Template-Type:ReDIF-Paper 1.0  
Title:Why Imitate, and if so, How? A Bounded Rational Approach to Multi-Armed Bandits 
Author-Name: Karl H. Schlag
Classification-JEL:C72, C79
Keywords:social learning, bounded rationality, imitation, multi-armed bandit, random matching, payoff increasing,
	replicator dynamic.
Abstract:We consider the situation in which individuals in a finite population must repeatedly choose an action
	yielding an uncertain payoff. Between choices, each individual may observe the performance of one other individual.
	We search for rules of behavior with limited memory that increase expected payoffs for any underlying payoff
	distribution. It is shown that the rule that outperforms all other rules with this property is the one that specifies
	imitation of the action of an individual that performed better with a probability proportional to how much better she
	performed. When each individual uses this best rule, the aggregate population behavior can be approximated by the
	replicator dynamic.
Length: pages
Creation-Date:1995-12
Revision-Date:  1996-03
File-URL: http://www.wiwi.uni-bonn.de/bgsepapers/bonsfb/bonsfb361.pdf
File-Format: application/pdf
File-Size: 324872 bytes
File-URL: http://www.wiwi.uni-bonn.de/bgsepapers/bonsfb/bonsfb361.ps
File-Format: Application/Postscript
Handle: RePEc:bon:bonsfb:361