1998 Fiscal Year Final Research Report Summary
Research on Generation of Internal Models in Games and Their Dynamics
| Project/Area Number |
09640454
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
物性一般(含基礎論)
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| Research Institution | The University of Tokyo |
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Principal Investigator |
IKEGAMI Takashi The University of Tokyo, The Graduate School of Arts and Sciences, Associated Professor, 大学院・総合文化研究科, 助教授 (10211715)
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| Co-Investigator(Kenkyū-buntansha) |
TAIJI Makoto The University of Tokyo, Institute of Statistical Mathematics, Associated Profes, 統計数理研究所・統計計算開発センター, 助教授 (10242025)
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| Project Period (FY) |
1997 – 1998
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| Keywords | Game / Internal Model / Cooperation / prisoner's dilemma / neural nets |
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| Research Abstract |
The purpose of this research is to propose a new modeling framework to study dynamical aspects of games and to discuss rationality of game players and social norms through the simulations of the model. The followings are the results from this project.
(a) We study the iterated prisoner's dilemma game as played by cognitive players, where each player optimizes his or her own future actions by making an internal model of the opponent's behavior. A kind of recurrent neural network called a dynamical recognizer (DR) is used to make these internal models, providing the advantage that the opponent's images are represented by complicated geometrical patterns in a context space of the DR.The dynamical behavior of these geometrical patterns will give a new chaotic dynamics by varying its dimensionality.
(b) The internal model of each player's behavior is constructed from a finite history, and various possible models can be generated from each history. That is, many internal models are equally acc
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urate in mimicking the opponent's behavior. If the optimized future action varies depending on which of the models is chosen, we construct branches in the world line to represent several possible future worlds. Depending on the game situation (e.g. the payoff structures, the length of past sequences to be considered, the uncertainty level in choosing models, etc.), the structures of the branching of world lines (i.e., of possible worlds) will vary. In some situations, the world line is surrounded by many possible worlds, each with different behaviors.
(c) The same approach is applied to the Rashevskyan game, where players move along his own spatial axis to take an advantageous position over the other player. Though those players are egocentric in principle, it is shown that some altruistic behavior will be performed as a dynamical attractor phase. The altruistic behavior is no longer attainable by merely having the opponent player's model as a Tit for Tat player. Rather players have to dynamically change his model of imitation to achieve mutual cooperation. Otherwise they go to a static noncooperative Nash solution. Less
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