Transforming the Future: Anticipation in the 20th Century

March 8, 2021

This week I’m reviewing Transforming the Future: Anticipation in the 20th Century, edited by Riel Miller, who was appointed as the Head of Foresight to UNESCO in 2012, and over 30 other contributors. I came across this book in the OECD Observatory of Public Sector Innovation’s Anticipatory Innovation Governance Working Paper (which is an excellent primer on anticipation in the public sector), and have subsequently seen it in multiple publications on anticipation and futures studies. The first chapters provide a definition of “futures literacy,” a foundational chapter for a discipline of Anticipation, and a theoretical framework for collective intelligence and knowledge creation processes (CIKC). The second half of the book consists of case studies from the UNESCO Futures Literacy Labs conducted around the world and examples the application of futures literacy to various topical domains. 

Why does anticipation matter for complexity-informed conflict resolution? 

In my latest post that outlined my Vision for Complexity-Informed Conflict Resolution, I focused on how the Carter School Peace Engineering Lab is planning to explore SenseMaker as a Peace Engineering approach. Re-reading the post, I’m realizing that there are many aspects of complexity-informed conflict resolution that still need to be fleshed out as a general mapping of where practice can go. 

Anticipation fits into the overall vision for two reasons: 

1. The future is becoming more and more uncertain as grand challenges like climate change, crises of democracy, and the global pandemic de-stabilize systems, and 

2. Historically, the peace studies field has developed in reaction to major conflicts and wars after they occur rather than proactively identifying future challenges for prevention. 

Because I’m interested in understanding how conflict resolution practice can scale, these macro dynamics are just as important as localized conflict dynamics. We are clearly about to encounter “known unknowns” that might entirely re-align the field around new domains of conflict. Increased uncertainty also calls for increased methodological flexibility that can rapidly mobilize large numbers of people to address large-scale problems. In general, peace scholarship should look forward, as many approaches to analysis might be too slow to cope with rapidly changing conflict formations. We are in a moment when peace studies can proactively embrace anticipatory thinking and prepare scalable conflict resolution mechanisms that contribute to human adaptability in this uncertain world. 

Futures Literacy and Anticipation 

Futures literacy is defined as: 

“The capacity to decipher and categorize as well as produce (design, conduct and interpret) explicit (volitional and intentional) processes of anticipatory knowledge creation, as a necessary and ordinary skill.”[1]

In designing a community sensemaking approach with SenseMaker, improving futures literacy could be an element of the outcomes for community participation- especially in the community sensemaking, data return, and intervention phases (of the overall participatory narrative inquiry approach). In a successfully institutionalized continuous capture story collection system, there would be opportunities to develop futures literacy for decision-makers in the public sector as well as in communities themselves. By adding this to the approach, it increases the options available for peacebuilders to play a role in the constructivist aspect of peace work- imagining how things should be and then figuring out steps that can be taken immediately. 

The book focuses on processes and systems to support anticipation because “anticipation is the only way that the future is actually expressed in the present.”[2] The book lays the framework of “using-the-future” by identifying anticipatory assumptions, reframing those assumptions, and then developing new questions and action steps based on a changed understanding of possible futures. These are the three phases of the UNESCO Futures Literacy Labs.[3] The first chapter of the book introduces a “Futures Literacy Framework” that grounds research in the Discipline of Anticipation established by the book. The framework is below: 

Futures Literacy Framework [4]

There is a dichotomy between “anticipation-for-the-future” (AfF) and “anticipation-for-emergence” (AfE)[5] that is essentially an ordered vs. complex approach in systems terms. AfE is similar to the SenseMaker approach, because the goal is ‘Using-the-future’ to understand the present on the basis of non-deterministic anticipatory systems.”[6] The authors strongly critique the probabilistic and normative anticipation-for-the-future approach, claiming that “AfF is the frame that legitimises and incentivises the grandiose claims being made by leaders worldwide that they can impose their will on tomorrow. In a nutshell, the imperative is to colonise tomorrow with today’s idea of tomorrow.”[7] The use of ‘colonization’ to describe future planning efforts is an interesting narrative intervention that positions anticipatory work for emergence as a decolonial process. Decolonizing the future, in this sense, means working with people to reflect on their own situation, imagine alternative futures, and work with anticipatory tools to shape it themselves. The anticipation-for-emergence approaches are ways to explore the “evolutionary potential of the present” (to use Dave Snowden’s language) by focusing on mapping the environment in a way that people can better come to grips with their own situation and contribute to a ‘solution’ through action. 

