Predictive Computing Decision Theory for Generative AI: A Probability–Cost–Benefit Framework for Rational Computational Action Selection

The increasing deployment of generative artificial intelligence (AI) systems has created new opportunities for anticipating future computational demand and preparing computational resources before actual requests materialize. Existing predictive computing research has investigated future demand forecasting, proactive inference preparation, infrastructure orchestration, GPU resource prediction, off-peak computation, and economic-aware resource allocation. However, an important theoretical problem remains: how should a predictive computing system determine whether a predicted future workload justifies an actual computational action under uncertainty? A highly probable future request does not necessarily justify precomputation when its preparation cost is excessive, while a relatively uncertain prediction may still justify computation when the potential savings are sufficiently large. Consequently, prediction accuracy alone cannot determine the rationality of predictive computation. This paper introduces Predictive Computing Decision Theory (PCDT), a decision-theoretic framework for determining computational actions through the joint evaluation of predicted demand probability, computational expenditure, potential reuse benefits, and operational risks. Rather than treating prediction confidence as a direct trigger for precomputation, PCDT formulates predictive computation as a sequential decision problem in which the system evaluates whether to execute computational tasks in advance, defer execution, reserve resources, select alternative infrastructure, prepare reusable computational states, or wait for actual demand. The proposed framework establishes a probability–cost–benefit decision model that incorporates expected computational savings, preparation expenditure, validation costs, storage requirements, opportunity costs, prediction errors, and service-level constraints. It further introduces risk-adjusted decision thresholds, asymmetric penalties for false-positive and false-negative predictions, value-of-information analysis, and multi-task budget allocation. A sequential decision mechanism is developed to support continuous reassessment as new information becomes available and as predicted demand evolves over time. The framework also distinguishes between the probability that future demand will occur, the probability that prepared resources will be successfully reused, and the probability that the resulting computational action will produce a net economic benefit. These probabilities represent different uncertainties and should not be treated as interchangeable. An experimental methodology is proposed to evaluate decision quality through expected utility, realized net savings, decision regret, risk-adjusted efficiency, resource utilization, and computational expenditure. The proposed research extends existing predictive computing architectures by establishing a formal theoretical foundation for translating uncertain demand predictions into economically and operationally rational computational decisions. Its practical effectiveness remains subject to empirical validation.