Facts and values: Decision analysis for complex public environmental decision problems

Abstract

Decision analysis can support complex public environmental problems in a consistent, structured way. My field of research is prescriptive decision theory, focusing on multi-criteria decision analysis (MCDA), specifically multi-attribute value theory and utility theory. After an introduction, I review MCDA for environmental decision-making from the view of operational research (OR). MCDA is widely used in practice, but uncertainty and integrating stakeholders is often absent in applications. The habilitation treatise then addresses four steps of MCDA processes: problem structuring (step A), making predictions (scientific evidence, facts; step B), eliciting stakeholder preferences (values; step C), and MCDA modeling to integrate facts and values (step D). For each, I review literature in which I embed my research, and identify knowledge gaps and future research directions. Steps A and C belong to the wider fields of behavioral OR and problem structuring. A review of problem structuring methods (PSMs) indicates a need for systematization and theory development. Regarding concrete PSMs, my research in step A focused on stakeholder analysis combined with social network analysis, and on generating objectives. Another research focus concerns biases occurring in steps A and C. Preference elicitation (step C) is especially sensitive to biases, and my work includes systematic comparison of elicitation methods. Moreover, we know from literature that preferences are constructed during decision-making, but we are far from understanding such processes. Another research interest is thus preference construction over time. We also started exploring online elicitation, whereby gamification might be beneficial. For making predictions (step B), there are less urgent research needs from the perspective of decision analysis. Many scientists focus on predicting outcomes of interventions to a natural or engineered system. However, making predictions based on literature, modeling, or expert input can be demanding and certainly requires solid interdisciplinary collaboration. The MCDA model (step D) integrates facts and values to identify best-performing alternatives that are robust across diverging stakeholder preferences and uncertainties. The common additive aggregation model rarely meets preferences; we proposed using other models. Moreover, environmental decisions are riddled with uncertainty, which was addressed in my research, e.g., using probability theory, expected expected utility theory (EEU), sensitivity analyses, and scenario planning. Literature indicates that uncertainty is neglected in applications of MCDA to environmental problems, which needs to be addressed. As summary, many identified research needs are rooted in behavioral OR. We require experimental and process research to overcome a serious lack of systematization in steps (A) and (C), and for supporting environmental decisions. We also require research regarding uncommon aggregation models and including uncertainty in practice (step D). Interesting research questions relate to combining scenario planning with MCDA. I conclude with an outlook on applied decision-making, gained in own Swiss and international cases in urban water management, river management, and flood risk research. Despite limits of MCDA in policy processes, MCDA can be highly useful to understand complex decisions, integrate stakeholders throughout the process, and identify consensus solutions. Stepwise, flexible MCDA could be a rewarding research avenue for reducing effort and costs when interacting with stakeholders. To increase the applicability of MCDA in practice, there is a need of simplifying our approaches without compromising on scientific rigor. I provide 13 selected publications in the appendix, organized along the four steps of MCDA.