Learning by imitation

Creative Commons License

Başçı E.

JOURNAL OF ECONOMIC DYNAMICS & CONTROL, vol.23, no.9-10, pp.1569-1585, 1999 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23 Issue: 9-10
  • Publication Date: 1999
  • Doi Number: 10.1016/s0165-1889(98)00084-0
  • Journal Name: JOURNAL OF ECONOMIC DYNAMICS & CONTROL
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.1569-1585
  • Keywords: learning, imitation, dynamic optimization, classifier systems, Kiyotaki-Wright model of money, EXCHANGE, MONEY
  • TED University Affiliated: No

Abstract

This paper introduces a learning algorithm that allows for imitation in recursive dynamic games. The Kiyotaki-Wright model of money is a well-known example of such decision environments. In this context, learning by experience has been studied before. Here, we introduce imitation as an additional channel for learning. In numerical simulations, we observe that the presence of imitation either speeds up social convergence to the theoretical Markov-Nash equilibrium or leads every agent of the same type to the same mode of suboptimal behavior. We observe an increase in the probability of convergence to equilibrium, as the incentives for optimal play become more pronounced. (C) 1999 Elsevier Science B.V. All rights reserved.