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This Festschrift celebrates Teddy Seidenfeld and his seminal contributions to philosophy, statistics, probability, game theory and related areas. The 13 contributions in this volume, written by leading researchers in these fields, are supplemented by an interview with Teddy Seidenfeld that offers an abbreviated intellectual autobiography, touching on topics of timeless interest concerning truth and uncertainty. Indeed, as the eminent philosopher Isaac Levi writes in this volume: "In a world dominated by Alternative Facts and Fake News, it is hard to believe that many of us have spent our life's work, as has Teddy Seidenfeld, in discussing truth and uncertainty." The reader is invited to share this celebration of Teddy Seidenfeld's work uncovering truths about uncertainty and the penetrating insights they offer to our common pursuit of truth in the face of uncertainty.
Sommario
An Interview with Teddy Seidenfeld.- The Value Provided by a Scientific Explanation.- A Gentle Approach to Imprecise Probability.- Foundations For Temporal Reasoning Using Lower Previsions Without A Possibility Space.- On the Equivalence of Normal and Extensive Form Representations of Games.- Dilation and Informativeness.- Playing with Sets of Lexicographic Probabilities and Sets of Desirable Gambles.- How to Assess Coherent Beliefs: A Comparison of Different Notions of Coherence in Dempster-Shafer Theory of Evidence.- Expected Utility in 3D.- On the Normative Status of Mixed Strategies.- On a Notion of Independence Proposed by Teddy Seidenfeld.- Coherent Choice Functions without Archimedeanity.- Quantifying Degrees of E-admissibility in Decision Making with Imprecise Probabilities.
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Thomas Augustin is Professor of Statistics at Ludwig-Maximilians-Universität München (LMU Munich), where he heads the "Foundations of Statistics and their Applications" Lab. His research interest is to develop set-valued methods for reliable statistical inference, decision making, and machine learning. For this, he utilizes
concepts from imprecise probabilities and partial identification to cope with different kinds of complex uncertainty, like non-randomly missing or coarsened data, non-standard measurement error, ambiguity,
conflicting information, and structural model indeterminacy.
Fabio G. Cozman is Professor of Computer Science at Escola Politécnica, Universidade de São Paulo (USP), Director of the Center for Artificial Intelligence at USP, with an interest in machine learning and knowledge/uncertainty representation. Engineer (USP) and PhD (Carnegie Mellon University, USA), he has served as Program and General Chair of the Conference on Uncertainty in Artificial Intelligence, Area Chair of the International Joint Conference on Artificial Intelligence, and Associate Editor of the Artificial Intelligence Journal, the Journal of Artificial Intelligence Research, and the Journal of Approximate Reasoning.
Gregory Wheeler is Professor of Philosophy and Computer Science at Frankfurt School of Finance & Management, where he heads the Center for Human & Machine Intelligence and is Academic Director of the Master of Applied Data Science program. His research interests concern the foundations of probability, bounded rationality, and decision-making under uncertainty involving underspecified models, conflicting information, computational resource bounds, and indeterminacy. He also co-founded Exaloan AG, a Frankfurt-based financial services software company, where he is Head of Machine Learning.
