

Solvency mainly discover the availability and fluctuations of one's cashflow, to reveal the reasons for one's balance and future trends, as well as to determine one's ability to obtain funds to repay debt and the flow Release space. the intention to repay reflects the debtor's decision which is made after weighting the benefits and cost of repaying the principal and interest. examining the repaying willingness reveals the debtor's passion to repay the outstanding obligations and the new debt, then based on which we can determine the security of principal and interest payment. risk preference is to examine individual's exact demand for credit, and the adjusting degree between the demand and the solvency, to derive the agent cost and the coverage rate of the new-issued debt.

Construction of a microscopic agent-based model for firms' dynamics

NASA Astrophysics Data System (ADS)

Iyetomi, Hiroshi; Aoyama, Hideaki; Fujiwara, Yoshi; Ikeda, Yuichi; Kaizoji, Taisei; Soma, Wataru

2005-07-01

A workable microscopic model for firms' dynamics has been constructed. The model consists of firm agents and a bank agent dynamics of which are described by balance sheets. The size distribution of firms and the temporal evolution of the bank show critical dependence on whether or not firms use perfect information on their financial conditions to draw up next production plans.

A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework

PubMed Central

Hernández, Luis; Baladrón, Carlos; Aguiar, Javier M.; Calavia, Lorena; Carro, Belén; Sánchez-Esguevillas, Antonio; Cook, Diane J.; Chinarro, David; Gómez, Jorge

2012-01-01

One of the main challenges of today's society is the need to fulfill at the same time the two sides of the dichotomy between the growing energy demand and the need to look after the environment. Smart Grids are one of the answers: intelligent energy grids which retrieve data about the environment through extensive sensor networks and react accordingly to optimize resource consumption. In order to do this, the Smart Grids need to understand the existing relationship between energy demand and a set of relevant climatic variables. All smart “systems” (buildings, cities, homes, consumers, etc.) have the potential to employ their intelligence for self-adaptation to climate conditions. After introducing the Smart World, a global framework for the collaboration of these smart systems, this paper presents the relationship found at experimental level between a range of relevant weather variables and electric power demand patterns, presenting a case study using an agent-based system, and emphasizing the need to consider this relationship in certain Smart World (and specifically Smart Grid and microgrid) applications.

Reciprocal relationship between proactive personality and work characteristics: a latent change score approach.

PubMed

Li, Wen-Dong; Fay, Doris; Frese, Michael; Harms, Peter D; Gao, Xiang Yu

2014-09-01

Previous proactivity research has predominantly assumed that proactive personality generates positive environmental changes in the workplace. Grounded in recent research on personality development from a broad interactionist theoretical approach, the present article investigates whether work characteristics, including job demands, job control, social support from supervisors and coworkers, and organizational constraints, change proactive personality over time and, more important, reciprocal relationships between proactive personality and work characteristics. Latent change score analyses based on longitudinal data collected in 3 waves across 3 years show that job demands and job control have positive lagged effects on increases in proactive personality. In addition, proactive personality exerts beneficial lagged effects on increases in job demands, job control, and supervisory support, and on decreases in organizational constraints. Dynamic reciprocal relationships are observed between proactive personality with job demands and job control. The revealed corresponsive change relationships between proactive personality and work characteristics contribute to the proactive personality literature by illuminating more nuanced interplays between the agentic person and work characteristics, and also have important practical implications for organizations and employees. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Diagnosing phosphorus limitations in natural terrestrial ecosystems in carbon cycle models

NASA Astrophysics Data System (ADS)

Sun, Yan; Peng, Shushi; Goll, Daniel S.; Ciais, Philippe; Guenet, Bertrand; Guimberteau, Matthieu; Hinsinger, Philippe; Janssens, Ivan A.; Peñuelas, Josep; Piao, Shilong; Poulter, Benjamin; Violette, Aurélie; Yang, Xiaojuan; Yin, Yi; Zeng, Hui

2017-07-01

Most of the Earth System Models (ESMs) project increases in net primary productivity (NPP) and terrestrial carbon (C) storage during the 21st century. Despite empirical evidence that limited availability of phosphorus (P) may limit the response of NPP to increasing atmospheric CO2, none of the ESMs used in the previous Intergovernmental Panel on Climate Change assessment accounted for P limitation. We diagnosed from ESM simulations the amount of P need to support increases in carbon uptake by natural ecosystems using two approaches: the demand derived from (1) changes in C stocks and (2) changes in NPP. The C stock-based additional P demand was estimated to range between -31 and 193 Tg P and between -89 and 262 Tg P for Representative Concentration Pathway (RCP) 2.6 and RCP8.5, respectively, with negative values indicating a P surplus. The NPP-based demand, which takes ecosystem P recycling into account, results in a significantly higher P demand of 648-1606 Tg P for RCP2.6 and 924-2110 Tg P for RCP8.5. We found that the P demand is sensitive to the turnover of P in decomposing plant material, explaining the large differences between the NPP-based demand and C stock-based demand. The discrepancy between diagnosed P demand and actual P availability (potential P deficit) depends mainly on the assumptions about availability of the different soil P forms. Overall, future P limitation strongly depends on both soil P availability and P recycling on ecosystem scale.

