Literature ESERA 2025

Accompanying Literature

Alvarado, R. (2023). Simulating Science: Computer Simulations as Scientific Instruments (1st ed. 2023). Synthese Library, Studies in Epistemology, Logic, Methodology, and Philosophy of Science: Bd. 479. Springer International Publishing; Imprint Springer. https://doi.org/10.1007/978-3-031-38647-3

Arnold, E. (2013). Experiments and Simulations: do they fuse? In E. Arnold & J. Duran (Hrsg.), Computer Simulations and the Changing Face of Scientific Experimentation (S. 46–75). Cambridge Scholars Publishing.

Beisbart, C. (2018). Are computer simulations experiments? And if not, how are they related to each other? European Journal for Philosophy of Science, 8(2), 171–204. https://doi.org/10.1007/s13194-017-0181-5

Develaki, M. (2019). Methodology and Epistemology of Computer Simulations and Implications for Science Education. Journal of Science Education and Technology, 28(4), 353–370. https://doi.org/10.1007/s10956-019-09772-0

Durán, J. M. (2020). What is a Simulation Model? Minds and Machines, 30(3), 301–323. https://doi.org/10.1007/s11023-020-09520-z

Durán, J. M. (2021). A Formal Framework for Computer Simulations: Surveying the Historical Record and Finding Their Philosophical Roots. Philosophy & Technology, 34(1), 105–127. https://doi.org/10.1007/s13347-019-00388-1

Durán, J. M. & Formanek, N. (2018). Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism. Minds and Machines, 28(4), 645–666. https://doi.org/10.1007/s11023-018-9481-6

Elgin, C. (2007). Understanding and the Facts. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 132(1), 33–42. http://www.jstor.org/stable/25471843

Eyert, F. (2020). Epidemie und Modellierung: Das Mathematische ist politisch. WZB Mitteilungen(168), 82–85.

Falvo, D. A. (2008). Animations and Simulations for Teaching and Learning Molecular Chemistry: International Journal of Technology in Teaching and Learning, 4(1), 68–77.

Frigg, R. & Hartmann, S. (2006). Models in Science. https://plato.stanford.edu/entries/models-science/#SemaModeRepr 

Frigg, R., Thompson, E. & Werndl, C. (2015). Philosophy of Climate Science Part II: Modelling Climate Change. Philosophy Compass, 10(12), 965–977. https://doi.org/10.1111/phc3.12297

Greca, I. M., Seoane, E. & Arriassecq, I. (2014). Epistemological Issues Concerning Computer Simulations in Science and Their Implications for Science Education. Science & Education, 23(4), 897–921. https://doi.org/10.1007/s11191-013-9673-7

Guala, F. (2002). Models, Simulations, and Experiments. In L. Magnani & N. J. Nersessian (Hrsg.), Model-Based Reasoning: Science, Technology, Values (S. 59–74). Springer US. https://doi.org/10.1007/978-1-4615-0605-8_4

Grüne-Yanoff, T. & Weirich, P. (2010). The Philosophy and Epistemology of Simulation: A Review. Simulation & Gaming, 41(1), 20–50. https://doi.org/10.1177/1046878109353470

Helgeson, C., Srikrishnan, V., Keller, K. & Tuana, N. (2021). Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions. Philosophy of Science, 88(2), 213–233. https://doi.org/10.1086/711501

Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626. https://doi.org/10.1007/s11229-008-9435-2

Krüger, D. & Upmeier zu Belzen, A. (2021). Kompetenzmodell der Modellierkompetenz – Die Rolle abduktiven Schließens beim Modellieren. Zeitschrift für Didaktik der Naturwissenschaften, 27(1), 127–137. https://doi.org/10.1007/s40573-021-00129-y

Kuckartz, U. & Rädiker, S. (2022). Qualitative Inhaltsanalyse. Methoden, Praxis, Computerunterstützung (5., überarbeitete Aufl.). Beltz Verlagsgruppe. 

