Modelling is the language of scientific discovery

By Steven Gray

Steven Gray (biography)

Modeling is the language of scientific discovery and has significant implications for how scientists communicate within and across disciplines. Whether modeling the social interactions of individuals within a community in anthropology, the trade-offs of foraging behaviors in ecology, or the influence of warming ocean temperatures on circulation patterns in oceanography, the ability to represent empirical or theoretical understanding through modeling provides scientists with a semi-standardized language to explain how we think the world works. In fact, modeling is such a basic part of human reasoning and communication that the formal practice of scientific modeling has been recently extended to include non-scientists, especially as a way to understand complex and poorly understood socio-environmental dynamics and to improve collaborative research. Although the field of participatory modeling has grown in recent years, there are still considerable questions about how different software tools common to participatory modeling can be used to facilitate communication and learning among diverse groups, which approaches are more or less suitable (given the nature of a community or environmental issue), and whether these approaches effectively lead to action-oriented outcomes.

Our SESYNC pursuit ( is an exciting journey to understand the role that participatory modeling plays in bridging understanding of environmental problems. Over the next two years, working with a large group of scientists, computational modelers, facilitators, and policy makers at SESYNC we plan to systematically explore the role that participatory modeling plays in interdisciplinary science and how the process of model co-construction can lead to coordinated implementation of solutions to shared environmental problems.

Stay tuned for what we find as we test these ideas with a diverse group of experts! And if you have experience with participatory modeling do let us know what you think!

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 currently the lead editor on the book ‘Environmental Modeling with Stakeholders: Methods, Theories and Applications’ (Springer 2016) and Principal Investigator on the SESYNC synthesis working group on Participatory Modeling for Action Oriented Outcomes.

5 thoughts on “Modelling is the language of scientific discovery”

  1. Modeling provides many languages for scientific discovery in core computer science and related practical development.

    I have written about program comprehension, symbolic analysis, problem solving, debugging software for maintenance, and currently for systems thinking. In all of those topics there is one or more models and languages to collect (combine) data into knowledge for learning.

    This is only one picture about different kind of semantics for software (rather narrow topic): Theory for many semantics is not clear, causing conflicts and misunderstanding.

    It may be “best science” to unify different point-of-views (approaches) into a more generic approach, if this helps in creating a new meta theory for further discovery for that discipline, and making tools for programming translations, which has been my business for 10 years ago.

    Atomistic thinking (semantics in the center) is one way for connecting everything. Some others are systems theory, systems thinking and ontologies. In modeling (ModelWare) metameta models (MDE) are a well known principle to help discovery and object-oriented programming.

    Every model is a system, and from every system multiple models can be created. This principle can be used in physics, chemistry, social sciences …. Unfortunately I have not links for them. I am only now metathinking and systemsthinking about future possiblities for science.

  2. Totally agreed and thanks for the additional links from the comments! Together with Prof. Heinrichs from Leuphana University and Ullrich Lorenz I have just finished a paper on this very topic. The model and the project that it is based on you will find here:
    Yet, there remains some scepticism amongst the science community that this explorative approach comparable to grounded theory and qualitative social research is proper scientific work. In my books are argue wha it definitely is: rather unique phenomena and those asking for future development cannot be examined empirically.
    Once our paper is released I am going to share a link here.

  3. “Modeling is the language of scientific discovery”


    Modeling is a language of scientific discovery ?

    I question the idea that there is only ONE language of scientific discovery. There may be more than one. Hence the question suggesting the indefinite rather than the definite article.

    Personally, I believe that there are several languages of scientific discovery, if not several languages of knowledge. And saying that is not, per se, to become unscientific and new-agey.

  4. It may be useful to exchange examples of participatory modelling. Here are three that I know of:

    Smetschka, B. and Gaube, V. (2013). Participatory Modelling, digital poster #620, from the First Global Conference on Research Integration and Implementation Canberra, Australia, online and at co-conferences in Germany, the Netherlands and Uruguay, 8-11 September, 2013.

    Newell, B. and Proust, K. (2013). Collaborative Conceptual Modelling, digital poster #564, from the First Global Conference on Research Integration and Implementation in Canberra, Australia, online and at co-conferences in Germany, the Netherlands and Uruguay, 8-11 September, 2013.

    Richardson, G., Andersen, D., Ackermann, F. and Eden, C. (2013). ScriptsMap: A Tool for Designing Multi-Method Policy-Making Workshops, digital poster #589, from the First Global Conference on Research Integration and Implementation in Canberra, Australia, online and at co-conferences in Germany, the Netherlands and Uruguay, 8-11 September, 2013.

    It would be great to hear from others.

    3/8/21: Links removed to the digital posters above, as they are no longer available online.

    • Here are a few from one of my projects:

      Reed MS, Bonn A, Broad K, Burgess P, Fazey IR, Fraser EDG, Hubacek K, Nainggolan D, Roberts P, Quinn CH, Stringer LC, Thorpe S, Walton DD, Ravera F, Redpath S (2013) Participatory scenario development for environmental management: a methodological framework. Journal of Environmental Management 128: 345-362.

      Prell C, Hubacek K, Reed MS, Burt, TP, Holden J, Jin N, Quinn C, Sendzimir J, Termansen M (2007) If you have a hammer everything looks like a nail: ‘traditional’ versus participatory model building. Interdisciplinary Science Reviews 32: 1-20.

      Reed MS, Hubacek K, Bonn A, Burt TP, Holden J, Stringer LC, Beharry-Borg N, Buckmaster S, Chapman D, Chapman P, Clay GD, Cornell S, Dougill AJ, Evely A, Fraser EDG, Jin N, Irvine B, Kirkby M, Kunin W, Prell C, Quinn CH, Slee W, Stagl S, Termansen M, Thorp S, Worrall F (2013) Anticipating and managing future trade-offs and complementarities between ecosystem services. Ecology & Society 18(1): 5


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