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Rebiasing: Managing automatic biases over time.

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  • 1Copenhagen Business School, Strategy and Innovation, Frederiksberg, Denmark.

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Summary
This summary is machine-generated.

Automatic preferences, or biases, can initially improve decision-making but become detrimental over time. Timely rebiasing, or reversing these biases, can lead to superior performance compared to debiasing or unbiased decision-making.

Keywords:
adaptivenessautomatic evaluationsautomatic preferencesbiasesdebiasingintuition

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Area of Science:

  • Cognitive Science
  • Computational Modeling
  • Decision Science

Background:

  • Automatic preferences influence choices before information is processed.
  • The adaptiveness of intuitive biases is debated, often studied in one-shot experiments.

Purpose of the Study:

  • To computationally model problem-solving incorporating automatic preferences.
  • To investigate the dynamic role of biases over extended decision-making periods.

Main Methods:

  • Developed a computational model of problem-solving with active trial and error.
  • Simulated decision-making outcomes across extended time spans to analyze bias effects.

Main Results:

  • Automatic preferences are beneficial early but detrimental later in decision-making.
  • Timely rebiasing (reversing initial preferences) significantly enhances performance.
  • Rebiasing outperforms debiasing and continuously unbiased decision-making.

Conclusions:

  • Automatic biases are manageable tools for superior performance, not just properties to be eliminated.
  • Strategic rebiasing, including changing decision-makers, offers a novel intervention for organizations.