Payoff Effects in Information Cascade Experiments

LISA R. ANDERSON (*) I. INTRODUCTION This article examines payoff effects in "information cascade" experiments. (1) Cascade experiments provide a unique environment to study errors because decisions are made publicly and in sequence, allowing people to learn from the decisions of others. However, there are no payoff interdependencies, so subjects are only concerned with maximizing their own expected payoffs which cannot be directly affected by the actions of others. This feature simplifies the analysis of the decision making process and makes it possible to focus more clearly on how subjects draw inferences from public decisions. Furthermore, errors are recursive in the sense that mistakes made by initial decision makers influence the decisions of those that come later in the sequence. This study focuses on learning and errors by analyzing data econometrically in a logistic error model. Econometric methods have gained popularity as a way to examine errors in experimental data. Harless and Camerer (1994) present a maximum likelihood estimation method to test the predictive power of various utility theories using data from 23 different experiments. McKelvey and Palfrey (1995 and 1998) use the logistic structure to estimate error rates with data from a variety of games. Palfrey and Prisbrey (1997) use a similar structure with a probit model to estimate error rates in a public goods experiment. The analysis presented here differs from these previous econometric studies of experimental data in the following way: in this model, errors are analyzed in a sequential structure so the errors of others must be taken into account, but there is no strategic element in the decision making process. This study expands on the error analysis presented in Anderson and Holt (1997) by examining error rates under different payoff conditions. Smith and Walker (1993a) present a model of decision making in which the effort of contemplating a decision is balanced against potential gains in payoffs. In addition, they survey the experimental literature in economics and psychology addressing the issue of payoff effects, and they find mixed results. Some studies report a decrease in error rates as payoffs increase. Others show little difference in results under payoff and no payoff treatments. However, there is a consensus that higher payoffs reduce the variance of decisions from the predicted Outcome. Testing their own theory in the context of a first price auction experiment, Smith and Walker (1993b) report that the amount and severity of error decreases as payoffs increase. Interestingly, behavior is not random, even when there is no monetary reward for a correct decision. Using data from lottery choice experiments , Wilcox (1993) finds that higher monetary payoffs affect behavior in complex situations, but not in relatively simple decision making environments. Harless and Camerer (1994) use econometric methods to compare error rates in experiments with real losses to those with hypothetical losses. They find that error rates are lower and variances are reduced in data with real losses. This article adds to the literature on the saliency of rewards by analyzing behavior in a different experimental framework and by using the logistic model to generate econometric estimates of error rates under no payoff, payoff, and double payoff conditions. The underlying behavioral model is an extension of the cascade model presented in Bikhchandani, Hirshleifer, and Welch (1992). Experimental procedures are discussed in section II. Section III discusses econometric details. Section IV reports results and section V concludes. II. PROCEDURES (2) For each session of this experiment, seven subjects were recruited from undergraduate economics courses at the University of Virginia. One subject was randomly chosen to be a monitor, and the remaining six subjects were decision markers. At the beginning of each period, the monitor threw a six-sided die to determine which of two urns was to be used for that period. …