Dynamic Adaptive Policymaking: An Approach to Planning Under Deep - - PowerPoint PPT Presentation

New Zealand Climate Change Research Institute Victoria University of Wellington 10 February 2015

Dynamic Adaptive Policymaking: An Approach to Planning Under Deep Uncertainty

• Prof. Dr. Warren E. Walker

The Problem: How to make policy in a deeply uncertain world

• “There are things that we know that we know; there

are things that we know that we don’t know; there are things that we don’t know that we don’t know.”

– Donald Rumsfeld, 2/12/2002

• How do we make policy taking into account the

things that we know that we don’t know, and safeguard against/prepare for the things that we don’t know that we don’t know?

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Outline

• The traditional (‘Predict-and-Act’) approach to

policymaking

• What is ‘deep uncertainty’?
• Why the traditional approach does not work under deep

uncertainty

• The inverted ‘Monitor-and-Adapt’ approach to

policymaking (with Dynamic Adaptive Policymaking (DAP) as an example)

A Framework for Traditional Policy Analysis

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Actors Involved in a Policy Analysis Study

Problem owner(s) (decisionmaker(s)) Stakeholders (policy arena actors) Methodologists (toolmakers) Policy analysts (decision advisors) Specialists (domain experts)

Summarizing the Traditional Approach to Policy Analysis*

6 * Warren E. Walker, “Policy Analysis: A Systematic Approach to Supporting Policymaking in the Public Sector”, Journal of Multicriteria Decision Analysis, Volume 9, No. 1-3 (2000), pp. 11-27.

Among the Assumptions

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• The future context(s)
• The system boundary
• The system model (elements; relationships among

elements)

• The relative importance of the outcomes

Large Losses Possible if Take Action Based on Wrong Predictions

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Large Needless Expenditures Possible if Take Action Based on Wrong Predictions

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Gross national product, USA (billions

• f 1958

dollars) Energy use (1015 Btu per year) 2,200 2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200

Historical trend continued

1970 1920 1929 1940 1950 1960 1910 1973 1930 1900 1890

20 40 60 80 100 120 140 160 180

1975 Conservation scenarios 2000 Actual 1990 1980 1977

Lessons About Predictions

• “Predictions are very difficult, especially about the future.”
• - Yogi Berra
• “In practice, decisionmaking is always in the face of

uncertainty… Waiting for an impossible absolute truth means never doing anything.”

• - D. Bradbury, letter to The Times, 6 January 2005
• Implementing policies that assume that the future will be a

continuation of the past is like driving a car while looking only into the rear view mirror.

• You cannot choose a scenario. The probability that any

specific scenario will be correct is zero.

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The Locations of Uncertainty in a Policy Analysis Study

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(X) Context (R) System model (O) System outcomes (W) Weights on the outcomes

Uncertainties are Found in the X,R,O,W Locations

• Uncertainties about the future (demographic,

economic, social developments, etc.)

• Uncertainties about the system (since (some of)

the key relationships determining system performance are insufficiently known)

• Uncertainties about the policy outcomes
• Uncertainties about the valuation of the outcomes

by different stakeholders

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Model-Based Decision Support Must Deal With Several Levels of Uncertainty at Each of the Locations

13 LEVEL Complete Certainty Level 1 Level 2 Level 3 Level 4 Total ignorance LOCATION Context (X) A clear enough future Alternate futures (with probabilities) A multiplicity of plausible futures Unknown future System Model (R) A single (deterministic) system model A single (stochastic) system model Several system models, with different structures Unknown system model; know we don’t know System Outcomes (O) A point estimate for each outcome A confidence interval for each

• utcome

A known range

• f outcomes

Unknown

• utcomes; know

we don’t know Weights

• n
• utcomes

(W) A single set of weights Several sets of weights, with a probability attached to each set A known range

• f weights

Unknown weights; know we don’t know

Today I Will Focus on Uncertainty About the Future Context

14 LEVEL Complete Certainty Level 1 Level 2 Level 3 Level 4 Total ignorance LOCATION Context (X) A clear enough future Alternate futures (with probabilities) A multiplicity of plausible futures Unknown future System Model (R) A single (deterministic) system model A single (stochastic) system model Several system models, with different structures Unknown system model; know we don’t know System Outcomes (O) A point estimate for each outcome A confidence interval for each

• utcome

A known range

• f outcomes

Unknown

• utcomes; know

we don’t know Weights

• n
• utcomes

(W) A single set of weights Several sets of weights, with a probability attached to each set A known range

