In the modern, evolving workplace there exists a need to support workers in adapting to novel demands and opportunities. Traditionally, human judgments are characterized by weighted strategies where tradeoffs between decision criteria are made. For example, college admissions committees consider multiple facets of an applicant and, often, a poor grade point average can be compensated by a superior entrance exam score. However, on occasions in which time stress or high workload exists, human judgments are typically rule-based where factors are not weighted. For instance, an air traffic controller is likely to diagnose potential problems in high workload situations based on a few salient cues.
This research will add to the growing body of knowledge concerning the way in which a decision environment influences the strategy used by decision makers. While characterizations such as weighted and rule-based strategies have been deduced from a multitude of experimental studies, no framework exists to model and predict shifts in judgment strategy in individual decision makers. The central theme of this research is to create a mathematical framework to model the shift from a weighted to a rule-based strategy, and vice versa, in work environments where workload and stress levels vary. This research will build upon existing work to create a model which infers judgment rules from human data. Multiple experiments will be conducted to compare the performances of the rule-based model with a commonly-used weighted model under varying workload and time stress situations. It is hypothesized that increasing workload and time stress will promote a systematic shift from weighted to rule-based strategies. This work will make a valuable contribution toward understanding decision making in complex, dynamic environments. One potential implication of this research is the design of an aiding mechanism that adapts to the needs of the user based on conditions of dynamic work environments.
