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Area of Science:

  • Cognitive Science
  • Computational Social Science
  • Artificial Intelligence

Background:

  • Human belief formation is complex and can lead to polarization.
  • Understanding the cognitive mechanisms behind belief formation is crucial.

Purpose of the Study:

  • To present two machine learning-based models of human belief formation.
  • To explain how polarized beliefs can emerge from similar information sources.

Main Methods:

  • Developed two computational models based on machine learning principles.
  • Model 1: Beliefs as deterministic functions fitting past data (training sets).
  • Model 2: Beliefs with a cost associated with complexity, leading to simplification.

Main Results:

  • Model 1 shows how differing data distributions, even slightly, can cause opposing deterministic beliefs.
  • Model 2 demonstrates that agents can disagree substantially due to differing simplification strategies, even with identical data.
  • Both models illustrate mechanisms for belief polarization.

Conclusions:

  • Machine learning theory provides valuable insights into human belief formation.
  • These models highlight how cognitive processes and data interpretation influence belief accuracy and agreement.
  • Findings suggest potential avenues for improving human judgment and consensus.