Diagnosing phosphorus limitations in natural terrestrial ecosystems in carbon cycle models.

PubMed

Sun, Yan; Peng, Shushi; Goll, Daniel S; Ciais, Philippe; Guenet, Bertrand; Guimberteau, Matthieu; Hinsinger, Philippe; Janssens, Ivan A; Peñuelas, Josep; Piao, Shilong; Poulter, Benjamin; Violette, Aurélie; Yang, Xiaojuan; Yin, Yi; Zeng, Hui

2017-07-01

Most of the Earth System Models (ESMs) project increases in net primary productivity (NPP) and terrestrial carbon (C) storage during the 21st century. Despite empirical evidence that limited availability of phosphorus (P) may limit the response of NPP to increasing atmospheric CO 2 , none of the ESMs used in the previous Intergovernmental Panel on Climate Change assessment accounted for P limitation. We diagnosed from ESM simulations the amount of P need to support increases in carbon uptake by natural ecosystems using two approaches: the demand derived from (1) changes in C stocks and (2) changes in NPP. The C stock-based additional P demand was estimated to range between -31 and 193 Tg P and between -89 and 262 Tg P for Representative Concentration Pathway (RCP) 2.6 and RCP8.5, respectively, with negative values indicating a P surplus. The NPP-based demand, which takes ecosystem P recycling into account, results in a significantly higher P demand of 648-1606 Tg P for RCP2.6 and 924-2110 Tg P for RCP8.5. We found that the P demand is sensitive to the turnover of P in decomposing plant material, explaining the large differences between the NPP-based demand and C stock-based demand. The discrepancy between diagnosed P demand and actual P availability (potential P deficit) depends mainly on the assumptions about availability of the different soil P forms. Overall, future P limitation strongly depends on both soil P availability and P recycling on ecosystem scale.

Mitigation of short-term disturbance negative impacts in the agent-based model of a production companies network

NASA Astrophysics Data System (ADS)

Shevchuk, G. K.; Berg, D. B.; Zvereva, O. M.; Medvedeva, M. A.

2017-11-01

This article is devoted to the study of a supply chain disturbance impact on manufacturing volumes in a production system network. Each network agent's product can be used as a resource by other system agents (manufacturers). A supply chain disturbance can lead to operating cease of the entire network. Authors suggest using of short-term partial resources reservation to mitigate negative consequences of such disturbances. An agent-based model with a reservation algorithm compatible with strategies for resource procurement in terms of financial constraints was engineered. This model works in accordance with the static input-output Leontief 's model. The results can be used for choosing the ways of system's stability improving, and protecting it from various disturbances and imbalance.

Agent-Based Intelligent Interface for Wheelchair Movement Control

PubMed Central

Barriuso, Alberto L.; De Paz, Juan F.

2018-01-01

People who suffer from any kind of motor difficulty face serious complications to autonomously move in their daily lives. However, a growing number research projects which propose different powered wheelchairs control systems are arising. Despite of the interest of the research community in the area, there is no platform that allows an easy integration of various control methods that make use of heterogeneous sensors and computationally demanding algorithms. In this work, an architecture based on virtual organizations of agents is proposed that makes use of a flexible and scalable communication protocol that allows the deployment of embedded agents in computationally limited devices. In order to validate the proper functioning of the proposed system, it has been integrated into a conventional wheelchair and a set of alternative control interfaces have been developed and deployed, including a portable electroencephalography system, a voice interface or as specifically designed smartphone application. A set of tests were conducted to test both the platform adequacy and the accuracy and ease of use of the proposed control systems yielding positive results that can be useful in further wheelchair interfaces design and implementation. PMID:29751603

Effect of in-office bleaching agents on physical properties of dental composite resins.

