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Wealth Distribution Evolution in an Agent-based Computational Economics Victor Romanov, Dmitry Yakovlev and Anna Lelchuk

The paperwork presented by Victor Romanov, Dmitry Yakovlev and Anna Lelchuk describes modification of wealth distribution among the customers. A simulation model created by them helps to analyze the following interrelated spheres, such as labour market, stock market, enterprise investment strategy, tax level.
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A simulation modeling approach for improving oral health outcomes of older adults Michael J. Widener, University at Buffalo

This interdisciplinary research focuses on improving the oral health of older adults as a means of enhancing their overall wellbeing and quality of life. Periodontal disease is a risk factor for other chronic illnesses, notably diabetes and cardiovascular disease. In order to identify policies that improve oral health for older adults, a dynamic modeling approach that considers community and individual level factors is utilized.
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Using simulation tools for strategic decision making Mark Paich. PricewaterhouseCoopers Technology Forecast 2010, Issue 1.

Dr. Mark Paich provides examples of Decisio’s success in using AnyLogic and other simulation tools to help executives at large enterprises make major transformation decisions.
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Investigating Output Accuracy for a Discrete Event Simulation Model and an Agent Based Simulation Model Mazlina Abdul Majid, Uwe Aickelin, Peer-Olaf Siebers. 2009 INFORMS at University of Warwick, June 25-27, Coventry, U.K.

In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study.
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Dynamic Learning in Human Decision Behavior for Evacuation Scenarios under BDI Framework Seungho Lee, Young-Jun Son. 2009 INFORMS at University of Warwick, June 25-27, Coventry, U.K.

A novel approach to represent learning in human decision behavior for evacuation scenarios is proposed under the context of an extended Belief-Desire-Intention framework. In particular, we focus on how a human adjusts his perception process (involving a Bayesian belief network) in Belief Module dynamically against his performance in predicting the environment as part of his decision planning function. To this end, a Q-learning algorithm (reinforcement learning algorithm) is employed and further developed.
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The Impact of Human Decision Makers’ Individualities on The Wholesale Price Contract’s Efficiency: Simulating The Newsvendor Problem Stavrianna Dimitriou, Stewart Robinson, Kathy Kotiadis. 2009 Winter Simulation Conference (WSC’09), December 13-16, Austin, TX, USA

Suppliers and retailers in the newsvendor setting need to submit their pricing and inventory decisions respectively, well before actual customer demand is realized. In the literature they have both been typically considered as perfectly rational optimizers, exclusively interested in their own respective benefits. Under the above set of conditions the wholesale price-only contract has long been analytically proven as inefficient.
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Hybrid Simulation and Optimization-Based Capacity Planner for Integrated Photovoltaic Generation with Storage Units Esfandyar M. Mazhari. 2009 Winter Simulation Conference (WSC’09), December 13-16, Austin, TX, USA

Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control difficult and requires storage units. The goal of this research is to develop a flexible capacity planning tool, which will allow us to obtain a most economical mixture of capacities from solar generation as well as storage while meeting reliability requirements against fluctuating demand and weather conditions. The tool is based on hybrid (system dynamics and agent-based models) simulation and meta-heuristic optimization.
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Agent-based Modeling and Simulation Charles M. Macal, Michael J. North. 2009 Winter Simulation Conference (WSC’09), December 13-16, Austin, TX, USA

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, mitigating the threat of bio-warfare, and understanding the factors that may be responsible for the fall of ancient civilizations.
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AnyLogic 4.0: Simulating Hybrid Systems with Extended UML-RT Andrei Borshchev. Simulation News Europe, No. 31 April 2001, pp 15-16

We outline a modelling approach aimed to capture sophisticated interdependencies of discrete and continuous behaviors in hybrid systems. The approach is essentially a hybrid extension of widely recognized object-oriented languages UML and UML-RT. It is fully supported by a new simulation tool AnyLogic 4.0 from Experimental Object Technologies.
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Decision Support Tool — Supply Chain Christian Wartha, Momtchil Peev, Andrei Borshchev, and Alexei Filippov. 2002 Winter Simulation Conference (WSC’02), December 8-11, 2002, San Diego, California, USA

We present a currently developed Decision Support Tool - Supply Chain (DST-SC). This is specialized domain oriented tool, which is an extension of the general purpose, UML-RT Hybrid Simulation kernel of AnyLogic by XJ Technologies. DST-SC allows high degree of flexibility with respect to the supply chain functionality being modeled, has the ability to handle large complex problems, and offers highly reusable model components, offering at the same time ease of use by non-experts in simulation.
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