Social Inference is Not Enough: A Decision-Value Framework for Diagnosis and Intervention

In strategic interactions, more accurate inference about an opponent does not necessarily translate into better decisions or higher payoffs. We develop a value-guided social inference framework to diagnose where this connection holds or breaks down. Building on value of information, the framework distinguishes three payoff comparisons relative to a prior-informed baseline: potential value, the improvement available from perfect knowledge; acquired value, the expected improvement enabled by an evidence procedure; and realized value, the informed policy's advantage in deployment, where evidence and opponent responses may differ from the source model. We characterize when remaining uncertainty has no decision value and connect payoff-relevant representations to cost-sensitive evidence selection and stopping. Controlled $2\times 2$ games isolate the absence of opportunities for better decisions, difficulty acquiring decision-relevant evidence, and loss of predicted gains under response mismatch. In two negotiation protocols with large language model agents, targeted interventions improve net return in the reported diagnostic comparisons by avoiding redundant identification, choosing cost-effective evidence, and verifying consequential but unreliable claims. Together, these results support a diagnostic account of how social inference contributes to interaction outcomes and illustrate how that account can guide what an agent learns, how it acts, and when it reassesses its model of the interaction.