PubMed

Mourouzis, Petros; Koulaouzidou, Elisabeth A; Helvatjoglu-Antoniades, Maria

2013-04-01

The physical properties of dental restorative materials have a crucial effect on the longevity of restorations and moreover on the esthetic demands of patients, but they may be compromised by bleaching treatments. The purpose of this study was to evaluate the effects of in-office bleaching agents on the physical properties of three composite resin restorative materials. The bleaching agents used were hydrogen peroxide and carbamide peroxide at high concentrations. Specimens of each material were prepared, cured, and polished. Measurements of color difference, microhardness, and surface roughness were recorded before and after bleaching and data were examined statistically by analysis of variance (ANOVA) and Tukey HSD post-hoc test at P < .05. The measurements showed that hue and chroma of silorane-based composite resin altered after the bleaching procedure (P < .05). No statistically significant differences were found when testing the microhardness and surface roughness of composite resins tested (P > .05). The silorane-based composite resin tested showed some color alteration after bleaching procedures. The bleaching procedure did not alter the microhardness and the surface roughness of all composite resins tested.

Quantitative Analysis of Intra Urban Growth Modeling using socio economic agents by combining cellular automata model with agent based model

NASA Astrophysics Data System (ADS)

Singh, V. K.; Jha, A. K.; Gupta, K.; Srivastav, S. K.

2017-12-01

Recent studies indicate that there is a significant improvement in the urban land use dynamics through modeling at finer spatial resolutions. Geo-computational models such as cellular automata and agent based model have given evident proof regarding the quantification of the urban growth pattern with urban boundary. In recent studies, socio- economic factors such as demography, education rate, household density, parcel price of the current year, distance to road, school, hospital, commercial centers and police station are considered to the major factors influencing the Land Use Land Cover (LULC) pattern of the city. These factors have unidirectional approach to land use pattern which makes it difficult to analyze the spatial aspects of model results both quantitatively and qualitatively. In this study, cellular automata model is combined with generic model known as Agent Based Model to evaluate the impact of socio economic factors on land use pattern. For this purpose, Dehradun an Indian city is selected as a case study. Socio economic factors were collected from field survey, Census of India, Directorate of economic census, Uttarakhand, India. A 3X3 simulating window is used to consider the impact on LULC. Cellular automata model results are examined for the identification of hot spot areas within the urban area and agent based model will be using logistic based regression approach where it will identify the correlation between each factor on LULC and classify the available area into low density, medium density, high density residential or commercial area. In the modeling phase, transition rule, neighborhood effect, cell change factors are used to improve the representation of built-up classes. Significant improvement is observed in the built-up classes from 84 % to 89 %. However after incorporating agent based model with cellular automata model the accuracy improved from 89 % to 94 % in 3 classes of urban i.e. low density, medium density and commercial classes. Sensitivity study of the model indicated that southern and south-west part of the city have shown improvement and small patches of growth are also observed in the north western part of the city.The study highlights the growing importance of socio economic factors and geo-computational modeling approach on changing LULC of newly growing cities of modern India.

Simulating Microdosimetry of Environmental Chemicals for EPA’s Virtual Liver

EPA Science Inventory

US EPA Virtual Liver (v-Liver) is a cellular systems model of hepatic tissues aimed at predicting chemical-induced adverse effects through agent-based modeling. A primary objective of the project is to extrapolate in vitro data to in vivo outcomes. Agent-based approaches to tissu...

Verifying Multi-Agent Systems via Unbounded Model Checking

NASA Technical Reports Server (NTRS)

Kacprzak, M.; Lomuscio, A.; Lasica, T.; Penczek, W.; Szreter, M.

2004-01-01

We present an approach to the problem of verification of epistemic properties in multi-agent systems by means of symbolic model checking. In particular, it is shown how to extend the technique of unbounded model checking from a purely temporal setting to a temporal-epistemic one. In order to achieve this, we base our discussion on interpreted systems semantics, a popular semantics used in multi-agent systems literature. We give details of the technique and show how it can be applied to the well known train, gate and controller problem. Keywords: model checking, unbounded model checking, multi-agent systems

Continuous Opinion Dynamics Under Bounded Confidence:. a Survey

NASA Astrophysics Data System (ADS)

Lorenz, Jan

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy. In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence. Second, it will give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions. Third, it will review the several extensions and the evolving phenomena which have been studied so far, and fourth it will state some open questions.

Effect of Climate Change and Transaction Costs on Performance of a Groundwater Market

NASA Astrophysics Data System (ADS)

Khan, H. F.; Brown, C.