Developing knowledge creation processes for conscious anticipation requires identifying and changing anticipatory assumptions (the right-hand side of the Futures Literacy Framework). Below is a picture of the glossary where these assumptions are defined: 

Anticipatory Assumptions Glossary[8]

You’ll notice that the anticipatory assumptions under AfE include strategic thinking for general-scalable repetition and wisdom-Tao-being. Essentially, anticipating for emergence requires wide adoption of general frameworks that can accommodate a wide variety of futures. The tie to Eastern Philosophy reminds me of Galtung’s Daoist Social Science Epistemology that I studied at the Galtung-Institut. In that line of thinking, Galtung derived rules for a Daoist epistemology that would counter Cartesian epistemology. It’s interesting how often complexity thinking and Eastern philosophy intersect. 

Overall, I look forward to returning to the citations in this book for my dissertation chapter on anticipation. One element that I chose not to focus on for this blog is the theoretical framework of Memory Evolutive Systems (in Ch. 3) that could give insight into tracking a vector theory of change with SenseMaker data. This theory connects to category theory in mathematics and can provide a “‘hybrid’ representation [that includes]: the relational and organized aspects of the system are captured in the structure of the successive configuration categories which give a snapshot of the state of the system at a given time, and the internal dynamics are captured by the ‘transitions’ between configurations which measure both the dynamic changes of states and the struc­tural changes (such as loss, or addition of components).”[9] As I continue to get deeper into the peace engineering community, I’m looking to make these mathematical connections that will inform the development of analytical capability for SenseMaker data at scale. A concept that provides good-enough representations of a system and accommodates multiple inputs is always welcome!

Event Plug

If you are interested in learning more about how futures literacy and anticipation can be utilized for peacebuilding and conflict resolution, you should register for the Carter School Peace Engineering Lab event on March 16: Peace Engineering through Anticipatory Innovation Governance. I will be hosting the conversation with Piret Toñurist and Angela Hanson from OPSI and we will be looking at practical ways to integrate anticipatory thinking into programming and policy design. You can register on the Carter School’s Eventbrite page here: https://www.eventbrite.com/e/peace-engineering-through-anticipatory-innovation-governance-tickets-143796367807

Note on the Works Cited: currently all of these are referenced to the book as a whole. Several authors contributed chapters, and so if properly cited these would likely be done by chapter.

[1] Riel Miller, ed., Transforming the Future: Anticipation in the 21st Century, 1st edition (Routledge, 2018). 58. 

[2] Miller. 19. 

[3] Miller. 104.

[4] Miller. 24.

[5] Miller. 20.

[6] Miller. This is the full set of terms in the glossary. 268. 

[7] Miller. 21. 

[8] Miller. This is the full set of terms in the glossary. 268. 

[9] Miller. 78.

Surfing Uncertainty

March 1, 2021

For this week’s reading reflection, I read Surfing Uncertainty by Andy Clark, a Professor of Philosophy at University of Edinburgh. I was first introduced to Clark’s work in 2018 when I had the pleasure to attend the HowtheLightGetsIn festival at Hay on Wye with Dave Snowden and the rest of the Cognitive Edge and Cynefin Centre crew. Clark presents a compelling case for an action-oriented predictive processing model of the brain that fits nicely with the “naturalized sensemaking” theory that grounds SenseMaker. My general goal for this text was not to gain a complete understanding of the embodied mind or the science behind it, but to uncover a few nuggets that would contribute to complexity-informed conflict resolution practice. Although the first half of the book was incredibly technical neuroscience, the later chapters (beginning with Chapter 6: Beyond Fantasy) provide connections between the mind and systems-level change. In this blog I’ll present a simplified account of Clark’s premise and explore some key terms and applications to conflict resolution. 

