Su H, Dong F, Liu Y, Zou R, and Guo H. 2017. Robustness-Optimality Tradeoff for Watershed Load Reduction Decision Making under Deep Uncertainty. Water Resour Manag:1-14.
Practical and optimal reduction of watershed loads under deep uncertainty requires sufficient search alternatives and direct evaluation of robustness. These requirements contribute to the understanding of the tradeoff between cost and robustness; while they are not well addressed in previous studies. This study thereby (a) uses preconditioning technique in Evolutionary Algorithm to reduce unnecessary search space, which enables a sufficient search; and (b) derives Robustness Index (RI) as a second-tier optimization objective function to achieve refined solutions (solved by GA) that address both robustness and optimality. Uncertainty-based Refined Risk Explicit Linear Interval Programming is used to generate alternatives (solved by Controlled elitist NSGA-II). The robustness calculation error is also quantified. Proposed approach is applied to Lake Dianchi, China. Results demonstrate obvious improvement in robustness after conducting sufficient search and negative robustness-optimality trade-offs, and provides a detailed characteristic of robustness that can serve as references for decision-making.
Robustness index Deep uncertainty Tradeoff Optimality Load reduction
Quinn, J. D., P. M. Reed and K. Keller (2017). “Direct policy search for robust multi-objective management of deeply uncertain socio-ecological tipping points.” Environmental Modelling & Software 92, http://dx.doi.org/10.1016/j.envsoft.2017.02.017: 125-141.
Managing socio-ecological systems is a challenge wrought by competing societal objectives, deep uncertainties, and potentially irreversible tipping points. A classic, didactic example is the shallow lake problem in which a hypothetical town situated on a lake must develop pollution control strategies to maximize its economic benefits while minimizing the probability of the lake crossing a critical phosphorus (P) threshold, above which it irreversibly transitions into a eutrophic state. Here, we explore the use of direct policy search (DPS) to design robust pollution control rules for the town that account for deeply uncertain system characteristics and conflicting objectives. The closed loop control formulation of DPS improves the quality and robustness of key management tradeoffs, while dramatically reducing the computational complexity of solving the multi-objective pollution control problem relative to open loop control strategies. These insights suggest DPS is a promising tool for managing socio-ecological systems with deeply uncertain tipping points.
Hamel, P. and BP Bryant (2017). Uncertainty assessment in ecosystem services analyses: Common challenges and practical responses. Ecosystem Services 24, 1-15. doi:10.1016/j.ecoser.2016.12.008
Abstract: Ecosystem services (ES) analyses are increasingly used to address societal challenges, but too often are not accompanied by uncertainty assessment. This omission limits the validity of their findings and may undermine the ‘science-based’ decisions they inform. We summarize and analyze seven commonly perceived challenges to conducting uncertainty assessment that help explain why it often receives superficial treatment in ES studies. We connect these challenges to solutions in relevant scientific literature and guidance documents. Since ES science is based on a multiplicity of disciplines (e.g. ecology, hydrology, economics, environmental modeling, policy sciences), substantial knowledge already exists to identify, quantify, and communicate uncertainties. The integration of these disciplines for solution-oriented modeling has been the focus of the integrated assessment community for many years, and we argue that many insights and best practices from this field can be directly used to improve ES assessments. We also recognize a number of issues that hinder the adoption of uncertainty assessment as part of standard practice. Our synthesis provides a starting point for ES analysts and other applied modelers looking for further guidance on uncertainty assessment and helps scientists and decision-makers to set reasonable expectations for characterizing the level of confidence associated with an ES assessment.
Readers interested in uncertainty and ES may also find a recent workshop report on “Motivating and Improving Uncertainty Assessment in ES” interesting as well:
Finally, those having or seeking to produce good examples of such assessment are encouraged to submit to a new special issue on the topic, with submissions due September 30, 2017.