2017-12-01

With surface water resources becoming increasingly stressed, groundwater extraction, much of it unmanaged, has increased globally. Incentive-based policies, such as the cap-and-trade system, have been shown to be useful in the context of groundwater management. Previous research has shown that optimal groundwater markets (i.e. incentives-based policy) outperforms water quotas (command and control policy) with regards to both economic and environmental outcomes. In this work, we investigate whether these advantages of a water market over water quotas hold when assumptions of perfect information are violated due to climate change and hydrogeologic heterogeneity. We also assess whether the benefits of a cap-and-trade system outweigh the costs of implementing it, and how changes in future climate affect the performance a cap-and trade system. We use a sub-basin of the Republican River Basin, overlying the Ogallala aquifer in the High Plains of the United States, as a case study. We develop a multi-agent system model where individual benefits of each self-interested agent are maximized subject to bounds on irrigation requirements and water use permits. This economic model is coupled with a calibrated physically based groundwater model for the study region. Results show that permitting farmers to trade results in increased economic benefits and reduced environmental violations. However, the benefits of trading are dependent on the total allocations and the resulting level of water demand. We quantify third party impacts and environmental externalities for different water allocations, and highlight the unequal distributional effects of uniform water allocations resulting in `winners' and `losers'. The study reveals that high transaction costs can reduce the efficiency of the cap-and-trade system even below that of water quotas. Future changes in climate are shown to significantly influence the dynamics of the water market, and emphasize the need to address climate sensitivity in the setup of water markets.

Seldon v.3.0

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

Berry, Nina; Ko, Teresa; Shneider, Max

Seldon is an agent-based social simulation framework that uniquely integrates concepts from a variety of different research areas including psychology, social science, and agent-based modeling. Development has been taking place for a number of years, previously focusing on gang and terrorist recruitment. The toolkit consists of simple agents (individuals) and abstract agents (groups of individuals representing social/institutional concepts) that interact according to exchangeable rule sets (i.e. linear attraction, linear reinforcement). Each agent has a set of customizable attributes that get modified during the interactions. Interactions create relationships between agents, and each agent has a maximum amount of relationship energy thatmore » it can expend. As relationships evolve, they form multiple levels of social networks (i.e. acquaintances, friends, cliques) that in turn drive future interactions. Agents can also interact randomly if they are not connected through a network, mimicking the chance interactions that real people have in everyday life. We are currently integrating Seldon with the cognitive framework (also developed at Sandia). Each individual agent has a lightweight cognitive model that is created automatically from textual sources. Cognitive information is exchanged during interactions, and can also be injected into a running simulation. The entire framework has been parallelized to allow for larger simulations in an HPC environment. We have also added more detail to the agents themselves (a"Big Five" personality model) and their interactions (an enhanced relationship model) for a more realistic representation.« less

Agent-Based Crowd Simulation Considering Emotion Contagion for Emergency Evacuation Problem

NASA Astrophysics Data System (ADS)

Faroqi, H.; Mesgari, M.-S.

2015-12-01

During emergencies, emotions greatly affect human behaviour. For more realistic multi-agent systems in simulations of emergency evacuations, it is important to incorporate emotions and their effects on the agents. In few words, emotional contagion is a process in which a person or group influences the emotions or behavior of another person or group through the conscious or unconscious induction of emotion states and behavioral attitudes. In this study, we simulate an emergency situation in an open square area with three exits considering Adults and Children agents with different behavior. Also, Security agents are considered in order to guide Adults and Children for finding the exits and be calm. Six levels of emotion levels are considered for each agent in different scenarios and situations. The agent-based simulated model initialize with the random scattering of agent populations and then when an alarm occurs, each agent react to the situation based on its and neighbors current circumstances. The main goal of each agent is firstly to find the exit, and then help other agents to find their ways. Numbers of exited agents along with their emotion levels and damaged agents are compared in different scenarios with different initialization in order to evaluate the achieved results of the simulated model. NetLogo 5.2 is used as the multi-agent simulation framework with R language as the developing language.

Primary health care lessons from the northeast of Brazil: the Agentes de Saúde Program.

PubMed

Cufino Svitone, E; Garfield, R; Vasconcelos, M I; Araujo Craveiro, V

2000-05-01

Market-led economic reforms are usually viewed as being in conflict with government-stimulated socioeconomic development for disadvantaged groups. Nevertheless, Ceará, a poor state in the Northeast of Brazil, has since 1987 pursued both of those strategies simultaneously. One part of that approach has been a program of nurse-directed auxiliary health workers serving about 5 million people--almost all the persons outside the capital city and half of those in the capital. The system requires that the auxiliaries, called agentes de saúde, live in the local communities that they serve. The health agents visit each home once a month to carry out a small number of priority health activities. While health agent positions are in high demand, the minimum-wage salary that the agents receive makes up only a small portion of the state budget. A key aspect of the system is timely and comprehensive information, which is based on agent visits and is managed by trained nurses. Since the health agents system was launched, there has been a rapid decline in infant mortality, a rapid rise in immunization, identification of bottlenecks limiting the utilization of other medical resources, and timely interventions in times of crisis. The health agents system has combined administrative decentralization with financial centralization during a period of electoral democratization. The system has strengthened Ceará's commitment to primary care even as market-oriented changes have reduced the overall role of government. The Ceará program is being copied throughout the Northeast and other regions of Brazil. The key role that nurses play in the Ceará program in organizing and leading a system of basic primary care in poor neighborhoods and rural areas may provide useful lessons for other countries. In addition, Ceará does not have many of the favorable characteristics of other countries that have successfully invested in primary health care. Ceará thus represents a more achievable model for other countries, where, like Brazil, income, educational levels, and land tenure equity are limited.

