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Computation-limited Bayesian updating: A resource-rational analysis of approximate Bayesian inference
Jian-Qiao Zhu1, Thomas L Griffiths1
1Department of Computer Science, Princeton University.
Psychological Review
|June 5, 2025
Summary
Intelligent systems face limited data and computation for belief updating. Resource-rational analysis explains why limited computation leads to conservative Bayesian updating, underweighting new evidence.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Information Theory
Background:
- Intelligent systems require data and computational capacity to update beliefs.
- These resources are inherently limited, posing challenges for belief updating.
Purpose of the Study:
- To introduce a resource-rational analysis of belief updating under constraints.
- To formalize data and computational limitations using information-theoretic principles.
Main Methods:
- Developed a resource-rational analysis framework.
- Applied information-theoretic principles to model belief updating constraints.
- Derived a novel belief updating rule.
Main Results:
- Identified an interaction between data and computational limitations.
- Showed that scarce computational resources hinder full data incorporation.
- The derived rule explains conservative Bayesian updating, where new evidence is often underweight.
Conclusions:
- Resource-rational analysis provides a framework for understanding belief updating under constraints.
- The derived rule offers a novel explanation for conservative Bayesian updating.
- The theory aligns with approximate Bayesian inference models.
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