Agent-Based Social Simulation and PSO
Consumers behavior can be modeled using a utility function that allows for measuring the success of an individuals decision, which consists of a tuple of goods an individual would like to buy and the hours of work necessary to pay for this purchase and consumption.The success of such a decision is measured by a utility function which incorporates not only the purchase and consumption of goods, but also leisure, which ad ditionally increases the utility of an individual.In this paper, we present a new agent based social simulation in which the decision finding pro cess of consumers is performed by Particle Swarm Optimization (PSO),a well-known swarm intelligence method.PSO appears to be suitable for the underlying problem as it is based on previous and current information, but also contains a stochastic part which allows for modeling the uncertainty usually involved in the hu man decision making process.We investigate the adequacy of different bounding strategies that map particles violating the underlying budget constraints to a feasible region.Experiments indicate that one of these bounding strategies is able to achieve very fast and stable convergence for the given optimization problem.However, an even more interesting ques tion refers to adequacy of these bounding strategies for the underlying social simulation task.
Agent-Based Modeling Social Simulation Particle Swarm Optimization PSO Consumers Behavior Decision Finding Process
Andreas Janecek Tobias Jordan Fernando Buarque de Lima-Neto
University of Vienna, Research Group Entertainment Computing, Austria Department of Economics and Business Engineering,Karlsruhe Institute of Technology (KIT), Germany Polytechnic School of Engineering, Computing Engineering Program University of Pernambuco, Recife/PE
国际会议
4th international Conference,ICSI2013(第4届群体智能国际会议)
哈尔滨
英文
63-73
2013-06-12（万方平台首次上网日期，不代表论文的发表时间）