Information Needs for Developing Robust Adaptive Strategies

1. Information Needs for Developing Robust Adaptive Strategies Robert Lempert Director, RAND Pardee Center for Longer Range Global Policy and the Future Human Condition Workshop on Climate Science Needed to Support Robust Adaptation Decisions Georgia Tech February 6, 2014

2. Climate-Related Decisions Poses Both Analytic and Organizational Challenges • Public planning should be: • Objective • Subject to clear rules and procedures • Accountable to public Climate-related decisions involve: • Incomplete information from new, fast-moving, and sometimes irreducibly uncertain science • Many different interests and values • Long-time scales • Near certainty of surprise How to make plans more robust and adaptable while preserving public accountability?

3. Concept of Decision Support Focuses Attention on How (in Addition to What) Information is Used Supply and demand of scientific information may be mismatched Decision support: • Bridges supply and demand with a focus on decision processes • Represents organized efforts to produce, disseminate, and facilitate the use of data and information to improve decisions • Includes as key elements: • Recognition that decision processes are at least as important as decision products • Co-production of knowledge between users and producers • Institutional stability • Design for learning Sarewitz and Pielke (2007) NRC (2009)

4. Should Climate Science Inform Exploratory, Rather Than Consolidative, Models? • Consolidative models: • Bring together all relevant knowledge into a single package which, once validated, can be used as a surrogate for the real world • Are often used for prediction • Exploratory models: • Map assumptions onto consequences, without privileging any one set of assumptions • Cannot be validated • Can, when used with appropriate decision processes and experimental designs, provide policy-relevant information Bankes (1993) Weaver et. al. (2013)

5. Consolidative Models Useful for ‘Predict Then Act’ Analysis • This traditional approach to risk management works well when the future • Isn’t changing fast • Isn’t hard to predict • Doesn’t generate much disagreement What will future conditions be? How sensitive is the decision to those conditions? Under those conditions, what is best near-term decision? Analytics aims to provide ranking of strategies, which implies consolidative models

6. Predict Then Act Methods Can Backfire in Deeply Uncertain Conditions • Uncertainties are underestimated • Competing analyses can contribute to gridlock • Misplaced concreteness can blind decisionmakers to surprise What will future conditions be? How sensitive is the decision to those conditions? Under those conditions, what is best near-term decision?

7. Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions Gross national product (trillions of 1958 dollars) • In the early 1970s forecasters made projections of U.S energy use based on a century of data 2.2 1975 Scenarios 2.0 1.8 1.6 1.4 1.2 Historical trend continued 1.0 1973 .8 1890 1970 1900 .6 1960 1973 1950 .4 1940 .2 1929 1920 1910 0 180 0 20 40 60 80 100 120 140 160 Energy use (1015 Btu per year)

8. Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions Gross national product (trillions of 1958 dollars) In the early 1970s forecasters made projections of U.S energy use based on a century of data … they all were wrong 2.2 1975 Scenarios 2.0 2000 Actual 2000 Actual 1.8 1.6 1990 1990 1.4 1.2 Historical trend continued 1980 1980 1.0 1977 1977 1973 .8 1890 1970 1900 .6 1960 1973 1950 .4 1940 .2 1929 1920 1910 0 180 0 20 40 60 80 100 120 140 160 Energy use (1015 Btu per year) Deep uncertainty occurs when the parties to a decision do not know or do not agree on the likelihood of alternative futures or how actions are related to consequences

9. RDM Manages Deeply Uncertain Conditions by Running the Analysis Backwards “RDM (Robust Decision Making) Process” • Start with a proposed strategy • Use multiple model runs to identify conditions that best distinguish futures where strategy does and does not meet its goals • Identify steps that can be taken so strategy may succeed over wider range of futures Proposedstrategy Identify vulnerabilities of this strategy Develop strategy adaptations to reduce vulnerabilities Analytics aims to provide concise summary of key tradeoffs, a useful function of exploratory models

10. Flood Risk Management Study for Ho Chi Minh City Provides an Example Over 15 years, HCMC has planned multi-billion dollar flood investments using best available projections

