Department Systems Analysis, Integrated Assessment and Modelling

Environmental Management can profit from formal Decision Analysis (DA) methodologies as they can help structuring the argumentation and negotiation processes, support combining scientific predictions with societal preferences, and they increase the transparency of argumentation during the decision-making process as well as after the decision has been taken. State of the art of environmental decision support based on DA considers the uncertainty in scientific predictions of the consequences of decision alternatives and a formal, quantitative description of the preferences of decision makers or stakeholders.

However this methodology mostly ignores the uncertainty in the quantification of the preferences and the ambiguity in the probability distributions that quantify uncertain knowledge (for both consequence prediction and preference quantification). This is a serious problem as individual preferences are uncertain, the aggregation to a “societal preference” is problematic, and the parameterization and elicitation processes add additional uncertainties. The overall goals of this project are to address these problems by

1. Extending the state of the art of decision support by accounting for the uncertainty in preferences and the ambiguity in probability distributions, and  
2. Investigating the relevance of the increase in uncertainty and the feasibility of the proposed approach in a case study about river rehabilitation prioritization decision making.

The methodology to address these topics will be to do Bayesian inference for a parameterized value or utility function using elicited preference information from stakeholders and to consider the ambiguity about prior information as imprecise probabilities in the form of sets of probability distributions.

References

Reichert, P., Langhans, S., Lienert, J. and Schuwirth, N. The Conceptual Foundation of Environmental Decision Support. Journal of Environmental Management 154, 316-332, 2015. doi.org/10.1016/j.jenvman.2015.01.053

Reichert, P. Towards a comprehensive uncertainty assessment in environmental research and decision support. Water Science and Technology 81(8), 1588–1596, 2020. doi.org/10.2166/wst.2020.032

Sriwastava, A. and Reichert, P., Reducing Sample Size Requirements by Extending Discrete Choice Experiments to Indifference Elicitation, submitted 2022.

Sriwastava, A. and Reichert, P., Bayesian Estimation of Value Function Parameters - Sensitivity to the Prior, in preparation 2022.