Clark’s story of the mind begins with an explanation of the brain as a prediction machine that continually processes incoming sensory input (i.e. sight or sound) by contextualizing it against prior experience and expectation. Unexpected sensory inputs are considered ‘errors’ which then alter layers ‘higher up in the hierarchy.’ This predictive processing model helps balance between maximizing accuracy and minimizing complexity of mental models to conserve the energy required for processing all of the incoming information.[1] Clark outlines three elements of predictive processing: evidence, prior knowledge, estimations of uncertainty.[2]

Clark augments this basic predictive processing model by incorporating action and the environment as scaffolding to support the brains predictions. Clark summaries ‘action-oriented predictive processing’ as the: 

“Combined mechanism by which perceptual and motor systems conspire to reduce prediction error using the twin strategies of altering predictions to fit the world, and altering the world to fit the predictions.”[3]

In Clark’s view, the brain does not try and achieve “some kind of action-neutral image of an objective realm,” but “delivers a grip upon affordances: the possibilities for action and intervention that the environment makes available to a given agent.”[4] The human brain, even at the most basic sensory levels, never attempts to achieve an ‘objective truth’ or reality. The only thing that matters is helping the human organism recognize the “environmental opportunities for organism-salient action and intervention” and creating a balance between the brain’s predictive model and the external environment. [5]

This notion calls into question a common theory of change held by conflict resolution practitioners: that if people had a more accurate (or objective) understanding of the ‘true nature’ or ‘root cause’ of a conflict, then they will be more likely to take action that contributes to peace. This theory has a long lineage that includes John Burton’s problem-solving workshops, dialogue approaches, and more recent efforts like Ricigliano’s SAT (structural, attitudinal, transactional) systems mapping approach. If Clark’s vision of the brain is correct, then conflict analysis should focus on generating novelty, surprise (error signals that change mental models), or scaffolds for action.

For the complex and structural conflicts that we face- like climate change or racism, complete understanding is illusive and by definition impossible. However, we do know that problems of this scale can be addressed by ‘parallel peacebuilding’ or ‘fractal engagement’ where people take action at their own level in a coherent way. The actions people take can be small. In some cases it might be talking to someone across party lines, starting a community garden, or hosting an event. Each action elicits a response from the external world that then can alter expectations (even at an unconscious level). The key is that people don’t need to have an elaborate theory of change to make the world more peaceful- they just need to know what they can do in the environment they are in. International organizations and donors that support peacebuilding work sink money into comprehensive systems maps because they can take action at the systems level. However, systems maps and dynamical flows mean nothing to the majority of people that have a much more constrained action repertoire. Surfing Uncertainty provides an explanation for this dynamic from a neuroscience perspective that can ground practice. 

Diagram Description automatically generated
Systems maps might obscure more than they show. Here is “The Face of Polarization” that I did for a class project with my colleague Sausan Ghosheh.

Implications for Practice

If we hold the following assumptions true,

  1. Conflicts occur in complex human systems where the nature of cause and effect can never truly be known,
  2. The human brain is fundamentally action-oriented and works to ‘get a grip’ on reality to take advantage of affordances, and
  3. Conflict analysis and resolution practitioners are subject to resource constraints that limit both their ‘action-repertoire’ for research and intervention

Then complexity-informed conflict resolution practitioners should

  1. Stop wasting time on systems mapping unless elements are bounded and ordered systems (i.e. financial flows). Deal with the complex domain in a way that dynamically supports action and decision-making rather than a top-level view that is “right” in it’s totality.
  2. Design all conflict analysis processes around identifying affordances or surprises (anything else introduces noise and no change)
  3. Try bottom-up action first and see if understanding at multiple levels in the system comes later

In a way, the complexity-informed conflict resolution approach should be isomorphic to Clark’s action-oriented predictive processing model to be effective. The more coherent approaches are with the fundamental workings of the brain, the more successful they can be. 

Next Steps

In my dissertation, I’m planning to do a chapter on “naturalized sensemaking” that incorporates work like Clark’s as the foundation and philosophy behind the complexity informed method that I’m testing with SenseMaker. By diving deeper into this literature, I’m hoping to pull out more assumptions or ‘first principles’ that will determine my decision-making practice along the way. As I’m documenting my process, I’m going to try and note how decisions are coherent with this knowledge from the natural sciences. 

Another challenge of this book is that I’m beginning to recognize that I see SenseMaker primarily as a practice mechanism rather than a tool for research (per se). Perhaps a better research question would be: Does SenseMaker help the complexity-informed conflict resolution practitioner get a ‘good enough grip’ on a conflict context to enable action at multiple levels in the systems?”

