Inferring Rule-Based Strategies in Dynamic Judgment Tasks: Toward a Noncompensatory Formulation of the Lens Model

Performers in time-stressed, information-rich tasks develop rule-based, simplification strategies to cope with the severe cognitive demands imposed by judgment and decision making. Linear regression modeling, proven useful for describing judgment in a wide range of static tasks, may provide misleading accounts of these heuristics. That approach assumes cue-weighting and cue-integration are well described by compensatory strategies. In contrast, evidence suggests that heuristic strategies in dynamic tasks may instead reflect rule-based, noncompensatory cue usage. We therefore, present a technique called genetics-based policy capturing (GBPC) for inferring noncompensatory rule-based heuristics from judgment data as an alternative to regression. In GBPC, rule-base representation and search uses a genetic algorithm, and fitting the model to data using multiobjective optimization to maximize fit on three dimensions: completeness (all human judgments are represented); specificity (maximal concreteness); and parsimony (no unnecessary rules are used). GBPC is illustrated using data from the highest and lowest scoring participants in a simulated dynamic, combat information center (CIC) task. GBPC inferred rule-bases for these two performers that shed light on both skill and error. We compare the GBPC results with regression-based lens modeling of the same data set, and discuss how the GBPC results allowed us to interpret the high scoring performer's highly significant use of unmodeled knowledge ( =1)revealed by lens model analysis. The GBPC findings also allow us to now interpret a similarly high use of unmodeled knowledge ( =1)in a previously published lens model analysis of a different data set collected in the same experimental task. We conclude by discussing training implica..