Including the Cost of Irreducible Uncertainty in the Policy Compression Framework
AI decision-support systems can benefit from anticipating biases in human decision-making. Many such biases may arise from human cognitive limitations. The policy compression framework models decision-making as a trade-off between reward maximization and the cognitive cost of encoding state-dependent action policies, formalized as the mutual information between states and actions (policy complexity). We argue that this account is incomplete because it treats conditional entropy--the irreducible uncertainty about which action should be selected given a state--as costless, even though empirical evidence suggests that it modulates reaction times. We therefore extend the framework by defining cognitive cost as the sum of policy complexity and a weighted conditional-entropy term, governed by a new parameter, $η$. The resulting optimal policy retains the standard exponential form but becomes sharper as $η$ increases, allowing policy precision to vary more independently of reward sensitivity. This modification implies that the standard policy compression framework may underestimate the cognitive cost of action selection, and it has the potential to better account for biases in human decision-making. At the same time, it introduces additional complexity for fitting the model to human data, which future work will need to address.