IEOR-DRO Seminar: Alessandro Arlotto (Duke)
IEOR-DRO Seminar: Alessandro Arlotto (Duke)303 MUDD
Abstract: We prove a central limit theorem for a class of additive processes that arise naturally in the theory of finite horizon Markov decision problems. The main theorem generalizes a classic result of Dobrushin (1956) for temporally non-homogeneous Markov chains, and the principal innovation is that here the summands are permitted to depend on both the current state and a bounded number of future states of the chain. We show through several examples that this added flexibility gives one a direct path to asymptotic normality of the optimal total reward of finite horizon Markov decision problems. The same examples also explain why such results are not easily obtained by alternative Markovian techniques such as enlargement of the state space. (Joint work with J. M. Steele. Financial support from the National Science Foundation under Grant No. 1553274 is gratefully acknowledged.)
Bio: Alessandro Arlotto is an Associate Professor of Business Administration, Mathematics, and Statistical Science at Duke University. Alessandro holds a primary appointment in the Decision Sciences area of Duke University’s Fuqua School of Business and secondary appointments in the departments of Mathematics and Statistical Science. Alessandro received his Ph.D. in 2012 from the University of Pennsylvania and joined Duke University in the same year.
Alessandro’s research interests are in probability, optimization and their applications to business and economics. His research has appeared in several journals including the Annals of Applied Probability, Management Science, Mathematics of Operations Research, Operations Research, and Stochastic Processes and their Applications. Alessandro is a recipient of the Faculty Early Career Development (CAREER) award from the National Science Foundation.
At Duke, Alessandro teaches the core course Probability and Statistics in the Daytime and Executive MBA programs as well as the Quantitative Business Analysis course for the Master in Management Studies. Alessandro also teaches the graduate course Stochastic Models.