On the Variational Costs of Changing Our Minds

2025-09-22

AI Summary

Humans often maintain prior beliefs despite contradictory evidence, selectively interpret information, or actively seek/avoid information—behaviors traditionally labeled “biases” yet potentially reflecting adaptive trade-offs between epistemic utility and cognitive/decisional costs. Method: We propose a variational Bayesian decision-theoretic framework that formally quantifies the informational cost of belief updating using KL divergence, integrates motivated reasoning, and models belief revision as an explicit utility–cost optimization. Contribution/Results: This framework provides a resource-rational, unified account of phenomena including confirmation bias and attitude polarization. Computational experiments successfully replicate multiple canonical cognitive biases and demonstrate the model’s efficacy in both predicting and correcting suboptimal belief-updating trajectories. By grounding seemingly irrational behavior in bounded rationality, our approach bridges motivational and computational accounts of human judgment, offering testable hypotheses for future empirical work in cognitive science and behavioral economics.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The human mind is capable of extraordinary achievements, yet it often appears to work against itself. It actively defends its cherished beliefs even in the face of contradictory evidence, conveniently interprets information to conform to desired narratives, and selectively searches for or avoids information to suit its various purposes. Despite these behaviours deviating from common normative standards for belief updating, we argue that such 'biases' are not inherently cognitive flaws, but rather an adaptive response to the significant pragmatic and cognitive costs associated with revising one's beliefs. This paper introduces a formal framework that aims to model the influence of these costs on our belief updating mechanisms. We treat belief updating as a motivated variational decision, where agents weigh the perceived 'utility' of a belief against the informational cost required to adopt a new belief state, quantified by the Kullback-Leibler divergence from the prior to the variational posterior. We perform computational experiments to demonstrate that simple instantiations of this resource-rational model can be used to qualitatively emulate commonplace human behaviours, including confirmation bias and attitude polarisation. In doing so, we suggest that this framework makes steps toward a more holistic account of the motivated Bayesian mechanics of belief change and provides practical insights for predicting, compensating for, and correcting deviations from desired belief updating processes.

Problem

• Modeling belief updating as variational decisions with cognitive costs
• Explaining confirmation bias through resource-rational belief mechanisms
• Quantifying belief change costs using Kullback-Leibler divergence metrics

Innovation

• Modeling belief updating as motivated variational decision
• Quantifying belief change cost with Kullback-Leibler divergence
• Using resource-rational model to emulate human biases