Citizen science and participatory modeling

By Rebecca Jordan and Steven Gray

As investigators who engage the public in both modeling and research endeavors we address two major questions: Does citizen science have a place within the participatory modeling research community? And does participatory modeling have a place in the citizen science research community?

Let us start with definitions. Citizen science has been defined in many ways, but we will keep the definition simple. Citizen science refers to endeavors where persons who do not consider themselves scientific experts work with those who do consider themselves experts (around a specific issue) to address an authentic research question. An authentic research question is one that has real life consequences. Therefore citizen science should be considered separate from outreach or formal/informal scientific education activities which serve an important purpose in creating a scientifically literate public, but do not result in the production of new scientific knowledge.

Similarly, participatory modeling also has several definitions. However, we use the definition that participatory modeling is a “purposeful learning process for action that engages the implicit and explicit knowledge of stakeholders to create formalized and shared representation(s) of reality” (https://ParticipatoryModeling.org). The field of participatory modeling lies at the intersection of participatory approaches to planning, computational modeling, and environmental modeling. The inclusion of stakeholders provides some unique insights that would otherwise not be available with models constructed by traditional experts alone. Therefore, similar to citizen science, the motivation for engaging stakeholders in modeling is to gain new understanding of a problem or system and to increase authentic research capacity, under the assumption that this will benefit collaborative problem-solving.

Given these definitions, on the surface, there are clear similarities between citizen science and participatory modeling, as both fields:

• are built around participation
• are problem-driven
• see benefits to collaboration.

Both fields also face a number of common questions about the social dynamics related to these participatory approaches. For example: who is initiating these research process, the public or scientists? Who defines the question to be answered, is it collaborative or top-down? How do these groups of modelers/scientists and stakeholders interact and how does this influence the research product? What happens to the information, knowledge or research products that result from these collaborations and who owns it?

Does citizen science have a place within the participatory modeling research community?

What can participatory modeling learn from citizen science? And how could participatory modeling incorporate citizen science? Arguably citizen science brings a broader range of engagement with stakeholders into a participatory modeling process. Citizen stakeholders not only help inform the building and testing of the model, but can also be involved in collecting the data that goes into the model. In addition, a number of citizen science projects embody a more collaborative, and in some cases co-created, process. In these projects citizen volunteers are seen as collaborators or even drivers of the research question, data collection and idea representation, data analysis and conclusions, and results-based decision-making.

And does participatory modeling have a place in the citizen science research community?

What can citizen science learn from participatory modeling? And how could citizen science incorporate participatory modeling? Many citizen science focused working groups (see https://participatorysciences.org/; Moderator note: In 2024 the Citizen Science Association changed its name to the Association for Advancing Participatory Sciences) have struggled with the identification of tools that help define the research and decision-making process. A particular contribution that participatory modeling can make is to provide communication, computer-assisted, and intellectual tools used to create models that support stakeholders’ satisfaction in decision-making outcomes. Further, because there are issues with the validity of participatory approaches, the participatory modeling research community has worked to create tools to enable stakeholders and organizers to make decisions about approaches for engagement (eg., see Beatrice Hedelin’s sustainable procedure framework).

Conclusion

The areas of overlap between citizen science and participatory modeling identified earlier suggest that strong on-going communication on shared areas of concern will be beneficial. Each field can learn from the other when studying social dynamics of interaction, such as who controls the research and the outcomes of knowledge co-creation (eg., for the latter, see Katrin Prager’s insights into co-creation). In addition, participatory modeling tools and processes to support decision-making can often expand the methods and influence of citizen science through careful consideration of research implementation (described in the blog post on linking co-creation and implementation science by Allison Metz and Annette Boaz). Thinking more broadly, citizen science and participatory modeling in collaboration can support civic ecology, for example, and other stakeholder driven stewardship projects in service of health, natural resources conservation, and science.

Biography: Rebecca Jordan is Professor of Environmental Education and Citizen Science in the Departments of Human Ecology and Ecology, Evolution, and Natural Resources at Rutgers University. She is the director of the Program in Science Learning, where she devotes most of her research effort to investigating public learning of science through citizen science and participatory modeling. She is a co-Principal Investigator of the Participatory Modelling pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

Biography: Steven Gray is an assistant professor in the Department of Community Sustainability at Michigan State University. His research focuses on understanding how individuals and groups make decisions about complex social-ecological systems and addresses questions about how values, attitudes, beliefs or local conditions influence human behavior toward the environment. This effort has recently led to a focus on understanding how collaborative modeling software tools help communities, resource managers, and other decision-makers understand, and to adapt to, the social impacts of climate and other environmental changes through iterative learning. He is the lead editor on the book ‘Environmental Modeling with Stakeholders: Methods, Theories and Applications’ (Springer 2016) and a co-Principal Investigator of the Participatory Modelling pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

This blog post is one of a series resulting from the second meeting in October 2016 of the Participatory Modeling pursuit. This pursuit is part of the theme Building Resources for Complex, Action-Oriented Team Science funded by the National Socio-Environmental Synthesis Center (SESYNC).