• f weights

Unknown weights; know we don’t know

Most Approaches for Dealing with Uncertainty about the Future are Problematic

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• Ignore uncertainty
• Assume the future is knowable (‘predict-and-act’  “optimal”

policy) (Level 1)

• Assume the future will (probabilistically) look like the past

(driving while looking only through rear-view mirror) ( ‘trend- based’ policy)(Level 2)

• Look for a policy that will do well in a few scenarios ( ‘static

robust’ policy) (Level 3)

• What if the experts do not know and /or stakeholders can not

agree on what the future might bring (Level 4) = “deep uncertainty”

Definition of Deep Uncertainty*

A situation in which the relevant actors do not know, or cannot agree upon:

– how the system works – how likely or plausible various future states are – how to value the various outcomes of interest

16 * Walker, Warren E., Robert J. Lempert, and Jan H. Kwakkel (2013). “Deep Uncertainty”, entry (pp. 395-402) in Saul I. Gass and Michael C. Fu (eds.), Encyclopedia of Operations Research and Management Science., 3rd Edition, New York: Springer.

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Four Approaches for Dealing with Deep (Level 4) Uncertainty

• Resistance: plan for the worst possible case or future

situation

– Likely to be very costly

• Resilience: whatever happens in the future, make sure

that the system can recover quickly

– Accepts short-term pain; focuses on recovery

• Static robustness: aim at reducing vulnerability in the

largest possible range of conditions

– May be difficult to change when conditions change

• Planned adaptation: plan to change over time, in case

conditions change

An Approach for Planned Adaptation: Dynamic Adaptive Policies (DAP)

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DAP Extends the Analysis

Features of an Adaptive Policy

• Tests assumptions (in the real world)
• Hedges against negative outcomes and
• ther uncertain events
• Steers toward positive outcomes
• Adapts to changing situations

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The DAP Approach (1)

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• Determine a set of goals
• Identify a promising basic policy and conditions

for its success

• Identify vulnerabilities of the policy (how it could

fail) and ways of protecting it

• Monitor progress toward the goals
• Adapt the policy as exogenous and endogenous

conditions change over time

Identifying Vulnerabilities: Use Non- Predictive Models (‘Exploratory Modeling’)

• Model is used to explore (“what if . . .”)
• Simultaneously takes into account context uncertainty,

model uncertainty, and uncertainty about weights (multi- dimensional sensitivity analysis)

• Objective: reason about system behavior ─ under what

circumstances would a policy succeed or fail (‘scenario discovery’)

• Basic idea: explore the consequences of the unresolved

uncertainties, and which would make a difference

• Requires a huge number of computer runs

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Key element of Dynamic Adaptive Policies: A monitoring system with related contingency actions to keep the policy on track

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MONITORING SYSTEM:

• Signposts and trigger values
• Are we still on track?
• Are corrective actions

needed?

• Do we need to implement

actions earlier or later?

• Is reassessment needed?

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What Happens After an Adaptive Action is Triggered? Two possibilities

• 1. Restart the DAP process again, from the new

situation (as shown on the DAP slide)

• 2. Identify promising pathways (i.e., sequences of

policy actions) in advance (e.g., to avoid lock-ins)

• We call this the Dynamic Adaptive Policy Pathway (DAPP)

approach*

• Based on triggers called ‘tipping points’, which are conditions

at which a policy begins to perform unacceptably

• Can be graphically represented as a ‘Metro map’

*M. Haasnoot, J.H. Kwakkel, and W.E. Walker (2013). “Dynamic Adaptive Policy Pathways: A New Method for Crafting Robust Decisions for a Deeply Uncertain World”, Global Environmental Change, 123: 485–498.

Example of an Adaptation Pathways Map

• Several paths will satisfy the policy objectives
• Some require many changes in policy; some require few
• Some cost more than others

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Example of an Adaptation Pathways Map

• Several paths will satisfy the policy objectives
• Some require many changes in policy; some require few
• Some cost more than others

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Current Research Involving DAP & DAPP

• Port of Rotterdam (“Flexible Port”)
• Schiphol Airport (Amsterdam)
• Thames Estuary (London)
• New York City
• Also (just beginning) water management in the

Danube Delta, Ho Chi Minh City, Croatian coast, Catalan harbors and coast, Ebro Delta, and the Elbe estuary/Port of Hamburg (in response to global climate change)

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Current Use of DAP: Adaptive Delta Management in the Netherlands

• Adaptive Delta

Management (ADM) is the cornerstone of the Dutch climate adaptation strategy in the water domain

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ADM – what is it?