[1] Andy Clark, Surfing Uncertainty: Prediction, Action, and the Embodied Mind (Oxford ; New York: Oxford University Press, 2016). 255.

[2] Clark. 250.

[3] Clark. 122.

[4] Clark. 171.

[5] Clark. 133.

Vision for Complexity-Informed Conflict Resolution

February 22, 2021

Over the past few weeks, multiple colleagues have asked me about my goals, vision, and where all my work around conflict resolution is headed. Through these conversations, I’ve realized a lot of the work I’ve done to set up pathways forward has been behind the scenes. I hope that this post will at least summarize the current state of my world. 

Note: The following post is my vision for complexity-informed conflict resolution at the Carter School, but it is not the official stance of the Carter School or the Peace Engineering Lab. As the Peace Engineering Lab develops, I hope that some of what follows proves to be a viable pathway forward and one of many successful trajectories for the Lab that will strengthen our peace engineering portfolio. 

Vision

My vision is for the Carter School to become a powerhouse for complexity-informed conflict resolution and peace engineering with:

  • A packaged approach for complexity-informed conflict resolution for practitioners who seek to use SenseMaker to map conflicts and develop intervention strategies
  • A public-facing Distributed Conflict Analysis Database consisting of SenseMaker micro-narratives and self-signification data hosted by the Carter School that can be a resource for peacebuilders and conflict scholars
  • Joint research and projects with the Peace Engineering Consortium on peace technology and decision-making support
  • Thought leadership in peace engineering and a community of like-minded scholars and practitioners
  • A stable budget/funding source to incubate experimental peace engineering approaches and accelerate the development and prototyping of innovative peace technologies

Below I’ll explain my efforts to make this dream a reality: 

Carter School Peace Engineering Lab

In my capacity as Peace Engineering Fellow at the Carter School, I’ve taken on the task of developing the Carter Schools Peace Engineering Lab under the guidance of Dean Alpaslan Ozerdem with a group of other interested students. In Fall 2020, our Peace Engineering group worked on bylaws to establish some basic Lab structures and develop the mission and vision. Although the bylaws have been tabled for the moment to give time for areas of work to emerge, it has given me a lot of time to reflect on what I hope to contribute. 

During this start-up phase of the Peace Engineering Lab, I have been struggling to place where conflict resolution practice fits within the larger Peace Engineering frame. The core question for me is- How can the Carter School best position itself given its expertise? I’m not an engineer by trade, although I’ve dabbled in ‘systems engineering’ and design work through past projects. 

Peace engineering has been defined by the Peace Engineering Consortium as, “The application of science and engineering principles for transdisciplinary systemic-level thinking to directly promote and support conditions for peace, and the safe and ethical deployment of emerging technologies.” Two parts interest me: the first being “transdisciplinary systemic level thinking” and the second being “emerging technologies.” The first half lends itself to complexity thinking and the second plays to my SenseMaker work. My colleagues are also interested in things like the ethics of technology in the humanitarian sphere, technology for negotiations, TechPlomacy, and designing digital spaces for conflict-resolution. These threads point to a focus for the Peace Engineering Lab on peace technology. 

My vision is for the Peace Engineering Lab to become a place where Carter School students can actively engage in (paid) projects that contribute to a research cluster around complexity-informed research methods, SenseMaker, and emerging technologies for Peace Engineering. 

Packaged Approach

The packaged approach will be developed through an initial flagship program: an overarching SenseMaker framework that is oriented toward mapping conflict in the United States, with an orientation to supporting better decisions for a future with more stories that we want to see. This framework will be the foundation for the Carter School’s Distributed Conflict Analysis Database- the first conflict database of self-interpreted micronarratives.

I’m prototyping this design, which might become part of my dissertation, and am hoping to have a draft completed by mid-March. Carter School Peace Week will mark the first participatory SenseMaker design workshop which will focus on designing signifiers that will contribute to racial healing and equity (Thursday, March 25th, from 10:00 am to 12:00 pm). This will be the first of several opportunities for the Carter School community to shape the design and ensure that the final framework is one that can scaffold conflict resolution practice from every cluster of expertise at the Carter School. The project will only be successful if the data informs practice for multiple subfields.

