INFORMS 2021 Program Book

INFORMS Anaheim 2021

MD03

more volatile after the implementation of GDPR than before. Interestingly, however, the opposite effect is found in the paid app market. Our results suggest that GDPR has significantly affected the competition in the app market. Also, the apps’ pricing strategies influence this impact. Our work contributes to multiple streams of the IS literature and provides meaningful insights for policymakers and firms. 3 - Herding Effects of Subjectivity on Emotional Polarization and Hate Speech in Online Political Discourse Amin Sabzehzar, Arizona State University, Tempe, AZ, United States A recent study by Pew Research Center shows that most Americans can only identify 60% of fact-based political statements from subjective opinions. This paper speaks to this issue by studying the influence of subjective comments on the quality of political discourse in the Reddit r/politics subreddit. Our results shed light on the effect of herding behavior in online political discourse by showing that the subjectivity of top-level comment triggers a subjective, high emotional, and low analytical political discourse. Further, we demonstrate the negative role of subjectivity on the quality of political discourse in terms of emotional polarization and hate speech. Our findings contribute to the discussion about social media use and political polarization, highlighting design implications for online platforms to battle political polarization. 4 - A Fair Framework for Unsupervised Outlier Detection Ensembles Moez Farokhnia Hamedani, University of South Florida, Tampa, FL, United States Outlier detection ensembles are among the most valuable ML-driven decision support systems. Extensive applications of performance metrics such as accuracy and computational complexity for the evaluation of ML-driven decisions have raised concerns about the fairness of the decisions with regards to different groups of entities. e.g. EM R. system auditing might be biased toward specific positions in the system that leads to biased managerial decisions which are made on the basis of the algorithmic outcomes. To address fairness, we propose a fair-framework for debiasing unsupervised ensembles. The proposed framework can be generalized to any ensemble, regardless of the aggregation strategies. Hybrid A Day in the Life of a Practitioner Informs Special Session: Informs Section on Practice Informs Special Session Session Chair: Sharon Arroyo, Boeing Company, Seattle, WA, 98124-2207, United States 1 - A Day in the Life of a Practitioner Sharon Arroyo, Boeing Company, Seattle, WA, 98124-2207, United States In this session, we will be discussing the various responsibilities of an OR practitioner’s role in an organisation. This session will be highly informative for graduate students and early career professionals who are looking forward to building an OR practitioner’s career. 2 - Panelist Sharon Arroyo, Boeing Company, Seattle, WA, 98124-2207, United States 3 - Panelist William Christian, Paygevity, Inc., Severn, MD, 21144-1905, United States, William Christian, DOD, MD, United States 4 - Panelist Rajeev Namboothiri, GE Research, John F. Welch Technology, Bangalore, 560066, India In Person: Leveraging PTC and Advanced Technologies to Increase Rail Capacity General Session Chair: Dharma Acharya, GE Transportation, a Wabtec Corporation, Ponte Vedra, FL, 32081-8471, United States Chair: Ken Kenjale, Wabtec 1 - Capacity and Planning Toward a Moving/Virtual Block Future Ken Kenjale, Wabtec Corporation, Pittsburgh, PA, United States We will discuss various approaches and pros and cons for utilizing moving/virtual block technologies for rail capacity and planning. Moving/Virtual blocks along with automated dispatching will help railroads increase rail capacity without any capital investment to improve physical infrastructure. MD05 CC Ballroom E / Virtual Theater 5 MD06 CC Room 303A

MD03 CC Ballroom C / Virtual Theater 3 Hybrid Michael H. Rothkopf Junior Researcher Paper Prize Sponsored: Auctions and Market Design Sponsored Session Chair: Robert Day, University of Connecticut, Storrs, CT, 06269-1041, United States 1 - Designing Approximately Optimal Search On Matching Platforms Alexander Wei, University of California-Berkeley, Berkeley, CA, United States We study the design of a two-sided matching market in which agents’ search is guided by a platform. The platform determines the rates at which agents of different types meet, while agents strategically accept or reject the potential partners whom they meet. We focus on the platform’s problem of optimal search design in a continuum matching market model where agents have symmetric pairwise preferences. The platform’s objective is to find meeting rates that maximize the equilibrium social welfare of the resulting game. Incentive issues arising from congestion and cannibalization make this design problem intricate. Nonetheless, we give an efficiently computable solution that achieves 1/4 the optimal social welfare. Our solution shows the platform can substantially limit choice while maintaining approximately optimal welfare through a carefully chosen search design. 2 - Award Presenter Rad Niazadeh, Chicago Booth School of Business, Chicago, IL, 94305-5008, United States 3 - When is Assortment Optimization Optimal? Will Ma, Columbia University, New York, NY, 02139-3516, United States A classical question in economics is whether complex, randomized selling protocols can improve a firm’s revenue beyond that of simple, take-it-or-leave-it posted prices. Myerson (1981) answered this question with an emphatic ``No’’ for a monopolist selling a single good. By contrast, for multiple goods, randomized lotteries can significantly increase revenue. We ask the same question for assortment optimization, where the firm cannot control the pricing but must decide on a set of substitute products to offer. To formalize such a question, we introduce a Bayesian mechanism design problem with fixed prices and ordinal customer preferences which captures assortment optimization. We show that generally, a top-k lottery can increase revenue, but for specific choice models the best deterministic assortment is revenue-optimal. MD04 CC Ballroom D / Virtual Theater 4 Hybrid Online Platform Design and User Engagement Sponsored: Information Systems Sponsored Session Chair: Qinglai He, Arizona State University, Tempe, AZ, 85281, United States 1 - Access to IT and Future of Work in the U.S. Leting Zhang, Temple University, Philadelphia, PA, 19122, United States, Taha Havakhor, Rajiv Sabherwal These digital transformations in working conditions post-COVID-19 can be resource-intensive for less-ready businesses. Capitalizing on the opportunity provided by the staggered introduction of stay-at-home orders across 48 states in the U.S. during the first wave of COVID-19 and natural variations in availability of IT services and resources in different regional (county-level) areas, we examine if the lack of sufficient access to businesses to IT resources posed as a barrier in the seamless transformation to WFH during those stay-at-home periods. Our findings show that counties without adequate business access to IT resources experience higher rates of unemployment after stay-at-home orders. We also identify the types of IT resources that help and hurt during the WFH transformation. 2 - How Data Privacy Regulations Affect Competition: Emprical Evidence From Mobile Application Market ““Ix” “Xi Wu, Temple University, Philadelphia, PA, 19122, United States, Min-Seok Pang Data has become a new type of asset for firms, provoking a discussion of data privacy and security. Government regulators start enacting privacy regulations to ensure the transparency of data collection and processing. How these regulations impact competition is a critical question that has not been thoroughly studied. Our study applies a DID framework to examine the effect of GDPR on the mobile app market. We find that the competition in the free app market has become

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