A multi-agent architecture for geosimulation of moving agents

NASA Astrophysics Data System (ADS)

Vahidnia, Mohammad H.; Alesheikh, Ali A.; Alavipanah, Seyed Kazem

2015-10-01

In this paper, a novel architecture is proposed in which an axiomatic derivation system in the form of first-order logic facilitates declarative explanation and spatial reasoning. Simulation of environmental perception and interaction between autonomous agents is designed with a geographic belief-desire-intention and a request-inform-query model. The architecture has a complementary quantitative component that supports collaborative planning based on the concept of equilibrium and game theory. This new architecture presents a departure from current best practices geographic agent-based modelling. Implementation tasks are discussed in some detail, as well as scenarios for fleet management and disaster management.

Advanced Contrast Agents for Multimodal Biomedical Imaging Based on Nanotechnology.

PubMed

Calle, Daniel; Ballesteros, Paloma; Cerdán, Sebastián

2018-01-01

Clinical imaging modalities have reached a prominent role in medical diagnosis and patient management in the last decades. Different image methodologies as Positron Emission Tomography, Single Photon Emission Tomography, X-Rays, or Magnetic Resonance Imaging are in continuous evolution to satisfy the increasing demands of current medical diagnosis. Progress in these methodologies has been favored by the parallel development of increasingly more powerful contrast agents. These are molecules that enhance the intrinsic contrast of the images in the tissues where they accumulate, revealing noninvasively the presence of characteristic molecular targets or differential physiopathological microenvironments. The contrast agent field is currently moving to improve the performance of these molecules by incorporating the advantages that modern nanotechnology offers. These include, mainly, the possibilities to combine imaging and therapeutic capabilities over the same theranostic platform or improve the targeting efficiency in vivo by molecular engineering of the nanostructures. In this review, we provide an introduction to multimodal imaging methods in biomedicine, the sub-nanometric imaging agents previously used and the development of advanced multimodal and theranostic imaging agents based in nanotechnology. We conclude providing some illustrative examples from our own laboratories, including recent progress in theranostic formulations of magnetoliposomes containing ω-3 poly-unsaturated fatty acids to treat inflammatory diseases, or the use of stealth liposomes engineered with a pH-sensitive nanovalve to release their cargo specifically in the acidic extracellular pH microenvironment of tumors.

Characterization of an Optically Integrated Mach-Zehnder Interferometer for the Detection of Biological Agents

DTIC Science & Technology

1997-04-01

nnf::1r+ •rer Model Ch ~~v"vristics Laser diode Seastar Optic Inc. a PMSEI0830LT015MD-G SM, OI and TS >.. = 830nm Photo diode Seastar Optic Inc. a...Ontario KIAOK2 Tel.: (613) 995-2971 Fax: (613) 996-0392 Toute demande de document doit etre adressee a: DIRECTEUR- GESTION DEL ’INFORMATION DE ... DE RECHERCHES POUR LA DEFENSE VALCARTIER, QUEBEC DREV - TM-9621 Unlimited distribution/Diffusion illimitee CHARACTERIZATION OF AN OPTICALLY

Literature Review on Systems of Systems (SoS): A Methodology With Preliminary Results

DTIC Science & Technology

2013-11-01

Appendix H. The Enhanced ISAAC Neural Simulation Toolkit (EINSTein) 73  Appendix I. The Map Aware Nonuniform Automata (MANA) Agent-Based Model 81...83  Figure I-3. Quadrant chart addressing SoS and associated SoSA designs for the Map Aware Nonuniform Automata (MANA) agent...Map Aware Nonuniform Automata (MANA) agent-based model. 85  Table I-2. SoS and SoSA software component maturation scores associated with the Map

