Teddy Seidenfeld

About

Teddy Seidenfeld holds the title of Herbert A. Simon University Professor of Philosophy and Statistics at Carnegie Mellon University. His research focuses on the foundational, conceptual, and methodological questions of broad importance within philosophy and statistics. As a distinguished faculty member, he contributes to the interdisciplinary environment of the Department of Philosophy, integrating insights from philosophy, logic, and statistical sciences to address complex issues in rationality, decision theory, and related fields.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Machine Learning
• Mathematical economics
• Statistics
• Philosophy
• Econometrics
• Economics
• Epistemology

Selected publications

• Extensions of Expected Utility Theory and Some Limitations of Pairwise Comparisons

  Figshare · 2018-06-30 · 40 citations

  articleOpen access

  We contrast three decision rules that extend Expected Utility to contexts where a convex set of probabilities is used to depict uncertainty: Γ-Maximin, Maximality, and E-admissibility. The rules extend Expected Utility theory as they require that an option is inadmissible if there is another that carries greater expected utility for each probability in a (closed) convex set. If the convex set is a singleton, then each rule agrees with maximizing expected utility. We show that, even when the opti…

  OA PDFDOI

• Non-Conglomerability for Finite-Valued, Finitely Additive Probability

  Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018-01-01 · 17 citations

  articleOpen access1st authorCorresponding

  We consider how an unconditional, finite-valued, finitely additive probability P on a countable set may localize its non-conglomerability (non-disintegrability). Non-conglomerability, a characteristic of merely finitely additive probability, occurs when the unconditional probability of an event P(E) lies outside the closed interval of conditional probability values, [infhe pi P(E|h), suphe pp(E|h)], taken from a countable partition p = hj:j=1,...}. The problem we address is how to identify event…

  PublisherOA PDFDOI

• Standards for Modest Bayesian Credences

  Philosophy of Science · 2017-09-13 · 9 citations

  article

  Gordon Belot argues that Bayesian theory is epistemologically immodest. In response, we show that the topological conditions that underpin his criticisms of asymptotic Bayesian conditioning are self-defeating. They require extreme a priori credences regarding, for example, the limiting behavior of observed relative frequencies. We offer a different explication of Bayesian modesty using a goal of consensus: rival scientific opinions should be responsive to new facts as a way to resolve their disp…

  PublisherDOI

• What finite-additivity can add to decision theory

  Statistical Methods & Applications · 2019-08-22 · 5 citations

  article
  PublisherDOI

• Subjective causal networks and indeterminate suppositional credences

  Synthese · 2019-12-17 · 3 citations

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  PublisherDOI

Frequent coauthors

• Joseph B. Kadane

  Carnegie Mellon University

  70 shared

• Mark J. Schervish

  Carnegie Mellon University

  67 shared

• Rafael B. Stern

  10 shared

• Ruobin Gong

  Rutgers Sexual and Reproductive Health and Rights

  7 shared

• Henry E. Kyburg

  3 shared

• Sébastien Destercke

  Heuristics and Diagnostics for Complex Systems

  3 shared

• Hailin Liu

  Fuzhou University

  3 shared

• Larry Wasserman

  3 shared

Similar researchers at Carnegie Mellon University

• Peter Spirtesh-46
• Alex John Londonh-39
• Richard Scheinesh-35