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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018-03-14
摘要
We study adaptive learning in a typical p-player game. The payoffs of the games are randomly generated and then held fixed. The strategies of the players evolve through time as the players learn. The trajectories in the strategy space display a range of qualitatively different behaviours, with 吸引子s that include unique fixed points, multiple fixed points, limit cycles and 混沌. In the limit where the game is complicated, in the sense that the players can take many possible actions, we use a generating-functional approach to establish the parameter range in which learning dynamics converge to a stable fixed point. The size of this region goes to zero as the number of players goes to infinity, suggesting that complex non-equilibrium behaviour, exemplified by 混沌, is the norm for complicated game
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