Recent Developments in Learning and Competition with Finite Automata (Extended Abstract)

  • Abraham Neyman
Conference paper

DOI: 10.1007/11944874_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4286)
Cite this paper as:
Neyman A. (2006) Recent Developments in Learning and Competition with Finite Automata (Extended Abstract). In: Spirakis P., Mavronicolas M., Kontogiannis S. (eds) Internet and Network Economics. WINE 2006. Lecture Notes in Computer Science, vol 4286. Springer, Berlin, Heidelberg


Consider a repeated two-person game. The question is how much smarter should a player be to effectively predict the moves of the other player. The answer depends on the formal definition of effective prediction, the number of actions each player has in the stage game, as well as on the measure of smartness. Effective prediction means that, no matter what the stage-game payoff function, the player can play (with high probability) a best reply in most stages. Neyman and Spencer [4] provide a complete asymptotic solution when smartness is measured by the size of the automata that implement the strategies.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Abraham Neyman
    • 1
  1. 1.Institute of Mathematics and Center for the Study of RationalityThe Hebrew University of JerusalemGivat Ram, JerusalemIsrael

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