## Readings on Modelling as an Epistemic Activity in Ecology The following list of suggested readings form part of a [resource set](https://osf.io/4qvjr/wiki/home/) curated to support discussion about the epistemic role of modelling, drawing on examples from within Ecology. Studies of modelling as an epistemic activity have drawn attention to the way that models can function as tools that are inextricably implicated in the dynamic processes of generating knowledge. This view of models as tools within philosophical studies of the sciences more generally, converges with accounts of modelling practices by scientists within the field of Ecology. For examples of this this convergence, see the following readings (and associated notes): #### Knuuttila, T., & Boon, M. (2011). How do models give us knowledge? The case of Carnot’s ideal heat engine. European Journal for Philosophy of Science, 1(3), 309. https://doi.org/10.1007/s13194-011-0029-3 - Tarja Knuuttila and Mieke Boon have both written independently on the philosophy of modelling practices and this collaboration offers a valuable starting point for exploring how the activity of modelling can contribute to scientific knowledge in a variety of ways. - Provides a tangible example from which to start exploring how modelling can be studied as an epistemic activity. In doing so, Knuuttila and Boon build on a growing appreciation for the view that the functions of modelling can go beyond integrating existing knowledge into a manipulable form of representation. - Highlights the diverse range of tasks for models in science, including in prediction, design of experiments, theory development, and as contributions to scientific understanding. - Treats models as tools (in that they are constructed entities that can be used by (skilled) humans for specific epistemic purposes). This approach highlights that, as with other tools, the epistemic value of models emerges through our interaction with them when pursing specific goals. - Draws attention to the range of steps involved in creating a model and suggests that, at each step, the use of models as epistemic tools both limits and affords interactions between elements of scientific practices in ways that scaffolds further scientific reasoning. #### Tredennick, A. T., Hooker, G., Ellner, S. P., & Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology, 102(6), e03336. https://doi.org/10.1002/ecy.3336 - Tredennick et al's central argument is that many modellers inappropriately apply model selection procedures, and that this confusion stems from failing to first specify the purpose of the modelling exercise. However, the paper provides an excellent case-study illustrating how both the modelling process is iterative, and secondly, how the overarching modelling goal should dictate what modelling methods are used. - The paper describes three worked examples of modelling using a single dataset for three different modelling goals, exploration, inference and prediction. Note that this categorisation of modelling goals is not agreed upon with any consensus in ecology, with other authors having proposed different taxonomies of modelling. - Collectively, the three worked examples nicely illustrate how model purpose influences model process, model form, and overall findings, highlighting how models act as epistemic tools or ways of knowing: at various points in the modelling process, modellers (and model users, e.g. decision-makers, such as environmental managers) generate knowledge by interacting with the model. The fact that the model just exists doesn’t reveal knowledge (it can, if the purpose is to synthesise and communicate knowledge, however). Rather, models generate knowledge when modellers interact and query the model using different modelling procedures, tools, subsets of variables and data. ### Further Reading: * Alexandrov, G. A., Ames, D., Bellocchi, G., Bruen, M., Crout, N., Erechtchoukova, M. et al. (2011). Technical assessment and evaluation of environmental models and software: Letter to the Editor. _Environmental Modelling & Software_, _26_(3), 328-336. * Allison, A. E. F., Dickson, M. E., Fisher, K. T., & Thrush, S. F. (2018). Dilemmas