11. Conditions have diverged from projections and the city is at significant risk

12. How Can HCMC Develop This Plan When Today’s Predictions Are No More Likely To Be Accurate? Today, HCMC seeks an innovative, integrated flood risk management strategy World Bank WPS-6465 (2013)

13. Simulation Model Projects Flood Risk From Estimates of Hazard, Exposure, and Vulnerability Risk Model Hazard Exposure Vulnerability • Future rainfall intensity • Height of the Saigon River • Population in the study area • Urban form • Vulnerability of population to flood depth Risk = Expected Number of People Affected By Floods Each year

14. Model Projections Depend on Values of Six Deeply Uncertain Parameters RainfallIncrease + 35% +0% Increase River Height 100 cm 20 cm Population 19.1 M 7.4 M Urban Form Growth in Center Growth in Outskirts Poverty Rate 25 % 2.4 % Vulnerability Not Vulnerable Very Vulnerable

15. Traditional Planning Asks “What Will The Future Bring?” Best Estimate Future Risk Model Risk to the Poor Infrastructure performance under best estimate future Risk to the Non-Poor

16. We Run HCMC’s Infrastructure Plan Through 1000 Different Combinations of Conditions Risk Model Alternative Future Future Infrastructure Under Alternative Future Risk to the Poor What factors best distinguish this region, where risk is reduced for both poor and non-poor? Risk to the Non-Poor

17. 1. Under What Future Conditions Is HCMC’s Infrastructure Vulnerable? Infrastructure fails to reduce risk when: > 6% increase in rainfall intensity, > 45 cm increase in river height, OR > 5% poverty rate 6% 45cm rise Infrastructure 45cm 5% In this region, risk is reduced for both poor and non-poor 6% increase

18. 2. Are Those Conditions Sufficiently Likely To Warrant Improving HCMC’s Plan? Even stakeholders with diverse views can agree that its worth considering improvements to current plan NOAA SLR + SCFC Subsidence 75 cm 45cm rise Infrastructure MONRE SLR Estimate 30 cm IPCC SREX Mid Value (20%) IPCC SREX High Value (35%) 6% increase

19. 1. Rely on current infrastructure We Consider A Range of Options for Integrated Flood Risk Management “Soft Options” 2. Raise Homes 3. Relocate Areas 4. Manage Groundwater 5. Capture Rain Water

20. How Will Adding “Soft” Options Improve Our Strategy? NOAA SLR + SCFC Subsidence (75 cm) Elevate (23%, 55cm) + Infrastructure MONRE SLR Estimate 30 cm IPCC SREX Mid Value (20%) IPCC SREX High Value (35%)

21. What Are Tradeoffs Between Robustness And Cost? Stakeholders debate about how much robustness they can afford (which is much more useful than debating what the future will be) Relocate (17%, 100cm) Rainwater (10%, 100cm) NOAA SLR + SCFC Subsidence (75 cm) Groundwater (7%, 55cm) Elevate (23%, 55cm) x Infrastructure MONRE SLR Estimate 30 cm High Cost Low Cost IPCC SREX Mid Value (20%) IPCC SREX High Value (35%)

22. RDM Approach Also Used to Help Develop New Plans for Managing Colorado River 2012 Bureau of Reclamation study, in collaboration with seven states and other users: • Assessed future water supply and demand imbalances over the next 50 years • Developed and evaluated opportunities for resolving imbalances

23. RDM Embeds Analytics in a “Deliberation with Analysis” Decision Support Process Participatory Scoping • Key products include: • Scenarios that illuminate vulnerabilities of plans • New or modified plans that address these vulnerabilities • Tradeoff curves that help decision makers choose robust strategies New Options Tradeoff Analysis Case Generation Scenario Exploration and Discovery Robust Strategy Deliberation Scenarios that Illuminate Vulnerabilities Analysis Deliberation with Analysis

24. Decision Structuring: Work with Decision Stakeholders to Define Objectives/Parameters Deliberation with Stakeholders • Metrics that reflect decision makers’ goals • Management strategies (levers) considered to pursue goals • Uncertain factors that may affect ability to reach goals • Relationships among metrics, levers, and uncertainties Information needed to organize simulation modeling 1. Decision Structuring Also called “XLRM”

