2015 Informs Annual Meeting

MB23

INFORMS Philadelphia – 2015

3 - Examining Change in Hospital Quality and Efficiency after ACA using Dynamic Network DEA Yasar Ozcan, Professor, Virginia Commonwealth University, P.O. Box 980203, Richmond, VA, 23298-0203, United States of America, ozcan@vcu.edu, Jaya Khushalani Dynamic Network DEA was used to examine change in both quality and efficiency of hospitals between 2009 and 2013, pre and post Affordable Care Act (ACA). Quality and efficiency improved significantly with no trade-off between the two. Urban and teaching hospitals were less likely to improve quality and efficiency together. 4 - Robust Decisions for the Partially Diversified Disease Management Model Shuyi Wang, Lehigh University, 200 W Packer Ave, Bethlehem, PA, United States of America, shw210@lehigh.edu We discuss a model to help pharmaceutical companies determine the optimal strategy under high uncertainty for a business model called the Partially Diversified Disease Management Model, which includes disease care pathways as well as health management, diagnostics&devices, and medication, and incentivizes patients’ health. Our MIP provides a tradeoff between diversification and specialization. 5 - When is the Outside Care Utilization Optimal for Acos? Trade-off Between Cost, Access, and Quality Tannaz Mahootchi, Postdoctoral Research Associate, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States of America, t.mahootchi@neu.edu Accountable Care Organizations (ACOs) are responsible for the health outcomes and the care expenses of their patients. We investigate the details of patient diversion process to an alternative provider when the primary ACO is experiencing congestion. ACOs choose the alternative provider based on the performance measures and the costs of patient diversion. We derive the transfer price and the performance measures that makes the diversion decision optimal. MB22 22-Franklin 12, Marriott Learning and Random Graphs Sponsor: Applied Probability Sponsored Session Chair: Marc LeLarge, INRIA-ENS, 23 Avenue d’Italie, Paris, France, marc.lelarge@ens.fr 1 - Typical Distances in Directed Random Graphs Mariana Olvera-Cravioto, Associate Professor, Columbia University, New York, NY, 10027, United States of America, mo2291@columbia.edu We study the distance between two randomly selected nodes in a directed configuration model under the assumption that the degree distributions have finite variance. In particular, we show that the distance grows logarithmically in the size of the graph. The method of proof uses a coupling between a graph exploration process and a weighted branching tree, since unlike the undirected case, we need to keep simultaneous control of both the in-degrees and the out- degrees. 2 - Competitive Contagion in Networks Moez Draief, Imperial College London and Huawei Research Paris, South Kensington Campus, London, United Kingdom, moez.draief@huawei.com There has been a growing interest, over the past few years, in studying models of competing products/opinions on social networks. The question of interest is what is the impact of the first adopters of a product on the outcome of a series of adoption by other nodes in the system influenced by those initial nodes. More precisely, the decision of a node to adopt a product is influenced by the behaviour of its neighbours in the social network. This raises challenging and intriguing mathematical, algorithmic and game theoretic questions. In this talk, I will present an overview of recent developments in this topic. 3 - Learning in Networks: Multi-armed Bandits with Structure Richard Combes, Assistant Professor, Centrale-Supelec, Plateau de Moulon, 3 rue Joliot-Curie, Gif-Sur-Yvette, 91192, France, richard.combes@supelec.fr The design of networks and online services can often be mapped to a multi-armed bandit problem with structure. With this approach, problems such as link adaptation, resource allocation, or ad-display optimization can be solved in a provably optimal manner. Namely, the learning speed of the proposed schemes matches a fundamental limit verified by any scheme. A review of the relevant mathematical tools and litterature is provided.

4 - Community Detection with the Non-backtracking Operator Marc LeLarge, INRIA-ENS, 23 Avenue d’Italie, Paris, France, marc.lelarge@ens.fr, Charles Bordenave, Laurent Massoulie Community detection consists in identification of groups of similar items within a population. In the context of online social networks, it is a useful primitive for recommending either contacts or news items to users. We will consider a particular generative probabilistic model for the observations, namely the so- called stochastic block model and prove that the non-backtracking operator provides a significant improvement when used for spectral clustering. 5 - Rumor Source Obfuscation Peter Kairouz, Graduate Research Assistant, University of Illinois at Urbana Champaign, 408 E Clark St, Apt. 6, Champaign, IL, 61820, United States of America, kairouz2@illinois.edu, Sewoong Oh, Pramod Viswanath Anonymous messaging platforms have recently emerged as important tools for sharing one’s thoughts without the fear of being judged by others. Such platforms are crucial in nations with authoritarian regimes where the right to free expression depends on anonymity. Existing messaging protocols are vulnerable against adversaries who can collect metadata. We introduce a novel messaging protocol and show that it spreads the messages fast and achieves perfect obfuscation of the source. MB23 23-Franklin 13, Marriott Role of Information in Large-scale Stochastic Resource Allocation Problems Sponsor: Applied Probability Sponsored Session 1 - Centralized Seat Allocation for Engineering Colleges in India Yash Kanoria, Assistant Professor, Columbia University, New York, NY, United States of America, ykanoria@columbia.edu The central government funds over 75 engineering colleges in India with 50,000 seats a year, and a diversity of programs and admissions criteria. We deploy a new, centralized, seat allocation mechanism, that accounts for the preferences of students as well as the admissions criteria for different colleges/programs using a deferred acceptance inspired approach. 2 - Learning to Optimize via Information-directed Sampling Daniel Russo, Stanford University, 218 Ayrshire Farm Lane, Apt. 102, Stanford, CA, 93405, United States of America, djrusso@stanford.edu, Benjamin Van Roy We offer a fresh, information-theoretic, perspective on the exploration/exploitation trade-off and propose a new algorithm—information- directed sampling—for a broad class of online optimization problems. We establish a general expected regret bound and demonstrate strong simulation performance for the widely studied Bernoulli, Gaussian, and linear bandit problems. Simple analytic examples show information-directed sampling can dramatically outperform Thompson sampling and UCB algorithms. 3 - Online Advertising Matching in the Large Market Jian Wu, Cornell University, Ithaca, NY, United States of America, jw926@cornell.edu, Peter Frazier, J. G. Dai We study online advertising matching in a large market asymptotic regime, in which the number of opportunities and the number of advertisers increase simultaneously. We develop a matching policy based on the LP solution to a certain deterministic problem. Under certain conditions, we prove that the policy is asymptotically optimal under the fluid-scaling to maximize click-through-rate (CTR) while satisfying all contractual agreements with overwhelming probability. 4 - Robust Scheduling in a Flexible Fork-join Network Yuan Zhong, Columbia University, 500 W. 120th Street, New York, NY, 10027, United States of America, yz2561@columbia.edu, Ramtin Pedarsani, Jean Walrand We consider a general flexible fork-join processing network, motivated by applications in e.g., cloud computing, manufacturing, etc, in which jobs are modeled as directed acyclic graphs, and servers are flexible with overlapping capabilities. A major challenge in designing efficient scheduling policies is the lack of reliable estimates of system parameters. We propose a robust scheduling policy that does not depend on system parameters, and analyze its performance properties. Chair: Kuang Xu, Stanford University, Stanford, CA, United States of America, kuangxu@stanford.edu

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