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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

Suggested Citation

  • Fernando Lozano & Jaime Lozano & Mario García, 2007. "An artificial economy based on reinforcement learning and agent based modeling," Documentos de Trabajo 3907, Universidad del Rosario.
    • Handle: RePEc:col:000092:003907
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    References listed on IDEAS

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    Cited by:

    1. Brini, Alessio & Tedeschi, Gabriele & Tantari, Daniele, 2023. "Reinforcement learning policy recommendation for interbank network stability," Journal of Financial Stability, Elsevier, vol. 67(C).

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