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Empirical content as a criterion for evaluating models.

Marc Jekel1

  • 1Social Cognition Center Cologne, University of Cologne, Richard-Strauss-Straße 2, 50931, Cologne, Germany. marc.jekel@uni-koeln.de.

Cognitive Processing
|March 22, 2019
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Summary

This study evaluates computational models of decision-making using empirical content criteria. It assesses the precision and universality of predictions to determine model usefulness and guide modifications.

Keywords:
Empirical contentFalsificationModel evaluationPrecisionTheory of scienceUniversality

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Area of Science:

  • Cognitive Science
  • Decision-Making Models
  • Philosophy of Science

Background:

  • Models in science are evaluated by their empirical content, which relates to the precision and universality of their predictions.
  • Karl Popper's criteria emphasize that useful models should make specific, broad predictions.
  • Kirsch's unifying computational model of decision-making is a recent development in cognitive science.

Purpose of the Study:

  • To critically evaluate Kirsch's computational model of decision-making.
  • To apply Popperian criteria of empirical content (precision and universality) to assess the model's utility.
  • To determine if the model can be dismissed or requires modification based on its predictive power.

Main Methods:

  • Applying the concept of empirical content to evaluate a computational model.
  • Assessing the degree of precision in the model's predictions.
  • Examining the level of universality across various events predicted by the model.
  • Using hypothetical empirical testing as a framework for model evaluation.

Main Results:

  • The evaluation framework highlights the importance of precise and universal predictions for model validation.
  • Specific aspects of Kirsch's model are critically examined against these criteria.
  • The study provides a method for dismissing or modifying models based on predictive failures.

Conclusions:

  • The utility of computational models in decision-making hinges on their empirical content.
  • Models with high precision and universality are more scientifically valuable.
  • This approach offers a rigorous method for advancing decision-making research through model refinement.