Samuli Reijula

Samuli Reijula is a University Lecturer in Theoretical Philosophy at the University of Helsinki, holding a permanent faculty position equivalent to US associate professor. He is also a docent (habilitation / adjunct professor) in Practical Philosophy and an Academy Research Fellow. Reijula is affiliated with the TINT Centre for Philosophy of Social Science and specializes in the philosophy of science, cognitive science, and institutional epistemology.

His research focuses on how science works as a distributed cognitive system, examining scientific problem-solving at multiple levels from individual scientists to research groups and scientific communities. Reijula investigates cognitive diversity, coping with uncertainty, epistemology of simulation modeling, foundations of scientific classification, and issues related to the science of science, scientific discovery, and science policy. He is particularly interested in viewing science as a collective cognitive system and understanding the social, cognitive, and institutional preconditions for well-functioning scientific research.

Reijula's recent publications explore topics including objectivity in academic recruitment, AI and epistemic humility, the Fediverse as a solution to epistemic challenges in the AI era, diversity in scientific communities, and the concept of Bildung university. His work often bridges philosophy with practical policy implications, addressing issues like inheritance tax policy, public authorities' use of social media platforms, and the philosophical foundations of open science.

He has received research funding from the Academy of Finland for his project 'Modeling the republic of science: Collaborative problem solving and collective rationality in scientific inquiry' (2020-2025). Reijula is scheduled to visit Carnegie Mellon University's Center for Formal Epistemology and Institute for Complex Social Dynamics in October-November 2024. His approach integrates philosophical analysis with computational methods, particularly agent-based modeling, to understand scientific communities as complex adaptive systems.