Decision Theory and Artificial Intelligence: An Anatomical Review of Output Contracts, Acquisition Geometries, and Epistemic Verticals

Abstract

This paper presents a detailed anatomical review of Miquel Noguer i Alonso's "Decision Theory and Artificial Intelligence" (2026). The reviewed work develops a unified Bayesian decision-theoretic framework that links feasible action spaces, reward functions, statistical estimation targets, and costly information acquisition policies. Central contributions include minimal output contracts for specified reward families (Theorem 4.5), regret propagation decompositions (Proposition 6.1), capacity-constrained optimal gates with complementary slackness (Theorem 7.1), an acquisition-output contract characterising compressed representations that preserve nonnegative-price routing decisions (Theorem 7.6), and a routing-geometry dichotomy between discrete decisions (information most valuable near action boundaries) and continuous quadratic sizing (precision gains favouring strongest signals) (Proposition 7.13). The framework accommodates supervised, unsupervised, and reinforcement learning, large language models, and typed probability interfaces such as JEV. A retrospective empirical study of 27,241 LLM cases evaluates cost-aware replacement routing. This review systematically examines the paper's strengths and limitations, situates it against at least ten competing models and theories in decision theory, statistical treatment rules, cascade deferral, metareasoning, and decision-focused learning, and articulates the ontological and epistemological verticals underlying the evolution of its central themes. Possible extensions and ideation pathways are proposed. All references follow Harvard style. KSCCN review article, 11 October 2026. Dr. Syed Muntasir Mamun, ORCID 0000-0001-6845-2853. The views expressed are those of the author.