Confidence-Aware Action Selection: Adjusting Decisions According to the Reliability of Predicted Outcomes

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Intelligent systems increasingly select actions according to predictions of their future consequences. However, a prediction with a high estimated outcome value may be unreliable, while a lower-valued prediction may be supported by substantially stronger evidence. Decision mechanisms that rank actions exclusively by predicted utility can therefore behave poorly when the predictive model is uncertain, miscalibrated, or operating outside its training distribution. This paper proposes Confidence-Aware Action Selection (CAAS), a general decision-making framework that adjusts action selection according to the estimated reliability of predicted outcomes. Given a current state and a set of candidate actions, CAAS estimates the expected utility and predictive uncertainty associated with each candidate, evaluates the reliability of the corresponding predictions, and selects an action using a confidence-sensitive decision rule. The framework distinguishes outcome variability from epistemic uncertainty, prediction calibration from ranking accuracy, and confidence in an individual prediction from confidence in the relative comparison between actions. It introduces uncertainty-adjusted utility, risk-sensitive selection, confidence-dependent verification, adaptive abstention, and value-of-information-based computation allocation. A mathematical formulation, training objectives, inference algorithm, and experimental protocol are presented. The framework is designed to be compatible with reinforcement learning, model-based planning, generative systems, autonomous agents, and other predictive decision processes. Its central hypothesis is that explicitly incorporating calibrated predictive reliability can improve decision quality and reduce harmful decisions under uncertainty, particularly when the consequences of incorrect actions are substantial. No experimental results are claimed in this paper. The effectiveness, computational advantages, and distinctiveness of CAAS must be established through controlled comparisons with uncertainty-aware decision-making, risk-sensitive optimization, selective prediction, and value-of-information methods.