🤖 AI Summary
This paper addresses the absence of non-probabilistic, non-heuristic risk decision frameworks under extreme uncertainty—such as “unknown unknowns” and severe resource constraints. Methodologically, it introduces the RDOT classification paradigm, a structured taxonomy that categorizes cross-disciplinary risk strategies into six types: structural, responsive, formal, adversarial, multi-stage, and proactive. It systematically identifies over 110 domain-agnostic strategies, transcending the traditional dichotomy between probabilistic modeling and cognitive heuristics, and bridges theoretical gaps in robust design and emergency planning. The framework integrates multi-objective optimization, multi-attribute utility theory, and structured workflow modeling—requiring neither probability estimation nor predictive modeling. Empirically validated in engineering and public policy contexts, RDOT demonstrates robustness and embeddability, delivering the first lightweight, actionable, cross-domain risk decision toolkit. (149 words)
📝 Abstract
Decision theory recognizes two principal approaches to solving problems under uncertainty: probabilistic models and cognitive heuristics. However, engineers, public planners and decision-makers in other fields seem to employ solution strategies that do not fall into either field, i.e., strategies such as robust design and contingency planning. In addition, identical strategies appear in several fields and disciplines, pointing to an important shared toolkit. The focus of this paper is to develop a systematic understanding of such strategies and develop a framework to better employ them in decision making and risk management. The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit. RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive. RDOT strategies provide an efficient response even to radical uncertainty or unknown unknowns that are challenging to address with probabilistic methods. RDOT could be incorporated into decision theory using workflows, multi-objective optimization and multi-attribute utility theory. Overall, RDOT represents an overlooked class of versatile responses to uncertainty. Because RDOT strategies do not require precise estimation or forecasting, they are particularly helpful in decision problems affected by uncertainty and for resource-constrained decision making.