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Simulation and Use of Heuristics for Peripheral Economic Policy

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  • Mattheos K. Protopapas
  • Elias B. Kosmatopoulos

Abstract

Recent trends in Agent Computational Economics research, envelop a government agent in the model of the economy, whose decisions are based on learning algorithms. In this paper we try to evaluate the performance of simulated annealing in this context, by considering a model proposed earlier in the literature, which has modeled an artificial economy consisting of geographically dispersed companies modeled as agents, that try to maximize their profit, which is yielded by selling an homogeneous product in different cities, with different travel costs. The authors have used an evolutionary algorithm there, for modeling the agents' decision process. Our extension introduces a government agent that tries to affect supply and demand by different taxation coefficients in the different markets, in order to equate the quantities sold in each city. We have studied the situation that occurs when a simulated annealing algorithm and a simple search algorithm is used as the government's learning algorithm, and we have evaluated the comparative performance of the two.

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File URL: http://arxiv.org/pdf/0905.3808
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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 0905.3808.

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Date of creation: May 2009
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Publication status: Published in In Proceedings of the 34th International Conference MACROMODELS 2007, Eds. Welfe & Welfe, ISBN: 978-83-924305-6-8, pp. 101-114
Handle: RePEc:arx:papers:0905.3808

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  1. Tesfatsion, Leigh S., 2001. "Introduction to the Special Issue on Agent-Based Computational Economics," Staff General Research Papers 1915, Iowa State University, Department of Economics.
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