By Robert Lempert and Marjolijn Haasnoot
In columnist Bret Stephens’ first blog post for the New York Times, published at the end of April, he highlights the uncertainty surrounding climate change, warns against overconfidence, and issues an invitation to dialogue. We agree that significant uncertainty exists regarding the future impacts of climate change and the costs of avoiding those impacts, that it is dangerous to ignore or downplay that uncertainty, and that acknowledging these uncertainties can provide a strong foundation for dialogue.
A vast literature exists on uncertainty and climate change. Most of it suggests that uncertainty is a reason for action. That said, it remains a significant challenge to determine what actions most effectively balance society’s many goals, in the presence of deep uncertainty about the likelihood of various futures and how our actions are related to consequences.
In exchanges with Stephens, both Andy Revkin and Costa Samaras highlighted the Society for Decision Making Under Deep Uncertainty as a community that brings together researchers and practitioners from all over the world. We develop, apply and exchange experiences on methods, approaches and case studies on decision making under deep uncertainty. Climate change is one of the policy domains (though not the only one) of great interest to many of the Society’s members. Continue reading
Please save the date for the 2017 DMDU meeting. The meeting will be hosted by the Oxford Martin School in Oxford, UK, on 14-15 November 2017, with a training on DMDU methodologies scheduled for 13 November 2017. For more information on the workshop, visit the website. The 2017 annual meeting theme is dealing with deep uncertainty in decision making across multiple scales. The workshop will tackle the challenges of decision making at many different scales, from the perspective of deep uncertainty. The theme of multiple scales embraces spatial scales, temporal scales and scales of governance.
by Julie Rozenberg, Economist at the World Bank in the Chief Economist Office for Sustainable Development.
This blog is a reposted from the World Bank.
In 2015, severe floods washed away a series of bridges in Mozambique’s Nampula province, leaving several small villages completely isolated. Breslau, a local engineer and one of our counterparts, knew that rebuilding those bridges would take months. Breslau took his motorbike and drove the length of the river to look for other roads, trails, or paths to help the villagers avoid months of isolation. He eventually found an old earth path that was quickly cleaned up and restored… After a few days, the villagers had an alternative to the destroyed bridge, reconnecting them to the rest of the network and the country.
What happened in the Nampula province perfectly illustrates how a single weather event can quickly paralyze transport connections, bringing communities and economies to a screeching halt. There are many more examples of this phenomenon, which affects both developing and developed countries. On March 30th, a section of the I-85 interstate collapsed in Atlanta, causing schools to close and forcing many people to work from home. In Peru, food prices increase in Lima when the carretera central is disrupted by landslides because agricultural products can’t be brought to market.
How can we help countries improve the resilience of their transport networks in a context of scarce resources and rising climate uncertainty?
by: Steven Popper
The Society for Decision Making under Deep Uncertainty held its first annual training day event on 15 November 2016 at World Bank Headquarters in Washington, DC. This was the day prior to the start of the DMDU Society’s annual two-day workshop. The Society’s leadership team has decided that a training day will precede future DMDU workshops under the direction of the chair for education and training in coordination with that year’s workshop organizing committee. This decision is a direct response to an interest expressed through the questionnaire on education and training distributed to the Society’s membership earlier in 2016. The survey disclosed not only an interest in such a session but a willingness to participate on the part of students, DMDU analysts and methodologists, and policy practitioners.
by: Patrick Reed
This blog reports on one of the session of the annual meeting of 2016. The sessoin was organized by Patrick Reed (Cornell University), Jan Kwakkel (TU Delft), Andrea Castelletti (Politecnico di Milano), Laura Bonzanigo (World Bank). Invited speakers were Julie Quinn (Cornell University) and Marc Jaxa-Rozen (TU Delft).
Session Focus: This session explored the interplay between short-term adaptive operations and their influence on long-term planning is particularly relevant for irreversible decisions for long-lived infrastructures that present complex ecological impacts, and must reliably meet multi-sectoral demands (e.g., reservoirs, energy production/transmission, transportation networks, etc.). A core theme throughout this whole session is that current DMDU frameworks that truly seek robustness must better exploit information feedbacks, tailor adaptivity so that triggered actions are contextually appropriate, and minimize lock in. The session was organized into three case study presentations, five posters, and an interactive serious table top game. This suite of multi-sector examples helped clarify emerging innovations and persistent challenges related to bridging the planning and management divide.
by Judy Lawrence and Robert Lempert
At the conclusion of the DMDU workshop at Deltares, The Netherlands in 2015, we identified political scientists as an additional group that could inform the discussions at the next annual workshop. Accordingly, we designed a problem session at the annual workshop at the World Bank in 2016, entitled: Deep Uncertainty and the Long-Term: Time, the policy challenge and enablers for policy persistence. Whether or not decision makers consider the implications of their decisions for future generations under changing conditions depends on a range of institutional, political, behavioural and ethical factors. One of these is the extent to which policy decisions are influenced by short-termism or presentist bias. This in turn, depends on the political context within which decisions are made.
