Events & Seminars

We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

Job postings provide unique insights about the demand for skills, tasks, and occupations. Using the full text of data from millions of online job postings, we train and evaluate a natural language processing (NLP) model with over 100 million parameters to classify job postings' occupation labels and salaries. To derive additional insights from the model, we develop a method of injecting deliberately constructed text snippets reflecting occupational content into postings. We apply this text injection technique to understand the returns to several information technology skills including machine learning itself. We further extract measurements of the topology of the labor market, building a ``jobspace'' using the relationships learned in the text structure. Our measurements of the jobspace imply expansion of the types of work available in the U.S. labor market from 2010 to 2019. We also demonstrate that this technique can be used to construct indices of occupational technology exposure with an application to remote work. Moreover, our analysis shows that data-driven hierarchical taxonomies can be constructed from job postings to augment existing occupational taxonomies like the SOC (Standard Occupational Classification) system.

The U.S. Securities and Exchange Commission (SEC) requires firms to notify investors in an SEC filing of a data breach if it constitutes a material event. Importantly, the determination of materiality lies with executives, which has resulted in firms failing to disclose breaches to the SEC or purposely delaying notifications. We draw from the behavioral theory of the firm and executive compensation literature to develop predictions about the influence of IT and non-IT executives’ compensation on firms’ SEC data breach notification delays. Given the possibility of competing priorities and goals of the two executive groups, we argue that increased IT executive compensation leads to fewer delays, whereas increased non-IT executive compensation has the opposite effect. Because corporate boards of directors have oversight and advise on firms’ cybersecurity matters, we argue that the cybersecurity intensity of the firm’s board (i.e., social ties to breached firms) moderates the relationships between IT and non-IT executive compensation and notification delays. To test our hypotheses, we constructed a panel dataset from public sources and performed a series of econometric analyses. Our results suggest that the influence of executive compensation on notification delays differs for IT and non-IT executives in the manner hypothesized. However, for both types of executives, the moderating influence of the board’s cybersecurity intensity works to increase notification delays. Counter to the conventional view that increased cybersecurity experience on the board benefits timely data breach notification, our findings suggest that greater board experience results in delays of timely communications about data breaches via 8-K filings.

Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps, via real-time bidding on ad exchanges. Each location is characterized by its win curve, i.e., the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform's objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest.

Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The regret under any policy is , where is the number of seasons, and that under our policy is . We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform.

Perception regarding a focal firm's corporate social responsibility (CSR) depends not only on itself but also on its known suppliers. This paper provides the first empirical evidence linking CSR and supply chain information disclosure together. We uncover robust evidence that listed firms voluntarily disclose environmentally responsible suppliers while selectively not disclosing" bad" ones, effectively greenwashing their supply chain image. This selective disclosure of green suppliers is prevalent among listed firms across the world. Such corporate behavior is increasing in public awareness of climate change, decreasing in regulations on CSR information transparency. It is more salient for firms who face higher competition or care more about their brand awareness, and for firms that are more profit-driven or held more by institutional investors. Firms that greenwash supply chains observe an increase in sales and valuation, suggesting that consumers and investors do not fully take greenwashing of listed firms into account.

Facility location models are ubiquitously involved in modern transportation and logistics problems. We present recent results of two types of facility-location models that involve (i) competition and probabilistic customer choice or (ii) location-dependent uncertain demand with ambiguously known distribution. For (i), we study a Stackelberg game that admits a bilevel mixed-integer nonlinear program (MINLP) formulation, and derive an equivalent, single-level MINLP reformulation and exploit the problem structures to derive valid inequalities, based on submodularity and concave overestimation, respectively. We also study various model extensions by considering general facility setup costs, multiple competitors, as well as other types of decisions for planning facilities. We conduct numerical studies to demonstrate that the exact algorithm significantly accelerates the computation of CFLP on large-sized instances that have not been solved optimally or even heuristically by existing methods. For (ii), we represent moment information of stochastic demand as piecewise linear functions of location decisions, and then develop an exact mixed-integer linear programming reformulation of a decision-dependent distributionally robust optimization model. Our results draw attention to the need of considering various impacts of competition and location choices on customer demand during strategic facility planning. (Papers related to the talk: https://arxiv.org/abs/2103.04259 ; https://www.sciencedirect.com/science/article/abs/pii/S0377221720309449)

Generalized Gamma process is a pure-jump subordinator with infinite activity, it can be used to construct a flexible two-parameter complete random measure, whose application has appeared in various areas. In this talk, we present an exact simulation algorithm to sample from the largest n jumps of a generalized Gamma process. The algorithm immediately implies a method to sample from the celebrated Poisson-Dirichlet distribution, we will illustrate this method with numerical examples. As an application of our algorithm, we review the construction of the Caron-Fox random graph and discuss a potential modification to its simulation algorithm.