A conceptual data model and modelling language for fields and agents

NASA Astrophysics Data System (ADS)

de Bakker, Merijn; de Jong, Kor; Schmitz, Oliver; Karssenberg, Derek

2016-04-01

Modelling is essential in order to understand environmental systems. Environmental systems are heterogeneous because they consist of fields and agents. Fields have a value defined everywhere at all times, for example surface elevation and temperature. Agents are bounded in space and time and have a value only within their bounds, for example biomass of a tree crown or the speed of a car. Many phenomena have properties of both fields and agents. Although many systems contain both fields and agents and integration of these concepts would be required for modelling, existing modelling frameworks concentrate on either agent-based or field-based modelling and are often low-level programming frameworks. A concept is lacking that integrates fields and agents in a way that is easy to use for modelers who are not software engineers. To address this issue, we develop a conceptual data model that represents fields and agents uniformly. We then show how the data model can be used in a high-level modelling language. The data model represents fields and agents in space-time. Also relations and networks can be represented using the same concepts. Using the conceptual data model we can represent static and mobile agents that may have spatial and temporal variation within their extent. The concepts we use are phenomenon, property set, item, property, domain and value. The phenomenon is the thing that is modelled, which can be any real world thing, for example trees. A phenomenon usually consists of several items, e.g. single trees. The domain is the spatiotemporal location and/or extent for which the items in the phenomenon are defined. Multiple different domains can coexist for a given phenomenon. For example a domain describing the extent of the trees and a domain describing the stem locations. The same goes for the property, which is an attribute of the thing that is being modeled. A property has a value, which is possibly discretized, for example the biomass over the tree crown extent. Properties sharing the same domain are grouped into a property set. The conceptual data model is translated into a physical data model in de Jong et al. (2016, presented in the same session). We have designed a modelling language that allows domain specialists to build models without the programming efforts required by many programming environments. The language is based on the ideas of map algebra. We have defined data types that are associated with a phenomenon. These data types determine the behavior of the language when used as arguments in operations. The result is a concise language in which fields and agents can be combined in operations. We test the language in a case study modelling exposure to air pollution of commuting children. References De Jong, K, M. de Bakker, D. Karssenberg. 2016. A physical data model for fields and agents. European Geosciences Union, EGU General Assembly, 2016, Vienna.

Impact of Different Policies on Unhealthy Dietary Behaviors in an Urban Adult Population: An Agent-Based Simulation Model

PubMed Central

Giabbanelli, Philippe J.; Arah, Onyebuchi A.; Zimmerman, Frederick J.

2014-01-01

Objectives. Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. Methods. We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. Results. Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. Conclusions. Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems. PMID:24832414

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

Balancing the Scholarship Demands of Forensics and Graduate Study.

ERIC Educational Resources Information Center

Kuper, Glenn

It is difficult to strike a balance between the demands placed on graduate students and those placed on graduate forensics assistants. The combination of duties as Graduate Forensics Assistants (GFAs)--baby sitters, confidants, teachers, travel agents, administrators, clerical workers, psychologists, proofreaders, authority figures, and finally,…

Nonlinear multi-analysis of agent-based financial market dynamics by epidemic system

NASA Astrophysics Data System (ADS)

Lu, Yunfan; Wang, Jun; Niu, Hongli

2015-10-01

Based on the epidemic dynamical system, we construct a new agent-based financial time series model. In order to check and testify its rationality, we compare the statistical properties of the time series model with the real stock market indices, Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index. For analyzing the statistical properties, we combine the multi-parameter analysis with the tail distribution analysis, the modified rescaled range analysis, and the multifractal detrended fluctuation analysis. For a better perspective, the three-dimensional diagrams are used to present the analysis results. The empirical research in this paper indicates that the long-range dependence property and the multifractal phenomenon exist in the real returns and the proposed model. Therefore, the new agent-based financial model can recurrence some important features of real stock markets.

Identification of chemical warfare agents using a portable microchip-based detection device

NASA Astrophysics Data System (ADS)

Petkovic-Duran, K.; Swallow, A.; Sexton, B. A.; Glenn, F.; Zhu, Y.

2011-12-01

Analysis of chemical warfare agents (CWAs) and their degradation products is an important verification component in support of the Chemical Weapons Convention and urgently demanding rapid and reliable analytical methods. A portable microchip electrophoresis (ME) device with contactless conductivity (CCD) detection was developed for the in situ identification of CWA and their degradation products. A 10mM MES/His, 0.4mM CTAB - based separation electrolyte accomplished the analysis of Sarin (GB), Tabun( GA) and Soman (GD) in less than 1 min, which is the fastest screening of nerve agents achieved with portable ME and CCD based detection methods to date. Reproducibility of detection was successfully demonstrated on simultaneous detection of GB (200ppm) and GA (278ppm). Reasonable agreement for the four consecutive runs was achieved with the mean peak time for Sarin of 29.15s, and the standard error of 0.58s or 2%. GD and GA were simultaneously detected with their degradation products methylphosphonic acid (MPA), pinacolyl methylphosphonic acid (PMPA) and O-Ethyl Phosphorocyanidate (GAHP and GAHP1) respectively. The detection limit for Sarin was around 35ppb. To the best of our knowledge this is the best result achieved in microchip electrophoresis and contactless conductivity based detection to date.

