Senthil Veeraraghavan

Jiding Zhang, Ken Moon, Senthil Veeraraghavan (Under Revision), Does Fake News Create Echo Chambers?.

Abstract: Platforms have come under criticism from regulatory agencies, policymakers, and media scholars for the unfettered spread of fake news online. A key concern is that, as fake news becomes prevalent, individuals may fall into online "echo chambers" that predominantly expose them only to fake news. Using a dataset reporting 30,995 individual households’ online activity, we empirically examine the reach of false news content and whether echo chambers exist. We find that the population is widely exposed to online false news. However, echo chambers are minimal, and the most avid readers of false news content regularly expose themselves to mainstream news sources. Using a natural experiment occurring on a major social media platform, we find that being exposed to false news content causes households to increase their exposure to countervailing mainstream news (by 9.1% in the experiment). Hence, a naive intervention that reduces the supply of false news sources on a platform also reduces the overall consumption of news. Based on a structural model of household decisions whether to diversify their online news sources, we prescribe how platforms should moderate false news content. We find that platforms can further reduce the size of echo chambers (by 12-18%) by focusing their content moderation efforts on the households that are most susceptible to consuming predominantly false news, instead of the households most deeply exposed to false news.

Amandeep Singh, Jiding Zhang, Senthil Veeraraghavan (Working), Fulfillment by Platform: Implications for Upstream Market Power.

Shiliang Cui, Xuanming Su, Senthil Veeraraghavan (2018), A Model of Rational Retrials in Queues, Operations Research (Forthcoming).

Shiliang Cui, Senthil Veeraraghavan, Jinting Wang, Yu Zhang (Working), In-Queue Observation and Abandonment.

Senthil Veeraraghavan, Li Xiao, Hanqin Zhang, Impatience and Learning in Queues.

Abstract: Customers often abandon waiting in queues when they get impatient. Prior literature on Markovian queues shows that it is not rational for customers to quit ``midway": Customers should either quit immediately on arrival (balk) or wait till the completion of their service. We show how in-queue abandonment behavior can be rational in queues because of learning. We compare how rational Bayesian customers make abandonment decisions under different information disclosures. Our paper reveals interesting features in waiting behavior, showing that customers can be (rationally) more patient in slower shorter queues, than in faster longer queues. Using stochastic comparisons, we demonstrate that customers who anticipate a congested system can become more impatient. Finally, we show that Bayesian customers may exhibit a more conservative threshold joining behavior compared to myopic customers with the same priors.

Chen Jin, Senthil Veeraraghavan, Chenguang (Allen) Wu, Service Pricing When Customers Collude.

Abstract: We model the presence of collusion amongst customers who wait to consume a service. In congested queues, individual customers seek to maximize their own payoff and compete with each other for limited resources. To overcome this disadvantage, some customers may collude as a group to seek and receive better benefits, often in the form of reduced prices for group members. Using a queuing framework, we characterize a service provider's optimal pricing and priority decisions when serving a market made up by individual customers and collusive customers. We demonstrate how individual customers may benefit from the presence of colluding groups when a single price is charged for the service. The presence of collusion enables individual customers to ``free-ride” and enjoy an increased consumer surplus. Under price discrimination, the service provider prefers to serve individual customers at a higher price, over collusive customers. However, despite the preference for individual customers, the service provider can improve the revenue by better service management, by prioritizing collusive customers and reducing their price benefit. Prioritization of collusive customers can also improve social welfare over the first come, first served (FCFS) policy, by better utilization and increased market coverage.

Laurens Debo, Uday Rajan, Senthil Veeraraghavan (2018), Signaling Quality via Long Lines and Uninformative Prices, M&SOM (Forthcoming).

Abstract: Firms sometimes nurture long lines, rather than raising prices to eliminate waiting times. We justify this practice by considering the informational role of a queue in a setting in which a rm can also adjust its price to signal its quality to uninformed consumers. When the proportion of informed consumers is low, to separate on price a high-quality rm must raise its price above the monopoly price. We show that there exist pooling equilibria in which firms instead charge a price lower than the monopoly price. We characterize two pooling equilibria. In both equilibria, a high-quality rm on average has a longer queue than a low-quality one, and long lines signal high quality. In the first one, the price is sufficiently low that short lines in turn signal low quality, so that the queue length almost perfectly reveals the type of the rm. In the second one, the price is in an intermediate range (but remains lower than the monopoly price), and short lines are an imperfect signal of quality. In each of the pooling equilibria, the high-quality firm earns a higher prot than in the separating equilibrium, despite the longer lines. Therefore, a rm will sometimes signal the quality of a new or improved product via long queues rather than by charging a high price to reduce the queue length.

Joseph Jiaqi Xu, Peter Fader, Senthil Veeraraghavan (2018), Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets, M&SOM (Forthcoming).

Abstract: Many firms have difficulty evaluating the impact of their pricing policy, which further inhibits their ability to properly design and implement dynamic pricing. We address this issue in the context of single-game ticket pricing for a Major League Baseball franchise. We develop and estimate a comprehensive demand model to help evaluate and design dynamic pricing policies for the franchise. Our model encompasses all relevant aspects of the demand generation process, including ticket quantity and stadium seat section choice. The demand model reveals factors that drive sport ticket revenue such as the effect of home team performance on the overall price sensitivity and the relationship between customers' arrival timing and product choice. We show that by leveraging these insights and allowing sufficient pricing flexibility, the franchise can achieve a potential revenue improvement of 17.2% through daily price re-optimization, which is comparable to that of a clairvoyant policy in which the future evolution of demand is assumed to be known.

Hummy Song and Senthil Veeraraghavan, “Quality of Care: An Operations Perspective of Health Care Quality”. In Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, edited by Tinglong Dai and Sridhar Tayur (Eds.), (Hoboken, NJ: John Wiley & Sons, 2018)

Ganesh Janakiraman, Mahesh Nagarajan, Senthil Veeraraghavan (2018), Simple Policies for Managing Flexible Capacity, M&SOM, 20 (2), pp. 333-346.

Abstract: In many scenarios, a fixed capacity is shared flexibly between multiple products. To manage such multi-product systems, firms need to make two sets of decisions. The first one requires setting an inventory target for each product and the second decision requires dynamically allocating the scarce capacity among the products. It is not known how to make these decisions optimally. In this paper, we propose easily implementable policies that have both theoretical and practical appeal. We first suggest simple and intuitive allocation rules that determine how such scarce capacity is shared. Given such a rule, we calculate the optimal inventory target for each product. We demonstrate analytically that our policies are optimal under two asymptotic regimes represented by high service levels (i.e. high shortage costs) and heavy traffic (i.e. tight capacity). We also demonstrate that our policies outperform current known policies over a wide range of problem parameters. In particular, the cost savings from our policies become more significant as the capacity gets more restrictive.