Jonathan St-Onge

Humans are unique in their ability to cooperate as groups to address large-scale collective action problems. This capacity emerges from the co-evolution of cultural psychology and group-level behaviors, resulting in multiscale dynamics from smallgroups (e.g., households) to large institutions (e.g., countries, firms). This thesis develops a framework for modeling the co-evolution of individuals and institutions from a group-based perspective. Drawing on insights from complex systems, philosophy, and cultural evolution, we show how traditional network dynamics are qualitatively transformed by adopting group-based formalisms that jointly represent institutional dynamics and individual behavior. As a first step, we recount the tumultuous history of group ontology, showing how assumptions about the existence of groups have shaped research methodologies throughout the twentieth century. Building on this foundation, we introduce a typology that maps conceptual views of groups onto modeling assumptions from the physics of higher-order interactions. This typology illustrates how progressively stronger ontological commitments—from momentary multi-way interactions to persistent and potentially misaligned group structures—enable richer representations of group dynamics. To formalize these ideas, we propose a group-based master equation framework that captures both enduring group structures and the co-evolution of cultural group traits. We then present two case studies in group-based modeling that reinterpret diffusion dynamics through a group-level lens. The first integrates institutions into contagion models, revealing a counterintuitive dynamic: higher infection rates can lead to smaller utbreaks due to a call for action–given how a contagion spreads, we might see varying institutional responses. The second examines the emergence of new skills in research groups, using programming in the humanities as a case study. Here, groups face a trade-off between accelerating their computational transition—potentially at the cost of overburdening individuals–or adapting more slowly to accommodate learning curves. Finally, we present preliminary empirical investigations grounded in our framework. One project models the co-evolution of COVID-19 contagion and government policy responses; the other examines the computational turn in the humanities, focus-ing on tensions between group-level incentives and individual costs of acquiring new skills. We conclude by reflecting on the theoretical challenge of integrating collective intentionality–how shared perceptions among individuals shape group behavior–into formal group-based models.

As a discipline of its own, the philosophy of science can be traced back to the founding of its academic journals, some of which go back to the first half of the twentieth century. While the discipline has been the object of many historical studies, notably focusing on specific schools or major figures of the field, little work has focused on the journals themselves. Here, we investigate contemporary philosophy of science by means of computational text-mining approaches: we apply topic-modeling algorithms to eight major philosophy of science journals, from the 1930s up until 2017. Based on the full-text content of some 15,897 articles, we identified 25 research themes and 8 thematic clusters that show how the research agenda of the philosophy of science has changed in its content over the course of the last eight decades, up to the philosophy of science we now know. We also show how each one of the journals contributed in its own way to this thematic evolution.