Extended Policy Compression Framework Incorporates Irreducible Uncertainty in Human Decision-Making Models
Researchers propose an extension to the policy compression framework that accounts for the cognitive cost of irreducible uncertainty—the inherent difficulty in choosing between actions even after considering all available information. The standard framework models decision-making as a trade-off between reward maximization and policy complexity (measured by mutual information), but treats the uncertainty about action selection as costless despite evidence it affects reaction times. The modification could improve predictions of human decision-making biases and has implications for designing AI systems that better anticipate human behavior.
The policy compression framework explains human decision-making as a balance between maximizing rewards and minimizing the cognitive effort required to encode state-dependent action policies. However, researchers argue this model overlooks a key component: conditional entropy, or the irreducible uncertainty about which action to select given a particular state. Empirical evidence suggests this uncertainty influences reaction times, yet the standard framework treats it as costless. The authors extend the framework by adding a weighted conditional-entropy term to the cognitive cost function, governed by a parameter η. This modification preserves the exponential form of optimal policies but allows policy precision to vary more independently of reward sensitivity. The authors acknowledge that while this extension could better account for human decision-making biases and improve AI decision-support systems, it introduces additional complexity for fitting models to empirical data—a challenge for future research.
What's missing
The paper does not provide empirical validation of the proposed framework against human behavioral data, nor does it specify which types of decision-making tasks or domains the extension is most applicable to. The authors acknowledge this limitation, noting that fitting the extended model to human data presents challenges requiring future work.
What different sources said
- arXiv q-bioCenter
Including the Cost of Irreducible Uncertainty in the Policy Compression Framework
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