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Building compressed causal models of the world.

David Kinney1, Tania Lombrozo2

  • 1Department of Philosophy and Program in Philosophy-Neuroscience Psychology, Washington University in St. Louis, United States.

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Agents create simplified causal models by balancing information compression and usefulness for decisions. They prefer simpler models when lost information isn't crucial for decision-making, as confirmed by multiple studies.

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Area of Science:

  • Cognitive Science
  • Philosophy of Science
  • Artificial Intelligence

Background:

  • Causal systems can be represented in multiple ways.
  • Agents must select variables and granularity for causal representations.

Purpose of the Study:

  • To develop a formal theory explaining how agents choose causal representations.
  • To investigate the trade-off between compression and informativeness in causal models.

Main Methods:

  • Utilized Bayesian networks, information theory, and decision theory.
  • Developed a formal model predicting preferences for causal representations.
  • Conducted seven empirical studies (N=2,546 total participants).

Main Results:

  • Agents prefer compressed causal models when information loss is minimal.
  • Agents sacrifice compression for informativeness if the lost data impacts decisions.
  • Empirical findings align with the developed theory's predictions.

Conclusions:

  • Causal representation is governed by a compression-informativeness trade-off.
  • This trade-off is modulated by the decision-theoretic value of information.
  • Compressed causal representations are central to human cognition and evaluation.