INFORMS 2021 Program Book

INFORMS Anaheim 2021

TC42

TC38 CC Room 210D In Person: Platforms, Networks, and New Markets General Session Chair: Fatemeh Navidi, University of Chicago, Chicago, IL, United States Co-Chair: Rad Niazadeh, Chicago Booth School of Business, Stanford, CA, 94305-5008, United States 1 - Structuring Question and Answer Communities Neha Sharma, Northwestern University Kellogg School of Management, Evanston, IL, United States, Achal Bassamboo, Gad Allon Q&A communities started as a supplement to customer service but now most firms have their own community for customer engagement and support. Typically, in such communities, a user posts a question; other users with more knowledge can then answer her question. The asker sees all the answers and then chooses the best answer. The user who provided the best answer gets reward points, while the asker gains value from getting an answer to her question.We analyze how users decide to participate in the community. We then study the change in participation and network structure due to users’ decisions and growth in knowledge. Finally, we study the number of questions asked and answered over time. We also find that a community designer faces a trade-off between generating traffic through increased participation and efficiency in terms of the number of answered questions. 2 - Food Delivery with Unknown Demand Fatemeh Navidi, University of Chicago, Chicago, IL, United States, Rene A. Caldentey, Ozan Candogan, Rad Niazadeh We model the service operation of a food delivery platform as an online decision making problem over discrete time horizon. At each time we need to jointly pick multiple restaurant rankings to show to the users based on their neighborhood and assign delivery routes to the drivers in a way that maximizes the generated revenue net routing cost. Given this model, we assume each restaurant is associated with an unknown attraction parameter, which captures the likelihood of generating an online order for the restaurant when is considered by a user. To learn these parameters, and therefore the demand, we can use the upcoming online orders over time as information feedback. Being equipped with this feedback mechanics, we design a polynomial time online learning algorithm that implicitly learns the attraction parameters and achieves a near-optimal multiplicative-additive regret. TC39 CC Room 211A In Person: Information Design and Incentive Management General Session Chair: Minjun Chang, Durham, NC, 27713, United States 1 - Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms Can Kucukgul, The University of Texas at Dallas, Richardson, TX, 75080, United States, Ozalp Ozer, Shouqiang Wang Many online retailing platforms offer time-locked sales campaigns as an innovative selling mechanism, whereby third-party sellers sell their products at a typically discounted price for a fixed time horizon of pre-specified length. To incentivize purchases, platforms provide some information on up-to-date sales as campaigns progress, in the hope of influencing an upcoming customer’s valuation of products. Using a dynamic Bayesian persuasion framework, we study how a revenue-maximizing platform should optimize its information policy for such a setting. We propose a heuristic policy that is easy-to-implement and numerically shown to perform well. Our policy yields significant profit improvement upon some naïve policies currently implemented in practice. Finally, we demonstrate the generality of our methodology by relaxing some informational assumptions. 2 - Containing the Outbreak of an Epidemic Minjun Chang, Duke University, Durham, NC, United States To contain the outbreak of an epidemic, public agencies need the population to take costly protective actions, such as vaccination or social distancing. Agencies track the origin of the disease on a network and inform individuals about their risks of being infected so as to persuade them to act. The more individuals protect themselves, the less likely the disease will spread to a particular node, which lower the incentive for one player to act. We study how public agencies should inform a population so as to mitigate such free riding incentive issues, and, ultimately minimize the impact of an outbreak.

TC40 CC Room 211B In Person: Equilibrium and Games in Mathematical Finance General Session

Chair: Moritz Voss, University of California, Los Angeles, Los Angeles 1 - Managing Projects in Virtual Settings: Information Exchange Networks and Project Performance Sukrit Pal, Doctoral Candidate, Michigan State University, East Lansing, MI, United States, Anand Nair Projects are increasingly being managed in virtual settings where project team members collaborate online. Collaborative information exchanges among team members result in the creation of complex communication network that can have non-trivial influence on the outcome due to the asynchronous nature of these exchanges and the lack of visual and non-verbal cues. We examine the impact of communication network characteristics on the number of issues closed within an open source software (OSS) development project. Additionally, the study examines the role of project managers’ active participation in these communication networks on project outcome. We analyzed a panel dataset comprising of 1842 OSS development projects spanning 104 weeks from the time of project initiation was carefully compiled for this research. 2 - Equilibrium Asset Pricing with Transaction Costs: Theory and Numerics Xiaofei Shi, Columbia University, New York, NY, United States In a risk-sharing economy we study how the price dynamics of an asset depends on its “liquidity”. An equilibrium is achieved through a system of coupled forward-backward SDEs, whose solution turns out to be amenable to an asymptotic analysis for the practically relevant regime of large liquidity. We also discuss how to leverage deep-learning techniques to obtain numerical solutions, and compare them with our asymptotic approximations. 3 - Trading With The Crowd Moritz Voss, UC Los Angeles, Los Angeles, CA, United States We study a multi-player stochastic differential game between financial agents who seek to liquidate their position in a risky asset in the presence of jointly aggregated transient price impact on the asset’s execution price along with taking into account a general price predicting signal. The unique Nash-equilibrium strategies reveal how each agent’s policy adjusts the predictive trading signal for the accumulated transient price distortion induced by all other agents’ price impact; and thus unfolds a direct link in equilibrium between the trading signal and the agents’ trading. We also formulate and solve the limiting mean field game and show how the finite-player Nash equilibrium strategies converge to the mean field game solution. TC42 CC Room 212B In Person: Environment, Energy, and Natural Resources Contributed Session Chair: Diwas Paudel, University of South Florida, Tampa, FL, United States 1 - Predictive Multi-microgrid Generation Maintenance, Formulation And Impact On Operations & Resilience This work proposes a framework that builds a seamless integration between sensor data and operational & maintenance (O&M) drivers in a multi-microgrid setting and demonstrates the value of this integration for improving multiple aspects of microgrids operations. The framework offers an integrated stochastic optimization model that jointly optimizes O&M. Operational uncertainty from renewables, demand, and market prices are modeled through scenarios. We use the model structure to develop a decomposition-based solution algorithm to ensure computational scalability. The model provides significant improvements in Farnaz Fallahi, GRA, Wayne State University, Detroit, MI, United States, Murat Yildirim, Jeremy Lin, Caisheng Wang Yize Chen, University of Washington, Seattle, WA, United States The proliferation of electric vehicles calls for reliable and efficient operations of EV charing stations, which are often limited by the charging capacity and electrical network constraints. In this talk, by taking the state-of-charge information into account, we formulate the EV charing problem as a throughput maximization problem. The resulting adaptive charing algorithm can not only serve the most charging sessions, it can only schedule the charing rate by respecting the charing rate and demand congestion constraints. terms of reliability, costs, generation availability, & resilience. 2 - Efficient EV Charging via Throughput Maximization

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