Informs Annual Meeting 2017

SB12

INFORMS Houston – 2017

SB12

across regions. To overcome these issues members of the Organ Procurement Transplant Network can propose a modification (i.e., a variance) to the current allocation system to allocate organs differently than the OPTN Policies. In this study, using optimization and simulation, we investigate how the implementation of the Statewide Sharing variance would affect access to and outcomes of transplantation for the state of Georgia over time. 2 - Comparison of Particle Filter Methods for the Estimation of Reproduction Number of Influenza Epidemics Xi Chen, University of Texas at Austin, 3500 Greystone Drive, Apt 271, Austin, TX, 78731, United States, carol.chen@utexas.edu Accurately estimating the parameters of the infectious disease progression model can efficiently help organizations determining the progression and severity of the disease and response properly and quickly. We propose a Posterior particle filter and two Single Statistic Posterior particle filters and compare them with Bootstrap Filter and Auxiliary Particle Filter to show their ability of accurately and consistently estimating the parameters in infectious disease progression models which are non-linear in nature. Besides, we show the importance of the re- sampling algorithm in helping improving the consistency of particle filters. 3 - Identifying Temporal Disease Progression Patterns using Electronic Medical Records Xiaochen Wang, PhD Student, Yale University, 300 George Street, Suite 503, New Haven, CT, 06511, United States, xiaochen.wang@yale.edu, Hongyu Zhao Wide availability of electronic medical records nowadays allows for a more comprehensive understanding about disease progression patterns and their associations with patients’ outcomes. We will describe a multi-layer continuous- time hidden Markov model to learn the hidden states of a disease and their progression on patients receiving different treatment. Our disease progression model can explicitly include demographic information and characterize treatment effects under different treatments. We will show how our model parameters can be estimated using the EM-based algorithms, and illustrate its usefulness through its application to a congestive heart failure patient cohort. 4 - Minimizing Health Compromising Behaviors via School-based Programs: An Application of Linear Programming Banafsheh Behzad, Assistant Professor, California State University, Long Beach, 1250 Bellflower Blvd., MS.850, CBA 440, Long Beach, CA, 90840, United States, banafsheh.behzad@csulb.edu, Niloofar Bavarian, Sheena Cruz We aimed to examine the minimum curriculum delivery levels of a school-based social-emotional and character development program needed to observe an impact on youth’s internalizing and externalizing behaviors and outcomes, while controlling for known barriers to implementation fidelity by using a linear programming model. 5 - Considering Efficacy-based Objective Functions for Dorfman Style Pooling using a Combo Assay Evan Mullins, Virginia Tech, 966 Nellie’s Cave Road, Blacksburg, VA, 24060, United States, evanm@vt.edu, Ebru Korular Bish, Doug Bish We derive performance metrics for a two-infection combo assay and develop an optimization model to capture classification accuracy with respect to a testing budget constraint and an optimization model that minimizes total cost to the testing center. We consider performance metrics influenced by the dilution effect and then modify them for the special case of no dilution effect. We propose three hypothetical sensitivity functions to quantify that quantify the dilution effect and analyze their effect on the performance metrics. The methodology is then evaluated by a numerical study based on for four distinct sub-groups. 6 - Optimal Budget Allocation for Infectious Disease Control Hamideh Anjomshoa, IBM.Research Australia, 204 Lygon Street, Carlton, Victoria, 3124, Australia, hamideh.a@au.ibm.com, Manoj Gambhir, Roslyn Hickson, Olivia Jayne Smith There are typically multiple possible intervention combinations that can be applied to control infectious disease outbreaks. We simulate the spatiotemporal disease transmission dynamics and subsequent impacts of intervention applications. We identify the optimisation framework to optimally allocate resources given the budgetary constraints which we demonstrate with a case study of dengue fever. The key challenge in this work is the nonlinear dynamics, combined with budget allocations between spatial regions.

332B Marketplace Design Sponsored: Manufacturing & Service Oper Mgmt,

Service Operations Sponsored Session Chair: Itai Ashlagi, Stanford University, Stanford, CA, 94305, United States, iashlagi@stanford.edu 1 - Communication Requirements and Informative Signaling in Matching Markets Itai Ashlagi, s, Stanford, CA, 94305, United States, iashlagi@stanford.edu, Mark Braverman, Yash Kanoria, Peng Shi We study how much communication is needed to and a stable matching in a two- sided matching market with private preferences. Segal (2007) and Gonczarowski et al. (2015) showed that in the worst case, any protocol that computes a stable matching requires the communication cost per agent to scale linearly in the total number of agents. In real-world markets with many agents, this communication requirement is implausibly high. This casts doubts on whether stable matchings can arise in large markets. We study markets with realistic structure on the preferences and information of agents, and show that in “typical” markets, a stable matching can be found with much less communication effort. Our protocols have good incentive properties and give insights on how to mediate large matching markets to reduce congestion. 2 - The Reverse Economics of (thin) Centralized Matching Markets Afshin Nikzad, Stanford University, 741A Homer Ave, Stanford, CA, 94301, United States, afshin.nikzad@gmail.com We study the monopoly and duopoly equilibria of centralized matching markets where there is externality for thickness. When these markets are “thick”, their equilibria are governed by the usual economic laws, e.g. the higher the demand, the higher the price. However, when these markets are “thin”, their economics could be very different. In particular, we characterize the Reverse Law of Demand, according to which, the wage paid to workers may go up when the labor supply goes up. We also characterize the Adverse Effect of Competition in the duopoly equilibrium, according to which, customers may face higher price (and therefore lower average welfare) under a duopoly than under a monopoly. 3 - Dynamic Reserve Prices for Repeated Auctions: Learning from Bids Hamid Nazerzadeh, CA, United States, nazerzad@marshall.usc.edu, Yash Kanoria A large fraction of online advertisements are sold via repeated second-price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: How can the auctioneer optimize the reserve prices by learning from the previous bids, while accounting for the long-term incentives and strategic behavior of the bidders? To this end, we consider a seller who repeatedly sells ex ante identical items via a second-price auction. Buyers’ valuations for each item are drawn i.i.d. from a distribution $F$ that is unknown to the seller. We find that if the seller attempts to dynamically update a common reserve price based on the bidding history, this creates an incentive for buyers to shade their bids, which can hurt revenue. When there is more than one buyer, incentive compatibility can be restored by using personalized reserve prices, where the personal reserve price for each buyer is set using the historical bids of other buyers. Such a mechanism asymptotically achieves the expected revenue obtained under the static Myerson optimal auction for $F$. Further, if valuation distributions differ across bidders, the loss relative to the Myerson benchmark is only quadratic in the size of such differences. If up- front fees are permitted, we show how the seller can determine such payments based on the bids of others to obtain an approximately incentive compatible- mechanism that extracts nearly all the surplus. 332C Health Care, Strategy and Policy Contributed Session Chair: Hamideh Anjomshoa, IBM Research Australia, Carlton, Victoria, Australia, hamideh.a@au.ibm.com 1 - Evaluating the Effects of the Implementation of Variances to the Current Allocation System on the State of Georgia Monica Gentili, University of Louisville, JB Speed Building, Room 304, Louisville, KY, 40292, United States, monica.gentili@louisville.edu, Shanthi Muthuswamy, Mohsen Mohammadi, Vinod Venkata Naga Sai The U.S. transplant community has long been concerned about disparities in access to and outcomes of transplantation. One of the alleged causes of disparity is administratively determined organ allocation boundaries that limit organ sharing SB13

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