Tools developed for decision making under conditions of uncertainty and change, need to be ‘fit’ for the changing environment and for the political context, to enable policies to persist over time and adapt to changing conditions. Or the political context could be changed using commitment devices. Thus, for successful implementation of policies that can persist over the long term or be adjusted as the world changes, we need to understand the drivers that motivate the actors.
by Marjolijn Haasnoot, Laura Bonzanigo
Tomorrow we will start our 4th annual meeting of the Society for Decision Making under Deep Uncertainty. Like last year we made word cloud of the titles of the presentation, abstract and posters. As expected ‘uncertainty’ is one of the most frequent words this year. However, this has not always been the case. If you look back at the word clouds from previous meetings (see picture below), you see this pops up in the second meeting, and in the third meeting this becomes DEEP uncertainty. Is uncertainty increasing?
Regarding the policy domains and topics that are addressed ‘infrastructure’ and ‘climate’ stand out in this year’s meeting. The topic of ‘water’ follows after that. In previous years water was more present, while in the first meeting that was less of a clear policy topic that stood out. ‘Climate’ as topic for deep uncertainty has always been there, although less apparent in the titles of last years meeting. You might also notice a change from ‘robust decision making/analysis’ in the first meeting towards ‘adaptation/adaptive decision making’. The most outstanding difference the infrastructure in this year’s meeting. We are very much looking forward to hear more …
Word clouds are made with: http://www.wordle.net/create
The World Bank will host the 2016 DMDU workshop in Washington DC, on November 16 and 17, 2016, with a training on DMDU methodologies scheduled for November 15th, 2016. There is still place for the training, but it is running out fast. Please confirm here by October 15 if you have not done that already, to make sure we save you a spot! The annual meeting is fully booked. Please let us know if you will not come so there will be place for others to attend. Download the programme. Continue reading
by Laura Tuck and Julie Rozenberg, Sustainable Development Practice Group, World Bank Group
We all face uncertainties.
What if the train’s late? What if it rains? What if traffic is bad? What if there’s a shift in government before the project starts?
Every day we’re hit by all the “what ifs” especially in our line of work at the World Bank Group, whether in the field or within our organization. But how do we best cope with this? Embracing uncertainties may be the answer.
The World Bank Group has been at the forefront of mainstreaming new methods to deal with uncertainties. In fact, you may not know this, but the World Bank is one of the founding members of the Society for Decision Making Under Deep Uncertainty.
Today’s decision makers face conditions of fast-paced, transformative, and often surprising change. Traditional decision analysis relies on point and probabilistic predictions. But under conditions of deep uncertainty, predictions are often wrong, and relying on them can prove costly and dangerous. Fortunately, new methods and processes now exist to help decision makers identify and evaluate robust and adaptive strategies, thereby making sound decisions in the face of these challenges. Continue reading
by Joseph Guillaume
At the iEMSs2016 conference in Toulouse, the session on Decision Making under Deep Uncertainty (see blog report) was accompanied by a more generic one on “Managing Uncertainty”, organised by Joseph Guillaume (Aalto University), Tony Jakeman (Australian National University), Holger Maier (The University of Adelaide), Jiri Nossent (Flanders Hydraulics Research and Vrije Universiteit Brussel) and Evelina Trutnevyte (ETH Zurich).
The session emphasised the diversity of approaches for managing uncertainty. Contributions notably covered sensitivity analysis, scenario analysis, parameter estimation and uncertainty quantification. While not directly tied to Decision Making under Deep Uncertainty, it is important to remember that these techniques form the foundations of our analyses – the means of addressing any uncertainty that is not treated as deep. As argued in a recent publication in Environmental Modelling and Software (Maier at al. 2016), multiple paradigms for modelling the future tend to co-exist, with different parts of an analysis focussed on capturing best available knowledge, quantifying uncertainty, and exploring multiple plausible futures. Continue reading
by Rob Lempert and Jan Kwakkel,
The 8th International Congress on Environmental Modeling and Software in Toulouse, France, on July 10-14, 2016, featured a track titled Advancing in Environmental Decision Making Under Deep Uncertainty: Emerging Tools and Challenges. The track was co-organized by Jan Kwakkel (Delft University of Technology), Patrick Reed (Cornell), Robert Lempert (RAND Corporation), and Marjolijn Haasnoot (Deltares). The track consisted of four sessions with four papers in each session.
