State-Transition Cost Modeling: Predicting How Candidate Operations Change Queues, Resource Pressure, and System State

Computational systems execute operations that modify not only their immediate outputs but also the state of the system in which subsequent operations must execute. An operation may increase queue length, release a resource, create downstream tasks, introduce contention, change cache conditions, or alter the feasibility and cost of future actions. Conventional cost estimation often focuses on the isolated execution time or resource requirements of an operation, while state-aware scheduling and control methods account for selected aspects of system dynamics. However, predicting the broader consequences of candidate operations before committing to them remains a useful research direction for adaptive computational decision-making. This paper proposes State-Transition Cost Modeling (STCM), a conceptual framework for predicting the effects of candidate operations on queues, resource pressure, and future system states. Rather than estimating only the immediate cost of an operation, STCM models the transition from the current system state to a distribution over possible future states and estimates the cumulative execution cost associated with each transition. The framework represents queues, resource utilization, pending tasks, dependencies, service rates, and relevant system conditions through a structured state representation. A transition predictor estimates the next-state distribution for each candidate operation, while a cost evaluator predicts immediate and downstream costs over configurable time horizons. A decision mechanism selects an operation according to predicted cost, uncertainty, resource constraints, and computational budget. The proposed formulation combines probabilistic state-transition prediction, multi-component cost estimation, uncertainty-aware action selection, and receding-horizon decision-making. It is intended to support applications such as task scheduling, distributed computing, workflow orchestration, resource allocation, and adaptive execution planning. This paper presents a mathematical formulation, model architecture, training objectives, inference algorithm, and experimental protocol. No experimental results are claimed. The central hypothesis—that explicitly modeling operation-induced state transitions improves computational decisions over immediate-cost estimation—must be established through controlled comparisons with relevant scheduling, queueing, model-predictive-control, and learned cost-estimation baselines.