Possibility theory for managerial and economic decision-making under uncertainty

Background: Managerial and economic decisions are routinely made under incomplete, vague, or ambiguous information that classical probability theory is not always well suited to represent, particularly when the uncertainty involved is epistemic or linguistic rather than purely a matter of chance. Methods: This is a conceptual review paper. It develops a taxonomy of uncertainty types relevant to managerial and economic decision-making, sets out the formal apparatus of possibility theory, and reviews methods for constructing possibility distributions, applying this apparatus to established managerial and economic decision problems drawn from the literature. Results: Only aleatory (chance-based) uncertainty is naturally suited to probabilistic treatment; epistemic, linguistic, model, completeness, value, recognition, and decision-related uncertainty are common in business practice but poorly served by forced probability estimates. Possibility theory, through crisp, fuzzy, and interval restrictions and six practical construction methods, applies directly to risk assessment, strategic planning, resource allocation, project scheduling, supplier selection, and negotiation, and to consumer behaviour, firm production decisions, macroeconomic forecasting, and investment decisions under Knightian uncertainty. Conclusion: Possibility theory merits a routine, taught place alongside probability theory in applied managerial and economic decision analysis, reserved specifically for uncertainty that is vague, judgement-based, and non-frequentist in character.