Bert Baumgaertner

I am a Professor of Philosophy at the University of Idaho. My work is situated at the intersections of cognitive science, philosophy, complex systems, and computational modeling. I am particularly interested in how scientists use diverse modeling techniques to understand the impacts of determinants of belief formation on complex biological and social phenomena.

My research explores questions about standards of evidence and their connections to opinion dynamics. I often use agent-based models and network theory as methodological tools, as well as survey studies. I am highly collaborative and interdisciplinary; my preference is to focus on questions that interest me rather than worry about disciplinary boundaries.

More recently, my work has expanded into analogy and metaphor as windows into human cognition and AI limitations. The MARC project (Metaphor Abstraction and Reasoning Corpus) uses community-created puzzles to probe what humans can do that AI systems cannot, and doubles as a tool for teaching AI literacy and ethics. I have also been investigating standards of evidence across political and scientific domains, and the philosophical and ethical dimensions of large language models and generative AI.

Opinion Dynamics & Social Epistemology

Modeling how beliefs form and spread through populations. Work on the preference for belief, echo chambers, reflective equilibrium, and how network structure shapes collective knowledge production.

Disease-Behavior Models

Computational models linking behavioral responses to epidemic dynamics. Research on risk tolerance, vaccination attitudes, and how transient prophylaxis drives epidemic waves.

Standards of Evidence

Investigating how individuals assess and gather evidence across scientific and political domains, including the effects of ideology and cognitive reflection on evidence-seeking behavior.

Analogy, Metaphor & AI Benchmarking

The MARC project explores how metaphorical reasoning reveals fundamental gaps between human and artificial intelligence. This work connects philosophy of language, cognitive science, and AI evaluation.

AI Literacy & Ethics

Building frameworks and tools for understanding generative AI, including its limits, biases, and societal impacts. Developing pedagogical approaches that make AI literacy accessible to non-technical audiences.