Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

• Published in: Econometrica Vol. 72; no. 3; pp. 781 - 822
• Main Authors: Houser, Daniel, Keane, Michael, McCabe, Kevin
• Format: Journal Article
• Language: English
• Published: Oxford, UK and Boston, USA Blackwell Publishing Ltd 01.05.2004; Econometric Society; Blackwell
• Subjects: A priori knowledge; Algorithms; Applications; Bayesian analysis; Bayesian decision theory; Bayesian method; Behavior; behavioral economics; Classification; Decision making; Decision making models; Decision theory; Dynamic programming; Econometrics; Economic models; Exact sciences and technology; experimental economics; Experiments; Fatalism; Games; Gibbs sampling; Heuristic; Heuristics; Insurance, economics, finance; Laboratories; Markov analysis; Markov processes; Mathematics; Modeling; Money; Multivariate analysis; Optimization; Parametric inference; Parametric models; Polynomials; Population; Probability and statistics; Probability theory and stochastic processes; Return on investment; Sciences and techniques of general use; Standard deviation; Statistics; Studies; Learning; Bayes estimation; Statistical method; Divergence; Error estimation; Classification; Problem solving; Statistical estimation; Econometrics; Algorithm; Decision rule
• ISSN: 0012-9682; 1468-0262; 1468-0262
• DOI: 10.1111/j.1468-0262.2004.00512.x

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.