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An artificial economy based on reinforcement learning and agent based modeling

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  • Fernando Lozano

    ()

  • Jaime Lozano

    ()

  • Mario García

    ()

Abstract

In this paper we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on convention. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions meaning that a firm is likely to behave as it neighbors if it observes that their actions lead to a good pay-off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of goverment in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize economy growth. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA algotithm combined wiht a multilayer perceptron as the function approximation for the action value function

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Bibliographic Info

Paper provided by UNIVERSIDAD DEL ROSARIO in its series DOCUMENTOS DE TRABAJO with number 003907.

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Length: 10
Date of creation: 30 Apr 2007
Date of revision:
Handle: RePEc:col:000092:003907

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Related research

Keywords: reinforcement learning; agent-based modeling; computational economics;

References

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  1. Ed Hopkins, . "Learning, Matching and Aggregation," ESE Discussion Papers 2, Edinburgh School of Economics, University of Edinburgh.
  2. Feltovich, Nick, 1999. "Equilibrium and reinforcement learning in private-information games: An experimental study," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1605-1632, September.
  3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  4. Tilman B�rgers & Rajiv Sarin, . "Learning Through Reinforcement and Replicator Dynamics," ELSE working papers 051, ESRC Centre on Economics Learning and Social Evolution.
  5. Thomas Brenner, 2004. "Agent Learning Representation - Advice in Modelling Economic Learning," Papers on Economics and Evolution 2004-16, Philipps University Marburg, Department of Geography.
  6. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
  7. Thomas J. Sargent & Francois R. Velde, 1998. "Optimal Fiscal Policy in a Linear Stochastic Economy," QM&RBC Codes 130, Quantitative Macroeconomics & Real Business Cycles.
  8. John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA.
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