Lookahead Uncertainty Resolution: Selecting Information-Gathering Actions According to Their Expected Reduction in Decision-Relevant Uncertainty

Intelligent systems frequently operate under incomplete information and must decide which information to acquire before committing to a subsequent action. Conventional information-gathering strategies may prioritize readily available observations, general uncertainty reduction, or fixed query sequences. However, not all uncertainty is equally relevant to the decision being considered, and an observation that reduces overall uncertainty may have little influence on the final decision. This paper introduces Lookahead Uncertainty Resolution (LUR), a conceptual framework for selecting the next information-gathering action according to its expected reduction in decision-relevant uncertainty. LUR evaluates candidate information-gathering actions before executing them. For each candidate action, the framework estimates the possible observations, the corresponding posterior belief updates, the expected reduction in decision-relevant uncertainty, and the computational or acquisition cost. The selected action is determined by its anticipated decision value rather than by information quantity alone. The framework distinguishes between general epistemic uncertainty, decision-relevant uncertainty, expected information gain, and expected value of sample information. This distinction is intended to prevent the system from spending resources on information that improves descriptive knowledge without materially improving the decision. The proposed architecture consists of five components: a decision-state representation, an information-action candidate generator, a prospective observation predictor, a decision-relevant uncertainty estimator, and a cost-aware action selector. A sequential formulation is developed to support repeated information acquisition, posterior updating, and termination when further information is unlikely to justify its cost. The framework also considers uncertainty in the predicted information value, action redundancy, observation reliability, and computational constraints. A set of testable hypotheses and a controlled experimental protocol are proposed to investigate whether prospective uncertainty estimation can improve decision quality and information-acquisition efficiency relative to random, fixed-order, maximum-entropy-reduction, and immediate-uncertainty-based strategies. The proposed experiments distinguish methodological feasibility from empirical effectiveness and do not assume that LUR necessarily outperforms existing information-gathering methods. LUR provides a general decision-oriented formulation for prospective information acquisition. Its central research proposition is that the next observation should be selected according to how much it is expected to improve the quality of a future decision, rather than simply how much uncertainty it is expected to remove.