A Probabilistic Modeling Approach to Decision Strategies: Predicting Expected Information Search and Decision Time in Multi-Attribute Choice Tasks with Varying Numbers of Attributes and Alternatives

It has been well established that individuals employ different decision strategies depending on the task environment, and these strategies differ in the amount of information search and time required to reach a decision. The present study developed probabilistic models for four representative decision strategies—additive, conjunctive, disjunctive, and lexicographic (including lexicographic semi-order)—and applied them to predict expected information search and decision time in multi-attribute decision-making tasks that varied in the number of attributes and alternatives. The modeling results showed that conjunctive and disjunctive strategies were strongly influenced by the number of attributes but were relatively unaffected by the number of alternatives. In contrast, the additive and lexicographic strategies were affected by both the number of attributes and alternatives, although the influence was smaller for the lexicographic strategy. To evaluate the predictive validity of these probabilistic models, their predictions were compared with those obtained through computer simulations based on an adaptive decision-maker model using the Mersenne Twister method, as well as with data from the previous psychological experiment. The comparative analyses revealed that the predictions generated by the probabilistic models were generally consistent with findings from prior empirical and simulation studies. These results suggest that even relatively simple mathematical models can successfully account for and predict variations in information search behavior and decision time leading to final choice outcomes.