of modelling and decision-making in environmental research. Environmental Modelling & Software, 99, 147-155. * Atanasova, N. (2015). Validating Animal Models. THEORIA. An International Journal for Theory, History and Foundations of Science, 30(2), 163–181. https://doi.org/10.1387/theoria.12761 * Augusiak, J., Van den Brink, P. J., & Grimm, V. (2014). Merging validation and evaluation of ecological models to ‘evaludation’: A review of terminology and a practical approach. _Ecological Modelling_, _280_, 117-128. * Babel, L., Vinck, D., & Karssenberg, D. (2019). Decision-making in model construction: unveiling habits. Environmental Modelling & Software. * Baetu, T. M. (2014). Models and the mosaic of scientific knowledge. The case of immunology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 45, 49–56. https://doi.org/10.1016/j.shpsc.2013.11.003 * Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J. et al. (2013). Characterising performance of environmental models. _Environmental Modelling & Software_, _40_, 1-20. * Bokulich, A. (2018). Using models to correct data: Paleodiversity and the fossil record. Synthese. https://doi.org/10.1007/s11229-018-1820-x * Bokulich, A. (2020). Towards a Taxonomy of the Model-Ladenness of Data. Philosophy of Science, 87(5), 793–806. https://doi.org/10.1086/710516 * Boon, M. (2019). Models as Epistemic Tools in the Engineering Sciences, Conference Presentation, ISH. * Boon, M. (2020). The role of disciplinary perspectives in an epistemology of scientific models. European Journal for Philosophy of Science, 10(3), 31. https://doi.org/10.1007/s13194-020-00295-9 * Boon, M., & Knuuttila, T. (2009). Models as Epistemic Tools in Engineering Sciences. In A. Meijers (Ed.), Philosophy of Technology and Engineering Sciences (pp. 693–726). North-Holland. https://doi.org/10.1016/B978-0-444-51667-1.50030-6 * Chang, H. (2014). Epistemic activities and systems of practice: Units of analysis in philosophy of science after the practice turn. In L. Soler, S. Zwart, M. Lynch, & V. Israel-Jost (Eds.), Science after the Practice Turn in the Philosophy, History, and Social Studies of Science (pp. 67–79). Taylor and Francis. * Chadarevian, S. de, & Hopwood, N. (2004). Models: The Third Dimension of Science. Stanford University Press. * Chin, C. (2011). Models as interpreters (with a case study from pain science). Studies in History and Philosophy of Science Part A, 42(2), 303–312. https://doi.org/10.1016/j.shpsa.2010.11.038 * Coelho, M. T. P., Diniz‐Filho, J. A., & Rangel, T. F. (2019). A parsimonious view of the parsimony principle in ecology and evolution. Ecography, 42(5), 968-976. * da Costa, N., & French, S. (2000). Models, Theories, and Structures: Thirty Years on. Philosophy of Science, 67, S116–S127. https://doi.org/10.2307/188662 * Francoeur, E. (1997). The Forgotten Tool: The Design and Use of Molecular Models. Social Studies of Science, 27(1), 7–40. https://doi.org/10.1177/030631297027001002 * Getz, W. M., Marshall, C. R., Carlson, C. J., Giuggioli, L., Ryan, S. J., Romañach, S. S. et al. (2017). Making ecological models adequate. Ecology Letters, 21(2), 153-166.[http://doi.wiley.com/10.1111/ele.12893](http://doi.wiley.com/10.1111/ele.12893) * Green, S. (2013). When one model is not enough: Combining epistemic tools in systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44(2), 170–180. https://doi.org/10.1016/j.shpsc.2013.03.012 * Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. _Ecological Modelling_, _135_(2-3), 147-186. * Frigg, R., & Hartmann, S. (2020). Models in Science. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Spring 2020). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/spr2020/entries/models-science/ * Francoeur, E. (1997). The Forgotten Tool: The Design and Use of Molecular Models. Social Studies of Science, 27(1), 7–40. https://doi.org/10.1177/030631297027001002 * Haag, D., & Kaupenjohann, M. (2001). Parameters, prediction, post-normal science and the precautionary principle—a roadmap for modelling for decision-making. * Klein, U. (2002). Experiments, models, paper tools: Cultures of organic chemistry in the nineteenth century. Stanford University Press. * Knuuttila, T. (2005). Models, Representation, and Mediation. Philosophy of Science, 72(5), 1260–1271. https://doi.org/10.1086/508124 * Knuuttila, T., & Boon, M. (2011). How do models give us knowledge? The case of Carnot’s ideal heat engine. European Journal for Philosophy of Science, 1(3), 309. https://doi.org/10.1007/s13194-011-0029-3 * Leonelli, S. (2007). What is in a model? Combining theoretical and material models to develop intelligble theories. In S. Leonelli, L. D. F. Costa, & H.-J. Rheinberger (Eds.), Modeling Biology: Structures, Behavior, Evolution (pp. 15–35). MIT Press. * Liu, Y., Althoff, T., & Heer, J. (2020). Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis. Proceedings from Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, New York, NY, USA. * Lloyd, E. A. (2018). The Role of “Complex” Empiricism in the Debates About Satellite Data and Climate Models. In E. A. Lloyd & E. Winsberg (Eds.), Climate Modelling: Philosophical and Conceptual Issues (pp. 137–173). Springer International Publishing. https://doi.org/10.1007/978-3-319-65058-6_6 * Morgan, M. S., & Morrison, M. (Eds.). (1999). Models as mediators: Perspectives on natural and social sciences. Cambridge University Press. * Morrison, M. (1999). Models as autonomous agents. In M. S. Morgan & M. Morrison (Eds.), Models as mediators: Perspectives on natural and social sciences. Cambridge University Press. * Myers, N. (2015). Tangible Media. In Rendering life molecular: Models, modelers, and excitable matter (pp. 7–40). Duke University Press. * Nersessian, N. J. (2012). Modeling practices in conceptual innovation: An ethnographic study of a neural engineering research laboratory. In U. Feest & F. Steinle (Eds.), Scientific concepts and investigative practice (pp. 245–270). De Gruyter. * Nilsen, E. B., Bowler, D. E., & Linnell, J. D. C. (2020). Exploratory and confirmatory research in the open science era. Journal of Applied Ecology. * Orzack, S. H. (2012). The philosophy of modelling or does the philosophy of biology have any use. Philos Trans R Soc Lond B Biol Sci, 367(1586), 170-180. * Ritson, S. (2021). Creativity and modelling the measurement process of the Higgs self-coupling at the LHC and HL-LHC. Synthese. https://doi.org/10.1007/s11229-021-03317-y * Ruhmkorff, S. (2017). Models and Experiments. Metascience, 26(1), 83–86. https://doi.org/10.1007/s11016-017-0160-7 * Rykiel Jr, E. J. (1996). Testing ecological models: the meaning of validation. _Ecological Modelling_, _90_(3), 229-244. * Schmolke, A., Thorbek, P., Deangelis, D. L., & Grimm, V. (2010). Ecological models supporting environmental decision making: a strategy for the future. Trends in Ecology & Evolution, 25(8), 479-486. * Shmueli, G. (2010). To Explain or to Predict. Statistical Science, 25(3), 289-310. https://doi.org/10.1214/10-STS330 * Suárez, M. (2019). Philosophy of Probability and Statistical Modeling [Preprint](https://www.cambridge.org/core/elements/philosophy-of-probability-and-statistical-modelling/C12D7946AE6E7224C08A0667DCB58A10) * Tal, E. (2012). The Epistemology of Measurement: A Model-Based Account [PhD Thesis, University of Toronto](https://www.proquest.com/openview/d698b0e2cdbec44a2207178a4c8e4a7f/1) * Travassos‐Britto, B., Pardini, R., El‐Hani, C. N., & Prado, P. I. (2021). Towards a pragmatic view of theories in ecology. Oikos. * Tredennick, A. T., Hooker, G., Ellner, S. P., & Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology. * Upmeier zu Belzen, A., Engelschalt, P., & Krüger, D. (2021). Modeling as Scientific Reasoning—The Role of Abductive Reasoning for Modeling Competence. Education Sciences, 11(9), 495. * Wartofsky, M. W. (1979). Models: Representation and the Scientific Understanding. Springer Netherlands. doi: 10.1007/978-94-009-9357-0 * Yen, Jian. (2021). Putting ecological modelling to the test. ARI Seminar, Science Week. Arthur Rylah Institute for Environmental Research. [Video recording](https://www.youtube.com/watch?v=DBR2yQR2ilw) - (starting 33min in following Peter Griffion's talk 'Identifying frog calls with Deep Learning AI').