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

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Climate-Related Decisions Poses Both Analytic and Organizational Challenges

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?

Public planning should be:• Objective

• Subject to clear rules and procedures

• Accountable to public

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Concept of Decision Support Focuses Attention on How (in Addition to What) Information is Used

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

Supply and demand of scientific information may be mismatched

Sarewitz and Pielke (2007) NRC (2009)

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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)

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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?

Under those conditions, what is

best near-term decision?

How sensitive is the decision to

those conditions?

Analytics aims to provide ranking of strategies, which implies consolidative models

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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?

Under those conditions, what is

best near-term decision?

How sensitive is the decision to

those conditions?

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Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions

• In the early 1970s forecasters made projections of U.S energy use based on a century of data

Gross national product (trillions of 1958 dollars)

2.22.0

1.81.6

1.4

1.2

1.0.8

.6

.4

.2

0180

Energy use (1015 Btu per year)

0

Historical trend

continued1970

1920 19291940

19501960

1910

1973

19731900

1890

20 40 60 80 100 120 140 160

1975 Scenarios

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Believing Forecasts of the Unpredictable Can Contribute to Bad Decisions

Gross national product (trillions of 1958 dollars)

2.22.0

1.81.6

1.4

1.2

1.0.8

.6

.4

.2

0180

Energy use (1015 Btu per year)

0

Historical trend

continued1970

1920 19291940

19501960

1910

1973

19731900

1890

20 40 60 80 100 120 140 160

2000 Actual

1990

19801977

1975 Scenarios2000 Actual

1990

19801977

In the early 1970s forecasters made projections of U.S energy use based on a century of data

… they all were wrong

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

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RDM Manages Deeply Uncertain Conditions by Running the Analysis Backwards

1. Start with a proposed strategy

2. Use multiple model runs to identify conditions that best distinguish futures where strategy does and does not meet its goals

3. 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

“RDM (Robust Decision Making) Process”

Analytics aims to provide concise summary of key tradeoffs, a useful function of exploratory models

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Over 15 years, HCMC has planned multi-billion dollar flood investments using best available projections

Flood Risk Management Study for Ho Chi Minh City Provides an Example

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

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Today, HCMC seeks an innovative, integrated flood risk management strategy

How Can HCMC Develop This Plan When Today’s Predictions Are No More Likely To Be Accurate?

World Bank WPS-6465 (2013)

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Simulation Model Projects Flood Risk From Estimates of Hazard, Exposure, and Vulnerability

Hazard

• Future rainfall intensity

• Height of the Saigon River

Exposure

• Population in the study area

• Urban form

Vulnerability

• Vulnerability of population to flood depth

Risk = Expected Number of People Affected By Floods Each year

Risk Model

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Model Projections Depend on Values of Six Deeply Uncertain Parameters

RainfallIncrease +0% + 35%

Increase River Height 20 cm 100 cm

Population7.4 M 19.1 M

Urban FormGrowth in Outskirts Growth in Center

Poverty Rate2.4 % 25 %

Vulnerability Not VulnerableVery Vulnerable

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Traditional Planning Asks “What Will The Future Bring?”

Infrastructure performance under best estimate future

Risk to the Non-Poor

Ris

k t

o t

he

Po

or

Risk Model

Best Estimate Future

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Ris

k t

o t

he

Po

or

Infrastructure UnderAlternative Future

Risk Model

We Run HCMC’s Infrastructure Plan Through 1000 Different Combinations of Conditions

Alternative Future Future

Risk to the Non-Poor

What factors best distinguish this

region, where risk is reduced for both

poor and non-poor?

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6% increase

45cm riseInfrastructure

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

5%

In this region, risk is reduced for both poor and non-poor

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6% increase

45cm rise

2. Are Those Conditions Sufficiently Likely To Warrant Improving HCMC’s Plan?

Infrastructure

IPCC SREX Mid Value (20%)

IPCC SREX High Value (35%)

MONRE SLREstimate 30 cm

NOAA SLR + SCFC Subsidence75 cm

Even stakeholders with diverse views can agree that its worth considering improvements to current plan

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We Consider A Range of Options for Integrated Flood Risk Management

1. Rely on current infrastructure

5. Capture Rain Water

“Soft Options”

3. Relocate Areas

4. Manage Groundwater

2. Raise Homes

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Infrastructure

NOAA SLR + SCFC Subsidence (75 cm)

IPCC SREX Mid Value (20%)

IPCC SREX High Value (35%)

Elevate(23%, 55cm)

+

How Will Adding “Soft” Options Improve Our Strategy?

