Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel

jan-kwakkel
Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are. This implies that, under deep uncertainty, it is possible to enumerate possible representations of the system, plausible futures, and relevant outcomes of interest, without being able to rank order them in terms of likelihood or importance.

There is an emerging consensus that effort needs to be devoted to making any decision regarding a complex system robust with respect to such uncertainties. A plan is robust if its expected performance is only weakly affected by deep uncertainty. Alternatively, a plan can be understood as being robust if no matter how the future turns out, there is little cause for regret (the so-called “no regrets” approach to decision making).

Over the last decade a new paradigm, known as ‘decision-making under deep uncertainty’, has emerged to support the development of robust plans. This paradigm rests on three key ideas: (i) exploratory modeling; (ii) adaptive planning; and, (iii) joint sense-making.

Exploratory modelling

Exploratory modeling allows examination of the consequences of the various irreducible uncertainties for decision-making. Typically, in the case of complex systems this involves the use of computational scenario approaches (see also the blog post by Laura Schmitt-Olabisi on Dealing with deep uncertainty: Scenarios).

A set of models that is plausible or interesting in a given context is generated by the uncertainties associated with the problem of interest, and is constrained by available data and knowledge. A single model drawn from the set is not a prediction. Rather, it is a computational ‘what-if’ experiment that reveals how the real world system would behave if the various assumptions this particular model makes about the various uncertainties were correct.

A single ‘what-if’ experiment is typically not that informative, other than suggesting the plausibility of its outcomes. Instead, exploratory modeling aims to support reasoning and decision-making on the basis of the set of models. Thus exploratory modeling involves searching through the set of models using (many-objective) optimization algorithms, and sampling over the set of models using computational design of experiments and global sensitivity analysis techniques. By searching through the set of models, one can identify which (combination of) uncertainties negatively affects the outcomes of interest. In light of this, actions can be iteratively refined to be robust with respect to these uncertainties.

Adaptive planning

Adaptive planning means that plans are designed from the outset to be altered over time in response to how the future actually unfolds. In this way, modifications are planned for, rather than taking place in an ad hoc manner. The flexibility of adaptive plans is a key means of achieving decision robustness.

This means that a wide variety of futures has to be explored. Insight is needed into which actions are best suited to which futures, as well as what signals from the unfolding future can be monitored in order to ensure the timely implementation of the appropriate actions. Adaptive planning thus involves a paradigm shift from planning in time, to planning conditional on observed developments.

Joint sense-making

Decision making on uncertain complex systems generally involves multiple actors who have to come to agreement. In such a situation, planning and decision-making require an iterative approach that facilitates learning across alternative framings of the problem, and learning about stakeholder preferences and trade-offs, in pursuit of a collaborative process of discovering what is possible.

Various decision analytic techniques can be used to enable a constructive learning process amongst the stakeholders and analysts. Decision analysis in this conceptualization must shift away from the a priori agreement on (or imposition of assumptions on) the probability of alternative states of the world and the way in which competing objectives are to be aggregated, with the aim of producing a preference ranking of decision alternatives. Instead decision analysis must shift to an a posteriori exploration of trade-offs amongst objectives and their robustness across possible futures. Decision analysis should move away from trying to dictate the right choice, and instead aim at enabling deliberation and joint sense-making amongst the various parties to decision. (For more on sense-making, see Bethany Laursen’s blog post on Making sense of wicked problems).

Closing remarks

Exploratory modeling, adaptive planning, and joint sense-making are the three key ideas that underpin the emerging paradigm of decision making under deep uncertainty. Various specific approaches that exemplify these ideas include (many-objective) robust decision-making, dynamic adaptive policy pathways, decision scaling, info-gap decision theory, adaptive policy making and assumption based planning. Notwithstanding the many technical differences that exist between these approaches, there is an increasing emphasis on what is shared. In practice also, increasingly people are adopting aspects from multiple approaches in order to offer context-specific support for making decisions under deep uncertainty.

What has your experience been with decision making under deep uncertainty? What methods have you found to be useful?

Further reading:
Bankes, S. C. (1993). Exploratory Modeling for Policy Analysis. Operations Research, 4, 3: 435-449.

Haasnoot, M., Kwakkel, J. H., Walker, W. E. and ter Maat, J. (2013). Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Global Environmental Change, 23: 485-498. Online (DOI): 10.1016/j.gloenvcha.2012.12.006

Herman, J. D., Reed, P. M., Zeff, H. B. and Characklis, G. W. (2015). How should robustness be defined for water systems planning under change. Journal of Water Resources Planning and Management, 141, 10. Online (DOI): 10.1061/(ASCE)WR.1943-5452.0000509

Kwakkel, J. H., Walker, W. E. and Haasnoot, M. (2016). Coping with the Wickedness of Public Policy Problems: Approaches for Decision Making under Deep Uncertainty. Journal of Water Resources Planning and Management. Online (DOI): 10.1061/(ASCE)WR.1943-5452.0000626

Lempert, R. J., Groves, D. G., Popper, S. W. and Bankes, S. C. (2006). A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios. Management Science, 52: 514-528. Online (DOI): 10.1287/mnsc.1050.0472

