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Learning within a Markovian Environment

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  • Javier Rivas

Abstract

We investigate learning in a setting where each period a population has to choose between two actions and the payoff of each action is unknown by the players. The population learns according to reinforcement and the environment is non-stationary, meaning that there is correlation between the payoff of each action today and the payoff of each action in the past. We show that when players observe realized and foregone payoffs, a suboptimal mixed strategy is selected. On the other hand, when players only observe realized payoffs, a unique action, which is optimal if actions perform different enough, is selected in the long run. When looking for efficient reinforcement learning rules, we find that it is optimal to disregard the information from foregone payoffs and to learn as if only realized payoffs were observed.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2008/13.

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Date of creation: 2008
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Handle: RePEc:eui:euiwps:eco2008/13

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Related research

Keywords: Adaptive Learning; Markov Chains; Non-stationarity; Reinforcement Learning;

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References

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  1. Ellison, Glenn & Fudenberg, Drew, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, MIT Press, vol. 110(1), pages 93-125, February.
  2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  3. Cross, John G, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, MIT Press, vol. 87(2), pages 239-66, May.
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Cited by:
  1. Rivas, Javier, 2013. "Probability matching and reinforcement learning," Journal of Mathematical Economics, Elsevier, vol. 49(1), pages 17-21.
  2. Yves Ortiz & Martin schüle, 2011. "Limited Rationality and Strategic Interaction: A Probabilistic Multi-Agent Model," Working Papers 11.08, Swiss National Bank, Study Center Gerzensee.

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