Managing Transitions Toward Adaptive Delta Infrastructure: A framework for improving the decision context stage

Because of the potential consequences of sea level rise, decision-making to set adaptive strategies to handle the uncertainty over these consequences is required. There are several decision-making methods to deal with deep uncertainty and attempt to contain the impacts of sea level rise in organized response as the future unfolds. These methods...

Multi-scenario multi-objective robust optimization under deep uncertainty: A posteriori approach

This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well as an alternative way for scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate and test the novel approach, we use the classic shallow lake problem. We compare the results...

On considering robustness in the search phase of Robust Decision Making: A comparison of Many-Objective Robust Decision Making, multi-scenario Many-Objective Robust Decision Making, and Many Objective Robust Optimization

In recent years, a family of approaches has emerged for supporting decision-making on complex environmental problems characterized by deep uncertainties and competing priorities. Many-Objective Robust Decision Making (MORDM), Multi-scenario MORDM and. Many-Objective Robust Optimization (MORO) differ with respect to the degree to which...

Long-Term Adaptive Flood Risk Management: Investing in coastal protection and stormwater management under deep uncertainty

Flood risk management is the process of analysing, assessing and (if required) reducing flood risks, in terms of economic costs and affected population or loss of life. The analysis is performed through a probabilistic approach in which the factors are represented by a probability distribution function to address uncertainties. However, relevant...

Robust Decision Support Methods: A Comparative Analysis

Methods for decision support in the context of deep uncertainty have been gaining interest in the context of complex and adaptive problems that are characterized by “tipping point” behaviours. Unlike a traditional “predict-then-act” methods, which determine policies based on specific predictions of future behaviour, these decision support...

Multi-scale-multi-models: from forecasts to strategy and operations in the Port of Rotterdam: A proof of concept of the design and implementation of a model ecosystem that supports the Port of Rotterdam Authority in their decision-making process

The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making

There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches being put forward in the literature. A key idea shared among these is the use of models for exploratory rather than predictive purposes. Exploratory modeling aims at exploring the implications for decision making of the...

Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty

A variety of model-based approaches for supporting decision-making under deep uncertainty have been suggested, but they are rarely compared and contrasted. In this paper, we compare Robust Decision-Making with Dynamic Adaptive Policy Pathways. We apply both to a hypothetical case inspired by a river reach in the Rhine Delta of the Netherlands...

Improving scenario discovery by bagging random boxes

Scenario discovery is a model-based approach to scenario development under deep uncertainty. Scenario discovery relies on the use of statistical machine learning algorithms. The most frequently used algorithm is the Patient Rule Induction Method (PRIM). This algorithm identifies regions in an uncertain model input space that are highly...