Sequential Search and Learning from Rank Feedback: Theory and Experimental Evidence

Abstract

This paper studies the effect of limited information in a sequential search setting where a single selection is to be made from a set of random potential options. We consider both a full-information problem, where the decision maker observes the exact value of each option as she searches, and a partial-information problem, in which the decision maker only learns the rank of the current option relative to the options that have already been observed. We develop a model that allows for a sharp contrast between search behavior in the two information settings, both theoretically and empirically. We present the results of an experiment that tests, and supports, the key prediction of our model analysis—limited information induces longer search. Our data further suggest systematic deviations from the theoretical benchmarks in both informational settings. Importantly, subjects in our partial-information conditions are prone to stop prematurely during early stages of the search process and to suboptimally continue the search during late stages. We propose a simple model that succinctly captures the interplay of two symmetric choice and judgment biases that have asymmetric (but opposing) effects on the length of search. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1902 . This paper was accepted by Teck-Hua Ho, behavioral economics.