Attribution, Forecasting, and the Cost of False Certainty

How should high-confidence labels be governed when evidence is partial and incentives are asymmetric? This review answers by treating decision calibration as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is cyber attribution and probabilistic match forecasting, where overconfident predictions can shift markets or defenses and thereby invalidate the data-generating process assumed by the model. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of sequential outcomes, behavioral signals, threat reports, relational graphs, and model confidence; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.

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