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Learning by Imitation in Games: Theory, Field, and Laboratory

Listed author(s):
  • Erik Mohlin
  • Robert Ostling
  • Joseph Tao-yi Wang

We exploit a unique opportunity to study how a large population of players in the field learn to play a novel game with a complicated and non-intuitive mixed strategy equilibrium. We argue that standard models of belief-based learning and reinforcement learning are unable to explain the data, but that a simple model of similarity-based global cumulative imitation can do so. We corroborate our findings using laboratory data from a scaled-down version of the same game, as well as from three other games. The theoretical properties of the proposed learning model are studied by means of stochastic approximation.

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File URL: http://www.economics.ox.ac.uk/materials/papers/13639/paper734.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 734.

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Date of creation: 28 Nov 2014
Handle: RePEc:oxf:wpaper:734
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Web page: https://www.economics.ox.ac.uk/
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