On heuristic and linear models of judgment: Mapping the demand for knowledge

Abstract

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from "as if" linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of "lens model" research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human and heuristic performance in the same tasks. Our results highlight the trade-off between linear models and heuristics. Whereas the former are cognitively demanding, the latter are simple to use. However, they require knowledge -and thus "maps"- of when and which heuristic to employ.