Philosophy of Science and Methodology
Through conceptual analysis techniques and formal modeling, Department of Philosophy research elucidates the theoretical foundations underlying rationality assumptions in diverse areas spanning science, statistics, economics, and artificial intelligence.
Faculty and students advance frameworks for knowledge representation, belief updating, causal analysis, strategic interaction, and more.
Projects in philosophy of science and methodology characterize learning dynamics, inference mechanisms, and decision architectures—yielding unified paradigms integrating empirical and mathematical discovery. Additional initiatives further extend existing formalisms or develop non-standard alternatives. There are opportunities for both specialized disciplinary contributions and courageous interdisciplinary collaborations.
Belief Revision
This research examines core assumptions, alternatives, and limitations related to Bayesian approaches for representing rational degrees of belief and revising beliefs and knowledge amid contradictory evidence. Faculty explore extensions for diachronic coherence, applications of non-standard analysis, and connections with causal learning and decision theory.
Cognitive Science and Philosophy of Mind
Initiatives build computational models integrating philosophy, psychology and neuroscience to elucidate causal learning, reasoning, decision-making, goals, and underlying cognitive architectures in human and machine intelligence. Methodological advances enable analyzing neural structures and resolve longstanding debates on appropriate techniques.
Computational Epistemology
Pioneering work establishes a unified framework synergizing formal and empirical methods of inquiry, leveraging computational learning theory and recursion-theoretic techniques to directly characterize reliable and efficient truth-finding across mathematical and scientific domains.
Learning Theory and Belief Revision
Faculty explore connections between formal learning theory and belief revision, developing frameworks for understanding how agents update beliefs in light of new evidence. This work bridges computational approaches to learning with philosophical accounts of rational belief change.
Inductive Logic and Statistics
Contributions link causal inference models and statistical decision theory while measuring degrees of probabilistic incoherence. Unique opportunities catalyze interdisciplinary training combining philosophy and computer science, including specialized data mining focused master’s degrees for doctoral students.
Philosophy of Social Science
Investigations focus on foundational questions in social scientific methodology, examining issues of causality, explanation, and theory construction in disciplines such as economics, sociology, and psychology. Research addresses the unique challenges of studying human behavior and social phenomena scientifically.
Rational Choice, Decision Theory and Game Theory
Research formalizes the analytical foundations of expected utility theory, sketching extensions that model complex risk attitudes and social contexts. Additional projects build interactive models of strategic reasoning and multiplayer decision-making, exploring connections between beliefs, preferences, and outcomes.
Theory of Causation
Cutting-edge research enables reliable discovery of causal structures from observational and experimental data via computational techniques with demonstrated effectiveness across disciplines as diverse as sociology, climate science, genetics, personalized medicine, education and public policy.