Abstract:
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.
