An Approach to Multi‐Criteria Decision Problems Under Severe Uncertainty

Abstract

Solving complex decision problems is a demanding task; it requires determining and evaluating the consequences of decision alternatives. To this end, uncertain factors that can only partly be influenced by the decision makers, and their interdependencies need to be considered. Scenarios focus on this part of the decision problem; they enable a systematic exploration of a multitude of possible future developments that are relevant for the decision including external events and decisions made. Scenarios are particularly useful when the problem is pervaded by severe uncertainties that cannot be quantified. For the evaluation of alternatives, multiple objectives and the potentially diverging preferences of the involved actors need to be respected. Multi‐criteria decision analysis aims at structuring the problem, evaluating the alternatives and supporting decision makers pursuing multiple goals. We propose an approach integrating scenarios and multi‐criteria decision analysis that focuses on the robustness of alternatives in complex, dynamic, uncertain and time‐bound situations. In this integrated framework, the scenarios provide the basis for evaluating a set of alternatives. Ideally, the set of scenarios considered captures all possible future developments. To appropriately explore this set, formal or analytical approaches to scenario construction generate a large number of scenarios. This challenges the decision makers' information‐processing capacity. To support them in managing the richness of information, a two‐fold approach that uses selection and aggregation is presented. By using a selection method, the scenarios that are deemed most relevant are identified, and their evaluations are presented in detail to decision makers. This approach is complemented by an aggregation of scenario evaluations on the basis of the decision makers' preferences. We present two approaches to facilitate the preference elicitation process. Copyright © 2013 John Wiley & Sons, Ltd.

Communicating Geographical Risks in Crisis Management: The Need for Research

“…Several authors have considered how qualitative and quantitative forms of analysis may be combined to address deep uncertainties, in particular the use of multiple scenarios to conduct several parallel quantitative analyses . Some have noted the potential of this approach to structure analyses for nuclear crisis management . However, these references have tended to use more quantitative and probabilistic methods that are not yet computationally feasible in the first few hours of a crisis.…”

Governance for (climate) change in American “Legacy” cities: a case study of Cleveland

“…The realization that robustness is necessary is not recent (e.g., Vijn and Molenaar, 1981;Ben-Haim, 2000). It has gained added currency with respect to the uncertainty of climate change consequences predicted for specific locations (e.g., Comes et al, 2013). Vibrant NEO illustrates perils of pursuing large-scale plans with long horizons without regard for their robustness.…”

“…They contradict current knowledge and prescriptions for generating scenarios to craft management strategies for complex social-ecological systems fraught with uncertainty that increases with the length of horizon considered (e.g., Cox, 2012;Comes et al, 2013;Matrosov et al, 2013;Ram and Montibeller, 2013). Vijn and Molenaar (1981) proposed that "The total set of all such assumptions/specifications for which the decision would have been the same is the robustness region."…”

Basin Flood Risk Management: A Territorial Data-Driven Approach to Support Decision-Making

“…These criteria must be understood in relative terms between the three basins and under the perspective of their availability and necessity. Time and resources are relevant criteria in a wide range of management decision-making processes (a thorough variety of examples are provided in [36] and specifically in flood risk management (e.g., [28]), from which other sub-criteria can be defined-acceptability level, for example, is a criterion closely related to time. Time expresses both the available time to intervene and the necessary time to implement measures.…”