After establishing the framework, the next step will be collecting the initial stories. To this end, I’ve been talking with my cohort about designing research projects using the Peace Engineering Lab’s framework. For example, a colleague interested in religious conflict resolution might collect stories from a religious community and then see how their experiences and perceptions contrast with other groups. A partnership with the TRHT initiative might provide a bottom-up approach to using stories for racial healing. Etc. etc. Each of these projects would contribute to the larger Carter School data set and the project as a whole. 

To ensure that there is also an academic component, I’ve had conversations with colleagues about working with a publisher to create an edited volume where each chapter would be from someone in the Carter School community that used SenseMaker to collect stories and use them to add sensemaking to their practice. This book would then provide the academic foundations of a Carter School approach to complexity-informed conflict resolution and benefit everyone who needs to publish along the way.

A major Peace Engineering component of the work will be exploring different applications for SenseMaker data in decision-making support. Initial ideas include: 

  • Data observatories:  Combining SenseMaker data visualizations of peoples’ perceptions of their lived experience and population, outcomes, or geographic data in a coherent decision-making platform. 
  • Weak signal detection and early warning systems: With SenseMaker data, we can create training sets of experiences that represent the predisposition for violence or peace, contextualized at the local level. The next stage of SenseMaker development would be to utilize these training sets for early warning. For example, through community sensemaking workshops, the SenseMaker practitioner identifies stories where people experienced violence (or were about to). The quantitative signification of these stories is run through the machine learning algorithm to identify the high-dimensional patterns in the signification. Then, as new stories are entered into SenseMaker, the algorithm could test the signification against the training datasets and alert the researcher if there is a pattern match. This would then enable decision-makers to take preventative action or further investigate trends. 
  • App integration: Integrating SenseMaker data collection into other apps with the SenseMaker API. (i.e. in UNM’s Peace Engineering ECHO platform or government surveys)
  • Data Ethics: exploring ethical issues around SenseMaker data collection, public story data, and developing policy.

SenseMaker Training

The next goal is to build capacity within the Carter School to run SenseMaker projects and analyze SenseMaker data. There are two goals I’m working toward: training from the Cynefin Centre for Applied Complexity and teaching a SenseMaker course.

Training

Yearly training slots will be made available to students through the Carter School’s Cynefin Centre SenseMaker license. I will also provide guidance and training for anyone taking up SenseMaker in their research projects.

SenseMaker Course

In spring 2022 (if everything goes as planned), I will teach a course on SenseMaker and participatory narrative inquiry. The course will position SenseMaker methodology as an intervention strategy for structural and complex conflicts. How can peace researchers utilize participatory narrative inquiry and SenseMaker to map complex conflict systems through self-interpreted lived experiences of people? How can the research process support “fractal engagement” by empowering people to take action by asking themselves “what can I do tomorrow to create more stories like the ones I want to see and fewer like the ones I don’t?” Throughout the course, students will develop coherent theory of change that understands the participatory narrative inquiry process as both a valuable research tool for understanding complex systems and a peacebuilding intervention.

Students will learn about how to approach conflict analysis and resolution from a complexity-informed perspective and gain the skills to use the innovative SenseMaker approach to narrative research for distributed conflict analysis. The course will cover the theoretical underpinnings of anthrocomplexity, naturalized sensemaking, and participatory narrative inquiry. Over the course of the semester, students will complete one cycle of the participatory narrative inquiry process using the Peace Engineering Lab’s SenseMaker framework for conflict resolution efforts in the United States (In development). The stages of the cycle include: project planning, story collection, catalysis (pre-analysis), sensemaking, return, and intervention. The semester project will consist of activities related to these different phases and a final reflection essay about how the SenseMaker approach would impact practice in the field. The course will also feature guest lectures from SenseMaker practitioners and complexity scholars from around the world who will share their practical experience in designing SenseMaker projects, developing story collection strategies in development contexts, and conducting analysis.