Cockerill, K., M. Armstrong, J. Richter, J. Okie. 2017. Environmental Realism: Challenging Solutions. Palgrave MacMillan 145p. ISBN 978-3-319-52824-3.
Abstract from Chapter 1: Why Challenge Solutions?
Labeling a problem ‘environmental’ creates a pervasive belief that science and technology can, should, and will generate solutions for issues ranging from pandemic disease to stream functions to nuclear contamination. These, however, are ‘wicked problems’ that defy simple or long-term solutions, but rather must be continually managed. Further, what are defined in the 21st century as ‘environmental problems’ are often the consequence of perceived ‘solutions’ implemented in a previous era.The perception of these issues as problems is derived, in part, from Enlightenment ideas segregating Homo sapiens from nature and a belief that humans can contain or control biophysical processes. Solutionist thinking and language perpetuates a self-referential problem-solution-problem cycle that begs the question of what constitutes a ‘solution’ and simultaneously elides the reality that human systems and biophysical systems are inseparable.
Gong, Min , Robert Lempert, Andrew M Parker, Lauren A. Mayer, Jordan Fischbach, Matthew Sisco, Zhamin Mao, David H. Krantz, and Howard Kunreuther. “Testing the Scenario Hypothesis: An Experimental Comparison of Scenarios and Forecasts for Decision Support in a Complex Decision Environment.” Environmental Modeling and Software 91 (2017): 135-55.
Decision support tools are known to influence and facilitate decisionmaking through the thoughtful construction of the decision environment. However, little research has empirically evaluated the effects of using scenarios and forecasts. In this research, we asked participants to recommend a fisheries management strategy that achieved multiple objectives in the face of significant uncertainty. A decision support tool with one of two conditions—Scenario or Forecast—encouraged participants to explore a large set of diversified decision options. We found that participants in the two conditions explored the options similarly, but chose differently. Participants in the Scenario Condition chose the strategies that performed well over the full range of uncertainties (robust strategies) significantly more frequently than did those in the Forecast Condition. This difference seems largely to be because participants in the Scenario Condition paid increased attention to worst-case futures. The results offer lessons for designing decision support tools.
Lahtinen, T. J., J. H. A. Guillaume, and R. P. Hämäläinen (2017), Why pay attention to paths in the practice of environmental modelling?, Environmental Modelling and Software, 92, 74–81, http://dx.doi.org/10.1016/j.envsoft.2017.02.019
Taking the ‘path perspective’ helps to understand and improve the practice of environmental modelling and decision making. A path is the sequence of steps taken in a modelling project. The problem solving team faces several forks where alternative choices can be made. These choices determine the path, together with the impact of uncertainties and exogenous effects. This paper discusses phenomena that influence the problem solvers’ choices at the forks. Situations are described where it can be desirable to re-direct the path or backtrack on it. Phenomena are identified that can cause the modelling project to get stuck on a poor path. The concept of a path draws attention to the interplay of behavioral phenomena and the sequential nature of modelling processes. This helps understand the overall effect of the behavioral phenomena. A path checklist is developed to help practitioners detect forks and reflect on the path of the modelling project.
Derbyshire, J. and Giovannetti, E. (2017) Understanding the failure to understand New Product Development failures: Mitigating the uncertainty associated with innovating new products by combining scenario planning and forecasting, Technological Forecasting & Social Change: http://www.sciencedirect.com/science/article/pii/S0040162516302980
In this paper we show that New Product Development (NPD) is subject to fundamental uncertainty that is both epistemic and ontic in nature. We argue that this uncertainty cannot be mitigated using forecasting techniques exclusively, because these are most useful in circumstances characteristic of probabilistic risk, as distinct from non-probabilistic uncertainty. We show that the mitigation of uncertainty in relation to NPD requires techniques able to take account of the socio-economic factors that can combine to cause present assumptions about future demand conditions to be incorrect. This can be achieved through an Intuitive Logics (IL) scenario planning process designed specifically to mitigate uncertainty associated with NPD by incorporating insights from both quantitative modelling alongside consideration of political, social, technological and legal factors, as-well-as stakeholder motivations that are central to successful NPD. In this paper we therefore achieve three objectives: 1) identify the aspects of the current IL process salient to mitigating the uncertainty of NPD; 2) show how advances in diffusion modelling can be used to identify the social-network and contagion effects that lead to a product’s full diffusion; and 3) show how the IL process can be further enhanced to facilitate detailed consideration of the factors enabling and inhibiting initial market-acceptance, and then the forecasted full diffusion of a considered new product. We provide a step-by-step guide to the implementation of this adapted IL scenario planning process designed specifically to mitigate uncertainty in relation to NPD.
