Daniel Herrmann is a decision theorist, formal epistemologist, and philosopher of AI. He develops mathematical and computational models of optimal reasoning and learning, with an eye towards understanding artificial agents, as well as agents who reason about themselves and how they are embedded in their world.
Some of Daniel’s recent work investigates belief-like representations in large language models, as well as how one should use evidence in decision making. He also uses evolutionary models to explain how conventions and meaningful linguistic systems emerge in populations.
Daniel completed his PhD in Logic and Philosophy of Science at the University of California, Irvine, and did his postdoctoral research at the University of Groningen.