An artificial economy based on reinforcement learning and agent based modeling
AbstractIn 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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- Thomas J. Sargent & Francois R. Velde, 1998. "Optimal Fiscal Policy in a Linear Stochastic Economy," QM&RBC Codes 130, Quantitative Macroeconomics & Real Business Cycles.
- Thomas Brenner, 2004.
"Agent Learning Representation - Advice in Modelling Economic Learning,"
Papers on Economics and Evolution
2004-16, Max Planck Institute of Economics, Evolutionary Economics Group.
- Brenner, Thomas, 2006. "Agent Learning Representation: Advice on Modelling Economic Learning," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 18, pages 895-947 Elsevier.
- 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.
- 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.
- T. Borgers & R. Sarin, 2010.
"Learning Through Reinforcement and Replicator Dynamics,"
Levine's Working Paper Archive
380, David K. Levine.
- Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
- Tilman B�rgers & Rajiv Sarin, . "Learning Through Reinforcement and Replicator Dynamics," ELSE working papers 051, ESRC Centre on Economics Learning and Social Evolution.
- Ed Hopkins, 1995.
"Learning, Matching and Aggregation,"
Game Theory and Information
- Ed Hopkins, . "Learning, Matching and Aggregation," Department of Economics 1996 : II, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins, . "Learning, Matching and Aggregation," ELSE working papers 033, ESRC Centre on Economics Learning and Social Evolution.
- Ed Hopkins, . "Learning, Matching and Aggregation," ESE Discussion Papers 2, Edinburgh School of Economics, University of Edinburgh.
- Ed Hopkins, . "Learning, Matching and Aggregation," Discussion Papers 1996-2, Edinburgh School of Economics, University of Edinburgh.
- Hopkins, E., 1995. "Learning, Matching and Aggregation," G.R.E.Q.A.M. 95a20, Universite Aix-Marseille III.
- John Duffy, 2004.
"Agent-Based Models and Human Subject Experiments,"
- 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.
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