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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.
Cognitive Psychology
|October 20, 2024
Summary
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.
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.
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