Warren E. Walker

Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world

Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper presents a model-driven approach supporting the development of promising adaptation pathways, and illustrates the approach using a hypothetical case. We use robust optimization over uncertainties related to climate change, land use, cause-effect relations, and policy efficacy, to identify the most promising pathways. For this purpose, we generate an ensemble of possible futures and evaluate candidate pathways over this ensemble using an Integrated Assessment Meta Model. We understand ‘most promising’ in terms of the robustness of the performance of the candidate pathways on multiple objectives, and use a multi-objective evolutionary algorithm to find the set of most promising pathways. This results in an adaptation map showing the set of most promising adaptation pathways and options for transferring from one pathway to another. Given the pathways and signposts, decision-makers can make an informed decision on a dynamic adaptive plan in a changing environment that is able to achieve their intended objectives despite the myriad of uncertainties.

Coping with the wickedness of public policy problems: Approaches for decision-making under deep uncertainty

In many planning problems, planners face major challenges in coping with uncertain and changing physical conditions, and rapid unpredictable socioeconomic development. How should society prepare itself for this confluence of uncertainty? Given the presence of irreducible uncertainties, there is no straightforward answer to this question. Effective decisions must be made under unavoidable uncertainty (Dessai et al. 2009; Lempert et al. 2003). In recent years, this has been labeled as decision making under deep uncertainty. Deep uncertainty means that the various parties to a decision do not know or cannot agree on the system and its boundaries; the outcomes of interest and their relative importance; the prior probability distribution for uncertain inputs to the system (Lempert et al. 2003; Walker et al. 2013); or decisions are made over time in dynamic interaction with the system and cannot be considered independently (Haasnoot et al. 2013a, b; Hallegatte et al. 2012). From a decision analytic point of view, this implies that there are a large number of plausible alternative models, alternative sets of weights to assign to the different outcomes of interest, different sets of inputs for the uncertain model parameters, and different (sequences of) candidate solutions (Kwakkel et al. 2010). Decision making under deep uncertainty is a particular type of wicked problem (Rittel and Webber 1973). Wicked problems are problems characterized by the involvement of a variety of stakeholders and decision makers with conflicting values and diverging ideas for solutions (Churchman 1967). What makes wicked problems especially pernicious is that even the problem formulation itself is contested (Rittel and Webber 1973). System analytic approaches presuppose a separation between the problem formulation and the solution. In wicked problem situations this distinction breaks down. Solutions and problem formulation are intertwined with each other. Depending on how a problem is framed, alternative solutions come to the fore; and, vice versa, depending on the available or preferred solutions, the problem can be framed differently. Even if there is agreement on the difference between observed and desired outcomes, rival explanations for the existence of this difference are available, and, hence, different solutions can be preferred. An additional factor adding to the wickedness is that decision makers can ill afford to be wrong. The consequences of any decision on wicked problems can be profound, difficult if not impossible to reverse, and result in lock-ins for future decision making. Planning and decision making in wicked problem situations should, therefore, be understood as an argumentative process: in which the problem formulation, a shared understanding of system functioning and how this gives rise to the problem, and the set of promising solutions, emerge gradually through debate among the involved decision makers and stakeholders (Dewulf et al. 2005). When even the problem formulation itself is uncertain and contested, planning and decision making requires an iterative approach that facilitates learning across alternative framings of the problem, and learning about stakeholder preferences and trade-offs, all in pursuit of a collaborative process of discovering what is possible (Herman et al. 2015). Modeling and optimization can play a role in facilitating this learning. They can help in discovering a set of possible actions that is worth closer inspection, and make the trade-offs among these actions more transparent (Liebman 1976; Reed and Kasprzyk 2009). Under the moniker of decision making under deep uncertainty, a variety of new approaches and tools are being put forward. Emerging approaches include (multiobjective) robust decision making (Kasprzyk et al. 2013; Lempert et al. 2006), info-gap decision theory (Ben Haim 2001), dynamic adaptive policy pathways (Haasnoot et al. 2013a, b), and decision scaling (Brown et al. 2012). A common feature of these approaches is that they are exploratory model-based strategies for designing adaptive and robust plans or policies. Although these frameworks are used in a wide variety of applications, they have been most commonly applied in the water domain, in which climate change and social change are key concerns that affect the long-term viability of current management plans and strategies. Liebman (1976) recognized that water resources planning problems are wicked problems in which modeling, simulation, and optimization cannot be straightforwardly applied. In recent years, this observation has been reiterated (Herman et al. 2015; Lund 2012; Reed and Kasprzyk 2009). If decision making under deep uncertainty is a particular type of wicked problem, to what extent do the recent methodological advances address some of the key aspects of what makes wicked problems wicked? To answer this question, the authors look at two exemplary approaches for supporting decision making under deep uncertainty: (multiobjective) robust decision making and dynamic adaptive policy pathways. This article first briefly outlines each approach, and then discusses some of the ongoing scientific work aimed at integrating the two approaches. This sets the stage for a critical discussion of these approaches and how they touch on the key concerns of supporting decision making in wicked problem situations.

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, and compare them with respect to the required tooling, the resulting decision relevant insights, and the resulting plans. The results indicate that the two approaches are complementary. Robust Decision-Making offers insights into conditions under which problems occur, and makes trade-offs transparent. The Dynamic Adaptive Policy Pathways approach emphasizes dynamic adaptation over time, and thus offers a natural way for handling the vulnerabilities identified through Robust Decision-Making. The application also makes clear that the analytical process of Robust Decision-Making is path-dependent and open ended: an analyst has to make many choices, for which Robust Decision-Making offers no direct guidance. This paper compares Robust Decision-Making (RDM) and Dynamic Adaptive Policy Pathways (DAPP).RDM and DAPP have different merits, which highlight their complementarity.RDM has a clear analytical process and the application is reasonably straight forward.DAPP offers a convenient framework for designing plans for dynamic adaptation over time.

Comment on “From Data to Decisions: Processing Information, Biases, and Beliefs for Improved Management of Natural Resources and Environments” by Glynn et al.

Glynn et al. (2017, https://doi.org/10.1002/2016EF000487) note the importance of engaging stakeholders in the process of public policymaking and analysis. In particular, they highlight the central role biases, beliefs, heuristics, and values play in such engagement. However, the framework they propose neglects uncertainty, which significantly restricts any ability to engage effectively with BBHV. We show how their papers narrow view can be widened to include aspects of risk and uncertainty.