2015 Informs Annual Meeting

TD25

INFORMS Philadelphia – 2015

TD25 25-Room 402, Marriott Economic Models and Analysis of Networks and Platforms Sponsor: Information Systems Sponsored Session Chair: Soumya Sen, University of Minnesota, Minneapolis, MN, United States of America, ssen@umn.edu 1 - Payments for Transactions Versus Payments for Discoveries: Theoretical Analyses Karthik Kannan, Purdue University, 403 W.State Street, West Lafayette, IN, 47907, United States of America, kkarthik@purdue.edu, Rajib Saha eBay.com in the U.S. charges payments for transactions but Taobao.com in China charges for discoveries. We theoretically study such payment schemes used by the platforms. We surprisingly find that when payments are for discoveries, the platform has an incentive to make welfare-decreasing matches between sellers and buyers. Similarly, in order for payments for transactions to be sustained, buyers and sellers should sufficiently value factors such as trust provided by the platform. 2 - The Impact of Online Word of Month on Channel Disintermediation for Information Goods Brian Lee, University of Connecticut, 2100 Hillside Road Unit 1041, Storrs, CT, 06268, United States of America, brian.lee@business.uconn.edu, Xinxin Li With the advance in digital technology, creators of intellectual products can sell their work directly to consumers without the help of publishers. In this study, we construct an analytical model to examine the role of online word of mouth (eWOM) in this trend of disintermediation. We find that eWOM may encourage creators to reintermediate publishers for high quality work. Our model makes predictions on when eWOM benefits publishers and for what types of products/creators it has the most impact. 3 - Electric Vehicle Power Plants: Carsharing Optimization with Smart Electricity Markets Micha Kahlen, Erasmus University Rotterdam, Burgemeester Oudlaan 50, Rotterdam, Netherlands, kahlen@rsm.nl, Wolf Ketter We study electric vehicles as power plants to bridge weather dependent energy shortages from wind and solar energy. Particularly, we are interested in the allocation of electric vehicles by making a trade-off between driving and storing electricity. This allocation is optimized in a first-price sealed bid auction with pricing signals from smart electricity markets and the availability of electric vehicles. Results show positive effects for drivers, carsharing operators, and the environment. 4 - Should You Go with ``Pay as You Go’’?:

3 - Replenishment and Fulfillment Based Aggregation for General Assemble-to-Order Systems Emre Nadar, Assistant Professor, Bilkent University, Department of Industrial Engineering, Bilkent University, Ankara, Turkey, emre.nadar@bilkent.edu.tr, Alan Scheller-wolf, Alp Akcay, Mustafa Akan We present an approximate dynamic programming method to optimizing Markovian assemble-to-order systems. We alleviate the computational burden by reducing the large state space of the problem via a novel aggregation method that builds upon certain component and product characteristics. We show the optimality of a lattice-dependent base-stock and rationing policy for the aggregate problem. We also derive finite error bound for the cost function of the aggregate problem under a mild condition. 4 - Optimal Manufacturing Policies for Engineer-to-Order Proteins Tugce Martagan, Eindhoven University of Technology, 5600 MB Eindhoven, Eindhoven, Netherlands, T.G.Martagan@tue.nl, Ananth Krishnamurthy We develop continuous state Markov decision models to optimize design decisions related to protein purification operations. We focus on engineer-to- order proteins with strict production requirements on quality and yield. We present a state aggregation mechanism to solve industry-size problems. Our models and insights are implemented in practice. Social Network Analytics Sponsor: Artificial Intelligence Sponsored Session Chair: Xi Wang, The University of Iowa, S343 PBB, Iowa City, IA, 52242, United States of America, xi-wang-1@uiowa.edu 1 - Optimizing Hurricane Warning Dissemination Problem for Evacuation Decision Making Dian Sun, Harbin Engineering University, 172 Princeton Ave. Apt 1, Buffalo, NY, 14226, United States of America, sundian@hrbeu.edu.cn, Yan Song, Zifeng Su Individual make evacuation decisions based on risk perception which can be socially influenced as evacuation warnings spread through social networks. In this study a formal model for evacuation warning dissemination in social networks through time is presented to characterize the social influence of the risk perception in the evacuation decision making process. Simulation models are developed to investigate the effects of community mixing patterns and the strength of ties on evacuation decision. 2 - Inferring User Location from Geographic and Social Network Da Xu, PhD Student, University of Utah, 1032 E 400 S, Apt 504B, Salt Lake City, UT, 84102, United States of America, Da.Xu@business.utah.edu, Xiao Fang The lack of tools to monitor the time-resolved locations of individuals constrains us to gain a deep understanding of human mobility. While, despite the diversity of people’s travel history, human mobility follows a high of temporal and spatial regularity. In this paper, we study the human mobility through social and geographic networks, give a deep insight to how individual mobility pattern and social network impact with each other, and build a probabilistic model to depict human mobility. 3 - Concurrent Diffusions of Information and Behaviors in Online Social Networks Shiyao Wang, University of Iowa, 634 Westgate St. Apt. 55, Iowa City, IA, 52246, United States of America, shiyao-wang@uiowa.edu, Kang Zhao Using the spread of the Ice Bucket Challenge (IBC) on Twitter as a case study, this research compared the concurrent diffusion patterns of both information and behaviors in online social networks. Individual behaviors related to IBC were detected by text mining techniques. Comparison between diffusion dynamics of information and behaviors at different levels revealed interesting differences and interactions between the two diffusion processes and laid foundations for future analytics. TD24 24-Room 401, Marriott

Optimal Design of Bucket Plans for Multi-unit Goods Manish Gangwar, Assistant Professor, ISB, ISB Campus, Gachibowli, Hyderabad, India, manish_gangwar@isb.edu, Hemant Bhargava

Among the class of nonlinear tariffs, “Three Part Tariff” is the most general tariff but it tends to focus on heavy users. Given the evident optimality of a bucket plan, we ask what are the pros and cons of alternative pricing models? We derive the closed-form expressions for commonly used demand function and specify a system of equations with economic interpretation for the general problem. We also examine the properties of optimal “Three Part Tariff” in the presence of a per- unit plan.

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