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Learning of Actions in Finitely Repeated Games

  • 2017.11.14
  • Event
Speaker: Mofei Zhao, Central University of Finance and Economics

Topic:

Learning of Actions in Finitely Repeated Games
 

Time & Date:

10:30am-12:00pm, 2017/12/15

Venue:

Room 502, Daoyuan Building, CUHK(SZ)

Speaker:

Mofei Zhao, Central University of Finance and Economics

Detail:

 

This paper studies a novel setting in game theory: a player may learn new
actions over time by observing the opponent's play. We investigate the impact
of such learning behavior in the context of finitely repeated games. In contrast
to related literature such as Kreps et al. (1982), we provide a framework with
full rationality and consistent stage-game payoffs for sustaining cooperation, which
bridges the gap between theories of finitely and infinitely repeated games. Even
if rational cooperation is impossible without learning, for instance in a Prisoner's
Dilemma, it can be sustained with approximate efficiency when players can learn
from one another. Cooperation does not have to be endowed in each player's initial
action set, but can be \taught" and enforced. When learning is imperfect, the set of
sustainable payoffs is not continuous, in the sense that no equilibrium exists when
learning is nearly perfect and the repeated games last for sufficiently many periods.