MONRE SLREstimate 30 cm

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NOAA SLR + SCFC Subsidence (75 cm)

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)

High Cost

Low Cost

Rainwater(10%, 100cm)

Elevate(23%, 55cm)

Groundwater(7%, 55cm)

MONRE SLREstimate 30 cm

IPCC SREX Mid Value (20%)

IPCC SREX High Value (35%)

xInfrastructure

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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

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RDM Embeds Analytics in a “Deliberation with Analysis” Decision Support Process

Scenarios that Illuminate

Vulnerabilities

Robust Strategy

Deliberation

AnalysisDeliberation with Analysis

Participatory Scoping

Scenario Exploration

and Discovery

Key products include:

1.Scenarios that illuminate vulnerabilities of plans

2.New or modified plans that address these vulnerabilities

3.Tradeoff curves that help decision makers choose robust strategies

New Options

Case Generation

Tradeoff Analysis

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Decision Structuring: Work with Decision Stakeholders to Define Objectives/Parameters

1. DecisionStructuring

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

Informationneeded to organize

simulation modeling

Also called “XLRM”

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Case Generation: Evaluate Strategy in Each of Many Plausible Futures

2. CaseGeneration

Simulating Futures

Large database of simulations

model results

(each element shows

performance of a strategy in one future)

• Strategy• Plausible

assumptions• Potential

outcomes

100s/1000s of cases

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Scenario Generation: Mine the Database of Cases to Identify Policy-Relevant Scenarios

Scenarios that illuminate

vulnerabilities of proposed

strategy

3. ScenarioDiscovery . . .. . ......

Un

cert

ain

inp

ut

vari

able

2

1. Indicate policy-relevant cases in database of simulation results

2. Statistical analysis finds low-dimensional clusters with high density of these cases

3. Clusters represent scenarios and driving forces of interest to decisionmakers

Uncertain input variable 1

Parameter 1

Par

amet

er 2 Strategy

successful

Strategy less successful

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Tradeoff Analysis: Help Decisionmakers to Compare Tradeoff Among Strategies

Robust strategy or

information to enable

decision-makers to make more

robust strategy

4. TradeoffAnalysis

Visualization helps decisionmakers

compare strategies

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Analysis with Colorado System Simulations Reveal Key Vulnerabilities

++

24,000 climate projections•Recent historic•Paleo records•Model projections•Paleo-adjusted model projections

Several demand projections

24,000 climate projections•Recent historic•Paleo records•Model projections•Paleo-adjusted model projections

Several demand projections

Baseline strategyBaseline strategy

Reclamation’s Colorado River Simulation SystemReclamation’s Colorado River Simulation System

++

RAND, RR-242-BOR

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Used Climate Information to Assess Vulnerabilities of Current Management Plans

Current plans fall short if:•Temperature greater than 2°F•Any decrease in precipitation

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Initial Actions(dependent on beliefs)

Initial Actions

Contingent Actions

Common Options Strategy

Analysis Can Support Deliberation Regarding Near- and Longer-Term Actions

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What about probabilities?

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RDM Considers Sets of Alterative Probability DistributionsExpected value of strategy s for distribution (x) is given by

HARD1.Choosing what strategies to consider2.Choosing what futures to consider3.Calculating the performance of strategy s in some future x4.Knowing – and convincing other people that you know – the true probability distribution

EASY•Calculating the integral for any once you have 1-3 above

Thus RDM considers many probability distributions over the set of futures x -- NOT a uniform distribution

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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, TreasuryAssumption

RAND, MG-679-CTRMP

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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.

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More InformationMore Information

http://www.rand.org/international/pardee/

http://www.rand.org/methods/rdmlab.html

Thank you!

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Flexible Options Can Prove Effective

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Research Portfolio Includes Developing and Evaluating Improved Tools

Tools to draw meaning from

informationUsers

Tools to represent

information

Analytic tools to facilitate RDMExploratory 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

Tool development efforts include:1.Facilitating design of strategies with “pareto-satisficing surfaces”2.Higher dimensional scenario discovery3.Adaptive sampling

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Portfolio Analysis Reveals Key TradeoffsP

erce

nt o

f Seq

uenc

esV

ulne

rabl

e in

Lo

wes

t Str

eam

flow

Con

ditio

ns

Lee Ferry Deficit Vulnerability Lake Mead Vulnerability

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“XLRM” Framework for theColorado River Basin Study

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Uncertain Factors (X) Options and Strategies (L)

Demand Conditions (6)

Supply Conditions (4)• Observed Resampled (103 traces)• Paleo Resampled (1,244 traces)• Paleo Conditioned (500 traces)• Downscaled GCM Projected (112

traces)

System Operations Conditions (2)

Options for demand reduction and supply augmentation (40)

Portfolios of many options designed to adjust over time in response to new information (4)•Near-term actions•Signposts•Contingent actions

Relationships or Models (R) Performance Metrics (M)

Colorado River Simulation System (CRSS)

• Water delivery (5)• Electric power (3),

Recreation (11), Ecological (5), Water quality (1), and Flood control (1)

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Colorado Basin Study Followed This Process

Deliberate:•Participants to decision define objections, options, and other parameters

Analysis:•Participants work with experts to generate and interpret decision-relevant information

Dozens of workshops with many stakeholders over

course of study

Interactivevisualizations

Revised instructions

1. Start with proposed policy and its goals

2. Identify futures where policy fails to meet its goals

3. Identify policies that address these vulnerabilities

4. Evaluate whether new policies are worth adopting