Decision Making under Deep Uncertainty

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For this week’s reading reflection, I read Decision Making under Deep Uncertainty a book that articulates several methods and approaches to decision analysis under conditions of uncertainty. The books contributors are members of the DMDU Society which defines ‘deep uncertainty’ as a condition, “when parties to a decision do not know, or cannot agree on, the system model that relates action to consequences, the probability distributions to place over the inputs to these models, which consequences to consider and their relative importance.”[1]

Part of my drive to create a “complexity-informed approach” to conflict resolution comes from the recognition that the world is becoming more complex and the future more uncertain. Issues like climate change, the coronavirus, and the collapse of the US empire are all examples of how unexpected events can instantly change the world as we know it. A centerpiece of a complexity-informed conflict resolution strategy is the creation of tools of processes that can be imbedded in various systems (i.e. government, M&E efforts, social service provision, international development) to help manage this increasing uncertainty by mapping the evolutionary potential of the present and enabling action across multiple levels of the system. 

The book was helpful on two accounts. It: 1. Provided insight into how a conflict-resolution oriented SenseMaker project could fit with other styles of decision-making support under uncertainty 2. Provided a pathway for integrating anticipatory innovation, decision-making support, and peace engineering approaches. 

Policy analysis in more stable systems can be conducted to determine which policy options are most likely to achieve desired outcomes. However, under deep uncertainty (or in the complex domain if using the Cynefin framework) predictions about the future don’t hold up and the relationship between policy and future states can’t be determined. DMDU outlines a “monitor and adapt” approach that contrasts with some of the traditional “predict and act” decision-making models. The DMDU approaches outlined in the book avoid simply ranking policy decisions, but expand the scope of what is considered and show tradeoffs between different policy options. This results in an interesting shift where the goal of decision-making is actually to defend a policy against a variety of future states. There are three main ideas the authors employ as part of their strategy: exploratory modeling, adaptive planning, and decision-support.  

The connection to peace engineering is clear: many of the scenarios described in the book are related to environmental engineering projects and dealing with topics like climate change. In these cases, exploratory scenarios can be generated by changing thresholds in existing quantitative data- i.e. number of feet of sea level rise. In such a case, a computational approach can work to calculate the robustness of a policy, because tipping points for when a policy should shift exist as a quantitative relationship (“once the seas rise 3 ft, then we need to focus on evacuations of the waterfront”). One of the challenges for peace research is that there are not the same quantitative metrics for policy adaptation thresholds. How do you measure if people are becoming more prone to violence? How can a policymaker know that their conflict resolution policy is improving peoples’ lives? Of course, surveys or public opinion polls could approximate sentiment, but they don’t provide a concrete picture of how perception is related to what is actually happening in peoples’ lives. Under DMDU approaches, a crucial aspect is identifying where decision-points would exist in response to future scenarios. An adaptive policy evolves as things unfold and certain thresholds trigger elements within the policy architecture. I believe that SenseMaker data could provide the quantitative equivalent of a threshold in such an approach to policy planning. 

There is enormous potential for SenseMaker and a complexity-informed conflict resolution approach to contribute a data sources for monitoring and decision-making support in this sphere of policymaking. Some of the principles behind DMDU (exploratory analysis and adaptive planning) could be linked to a combination of SenseMaker data outputs (the human terrain) and the outputs of other sensor networks. On a Peace Engineering Consortium call last Friday, members were discussing a peace engineering project that would track gunshots with SMART city infrastructure to monitor ‘negative peace’ in the city. By combining that data with the perceptions and impactful experiences of people in a neighborhood with high gun violence, peace engineers could have a powerful understanding of the conflict system in the city and a variety of mechanisms for weak signal detection. For example, the gunshot sensor could trigger proactive response where SenseMaker data provides insight into what to do and how to work with the community to get it done. Quantitative data provides the what, and self-signified stories provide the how. 

Although this is enough for this post, I hope to continue to explore the DMDU approaches and specific applications of SenseMaker. This might include drafting a policy proposal that would include sections on: Policy architecture, generation of policy alternatives, generation of scenarios, robustness metrics (regret or satisficing), and vulnerability analysis. Ultimately, this could become part of the policy-making wing of a conflict resolution SenseMaker project in the states. Because I’m hoping to work with governments on how to institutionalize complexity-informed approaches, DMDU was helpful in grasping the language and concerns of that realm. 

Next week I’ll be posting about Andy Clark’s Surfing Uncertainty and incorporating notes from interesting conversations I had with Bob Polk and Solon Simmons about sensemaking, root narrative, and strategy.

[1] “DMDU Society,” DMDU Society, accessed February 16, 2021,

Marchau, Vincent A. W. J., Warren E. Walker, Pieter J. T. M. Bloemen, and Steven W. Popper. Decision Making under Deep Uncertainty: From Theory to Practice. 1st edition. Springer, 2019.