Highly idealized models of scientific inquiry as conceptual systems

• Published in: European journal for philosophy of science Vol. 14; no. 3; p. 39
• Main Author: Pesonen, Renne
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2024; Springer Nature B.V
• Subjects: Cognition & reasoning; Cognitive ability; Education; Epistemology; Hypotheses; Mathematical models; Paper in General Philosophy of Science; Philosophy; Philosophy of Science; Science; Simulation; Simulation models; Skills; Agent-based models; Mental simulation; Scientific concepts; Social epistemology
• ISSN: 1879-4912; 1879-4920; 1879-4920
• DOI: 10.1007/s13194-024-00601-9

The social epistemology of science has adopted agent-based computer simulations as one of its core methods for investigating the dynamics of scientific inquiry. The epistemic status of these highly idealized models is currently under active debate in which they are often associated either with predictive or the argumentative functions. These two functions roughly correspond to interpreting simulations as virtual experiments or formalized thought experiments, respectively. This paper advances the argumentative account of modeling by proposing that models serve as a means to (re)conceptualize the macro-level dynamics of complex social epistemic interactions. I apply results from the epistemology of scientific modeling and the psychology of mental simulation to the ongoing debate in the social epistemology of science. Instead of considering simulation models as predictive devices, I view them as artifacts that exemplify abstract hypothetical properties of complex social epistemic processes in order to advance scientific understanding, hypothesis formation, and communication. Models need not be accurate representations to serve these purposes. They should be regarded as pragmatic cognitive tools that engender rather than replace intuitions in philosophical reasoning and argumentation. Furthermore, I aim to explain why the community tends to converge around few model templates: Since models have the potential to transform our intuitive comprehension of the subject of inquiry, successful models may literally capture the imagination of the modeling community.