Journal
ECONOMETRICA
Volume 72, Issue 3, Pages 781-822Publisher
BLACKWELL PUBL LTD
DOI: 10.1111/j.1468-0262.2004.00512.x
Keywords
dynamic programming; Gibbs sampling; Bayesian decision theory; experimental economics; behavioral economics; heuristics
Ask authors/readers for more resources
Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label Near Rational, Fatalist, and Confused. There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available