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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- Young, H. Peyton, 2009. "Learning by trial and error," Games and Economic Behavior, Elsevier, vol. 65(2), pages 626-643, March.
- Huck Steffen & Sarin Rajiv, 2004.
"Players With Limited Memory,"
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- 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.
- 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, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
- Sarin, Rajiv, 2000. "Decision Rules with Bounded Memory," Journal of Economic Theory, Elsevier, vol. 90(1), pages 151-160, January.
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