Agent-based modelling has become a well-established method in social epistemology and philosophy of science but the question of what kind of explanations these models provide remains largely open. This paper is dedicated to this issue. It starts by distinguishing between real-world phenomena, real-world possibilities, and logical possibilities as different kinds of targets which agent-based models can represent. I argue that models representing the former two kinds provide how-actually explanations or causal how-possibly explanations. In contrast, models that represent logical possibilities provide (...) epistemically opaque how-possibly explanations. While highly idealised ABMs in the form in which they are initially proposed typically fall into the last category, the epistemic opaqueness of explanations they provide can be reduced by validation procedures. To this purpose, an examination of results of simulations in terms of classes of models can be particularly helpful. I illustrate this point by discussing a class of ABMs of scientific interaction and the claim that a high degree of interaction can impede scientific inquiry. (shrink)

In this introduction to the Topical Collection on Social and Cognitive Diversity in Science, we map the questions that have guided social epistemological approaches to diversity in science. Both social and cognitive diversity of different types is claimed to be epistemically beneficial. The challenge is to understand how an increase in a group’s diversity can bring about epistemic benefits and whether there are limits beyond which diversity can no longer improve a group’s epistemic performance. The contributions to the Topical Collection (...) discuss various proposals to maintain an appropriate amount of cognitive diversity in science, for instance, by recruiting and retaining practitioners from underrepresented social groups, providing incentives for explorative and risky research, encouraging interdisciplinary collaborations and stakeholder participation in research, requiring industry scientists to share their evidence, and developing strategies to encounter politically motivated attempts to manufacture doubt. To be successful, efforts to promote diversity in science should anticipate risks related to institutional interventions, navigate trade-offs between different types of epistemically good outcomes, and identify hidden costs that such policies may cause for various actors. Such efforts need to be assessed not only from an epistemic perspective but also from the point of view of fairness and the political legitimacy of scientific institutions. (shrink)

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. (shrink)