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

Abstract

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 uses multi-objective optimization to maximize fit on three dimensions: a) completeness (all human judgments are represented); b) specificity (maximal concreteness); and c) 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.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2003
Accession Number
ADA436779

Entities

People

  • Alex Kirlik
  • Ling Rothrock

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Applied Psychology
  • Cognition
  • Cognitive Systems Engineering
  • Combat Information Centers
  • Commercial Aircraft
  • Data Sets
  • Genetic Algorithms
  • Human Factors Engineering
  • Human-Robot Interaction
  • Information Processing
  • Judgment
  • Psychology
  • Systems Engineering
  • Training
  • Transport Aircraft
  • Uss Vincennes

Readers

  • Artificial Intelligence
  • Regression Analysis.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Biotechnology