Registration link:
https://www.wjx.top/vm/OWTqAdc.aspx#
Speaker
Fabrizio Ruggeri (B.Sc. Mathematics Milano, M.Sc. Statistics Carnegie Mellon, Ph.D. Statistics Duke) is Senior Fellow at the Istituto di Matematica Applicatae Tecnologie Informatiche (IMATI) in Milano of CNR (Consiglio Nazionale delle Ricerche) where he had been a researcher from 1988 to May 2023 (as Research Director since 2001). He is member of the Faculty of the Ph.D. programme in Mathematics at the universities of Milano-Bicocca and Pavia.
He had various international appointments, including Adjunct Professor at Queensland University of Technology (Brisbane, Australia), International Professor Affiliate at Polytechnic Institute (New York University, USA), Chair of Excellence at Universidad Carlos III and ICMAT-CSIC (Madrid, Spain) and Faculty of the Ph.D. programme in Statistics at the Universidad de Valparaiso (Chile). He is the President-Elect of the International Statistical Institute (ISI) for the 2023-2025 term, after which he will serve as President from 2025 to 2027. He has been President of ENBIS (European Network for Business and Industrial Statistics), ISBA (International Society for Bayesian Analysis) and ISBIS (International Society for Business and Industrial Statistics), and ISI Vice President. He is a Fellow of IMS (Institute of Mathematical Statistics), ASA (American Statistical Association) and ISBA (which also awarded him the first Zellner Medal), and ENBIS Honorary Member. He is Editor-in-Chief of Applied Stochastic Models in Business and Industry and Wiley StatsRef, an online encyclopedia. He is the Director of the Applied Bayesian Statistics summer school organized by CNR-IMATI since 2004 and Chair of the series of workshops on Bayesian Inference in Stochastic Processes. He is author of over 200 articles (including 120 in refereed journals) and author/editor of 6 books.
His interests are mostly in Bayesian Statistics and Decision Analysis, especially about robustness, stochastic processes and industrial applications, mainly in reliability. His interests also cover other areas, in particular concentration functions, wavelets and, more recently, healthcare frauds and Adversarial Risk Analysis.
Course Description
The course will introduce the basic concepts of Adversarial Risk Analysis (ARA) and the work the lecturer has been doing in the field in the past few years. ARA is a relatively new area of research that informs decision-making when facing intelligent opponents and uncertain outcomes. It is a decision-theoretic alternative to classical game theory that uses Bayesian subjective distributions to model the goals, resources, beliefs, and reasoning of the opponent. It enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities and the type of strategic calculation that the opponent is using. Within that framework, the analyst then solves the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility, accounting for the uncertainty she has about the opponent.
Prerequisite
Some knowledge of Bayesian Statistics, although an introductory lecture is planned
Reference
Banks, D.L., Aliaga, J.M.R., & Rios Insua, D. (2015). Adversarial Risk Analysis (1st ed.). Chapman and Hall/CRC.
https://doi.org/10.1201/b18653
Target Audience
Undergraduate students (only senior)，Graduate students
Teaching Language
English
