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Profiling dynamic decision-makers.

Konrad Grabiszewski1, Alex Horenstein2

  • 1Mohammed Bin Salman College, King Abdullah Economic City, Saudi Arabia.

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|April 14, 2022
PubMed
Summary

This study introduces a new method to profile dynamic decision-makers based on response times. The developed profiling method accurately describes reasoning processes and predicts decision-making success.

Area of Science:

  • Cognitive Science
  • Behavioral Economics
  • Decision Analysis

Background:

  • Decision-making is a fundamental aspect of both professional and personal life.
  • Individuals exhibit significant variability in their decision-making processes.
  • A standardized method for classifying dynamic decision-makers is currently lacking.

Purpose of the Study:

  • To develop and validate a profiling method for classifying dynamic decision-makers.
  • To establish a system that can describe and predict individual decision-making performance.
  • To analyze differences in decision-making strategies.

Main Methods:

  • Equating dynamic decision-making with backward induction.
  • Utilizing response times as the primary data for profile construction.

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  • Testing the profiling method using data from 22 finite dynamic scenarios on the 'Blues and Reds' mobile app.
  • Main Results:

    • The developed profiling method demonstrates both descriptive and predictive power.
    • Subject profiles correlate with their reasoning processes.
    • The method successfully forecasts the likelihood of accurate backward induction.

    Conclusions:

    • The proposed profiling method offers a robust approach to understanding individual differences in dynamic decision-making.
    • The method's predictive capabilities were validated across a large and diverse dataset.
    • This research provides a novel tool for analyzing and potentially improving decision-making strategies.