Establishing a Community of Practice

To become a powerhouse for complexity-informed conflict resolution, thought leadership is essential. The network of SenseMaker and complexity scholars and practitioners has consistently helped me in my own journey to becoming a complexity-informed conflict resolution practitioner. I hope to expose Peace Engineering Lab members to some of these thought leaders through multiple events hosted by the Peace Engineering Lab. I’m bringing in Angela Hanson and Piret Toñurist from the OECD Observatory of Public Sector Innovation to discuss anticipatory innovation governance. Graham Day will be sharing his experiences in applying a complexity approach to post-conflict reconstruction as a high-level UN official. Dave Snowden will share the Cynefin Framework, anthrocomplexity, and his unique approach to conflict resolution with SenseMaker. I hope that the Peace Engineering Lab will be a place where practitioners go to hear conversations between people at the cutting edge of the field. 

Note: An update will be posted next week with the links to register for these public events. If you are interested in presenting your work on complexity/SenseMaker to the Carter School Community, please shoot me an email at keggers@masonlive.gmu.edu

Stable Budget

Establishing a new Lab and building a new peace engineering field requires resources. I believe that people need to be paid for their work or that there should be reciprocity for any labor. Unfortunately, the Peace Engineering Lab is still in its early development where the sources of stable funding are still uncertain. This is normal for any entrepreneurial project. I have been exploring the following models to make the Lab sustainable: 

The first and most obvious route is applying for sponsored projects and grant opportunities that further the Lab’s mission. The second is applying for opportunities internal to George Mason and the Carter School. The third, a social enterprise model, is by far the most interesting and easiest to achieve in the current phase. 

I am developing a fee-for-service model for the Peace Engineering Lab so that students and other Lab members can provide consulting on SenseMaker projects and anything else that falls under the purview of the Lab. The Peace Engineering Lab would be able to develop proposals through a menu of options (i.e. packaged SenseMaker project implementation, conflict-resolution training for engineers, data ethics consultation). This gives the Lab flexibility in taking on projects and also would allow for some savings that could then be re-invested into the Labs incubator/accelerator (which are currently aspirational). Additionally, students working with the Lab would have the benefit of gaining real world experience as paid consultants, practitioners and researchers.  

In Closing

I hope that this clarifies some of my designs for bringing complexity-informed conflict resolution to the Carter School. Every week in the PhD. program brings new opportunities and horizons and is truly a gift (although I must be careful not to work myself into the ground). I feel fortunate to be working on building a community around the work that I love in the best conflict resolution program in the world. 

If you have any thoughts about anything that I shared above, please don’t hesitate to contact me at keggers@masonlive.gmu.edu

This is all a work in progress and any comments, criticisms, or ideas for partnership are welcome!

Decision Making under Deep Uncertainty

February 16, 2021
Image result for decision making under deep uncertainty

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, https://www.deepuncertainty.org/.

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.

Coherence in Thought and Action

February 8, 2021

This week’s reading was Coherence in Thought and Action by Paul Thagard. I chose to read this book because it provides insight into a “naturalized epistemology” and sensemaking that will ground my work with SenseMaker and conflict resolution. Naturalized epistemology means that our ‘ways of knowing’ are limited by how our brains and bodies function. By taking the learnings from the natural sciences (i.e. neuroscience, biology, physics) we can ensure that our inquiries avoid cognitive traps and account for both the strengths and weaknesses of the human brain.

For example, one of these traps is “retrospective coherence” defined by Snowden as “attributing cause in cases where complex historical events represent a unique pattern that will only repeat by accident.”[1] Because humans are pattern-based animals, attributing cause comes naturally. This can become dangerous when the past events are used as a template for generating foresight. For peacebuilders, this is especially important because peace work is a balance between quickly understanding the disposition of a conflict system and taking action to stop violence. If a conflict resolution practitioner relies on experience of a past conflict or case study, then they risk applying the wrong strategy to the new context.

So back to Thagard and the relevance of the book for sensemaking. 