• A combination of existing concepts: adaptation pathways, tipping points, integrated

water resources management, scenarios

• A ‘narrative’, a method that mobilizes policymakers, decisionmakers, and politicians

around a shared approach ADM – what is it not?

• A quantitative modelling method to compare and select strategies

ADM – what did it do (2010 – 2014)?

• Prevented dead-lock, mobilized parties, provided consistency in strategic

development

• Created the conditions for actual Adaptive Delta Management (2015-2100)
• Flexible strategies

Agreement on long-term options (change speed or switch) Preparatory measures where necessary Development of a monitoring and evaluation program Announced: research program (e.g. on assumptions) Announced: further plans will be ready in time

Adaptive Delta Management in the Netherlands (1)

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Adaptive Delta Management in the Netherlands (2)

• In the ADM methodology developed in

the Delta Programme, adaptivity is woven into the strategy development at two different levels:

– at the level of the individual strategies: strategies are designed to be able to speed up or slow down following actual social-economic and climatic developments – at the level of the set of the strategies as a whole: the set as a whole is designed to be able to switch from one strategy to the other (adaptation pathways)

Actual Use of DAPP in the Netherlands

“In the Delta Programme, adaptive delta

management is the basic principle. Typical features of this approach are that interventions are not necessarily dimensioned on the basis of the worst- case scenario, and that no fixed final picture is assumed for 2100.”

Delta Programme 2015: Working on the Delta ─ The decisions to keep the Netherlands safe and liveable, The Ministry of Infrastructure and the Environment, September 2014, p. 138.

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Conclusions

• Uncertainty is always present in strategic policymaking
• Ignoring uncertainty is a terrible idea
• Dynamic adaptive policies (and dynamic adaptive

policy pathways) are good ways of dealing with deep uncertainty

– Get implementation under way – Allow adaptations of policy over time as new solutions are developed, values change, and other external events take place – Enable learning from experience over time

• DAP Motto: ‘Be Prepared’

Some Relevant References

33 Warren E. Walker, S. Adnan Rahman, and Jonathan Cave (2001). “Adaptive Policies, Policy Analysis, and Policymaking”, European Journal of Operational Research, Vol. 128, No. 2, pp. 282-289. W.E. Walker, P. Harremoës, J. Rotmans, J.P. van der Sluijs, M.B.A. van Asselt, P. Janssen, and M.P. Krayer von Krauss (2003). “Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support”, Integrated Assessment, Vol. 4, No. 1, pp. 5-17. Warren E. Walker, Vincent A.W.J. Marchau, and Darren Swanson (2010). “Addressing Deep Uncertainty Using Adaptive Policies: Introduction to Section 2”, Technological Forecasting & Social Change, Vol. 77, No. 6, pp. 917-923. Warren E. Walker, Robert J. Lempert, and Jan H. Kwakkel (2013). “Deep Uncertainty”, entry (pp. 395-402) in S. Gass and

• M. Fu (eds.), Encyclopedia of Operations Research and Management Science, 3rd Edition. New York: Springer.

Steven Bankes, Warren E. Walker, and Jan H. Kwakkel (2013). “Exploratory Modeling and Analysis”, entry (pp. 532-537) in

• S. Gass, and M..Fu (eds.), Encyclopedia of Operations Research and Management Science., 3rd Edition, New York:

Springer. Marjolijn Haasnoot, Jan H. Kwakkel, and Warren E. Walker (2013). “Dynamic Adaptive Policy Pathways: A New Method for Crafting Robust Decisions for a Deeply Uncertain World”, Global Environmental Change, Vol. 23, No. 2, pp. 485–498.. Warren E. Walker, Marjolijn Haasnoot, and Jan H. Kwakkel (2013). “Adapt or Perish: A Review of Planning Approaches for Adaptation Under Deep Uncertainty”, Sustainability Vol. 5, No. 3, pp. 955-979. W.E. Walker., V.A.W.J. Marchau, and J.H. Kwakkel (2013). “Uncertainty in the Framework of Policy Analysis”, Chapter 9 in W.E. Walker and W.A.H. Thissen (eds.), Public Policy Analysis : New Developments. New York: Springer.

Questions? Comments?

Contact: w.e.walker@tudelft.nl

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