Teddy Seidenfeld-Department of Philosophy - Carnegie Mellon University

Teddy Seidenfeld

Herbert A. Simon University Professor of Philosophy and Statistics

Office: Baker Hall 135J
Phone: 412.268.2209


Teddy Seidenfeld (H.A. Simon University Professor of Philosophy and Statistics) works on foundations at the interface between philosophy and statistics, often being concerned with problems that involve multiple decision makers. For example, in collaboration with M.J. Schervish and J.B. Kadane (Statistics, CMU), they relax the norms of Bayesian theory to permit a unified standard, both for individuals acting as separate decision makers and collectively, in forming a cooperative group agent. By contrast, this is an impossibility for strict Bayesian theory. For a second example, in collaboration with Larry Wasserman (Statistics, CMU), they examine the short-run consequences of using Bayes rule for updating a set of expert Bayesian opinions with shared information. They focus on anomalous cases (they call dilation), where an experiment is certain to result in new evidence that increases the experts: uncertainty about an event of common interest where uncertainty is reflected in the extent of probabilistic disagreements among the experts.

His current collaborations with Kadane and Schervish incude a theory for indexing the degree of incoherence in non-Bayesian statistical decisions, work on the representation of coherent choice-functions using sets of probabilitis, and investigations involving scoring rules for probabilistic forecasts. The three also work together on the development of finitely additive expectations for unbounded random variables.

A selection of Seidenfeld’s papers and some recent presentations are found below, clustered by topic area.

Selection of research papers

Relating to Coherence and Decision Theory Relating to Consensus Relating to Dilation of Sets of Probabilities Relating to Finite Additivity Relating to R.A.Fisher Relating to the Value of Information Relating to other issues in Probability and Statistical Theory Relating to Scoring Rules

Some recent presentations