Thagard defines making sense as “the activity of fitting something puzzling into a coherent pattern of mental representations that include beliefs, goals, and actions.[2] The main focus of the book to define this coherence as a constraint satisfaction. As a philosopher, Thagard proposes a “cognitive naturalism approach” to philosophical problems because inference and decision-making are “not serial but “largely unconscious process in which many pieces of information are combined in parallel into a coherent whole.”[3] If this is how the human mind works, it seems foolish to claim that belief should be justified on a logical process where each link in reasoning is derived from a set of truth-tested propositions. Thagard defines philosophical problems as coherence problems where the solution (maximizing coherence) “is a matter of maximizing satisfaction of a set of positive and negative constraints.”[4] There is a positive constraint between elements that fit together (they are coherent) and a negative constraint when elements do not fit together (they are not coherent). When there is a negative constraint between elements, one of them has to be rejected to maintain coherence.  Based on these constraints, elements are then sorted into two sets- accepted and rejected- and the answer to the coherence problem is the distribution of the elements in this partition. To solve these coherence problems, Thagard takes a computational approach and outlines algorithms that could be used to calculate the solution that maximizes coherence. I don’t think that the computational approach will ever make sense for conflict resolution due to the amount of uncertainty about what important elements are, but theoretical approach can shape how conflict resolution practitioners try and change beliefs of people in conflict.  

Constraints come up frequently in the complexity world and it is important to distinguish Thagard’s definition from the way they are used by SenseMaker or Cynefin practitioners (my camp). I tend to think of constraints as connections between elements in a system or boundaries of the system. In complexity, a practitioner can change constraints to stimulate the emergence of a desired behavior or activity. There are many of Dave’s blogposts that do deeper dives into a theory of constraints that you can find in the notes of this post. In Coherence in Thought and Action constraints are defined as the degree of fit between elements in a coherence problem. 

What implications does cognitive naturalism have for conflict resolution? 

One strategy to make change in a complex system is to create a coherent portfolio of safe-to-fail probes (small, low-resource experiments). Safe-to-fail probes are a way to test relationships between elements in a complex system before investing resources in a large-scale intervention. If a probe shows promise, it can be scaled up, if not, then it can be scaled down or stopped. In a conflict context, this could mean testing a new narrative strategy with a social justice group, hosting an event to bring people together, or sharing a cluster of stories from SenseMaker data with different groups in parallel. “Solving” complex conflicts require actions at multiple levels in a system rather than investing in an all-encompassing peace deal (for example). Rather than making decisions based infallible information, we can begin to shift to thinking of coherence problems.  “Will this action facilitate a better understanding of the conflict I’m working in and thereby move the system toward a more desirable place.” We can determine if an action actually helps achieve a goal through “explanatory coherence” or “Does this make sense?” Joint action in a portfolio of safe-to-fail interventions could be considered coherent if there is a plausible theory of change behind each one. 

What does coherence mean for this type of intervention strategy? If people have contradictory ideas for experiments based on different theories of change, how can that be accommodated in the portfolio? Here, Thagard’s discussion of the coherence problems democratic deliberation and practical reasoning can be useful. Thagard says, “The elements in deliberative coherence are actions and goals, and the primary positive constraint is facilitation: if an action facilitates a goal, then there is a positive constraint between them.”[5] In a sense, a safe-to-fail portfolio is a bounded coherence problem- contradictory theories of change can be tested in a safe-to-fail portfolio and then accepted or rejected based on if the system empirically demonstrates that the action facilitates movement toward a desirable goal. The “accepted actions” that seem to be most coherent can then be scaled up. 

Conclusion

This book was helpful in understanding some alternative interpretations of coherence, constraints and the meaning of a “naturalized approach.” As I continue developing the conflict resolution methodology with SenseMaker, the connection between philosophy and psychology are areas that will need to be strengthened to justify my approach. I’m at a stage where I’m familiar with the words and their basic usages, but still grasping at a detailed understanding. At any rate, I hope this post is intelligible! 

This week I’ll be reading “Decision-making under Deep Uncertainty” which I think will carry through similar themes. In next week’s post, I’ll be reflecting on the implications of that book for decision-making and “complexity-informed conflict resolution strategies” and how SenseMaker can potentially help.

Works Cited

Snowden, David. “Naturalizing Sensemaking.” Informed by Knowledge: Expert Performance in Complex Situations, 2010, 223–34.

Thagard, Paul. Coherence in Thought and Action. Reprint edition. A Bradford Book, 2002.

[1] David Snowden, “Naturalizing Sensemaking,” Informed by Knowledge: Expert Performance in Complex Situations, 2010, 223–34.227. 

[2] Paul Thagard, Coherence in Thought and Action, Reprint edition (A Bradford Book, 2002). xi.

[3] Thagard. 3.

[4] Thagard. 16.

[5] Thagard.127.