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Predicting human decisions with behavioural theories and machine learning.

Ori Plonsky1, Reut Apel2, Eyal Ert3

  • 1Technion - Israel Institute of Technology, Haifa, Israel. plonsky@technion.ac.il.

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Summary
This summary is machine-generated.

A new hybrid model, BEAST gradient boosting (BEAST-GB), accurately predicts human decisions under risk. This approach integrates behavioral theory with machine learning, outperforming existing models and neural networks.

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Area of Science:

  • Cognitive Science
  • Machine Learning
  • Behavioral Economics

Background:

  • Predicting human decisions under risk and uncertainty is a persistent challenge.
  • Existing models often fail in simplified scenarios like lottery choices.

Purpose of the Study:

  • Introduce BEAST gradient boosting (BEAST-GB), a hybrid model combining behavioral theory and machine learning.
  • Evaluate BEAST-GB's predictive accuracy and generalization capabilities.

Main Methods:

  • Developed BEAST gradient boosting (BEAST-GB) by integrating the BEAST behavioral theory with machine learning.
  • Tested BEAST-GB in the CPC18 competition for predicting risky choice.
  • Validated performance on two large datasets against neural networks and existing behavioral models.

Main Results:

  • BEAST-GB won the CPC18 competition.
  • Achieved superior predictive accuracy compared to neural networks and numerous behavioral models.
  • Demonstrated robust generalization across diverse experimental contexts.

Conclusions:

  • Integrating machine learning with behavioral theory enhances prediction of human decision-making.
  • BEAST-GB offers a powerful framework for understanding and predicting behavior, even improving the underlying theory.