Holder： Amos Golan（American University）
Time：2025-12-09 15:10-17:00
Location：Room 217, Guanghua Building 2
Abstract:
An information-theoretic maximum entropy (ME) model provides an alternative approach to finding solutions to partially identified models. In these models, we can identify only a solution set rather than point-identifying the parameters of interest, given our limited information. Based on this incomplete information, how should one make a prediction, choose a treatment, or make other decisions to maximize welfare? The choice depends on whether the criterion is Bayesian, maximizing the minimum welfare (maximin), minimizing the maximum risk (Wald, 1944), information-theoretical, or other. A seminal paper by Manski (2021) discusses the first three of these approaches, focusing on a statistical decision theory approach that minimizes the maximum risk or regret (minimax regret, or MMR). In this talk I propose using an information-theoretical maximum entropy (ME) approach as an alternative. I compare it to other commonly used approaches. Using Manski’s simulations for a missing data and a treatment problem, including an empirical example, I will show that the ME performs the same or better than MMR. In additional simulations, ME dominates various other statistical decision functions. ME has an axiomatic underpinning and is computationally efficient. (This talk is based on joint work with Jeffrey Perloff,)
About the Speaker:
Amos Golan is Professor of Economics at American University (CAS) and Director of AU’s Info-Metrics Institute. His research focuses on information, information processing, and optimal decision rules under limited information—work that has helped develop and apply the Info-Metrics framework across economics and related fields. He holds a PhD from the University of California, Berkeley, and MS/BA degrees from the Hebrew University of Jerusalem. Golan is a Fellow of the American Association for the Advancement of Science, an External Professor at the Santa Fe Institute, and an Elected Member of the International Statistical Institute. His honors include the International Journal of Forecasting’s 2020–2021 Outstanding Paper Award, a NSF/ASA/Census Senior Research Fellowship, and senior or visiting appointments at Oxford, the Rimini Center for Economic Analysis, and the European Institutes for Advanced Studies.
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