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 (C=1) revealed by lens model analysis. The GBPC findings also allow us to now interpret a similarly high use of unmodeled knowledge (C=1)in a previously published lens model analysis of a different data set collected in the same experimental task. We conclude by discussing training implications, and also prospects for the development of integrated GBPC models of both human judgment and the task environment, thus providing a noncompensatory formulation of the lens model (a genetics-based lens model, or GBLM) of the integrated human-environment system.