Patient no-shows for scheduled medical appointments are of great concern for many health care providers. In this paper, we tackle the no-show problem by applying insights from behavioral science. Specifically, we ''nudge'' patients into arriving for their scheduled appointment using text reminders of their upcoming visit. We conduct a field experiment at an outpatient specialty clinic where we add to the standard message, an additional line of text that indicates a potentially long wait for the next available appointment (we call this intervention ''waits framing''). Based on a difference-in-differences estimation strategy, we find that waits framing messaging significantly reduced no-shows by a factor of 28.6%. In addition, we find that patients with greater sensitivity to wait, such as those with urgent conditions and those willing to select unpopular slots, are more responsive to the nudge. Through a laboratory experiment, we uncover the mechanism that underlies the nudge---waits framing serves to trigger loss- aversion pertaining to the individual's position in the queue, thereby increasing the perceived cost of missing an appointment. Through the combination of field and designed lab studies, we provide both external and internal validity to the effects of waits framing, and identify the underlying mechanism and heterogeneity in response. Our results have significant implications for clinical operations. At the study site, the resulting improvement in capacity utilization and patient throughput led to a 5.2% increase in clinic revenue. Our findings contribute to the literature on behavioral queuing by showing that through appropriately framed messages, queue operators can tap into the behavioral biases of individuals in order to engender a desired queuing response such as a reduction in queue abandonment.

Agricultural open burning, i.e., the practice of burning crop residue in harvested fields to prepare land for sowing a new crop, is well-recognized as a significant contributor to CO2 and black-carbon emissions, and long-term climate change. Low-soil-tillage practices using a specific agricultural machine called Happy Seeder, which can sow the new seed without removing the previous crop residue, have emerged as the most effective and profitable alternative to open burning. However, given the limited number of Happy Seeders that the government can supply, and the fact that farmers incur a significant yield loss if they delay sowing the new crop, farmers are often unwilling to wait to be processed by the Happy Seeder and, instead, decide to burn their crop residue. We study how the government can use effective information-disclosure policies in the operation of Happy Seeders to minimize agricultural open burning. A Happy Seeder is assigned to process a group of farms in an arbitrary order. The government knows, but does not necessarily disclose, the Happy Seeder’s schedule at the start of the sowing season. Farmers incur a disutility per unit of time while waiting for the Happy Seeder due to the yield loss as a result of late sowing of the new crop. If the Happy Seeder processes a farm, then the farmer gains a positive utility. At the beginning of each period, each farmer decides whether to burn her crop residue or to wait, given the information provided by the government about the Happy Seeder’s schedule. We propose a class of information-disclosure policies, which we refer to as dilatory policies that provide no information to the farmers about the schedule until a pre-specified period and then reveal the entire schedule. By obtaining the unique symmetric Markov perfect equilibrium under any dilatory policy, we show that the use of an optimal dilatory policy can significantly lower the number of farms burnt compared to that under the full- disclosure and the no-disclosure policies. Using data from the rice-wheat crop system in northwestern India – an area of the world with the highest prevalence of open burning – we conduct a comprehensive case study and demonstrate that the optimal dilatory policy can reduce CO2 and black-carbon emissions by at least 14%.

Newcomers are important for the survival of online communities, but their contributions often receive negative reactions and comments from established members. Online communities realize that such negativity can take a toll on newcomers and harm the creation of user­-generated content. We study a novel intervention aimed at reducing hostility toward newcomers: a “newcomer nudge” that informs community members when they are interacting with a newcomer’s post and asks them to be more lenient toward its creator. Taking advantage of granular data from a large deal­-sharing community and a natural experiment, we use a difference-­in­-differences approach and find robust evidence that the newcomer nudge induced members to write 46% more responses per day with 10% fewer negative words during the first two days after a deal was published. Our results show that the nudge­-induced change in behavior toward newcomers increased newcomer retention. However, we also observe that before the nudge, newcomers’ second posts received more net votes (upvotes minus downvotes) than their first posts. After the nudge, newcomers’ subsequent posts were less popular than their first posts, which indicates that the nudge interrupted newcomers’ learning curve by suppressing helpful feedback.