Probability-Based Models of Spatial Search

Abstract

The author develops and explores probability-based decision strategies for the consumer spatial search problem. These strategies extend experimental and analytical work in psychology on repeated choice among alternatives with varying reinforcement rates. Normative models postulate that the searcher attempts to maximize the frequency with which the lowest acquisition cost will be achieved over a multiple-trip horizon. A behavioral model postulates that the searcher maximizes the frequency with which the maximum utility will be achieved over a multiple-trip horizon. These models encompass a wide range of behavioral factors, including the perceived travel cost, decision foresight, learning, price motivation, and risk attitudes. Sensitivity experiments conducted with the models establish their basic characteristics and assess the influence of these behavioral factors on the spatial search process.