Landriscina, F. (2013). Simulation and learning: A model-centered approach. Springer. https://doi.org/10.1007/978-1-4614-1954-9

Martín, E. & Ariza, Y. (2024). A Didactic and Metatheoretical Characterization of Computational Simulations in Science Education. Science & Education, 1–21.

Morgan, M. S. (2000). Experiments without material intervention: Model experiments, virtual experiments and virtually experiments, 216–235. 

Morrison, M. (2009). Models, measurement and computer simulation: the changing face of experimentation. Philosophical Studies, 143(1), 33–57. https://doi.org/10.1007/s11098-008-9317-y

Müller, S. & Reiners, C. (2022). Pre-service Chemistry Teachers' Views about the Tentative and Durable Nature of Scientific Knowledge. Science & Education, 32(6), 1813–1845. https://doi.org/10.1007/s11191-022-00374-8 

Orgill, M., York, S. & MacKellar, J. (2019). Introduction to Systems Thinking for the Chemistry Education Community. Journal of Chemical Education, 96(12), 2720–2729. https://doi.org/10.1021/acs.jchemed.9b00169

Parke, E. C. (2014). Experiments, Simulations, and Epistemic Privilege. Philosophy of Science, 81(4), 516–536. https://doi.org/10.1086/677956

Parker, W. S. (2020). Evidence and Knowledge from Computer Simulation. Erkenntnis(87), 1521–1538. https://doi.org/10.1007/s10670-020-00260-1

Rost, M., Sonnenschein, I., Möller, S. & Lembens, A. (2023). Don’t we know enough about models? Integrating a replication study into an introductory chemistry course in higher education. Chemistry Teacher International, 0(0). https://doi.org/10.1515/cti-2022-0032

Roush, S. (2018). The epistemic superiority of experiment to simulation. Synthese, 195(11), 4883–4906. https://doi.org/10.1007/s11229-017-1431-y

Seoane, M. E., Greca, I. M. & Arriassecq, I. (2022). Epistemological aspects of computational simulations and their approach through educational simulations in high school. SIMULATION, 98(2), 87–102. https://doi.org/10.1177/0037549720930084

Schwedler, S. & Kaldewey, M. (2020). Linking the submicroscopic and symbolic level in physical chemistry: how voluntary simulation-based learning activities foster first-year university students’ conceptual understanding. Chemistry Education Research and Practice, 21(4), 1132–1147. https://doi.org/10.1039/C9RP00211A

Terzer, E. & Upmeier zu Belzen, A. (2008). Naturwissenschaftliche Erkenntnisgewinnung durch Modelle – Modellverständnis als Grundlage für Modellkompetenz. Vorab-Onlinepublikation. https://doi.org/10.4119/UNIBI/ZDB-V16-I1-182 (33-56 Seiten / Zeitschrift für Didaktik der Biologie (ZDB) - Biologie Lehren und Lernen, Bd. 16 (2007).

Upmeier zu Belzen, A. & Krüger, D. (2010). Modellkompetenz im Biologieunterricht: Struktur und Entwicklung. Zeitschrift für Didaktik der Naturwissenschaften, 16, 41–57.

Upmeier zu Belzen, A. & Krüger, D. (2019). Modelle als methodische Werkzeuge begreifen und nutzen: Empirische Befunde und Empfehlungen für die Praxis. In J. Groß, M. Hammann, P. Schmiemann & J. Zabel (Hrsg.), Biologiedidaktische Forschung: Erträge für die Praxis (1st ed. 2019, S. 129–146). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-58443-9_8

Upmeier zu Belzen, A., Krüger, D. & van Driel, J. (Hrsg.). (2019). Models and Modeling in Science Education: Bd. 12. Towards a Competence-Based View on Models and Modeling in Science Education (1st ed. 2019). Springer International Publishing; Imprint Springer. https://doi.org/10.1007/978-3-030-30255-9

Winsberg, E. (2010). Science in the age of computer simulation. The Univ. of Chicago Pr. 

Winsberg, E. (2019). Computer Simulations in Science. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/simulations-science/