Extended Policy Compression Framework Incorporates Irreducible Uncertainty in Human Decision-Making Models
A new paper accepted at the 2026 Hybrid Human-Artificial Intelligence conference proposes extending the 'policy compression' framework for human decision-making by adding a cost term for irreducible uncertainty in action selection. The standard framework models cognition as a trade-off between reward maximization and the complexity of encoding state-dependent actions, but treats residual uncertainty as free — despite evidence that it slows reaction times. The extension could improve AI decision-support systems by better capturing human cognitive biases, though it also complicates model fitting.
The policy compression framework is an established model of human decision-making that treats cognitive cost as the mutual information between environmental states and chosen actions — essentially penalizing complex, state-sensitive policies. Researchers Álvaro Garrido-Pérez and colleagues argue this formulation is incomplete because it assigns no cost to conditional entropy, the irreducible uncertainty about which action to take in a given state, even though empirical data links this uncertainty to longer reaction times. To address this, they introduce a modified framework in which cognitive cost equals the sum of policy complexity and a weighted conditional-entropy term, governed by a new free parameter η. The resulting optimal policy retains the familiar exponential (softmax) form but becomes sharper — more decisive — as η increases, allowing policy precision to vary more independently of reward sensitivity than the original model permits. The authors suggest the standard framework may systematically underestimate the cognitive cost of action selection, which could cause AI decision-support systems to misjudge human behavior. They acknowledge that the added parameter introduces new challenges for fitting the model to empirical human data, which they flag as a priority for future work. The paper was accepted at the 5th International Conference on Hybrid Human-Artificial Intelligence (2026).
What's missing
The paper does not report empirical validation of the extended model against human behavioral data; the modification is currently theoretical. The specific empirical studies cited as evidence that conditional entropy modulates reaction times are not identified in the abstract, making it difficult to assess the strength of that motivating evidence.
What different sources said
- arXiv q-bioCenter
Including the Cost of Irreducible Uncertainty in the Policy Compression Framework
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