strategiescovering

Strategiescovering is a term used in decision theory and related fields to describe a framework for selecting a compact set of strategies that collectively address all relevant contingencies or outcomes in a given problem. The phrase is not widely standardized, but it is often applied wherever multiple scenarios must be managed by a limited set of actions.

A strategy cover, in a finite game or decision problem, is a subset of strategies such that for every scenario or state, there exists at least one strategy in the subset that meets a predefined objective. This objective may be a minimum payoff, feasibility under uncertainty, or a degree of robustness across scenarios. The exact notion of coverage depends on the problem’s goals, such as guaranteeing a threshold payoff or ensuring broad applicability of the chosen strategies.

From a computational perspective, finding the smallest effective strategy cover often corresponds to a set cover or hitting set problem. These problems are generally NP-hard, so practical approaches rely on approximation algorithms, greedy methods, or problem-specific structure to obtain tractable solutions with performance guarantees.

Applications of strategiescovering appear in robust planning under uncertainty, contingency planning in operations research, security and defense, portfolio design under multiple market scenarios, and strategy synthesis in multi-agent environments. The term highlights the trade-off between breadth of coverage and parsimony of the strategy set, and it connects to related concepts such as set cover, robust optimization, and plan generation.