Modeling decisions from experience: How models with a set of parameters for aggregate choices explain individual choices

  • Neha Sharma (Author)
    Indian Institute of Technology ,Mandi, India
  • Varun Dutt (Author)
    Indian Institute of Technology ,Mandi, India

Identifiers (Article)

Abstract

One of the paradigms (called “sampling paradigm”) in judgment and decision-making involves decision-makers sample information before making a final consequential choice. In the sampling paradigm, certain computational models have been proposed where a set of single or distribution parameters is calibrated to the choice proportions of a group of participants (aggregate and hierarchical models). However, currently little is known on how aggregate and hierarchical models would account for choices made by individual participants in the sampling paradigm. In this paper, we test the ability of aggregate and hierarchical models to explain choices made by individual participants. Several models, Ensemble, Cumulative Prospect Theory (CPT), Best Estimation and Simulation Techniques (BEAST), Natural-Mean Heuristic (NMH), and Instance-Based Learning (IBL), had their parameters calibrated to individual choices in a large dataset involving the sampling paradigm. Later, these models were generalized to two large datasets in the sampling paradigm. Results revealed that the aggregate models (like CPT and IBL) accounted for individual choices better than hierarchical models (like Ensemble and BEAST) upon generalization to problems that were like those encountered during calibration. Furthermore, the CPT model, which relies on differential valuing of gains and losses, respectively, performed better than other models during calibration and generalization on datasets with similar set of problems. The IBL model, relying on recency and frequency of sampled information, and the NMH model, relying on frequency of sampled information, performed better than other models during generalization to a challenging dataset. Sequential analyses of results from different models showed how these models accounted for transitions from the last sample to final choice in human data. We highlight the implications of using aggregate and hierarchical models in explaining individual choices from experience.

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Published
2017-10-06
Language
en
Keywords
Aggregate choice; individual choice; sampling paradigm; decisions from experience; computational models; likelihood