Participatory Modeling and Decision Processes

Start the analysis with the decision-makers in the room

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Why the Usual Process Fails

Having the analysts go off to do research and come back with a recommendation to hand to decision-makers doesn't really work. The process becomes opaque, and people who feel their interests and preferences aren't served by the recommendation can inevitably find (valid) nitpicks in the methods—nitpicks that they believe reflect a failure to account for things they think are important, or in the worst case represent question-begging or cherry-picking.

There is a better way. When the decision-makers see themselves in the process and have visibility on how results are derived, even decision-makers who are ideologically at odds with one another can agree on the results of the process because they were part of it. Below is a guide I wrote to community-driven, science-informed climate resilience planning. It is written for climate resilience specifically, but the process it describes is illustrative of participatory modeling processes more broadly.

A Guide to Community-Driven, Science-Informed Climate Resilience Planning

Context 1

For good outcomes, the "community-driven" bit is not optional because communities understand how they function, what they value, and what makes them resilient; outside technocrats don't. The science-informed bit is similarly not optional. Resources are limited and the impending harm is substantial1. We can't afford to implement every intervention at once, and scenario-based scientific modeling is our best and only tool to figure out which interventions are likely to accomplish the things the community wants them to accomplish.

Context 2

Resilience means different things to different people but has one definition: a resilient system is one that can recover from a potentially disruptive event 1) without permanent loss of function or structural harm, 2) quickly enough that knock-on effects of short-term disruption don't prevent full recovery2.

Let's step back now and define what a system is. Consider 1) a person made up of organs made of cells that interact in ways that keep the person alive, 2) a natural ecosystem made up of living things interacting in ways that sustain the ecosystem, and 3) a community made up of people interacting in ways that allow the community to flourish. A system is a collective entity made of individual interconnected components that is greater than the sum of its parts; the connections form the structure of the system and the interactions form the function of the system.

Resilience means different things to different people, but every version I've come across boils down to the core definition above.

The Steps

  1. Perform background research. Make preliminary lists of salient climate hazards, mechanisms of harm, and relevant existing adaptive capacity. If sufficient background knowledge on community values, institutions, and practices is available, use the values-rules-knowledge framework3 to pose a preliminary description of the decision context. Accept in your heart that you will throw away 95% of this material; you gather it so you're able to initiate and intelligently engage in conversation with community members in subsequent steps.
  2. Engage with community. What climate hazards are they concerned with? What harms do they care about? What are the impacts on the structure and function of the community? What tangible, observable outcomes will make them feel that our climate resilience interventions have been successful? What specific interventions are they interested in, what interventions will they accept, and what interventions are plausible? What else can you learn about the community that informs the decision context? There is some element of education that comes in here because your scientific expertise is relevant, but the central goal remains to learn from the community.
  3. Keep the community in the loop as you generate defensible scientific estimates of how the interventions the community is open to and interested in will affect the outcomes they care about. Precise estimates with realistically complex modeling exercises are often prohibitively expensive and take too long; rough estimates with simplified modeling exercises are manageable and plenty actionable if you're clever about it4. Anywhere the science isn't settled5 you run a handful of scenarios representing each way it could plausibly go6. There's more to the technical work but it's nerd stuff you probably don't want to hear about right now—feel free to read my papers. But the most important part is to get buy-in from community stakeholders on every step of the modeling process. Not everybody will get what they want in the end, but if they participate in and approve every major step in the analytical process, they'll accept the result in most cases.7
  4. Bring it back to the community. Discuss what succeeds or fails based on their chosen criteria. What interventions give the best bang for their buck from the community's perspective. Are there community preferences that come up which aren't captured in the criteria? Are we missing something important to the community that didn't come up before? If the success of an intervention depends on something we can't be certain of, how much does the community care about that uncertainty? Can interventions be tweaked to account for that uncertainty?
  5. Loop back to step 3 to integrate new information and community guidance until a decision is reached.

Notes

  1. I say "impending", not "potential" because while individual instances of harm may not be predictable (a specific flood, drought, wildfire, coastal erosion pattern), when we consider how these instances occur over time, substantial levels of harm are statistically guaranteed across all credible models. ↩
  2. Item 2 here is redundant with 1, but bears mentioning because it may not be obvious. ↩
  3. Worth a google if unfamiliar; this document is already too long and tangent-full for me to get into it here. ↩
  4. See Johnson & Geldner, 2019, Annual Review of Resource Economics for what I mean by that. ↩
  5. Which RCP or SSP is closest to the actual future? Which regional down-scaling model best estimates how weather patterns will change as a function of large scale climate variables? How will the local economy and population change over time? How will those be affected by national- and global-scale climate impacts; how will AMOC collapse or widespread crop failures or massive climate-induced migration interact with our choices? ↩
  6. See the same paper as footnote 4: Johnson & Geldner, 2019, Annual Review of Resource Economics. ↩
  7. As evidence, consider the consistency with which the Louisiana Coastal Master Plan passes through Louisiana's legislature with near unanimous approval every planning cycle. ↩

Getting Started

Please reach out if you have any interest in implementing such a process in your organization, or in lobbying for such a process in an organization making decisions to which you are a stakeholder.

Contact

nathangeldnerconsulting@gmail.com