iMuseumA: An Agent-Based Context-Aware Intelligent Museum System

PubMed Central

Ayala, Inmaculada; Amor, Mercedes; Pinto, Mónica; Fuentes, Lidia; Gámez, Nadia

2014-01-01

Currently, museums provide their visitors with interactive tour guide applications that can be installed in mobile devices and provide timely tailor-made multimedia information about exhibits on display. In this paper, we argue that mobile devices not only could provide help to visitors, but also to museum staff. Our goal is to integrate, within the same system, multimedia tour guides with the management facilities required by museums. In this paper, we present iMuseumA (intelligent museum with agents), a mobile-based solution to customize visits and perform context-aware management tasks. iMuseumA follows an agent-based approach, which makes it possible to interact easily with the museum environment and make decisions based on its current status. This system is currently deployed in the Museum of Informatics at the Informatics School of the University of Málaga, and its main contributions are: (i) a mobile application that provides management facilities to museum staff by means of sensing and processing environmental data; (ii) providing an integrated solution for visitors, tour guides and museum staff that allows coordination and communication enrichment among different groups of users; (iii) using and benefiting from group communication for heterogeneous groups of users that can be created on demand. PMID:25390409

Model of mobile agents for sexual interactions networks

NASA Astrophysics Data System (ADS)

González, M. C.; Lind, P. G.; Herrmann, H. J.

2006-02-01

We present a novel model to simulate real social networks of complex interactions, based in a system of colliding particles (agents). The network is build by keeping track of the collisions and evolves in time with correlations which emerge due to the mobility of the agents. Therefore, statistical features are a consequence only of local collisions among its individual agents. Agent dynamics is realized by an event-driven algorithm of collisions where energy is gained as opposed to physical systems which have dissipation. The model reproduces empirical data from networks of sexual interactions, not previously obtained with other approaches.

Post-processing techniques to enhance reliability of assignment algorithm based performance measures : [technical summary].

DOT National Transportation Integrated Search

2011-01-01

Travel demand modeling plays a key role in the transportation system planning and evaluation process. The four-step sequential travel demand model is the most widely used technique in practice. Traffic assignment is the key step in the conventional f...

An integrated and dynamic optimisation model for the multi-level emergency logistics network in anti-bioterrorism system

NASA Astrophysics Data System (ADS)

Liu, Ming; Zhao, Lindu

2012-08-01

Demand for emergency resources is usually uncertain and varies quickly in anti-bioterrorism system. Besides, emergency resources which had been allocated to the epidemic areas in the early rescue cycle will affect the demand later. In this article, an integrated and dynamic optimisation model with time-varying demand based on the epidemic diffusion rule is constructed. The heuristic algorithm coupled with the MATLAB mathematical programming solver is adopted to solve the optimisation model. In what follows, the application of the optimisation model as well as a short sensitivity analysis of the key parameters in the time-varying demand forecast model is presented. The results show that both the model and the solution algorithm are useful in practice, and both objectives of inventory level and emergency rescue cost can be controlled effectively. Thus, it can provide some guidelines for decision makers when coping with emergency rescue problem with uncertain demand, and offers an excellent reference when issues pertain to bioterrorism.

Urban Form Energy Use and Emissions in China: Preliminary Findings and Model Proof of Concept

