Synthesis of knowledge about participatory modeling: How a group’s perceptions changed over time

Community member post by Rebecca Jordan

Rebecca Jordan (biography)

How do a group’s perceptions change over time, when members across a range of institutions are brought together at regular intervals to synthesize ideas? Synthesis centers have been established to catalyze more effective cross-disciplinary research on complex problems, as described in the blog post ‘Synthesis centers as critical research infrastructure‘, by Andrew Campbell.

I co-led a group synthesizing ideas about participatory modeling as one of the activities at the National Socio-Environmental Synthesis Center (SESYNC). We met in Annapolis, Maryland, USA, four times over three years for 3-4 days per meeting. Our task was to synthesize what is known about participatory modeling tools, processes, and outcomes, especially in environmental and natural resources management contexts.

The group defined participatory modeling as a “purposeful learning process for action that engages the implicit and explicit knowledge of stakeholders to create formalized and shared representation(s) of reality” ( In its idealized form, participatory modeling involves stakeholders in co-formulating the problem and the solution or decision-making outcomes. In some cases, stakeholders also co-generate – with expert modelers – the shared representation or model.

Here, I discuss two representations generated, respectively, at the first and last meetings and shown in the figures below. These representations are the result of combining models generated by the participatory modeling experts present at each meeting. Individuals were given the following prompt: “create a model using pen and paper that reflects the participatory modeling process”. The sheets of paper were then collected and aggregated, following which I created a digitized version.

Representation generated at the first meeting by the participatory modeling group (source: Rebecca Jordan)

Representation generated at the last (fourth) meeting by the participatory modelling group (source: Rebecca Jordan)

Comparing the figures generated at the first and last meetings, it can be seen that both feature models, cycles, multiple scales, inclusion, and exclusion of participants.

But there are four major differences. Compared to the last meeting figure, the first meeting figure:

  1. is process oriented, organized as steps,
  2. features explicit theories,
  3. lacks realistic pictures including people, and
  4. lacks explicit mention of researchers.

My impression is that these differences also framed the changes in group discussion during the meeting processes.

One change was that the participatory modeling experts became more comfortable with each other allowing for a more creative flow of ideas and a more comfortable discourse. They also became more familiar with the ideas being represented in the different disciplines and could talk more freely about these ideas.

If we take the two representations as indicative of the change in the way that participants viewed the participatory modeling process, then I suggest that the group became somewhat humbled by the limitations in the research about (and the institutions that house) participatory processes in general. Not only did we read about, and discuss at length, the processes and tools within multiple cases, but we also confronted the socio-economic and political challenges that people across the globe face. In addition, we recognized the complex layers of uncertainty embedded within natural and social systems. The figure from the final meeting depicts a more reflective and personalized perspective on the participatory process that encompasses a much greater scale.

What stood out to me was the increased appreciation for the multiple layers of the processes by which people gather information and learn. While the group began with discussions about the inherent complexity in governance processes and the extent of varying stakeholder needs, the group ended the series of meetings with greater recognition of neurology, cognition, identity, culture, and the researcher biases that are all part of participatory engagement.

While these are personal reflections, I am interested in what you see in the change across the two figures. For me, better capturing the complexity that arose in our discussions has great potential to improve participatory modeling and the research that uses it. What do you think?

Biography: Rebecca Jordan is Professor and Department Chair of Community Sustainability in the College of Agriculture and Natural Resources at Michigan State University, Michigan, USA. She devotes most of her research effort to investigating public learning of science through citizen science and participatory modeling. She was a co-Principal Investigator of the Participatory Modelling pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

This blog post resulted from the Participatory Modeling pursuit which was part of the theme Building Resources for Complex, Action-Oriented Team Science funded by the National Socio-Environmental Synthesis Center (SESYNC).

What can interdisciplinary collaborations learn from the science of team science?

Suzi Spitzer (biography)

How can we improve interdisciplinary collaborations? There are many lessons to be learned from the Science of Team Science. The following ten lessons summarize many of the ideas that were shared at the International Science of Team Science Conference in Galveston, Texas, in May 2018.

1. Team up with the right people
On the most basic level, scientists working on teams should be willing to integrate their thoughts with their teammates’ ideas. Participants should also possess a variety of social skills, such as negotiation and social perceptiveness. The most successful teams also encompass a moderate degree of deep-level diversity (values, perspectives, cognitive styles) and include women in leadership roles. Continue reading

CoNavigator: Hands-on interdisciplinary problem solving

Community member post by Katrine Lindvig, Line Hillersdal and David Earle

How can we resolve the stark disparity between theoretical knowledge about interdisciplinary approaches and practical applications? How can we get from written guidelines to actual practices, especially taking into account the contextual nature of knowledge production; not least when the collaborating partners come from different disciplinary fields with diverse expectations and concerns?

For the past few years, we have been developing ways in which academic theory and physical interactions can be combined. The result is CoNavigator – a hands-on, 3-dimensional and gamified tool which can be used:

  • for learning purposes in educational settings
  • as a fast-tracking tool for interdisciplinary problem solving.

CoNavigator is a tool which allows groups to collaborate on a 3-dimensional visualisation of the interdisciplinary topography of a given field or theme. It addresses the contextual and local circumstances and the unique combinations of members in collaborative teams. CoNavigator is therefore short for both Context Navigation and Collaboration Navigation. The process of applying the tool takes around 3 hours.

Using CoNavigator

CoNavigator is composed of writable tiles and cubes to enable rapid, collaborative visualisation, as shown in the first figure below. The tactile nature of the tool is designed to encourage collaboration and negotiation over a series of defined steps.

Making the Tacit Visible and Tangible

Each participant makes a personal tool swatch. By explaining their skills to a person with a completely different background, the participant is forced to re-evaluate, re-formulate, and translate skills in a way that increases their own disciplinary awareness. Each competency that is identified is written onto a separate tool swatch.

Katrine Lindvig (biography)

Line Hillersdal (biography)

David Earle (biography)

Continue reading

Models as narratives

Community member post by Alison Singer

Alison Singer (biography)

I don’t see the world in pictures. I mean, I see the world in all its beautiful shapes and colors and shadings, but I don’t interpret the world that way. I interpret the world through the stories I create. My interpretations of these stories are my own mental models of how I view the world. What I can do then, to share this mental model, is create a more formalized model, whether it is a simple picture (in my case a very badly drawn one), or a system dynamics model, or an agent-based model. People think of models as images, as representations, as visualizations, as simulations. As tools to represent, to simplify, to teach, and to share. And they are all these things, and we need them to function as these things, but they are also stories, and can be interpreted and shared as such. Continue reading