2016 INFORMS Annual Meeting Program

TB41

INFORMS Nashville – 2016

4 - Data Analysis And Experimental Design For Accelerated Life Testing With Heterogeneous Group Effects Kangwon Seo, PhD Candidate, Arizona State University, 699 S Mill Ave., Tempe, AZ, 85281, United States, kseo7@asu.edu, Rong Pan In accelerated life tests (ALTs), complete randomization is hardly achieved because of economic or engineering constraints. Typical experimental protocols such as subsampling or random blocks in ALT result in a grouped structure of test units, which leads to correlated lifetime observations. In this talk, generalized linear mixed model (GLMM) approach is proposed to analyze ALT data and find the optimal ALT design with consideration of heterogeneous group e ffects. First, we will demonstrate how the random group effects of ALT affect the life-stress relationship. Second, D-optimal ALT test plan will be derived when we run experiments with multiple test chambers.

2 - Optimal Policies In Decentralized Stochastic Control: Existence And Approximations Serdar Yuksel, Queen’s University, Kingston, ON, K7L 3N6, Canada, yuksel@queensu.ca We will study optimal solutions in decentralized stochastic control. First, strategic measures will be introduced; these are probability measures induced by admissible policies. Properties such as convexity and compactness will be studied leading to existence of and structural results for optimal policies. Finally, asymptotic optimality of finite model representations will be established. These lead to asymptotic optimality of quantized control policies, so that one can construct a sequence of finite models obtained through the quantization of measurement and action spaces whose solutions converge to the optimal cost. Witsenhausen’s counterexample will be a running case study. 3 - Easy Affine Mdps: Theory Matthew J. Sobel, Case Western Reserve University, matthew.sobel@case.edu, Jie Ning An MDP with continuous state and action vectors is shown to have an extremal optimal policy if it has affine immediate rewards and dynamics, decomposable constraints on the actions, and maximizes the expected present value of the rewards. Identifying an optimal policy and computing its value function reduces to solving a small system of auxiliary equations. This exorcises the curse of dimensionality. The same structure in a sequential game yields the existence and simple characterization of an extremal Nash equilibrium. A companion paper with algorithms and applications is in another session. Chair: Woo Chang Kim, Associate Professor, KAIST, KAIST 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea, Republic of, wkim@kaist.ac.kr Co-Chair: Changle Lin, Princeton University, Jersey City, NJ, United States, changlel@princeton.edu 1 - Robo-advisor And Personalized Asset & Liability system Changle Lin, Merrill Lynch Wealth Management, changlelin1@gmail.com 2 - Robo-advisor: Goal-based Investing And Gamification Paolo Sironi, IBM, paolo.sironi@de.ibm.com The WM industry changes from distribution of products to channel of financial advice. Robo-Advisors need to innovate FIN not only TECH, embrace Goal Based Investing, move beyond MPT and craft better inter-temporal understanding of future performance of actual products, liabilities and portfolios against targets (asset values or post-retirement income). Probabilistic Scenario Optimisation facilitates the shift from market-oriented to client-centric allocations. Fears and ambitions enter the new equation if investors change their behaviour: Gamification could help to learn what money is for, how to invest and what to believe in when setting investment goals across scenarios. 3 - Robo-advisor: Application Of Advanced Portfolio Technology Woo Chang Kim, KAIST, woochang.kim@gmail.com 4 - Evidence-based Improvements To Investor Behavior: Betterment’s Approach Daniel Egan, Betterment, 61 W. 23rd Street, 4th Floor, New York, NY, 10010, United States, dan@betterment.com As the largest independent robo-advisor, Betterment relies on technology to further its mission of providing affordable, personalized advice to investors, regardless of their account size. Dan Egan, Vice President of Behavioral Finance and Investing at Betterment, will discuss the firm’s efforts to build features that address behavioral biases that cause retail investors to make systematic investment “mistakes,” including under-saving and excessive trading. He will also discuss Betterment’s internal culture and how it contributes to a focus on innovation and to ongoing, evidence-based behavioral improvements. TB41 207C-MCC Quantitative Methods in Finance XII Sponsored: Financial Services Sponsored Session

TB39 207A-MCC Applied Probability and Simulation I Sponsored: Applied Probability Sponsored Session

Chair: Henry Lam, University of Michigan, 500 S. State Street, Ann Arbor, MI, 48109, United States, khlam@umich.edu 1 - Rare Event Estimation For Gaussian Random Vectors Ton Dieker, Columbia University, dieker@columbia.edu, Richard Gabriel Birge We present a new technique for estimating the probability P(g(X)>x), where X is a Gaussian random vector and g is a function for which the probability becomes a rare event probability. In this setting, direct Monte Carlo is computationally expensive. We establish quantitative properties on the performance of our technique and illustrate them through numerical examples. 2 - On Adaptive Recursion For Integral Optimization Raghu Pasupathy, Purdue University, pasupath@purdue.edu We consider \emph{integral optimization problems}, that is, high-dimensional optimization problems where the objective function is expressed as an integral that can only be approximated using numerical quadrature. For efficient optimization, we propose an adaptive line-search recursion that dynamically determines the extent of work to be exerted during numerical quadrature. Assuming a general quadrature error-rate, we prove consistency and sample complexity results. The achieved rate in all cases is \emph{optimal} in a certain sense that we make clear. 3 - Three Asymptotic Regimes For Ranking And Selection With General Sample Distribution Yi Zhu, Northwestern University, yizhu2020@u.northwestern.edu In this paper, we study three asymptotic regimes that can be applied to ranking and selection (R&S) problems with general sample distributions. These asymptotic regimes are constructed by sending problem parameters (probability of incorrect selection, difference between the best and second best system) to zero. We establish asymptotic validity of the corresponding R&S procedures under each regime. We also analyze the connection among different asymptotic regimes and compare their pre-limit performances. TB40 207B-MCC Markov Decision Processes: Theory Sponsored: Applied Probability Sponsored Session Chair: Matthew J. Sobel, Emeritus Professor, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH, 44106-7235, United States, matthew.sobel@case.edu Co-Chair: Jie Ning, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH, 44106-7235, United States, jie.ning@case.edu 1 - Atomless Discounted Markov Decision Processes With Multiple Criteria Eugene A Feinberg, Stony Brook University, eugene.feinberg@stonybrook.edu, Aleksey Piunovskiy A Markov Decision Process (MDP) is called atomless if the initial distribution and transition probabilities are atomless. We show that, for an atomless MDP with multiple cost functions, for an arbitrary policy there is a nonrandomized stationary policy with the same vector of the total expected discounted costs. We also discuss the relevance of this result to Lyapunov’s convexity theorem, to the classic results by Dvoretzky, Wald, and Wolfowitz on sufficiency of nonrandomized policies for atomless decision problems, and to our previous results on sufficiency of nonrandomized Markov policies for atomless MDPs.

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