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

Aden, Nathaniel; Qin, Yining; Fridley, David

Urbanization is reshaping China's economy, society, and energy system. Between 1990 and 2008 China added more than 300 million new urban residents, bringing the total urbanization rate to 46%. The ongoing population shift is spurring energy demand for new construction, as well as additional residential use with the replacement of rural biomass by urban commercial energy services. This project developed a modeling tool to quantify the full energy consequences of a particular form of urban residential development in order to identify energy- and carbon-efficient modes of neighborhood-level development and help mitigate resource and environmental implications of swelling cities. LBNL developedmore » an integrated modeling tool that combines process-based lifecycle assessment with agent-based building operational energy use, personal transport, and consumption modeling. The lifecycle assessment approach was used to quantify energy and carbon emissions embodied in building materials production, construction, maintenance, and demolition. To provide more comprehensive analysis, LBNL developed an agent-based model as described below. The model was applied to LuJing, a residential development in Jinan, Shandong Province, to provide a case study and model proof of concept. This study produced results data that are unique by virtue of their scale, scope and type. Whereas most existing literature focuses on building-, city-, or national-level analysis, this study covers multi-building neighborhood-scale development. Likewise, while most existing studies focus exclusively on building operational energy use, this study also includes embodied energy related to personal consumption and buildings. Within the boundaries of this analysis, food is the single largest category of the building energy footprint, accounting for 23% of the total. On a policy level, the LCA approach can be useful for quantifying the energy and environmental benefits of longer average building lifespans. In addition to prospective analysis for standards and certification, urban form modeling can also be useful in calculating or verifying ex post facto, bottom-up carbon emissions inventories. Emissions inventories provide a benchmark for evaluating future outcomes and scenarios as well as an empirical basis for valuing low-carbon technologies. By highlighting the embodied energy and emissions of building materials, the LCA approach can also be used to identify the most intensive aspects of industrial production and the supply chain. The agent based modeling aspect of the model can be useful for understanding how policy incentives can impact individual behavior and the aggregate effects thereof. The most useful elaboration of the urban form assessment model would be to further generalize it for comparative analysis. Scenario analysis could be used for benchmarking and identification of policy priorities. If the model is to be used for inventories, it is important to disaggregate the energy use data for more accurate emissions modeling. Depending on the policy integration of the model, it may be useful to incorporate occupancy data for per-capita results. On the question of density and efficiency, it may also be useful to integrate a more explicit spatial scaling mechanism for modeling neighborhood and city-level energy use and emissions, i.e. to account for scaling effects in public infrastructure and transportation.« less

Bacteriophage T4 as a Nanoparticle Platform to Display and Deliver Pathogen Antigens: Construction of an Effective Anthrax Vaccine.

PubMed

Tao, Pan; Li, Qin; Shivachandra, Sathish B; Rao, Venigalla B

2017-01-01

Protein-based subunit vaccines represent a safer alternative to the whole pathogen in vaccine development. However, limitations of physiological instability and low immunogenicity of such vaccines demand an efficient delivery system to stimulate robust immune responses. The bacteriophage T4 capsid-based antigen delivery system can robustly elicit both humoral and cellular immune responses without any adjuvant. Therefore, it offers a strong promise as a novel antigen delivery system. Currently Bacillus anthracis, the causative agent of anthrax, is a serious biothreat agent and no FDA-approved anthrax vaccine is available for mass vaccination. Here, we describe a potential anthrax vaccine using a T4 capsid platform to display and deliver the 83 kDa protective antigen, PA, a key component of the anthrax toxin. This T4 vaccine platform might serve as a universal antigen delivery system that can be adapted to develop vaccines against any infectious disease.

Autonomy for Constellation

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Szczur, Martha R. (Technical Monitor)

2000-01-01

The newer types of space systems, which are planned for the future, are placing challenging demands for newer autonomy concepts and techniques. Motivating these challenges are resource constraints. Even though onboard computing power will surely increase in the coming years, the resource constraints associated with space-based processes will continue to be a major factor that needs to be considered when dealing with, for example, agent-based spacecraft autonomy. To realize "economical intelligence", i.e., constrained computational intelligence that can reside within a process under severe resource constraints (time, power, space, etc.), is a major goal for such space systems as the Nanosat constellations. To begin to address the new challenges, we are developing approaches to constellation autonomy with constraints in mind. Within the Agent Concepts Testbed (ACT) at the Goddard Space Flight Center we are currently developing a Nanosat-related prototype for the first of the two-step program.

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System

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

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas,more » and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.« less

Stress in Parents of Children with Autism Spectrum Disorder: An Exploration of Demands and Resources

ERIC Educational Resources Information Center

Krakovich, Teri M.; McGrew, John H.; Yu, Yue; Ruble, Lisa A.

2016-01-01

We applied the ABCX model of stress and coping to assess the association between child and family demands, school-based resources (i.e., parent-teacher alliance and COMPASS, a consultation intervention), and two measures of parent stress: perceptions of the demands of raising a child (Child domain) and reactions to those demands (Parent domain).…

Consensus pursuit of heterogeneous multi-agent systems under a directed acyclic graph

NASA Astrophysics Data System (ADS)

Yan, Jing; Guan, Xin-Ping; Luo, Xiao-Yuan

2011-04-01

This paper is concerned with the cooperative target pursuit problem by multiple agents based on directed acyclic graph. The target appears at a random location and moves only when sensed by the agents, and agents will pursue the target once they detect its existence. Since the ability of each agent may be different, we consider the heterogeneous multi-agent systems. According to the topology of the multi-agent systems, a novel consensus-based control law is proposed, where the target and agents are modeled as a leader and followers, respectively. Based on Mason's rule and signal flow graph analysis, the convergence conditions are provided to show that the agents can catch the target in a finite time. Finally, simulation studies are provided to verify the effectiveness of the proposed approach.

Energy supply and demand modeling. (Latest citations from the NTIS data base). Published Search

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

Not Available

1992-10-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)