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LEARNING, LOCAL INTERACTION, AND COORDINATION
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DOI：10.2307/2951493.png)
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摘要
En 中文
This paper discusses the dynamic implications of learning in a large population coordination game, focusing on the structure of the matching process which describes how players meet. As in Kandori, Mailath, and Rob (1993) a combination of experimentation and myopia creates ''evolutionary'' forces which lead players to coordinate on the risk dominant equilibrium. To describe play with finite time horizons it is necessary to consider the rates at which the dynamic systems converge. In large populations with uniform matching, play is determined largely by historical factors. In contrast, when players interact with small sets of neighbors it is more reasonable to assume that evolutionary forces may determine the outcome.
Keyword:
LEARNING
NEIGHBORS
COORDINATION
RATES OF CONVERGENCE
AI总结
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