25. Case Generation: Evaluate Strategy in Each of Many Plausible Futures Simulating Futures • Strategy • Plausible assumptions • Potential outcomes Large database of simulations model results (each element shows performance of a strategy in one future) 2. Case Generation 100s/1000s of cases

26. Scenario Generation: Mine the Database of Cases to Identify Policy-Relevant Scenarios Indicate policy-relevant cases in database of simulation results Statistical analysis finds low-dimensional clusters with high density of these cases . . . . . . .. . . Scenarios that illuminate vulnerabilities of proposed strategy . 3. Scenario Discovery Uncertain input variable 2 Strategy successful Uncertain input variable 1 Parameter 2 Clusters represent scenarios and driving forces of interest to decisionmakers Strategy less successful Parameter 1

27. Tradeoff Analysis: Help Decisionmakers to Compare Tradeoff Among Strategies Visualization helps decisionmakers compare strategies Robust strategy or information to enable decision-makers to make more robust strategy 4. Tradeoff Analysis

28. Analysis with Colorado System Simulations Reveal Key Vulnerabilities + Baseline strategy 24,000 climate projections • Recent historic • Paleo records • Model projections • Paleo-adjusted model projections Several demand projections + Reclamation’s Colorado River Simulation System RAND, RR-242-BOR

29. Used Climate Information to Assess Vulnerabilities of Current Management Plans • Current plans fall short if: • Temperature greater than 2°F • Any decrease in precipitation

30. Analysis Can Support Deliberation Regarding Near- and Longer-Term Actions Common Options Strategy Initial Actions Contingent Actions Initial Actions (dependent on beliefs)

31. What about probabilities?

32. RDM Considers Sets of Alterative Probability Distributions Expected value of strategy s for distribution r(x) is given by Thus RDM considers many probability distributions over the set of futures x -- NOT a uniform distribution

33. Some Strategies Are Robust Over a Wide Range of Probability Estimates • This chart: • Shows expected cost to taxpayers from re-authorizing U.S. Terrorism Risk Insurance Act • Quoted on floor of US Senate by a proponent • Called “insidious” by opponents • Usefully informed Congressional debate CBO, Treasury Assumption RAND, MG-679-CTRMP

34. Observations • Predict-the-act approaches promote restricted view of demand for climate information • Generally seek to aggregate information into consolidative models aimed at prediction • “Backwards” analyses like RDM effectively employ a much wider range of products from climate science • Integrated models of entire system (exploratory and consolidative) • Models of individual components of full system • Scientific understanding not well-embodied in models • Any probabilistic information, however imprecise • RDM creates demand for decision support methods and tools for managing and summarizing large and diverse sets of information in a decision-relevant context.

35. More Information http://www.rand.org/international/pardee/ http://www.rand.org/methods/rdmlab.html Thank you!

37. Flexible Options Can Prove Effective

38. Research Portfolio Includes Developing and Evaluating Improved Tools • Analytic tools to facilitate RDM • Exploratory modeling methods run computer simulation models many times to create a database that links a wide range of assumptions to their consequences • Scenario Discovery methods uses cluster analysis on these databases of model results to simply characterize the future conditions where a the proposed strategy does not meet its goals Tools to represent information Tools to draw meaning from information Users Tool development efforts include: Facilitating design of strategies with “pareto-satisficing surfaces” Higher dimensional scenario discovery Adaptive sampling

39. Portfolio Analysis Reveals Key Tradeoffs Percent of SequencesVulnerable in Lowest Streamflow Conditions Lee Ferry Deficit Vulnerability Lake Mead Vulnerability

40. “XLRM” Framework for theColorado River Basin Study

41. Colorado Basin Study Followed This Process Interactive visualizations Start with proposed policy and its goals Identify futures where policy fails to meet its goals • Analysis: • Participants work with experts to generate and interpret decision-relevant information • Deliberate: • Participants to decision define objections, options, and other parameters Evaluate whether new policies are worth adopting Identify policies that address these vulnerabilities Revised instructions Dozens of workshops with many stakeholders over course of study