Registration for our annual workshop in Washington DC (November 16-17) is now closed.
This year’s workshop is organized around two key elements. First, the workshop will include 6 “problem-solving” sessions during which we will have group discussions around practical problems our Society members face in their work, and possible solutions. These will not be typical panel sessions since the audience will be actively involved. Second, the workshop will use posters as the primary means for participants to present their current work. The workshop will integrate posters in three ways: (1) pitches in the problem-solving sessions for posters that speak to the problems described; (2) pitches in poster sessions; (3) informal discussions around posters during all breaks and social times. You will find below a short description of the 6 sessions that were selected for group discussions. Continue reading
by Marjolijn Haasnoot (Deltares, Delft University), Judy Lawrence (New Zealand Climate Change Research Institute), Robert Lempert (RAND), Nidhi Kalra (RAND), Jan Kwakkel (Delft University)
In early November 2015 about 100 scientists and practitioners from all over the world shared their experiences and methods at the third annual meeting of the Society for Decision Making Under Deep Uncertainty in Delft, Netherlands. The workshop was hosted by Deltares, Delft University of Technology, and UNESCO-IHE and co-organized with RAND, the World Bank, Victoria University of Wellington’s Climate Change Research Institute, and staff of the Dutch Delta Programme.
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.
Trindade, B. C., P. M. Reed, J. D. Herman, H. B. Zeff and G. W. Characklis (2017). “Reducing regional drought vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty.” Advances in Water Resources 104, https://doi.org/10.1016/j.advwatres.2017.03.023: 195-209.
Emerging water scarcity concerns in many urban regions are associated with several deeply uncertain factors, including rapid population growth, limited coordination across adjacent municipalities and the increasing risks for sustained regional droughts. Managing these uncertainties will require that regional water utilities identify coordinated, scarcity-mitigating strategies that trigger the appropriate actions needed to avoid water shortages and financial instabilities. This research focuses on the Research Triangle area of North Carolina, seeking to engage the water utilities within Raleigh, Durham, Cary and Chapel Hill in cooperative and robust regional water portfolio planning. Prior analysis of this region through the year 2025 has identified significant regional vulnerabilities to volumetric shortfalls and financial losses. Moreover, efforts to maximize the individual robustness of any of the mentioned utilities also have the potential to strongly degrade the robustness of the others. This research advances a multi-stakeholder Many-Objective Robust Decision Making (MORDM) framework to better account for deeply uncertain factors when identifying cooperative drought management strategies. Our results show that appropriately designing adaptive risk-of-failure action triggers required stressing them with a comprehensive sample of deeply uncertain factors in the computational search phase of MORDM. Search under the new ensemble of states-of-the-world is shown to fundamentally change perceived performance tradeoffs and substantially improve the robustness of individual utilities as well as the overall region to water scarcity. Search under deep uncertainty enhanced the discovery of how cooperative water transfers, financial risk mitigation tools, and coordinated regional demand management must be employed jointly to improve regional robustness and decrease robustness conflicts between the utilities. Insights from this work have general merit for regions where adjacent municipalities can benefit from cooperative regional water portfolio planning.
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.
Shortridge, J., Guikema, S. & Zaitchik, B. (2017) Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change. Climatic Change 140: 323. doi:10.1007/s10584-016-1845-4
Abstract: In the face of deeply uncertain climate change projections, robust decision frameworks are becoming a popular tool for incorporating climate change uncertainty into water infrastructure planning. These methodologies have the potential to be particularly valuable in developing countries where extensive infrastructure development is still needed and uncertainties can be large. However, many applications of these methodologies have relied on a sophisticated process of climate model downscaling and impact modeling that may be unreliable in data-scarce contexts. In this study, we demonstrate a modified application of the robust decision making (RDM) methodology that is specifically tailored for application in data-scarce situations. This modification includes a novel method for generating transient climate change sequences that account for potential variable dependence but do not rely on detailed GCM projections, and an emphasis on identifying the relative importance of data limitations and uncertainty within an integrated modeling framework. We demonstrate this methodology in the Lake Tana basin in Ethiopia, showing how the approach can highlight the vulnerability of alternative plans across different time scales and identify priorities for research and model refinement. We find that infrastructure performance is particularly sensitive to uncertainty in streamflow model accuracy, irrigation efficiency, and evaporation rates, suggesting that additional research in these areas could provide valuable insights for long-term infrastructure planning. This work demonstrates how tailored application of robust decision frameworks using simple modeling approaches can provide decision support in data-scarce regions where more complex modeling and analysis may be impractical.