Almeida, S., Holcombe, E. A., Pianosi, F. and Wagener, T. (2017). Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change, Nat. Hazards Earth Syst. Sci., 17, 225-241, doi:10.5194/nhess-17-225-2017.
Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables – the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.
Malekpour, S., Brown, R. R., de Haan, F. J., & Wong, T. H. F. (2017). Preparing for disruptions: A diagnostic strategic planning intervention for sustainable development. Cities, 63, 58–69. http://doi.org/10.1016/j.cities.2016.12.016
Despite the emphasis on sustainable development in some of the contemporary planning and policy rhetoric, we face an implementation deficit in practice. The impediments to the widespread adoption and successful implementation of sustainable infrastructure in cities’ critical sectors—such as water, energy or transport—are varied and complex. Although the scholarship has made some attempts to understand and categorize those impediments, not much has been said about how to identify them in a specific practical context. This study proposes a model for a diagnostic intervention in the ongoing process of strategic infrastructure planning, as a way of revealing context-specific impediments. The diagnostic intervention incorporates an explicit and reflexive consideration of short-term barriers and long-term disruptors into the strategic planning process, and assists with drafting the required coping strategies. The intervention has been tested in water infrastructure planning for one of the world’s largest urban renewal areas in Melbourne, Australia. This trial application provided promising outcomes for addressing the implementation deficit of sustainable development: it created a platform for various stakeholder groups to engage in explicit discussions on their confronted problems, which often have trans-organizational causes and impacts; it enabled reflexivity within the ongoing planning process; and, it helped to consider a large portfolio of future uncertainties to provide an enabling condition for more robust decisions to be made. Moreover, the trialed intervention provided empirical evidence in support of the scholarly discourse which contends that sustainable infrastructure delivery is not only about the development of technical solutions, but is also about the development of processes and tools that support the widespread adoption and successful implementation of those solutions in the face of wide-ranging impediments.
Hermans, Leon M., Marjolijn Haasnoot, Judith ter Maat, Jan H. Kwakkel. (2017). Designing monitoring arrangements for collaborative learning about adaptation pathways. Environmental Science & Policy 69: 29-38. DOI: 10.1016/j.envsci.2016.12.005 . https://authors.elsevier.com/a/1UFzx5Ce0rOGPN
Adaptation pathways approaches support long-term planning under uncertainty. The use of adaptation pathways implies a systematic monitoring effort to inform future adaptation decisions. Such monitoring should feed into a long-term collaborative learning process between multiple actors at various levels. This raises questions about who should monitor what, when and for whom. We formulate an approach that helps to address these questions, developed around the conceptual core offered by adaptive policy pathways methods and their notion of signposts and triggers. This is embedded in a wider approach that revisits the critical assumptions in underlying basic policies, looks forward to future adaptation decisions, and incorporates reciprocity in the organization of monitoring and evaluation. The usefulness and practical feasibility of the approach is studied for a case of the Delta Programme in the Netherlands, which incorporated adaptation pathways in its planning approach called adaptive delta management. The case results suggest that our approach adds value to existing monitoring practices. They further show that different types of signposts exist. Technical signposts, in particular, need to be distinguished from political ones, and require different learning processes with different types of actors.