Computational Models of Decision Making

Abstract

This chapter introduces computational models of decision making as worthy successors to the traditional, algebraic utility framework that has dominated the field. It provides an overview of several different computational modeling approaches before providing a detailed example of perhaps the most well-established of these, based on sequential sampling of information and evidence accumulation. It is shown how this approach can account for common paradoxes in decision behavior, and how it can be extended to a variety of tasks and response modes while retaining the same basic cognitive principles. The chapter concludes with an illustration of how process-tracing methods that capture the information acquisition and response processes can help to evaluate computational models of decision making and discriminate among them.

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