Adaptive Learning and Quasi Fictitious Play in “Do-It-Yourself Lottery” with Incomplete Information

  • Takashi Yamada
  • Takao Terano
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 229)


This study investigates a kind of guessing game, “do-it-yourself lottery” (DIY-L), with two types of players, adaptive learning and quasi fictitious play, by agent-based computational economics approach. DIY-L is a multi-player and multi-strategy game with a unique but skew-symmetric mixed strategy equilibrium. Here computational experiments are pursued to see what kind of game dynamics is observed and how each type of players behaves and learns in DIY-L by changing the game setup, learning parameters, and the number of each type of players. The main results are twofold: First a player who firstly and immediately learns to keep submitting the smallest integer becomes a winner in three-player games. Second, in four-player games, while the quasi fictitious play agent wisely wins when the other three players are all adaptive learners, one of the adaptive learners successfully makes advantage of the behaviors of quasi fictitious play agents when there are plural quasi fictitious play agents.


Adaptive Learning Normal Form Game Mixed Strategy Equilibrium Fictitious Play Mixed Strategy Nash Equilibrium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan

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