Non-probabilistic decision making with memory constraints
The single decision maker chooses one of the actions repeatedly. She chooses the action with the highest weighted average of the past payoffs. In the long run either the action with highest expected payoff or the action with highest minimal payoff is chosen depending on how weights evolve.
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References listed on IDEAS
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- Young, H. Peyton, 2009. "Learning by trial and error," Games and Economic Behavior, Elsevier, vol. 65(2), pages 626-643, March.
- Sarin, Rajiv, 2000. "Decision Rules with Bounded Memory," Journal of Economic Theory, Elsevier, vol. 90(1), pages 151-160, January.
- Sarin, R. & Vahid, F., 1999.
"Predicting how People Play Games: a Simple Dynamic Model of Choice,"
Monash Econometrics and Business Statistics Working Papers
12/99, Monash University, Department of Econometrics and Business Statistics.
- Sarin, Rajiv & Vahid, Farshid, 2001. "Predicting How People Play Games: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 34(1), pages 104-122, January.
- Steffen Huck & Rajiv Sarin, 2000.
"Players with Limited Memory,"
Econometric Society World Congress 2000 Contributed Papers
1645, Econometric Society.
- Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
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