Walker, W. E., Haasnoot, M. and Kwakkel, J. H. (2013). Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty. Sustainability, 5: 955-979. Online (DOI): 10.3390/su5030955

Biography: Jan Kwakkel is an associate professor at Delft University of Technology in the faculty of Technology, Policy and Management. He has a background in systems engineering and policy analysis for transport systems. His current research focuses on supporting decision making under deep uncertainty. This involves the development of taxonomies and frameworks for uncertainty analysis and adaptive planning, as well as research on model-based scenario approaches for designing adaptive plans. He has applied his research in various domains, including transportation, energy systems, and health. His primary application domain is climate adaptation in the water sector. A secondary research interest is in text mining of science and patent databases. His research is currently funded for four years through a personal development grant of the Dutch National Science Foundation.

Learning to tackle wicked problems through games / Aprendiendo a hacer frente a problemas perversos a través de los juegos/ Apprendre à affronter les problèmes sournois à travers les jeux

Community member post by Claude Garcia, Anne Dray and Patrick Waeber

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Claude Garcia (biography)

A Spanish version and a French version of this post are available

Can we help the next generation of policy makers, business leaders and citizens to become creative, critical and independent thinkers? Can we make them aware of the nature of the problems they will be confronted with? Can we strengthen their capacity to foster and lead stakeholder processes to address these problems?

Yes. Continue reading

Scoping: Lessons from environmental impact assessment

Community member post by Peter R. Mulvihill

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Peter R. Mulvihill (biography)

What can we learn about the role and importance of scoping in the context of environmental impact assessment?

“Closed” versus “open” scoping

I am intrigued by the highly variable approaches to scoping practice in environmental impact assessment and the considerable range between “closed” approaches and more ambitious and open exercises. Closed approaches to scoping tend to narrow the range of questions, possibilities and alternatives that may be considered in environmental impact assessment, while limiting or precluding meaningful public input. Of course, the possibility of more open scoping is sometimes precluded beforehand by narrow terms of reference determined by regulators.

When scoping is not done well, it inevitably compromises subsequent steps in the process. Continue reading

Transkillery! What skills are needed to be a boundary crosser?

Community member post by Dena Fam, Tanzi Smith and Dana Cordell

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Dena Fam (biography)

What skills and dispositions are required by researchers and practitioners in transdisciplinary research and practice in crossing boundaries, sectors and paradigms?

The insights here come from interviews with 14 internationally recognized transdisciplinary researchers and practitioners, chosen from a diverse range of research and practice-based perspectives.

tanzi-smith
Tanzi Smith (biography)

Here we focus on:

1) skills for specific tasks such as facilitation of a meeting, crafting a well-written report, and communicating effectively across disciplines; and,

cordell
Dana Cordell (biography)

2) dispositions, attitudes, orientations and temperaments of an effective researcher/practitioner, i.e., as a way of being.

 

Six categories of skills and dispositions

The core skills and dispositions of an exceptional transdisciplinary researcher/practitioner can be grouped into six categories, illustrated in the figure below. Continue reading

Argument-based tools to account for uncertainty in policy analysis and decision support

Community member post by Sven Ove Hansson and Gertrude Hirsch Hadorn

sven-ove-hansson
Sven Ove Hansson (biography)

Scientific uncertainty creates problems in many fields of public policy. Often, it is not possible to satisfy the high demands on the information input for standard methods of policy analysis such as risk analysis or cost-benefit analysis. For instance, this seems to be the case for long-term projections of regional trends in extreme weather and their impacts.

gertrude-hirsch-hadorn
Gertrude Hirsch Hadorn (biography)

However, we cannot wait until science knows the probabilities and expected values for each of the policy options. Decision-makers often have good reason to act although such information is missing. Uncertainty does not diminish the need for policy advice to help them determine which option it would be best to go for.

When traditional methods are insufficient or inapplicable, argument-based tools for decision analysis can be applied. Such tools have been developed in philosophy and argumentation theory. They provide decision support on a systematic methodological basis. Continue reading

Enabling co-creation: From learning cycles to aligning values, rules and knowledge

Community member post by Lorrae van Kerkhoff

lorrae-van-kerkhoff
Lorrae van Kerkhoff (biography)

How do we improve? In the context of sustainable development, we continually confront the question of how we can develop meaningful and positive actions towards a ‘better’ world (social, ecological, economic outcomes) despite inherent uncertainties about what the future holds.

Co-creation is one concept among several that seek to reorientate us from simplistic, largely linear ideas of progress towards more nuanced, subtle ideas that highlight that there are many different aspects of ‘progress’, and these can be deeply contested and challenging to reconcile. Enabling co-creation, then – or operationalizing it – means finding practical ways to work together, to deal with our different experiences, aspirations and expectations as well as the uncertainties of the future.

Co-creation sits within a learning paradigm that suggests engagement, social and mutual learning, adaptation and flexibility are key to enabling action in the face of uncertainty. But how do we think about learning? Continue reading