Thursday, November 17, 2022
Seminar held in JMHH 350
Presenter: Francisco Castro
Title: Randomized FIFO Mechanisms
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.
Thursday, November 10, 2022
Seminar held in JMHH 350
Presenter: Can Zhang
Title: Farm Equipment Sharing in Emerging Economies
In emerging economies, there is a growing number of farm equipment sharing platforms that connect smallholder farmers with tractor owners who are willing to fulfill farmers’ requests for mechanization services. Due to the small farm sizes and the low digital literacy in rural areas of emerging economies, these platforms often rely on the so-called “booking agents” to collect demand from individual farmers and submit the aggregated demand on the platform (rather than having individual farmers submit their service requests). This paper studies how the presence of such booking agents affects the platform’s optimal pricing and wage decisions and equilibrium outcomes. Our analysis reveals several insights. First, we show that the platform should consider paying out a higher percentage of the price to booking agents when the number of tractors on the platform increases, when tractor owners’ operating cost decreases, and surprisingly, when the platform puts more emphasis on its profit (over farmer surplus). Second, in contrast to conventional sharing settings, we show that in the presence of booking agents, an increase in the number of service providers (i.e., tractors) may lead to a lower optimal platform commission. Finally, while efforts to enhance the supply side of the platform (e.g., increasing the number of tractors or reducing tractor owners’ operating cost) generally lead to a decrease in price and an increase in total farmer surplus, we find that reducing booking agents’ demand aggregation cost can lead to an increase in price and a decrease in total farmer surplus.
Thursday, November 3, 2022
Seminar held in JMHH 350
Presenter: Itai Gurvich
Title: Near-Optimal Policies for Dynamic Matching
We consider centralized dynamic matching markets with finitely many agent types and heterogeneous match values. A network topology determines the feasible matches in the market and the value generated from each match. An inherent trade-off arises between short- and long-term objectives. A social planner may delay match decisions to thicken the market and increase match opportunities to generate high value. This inevitably compromises short-term value, and the planner may match greedily to maximize short-term objectives.
We characterize networks where this tradeoff is moot and introduce matching policies that achieve the optimal balance between short- and long-term objectives. This balance depends, we show, on the general position gap, a measure that quantifies the demand-imbalance in the network. The optimal policies highlight the fundamental difference between multi-way and two-way networks.
The talk is based on joint work with Suleyman Kerimov and Itai Ashlagi
Tuesday, October 25, 2022
Seminar held in JMHH 350
Presenter: Grace Gu
Title: Technology Fragmentation, Platform Investment, and Complementary Innovation
Complementor innovation is an essential form of value co-creation in open platform ecosystems. However, the increasing platform fragmentation, i.e., users in the ecosystem adopting different versions of the platform technologies, has significantly hindered complementor innovation. For instance, there are a dozen different versions of the Android operating system currently running among Android devices, which increases the cost of complementor innovation as app developers must exhaustively test on different versions of the operating system before releasing new updates. Reducing platform fragmentation is a complex coordination problem involving multiple parties in the ecosystem, thus it is unclear whether and how a platform’s efforts to fight fragmentation would affect complementor innovation. Focusing on recent efforts by Google to address Android fragmentation, we find that while the platform investment does not immediately reduce Android fragmentation, app developers respond to the platform’s action by significantly increasing their innovation efforts in updating apps shortly after the platform investment. We find support of two possible explanations for the developers’ forward-looking behavior: a lower anticipated cost structure and a higher perceived platform value in future. Our research highlights the role of platform commitment to improve platform infrastructure in complementor innovation and provides implications for platform investment and intervention.
Thursday, October 20, 2022
Seminar held in JMHH 260
Presenter: Michael Freeman
Title: Continuity of Care Increases Physician Productivity in Primary Care
Continuity of care, defined as an ongoing therapeutic relationship between a patient and a physician, is a defining characteristic of primary care. However, arranging a consultation with one’s regular doctor is increasingly difficult as practices face physician shortages. We study the effect of declining care continuity on the productivity of physicians by analyzing data of over 10 million consultations in 381 English primary care practices over a period of 11 years. Specifically, we examine whether a consultation with the patient’s regular doctor is more productive than with another doctor in the practice. Using statistical models that account for confounding and selection bias and restricting the sample to consultations with patients who had at least three consultations over the past two years, we find that the time to a patient’s next visit is on average 18.1% (95% CI: [16.9%, 19.2%]) longer when the patient sees the doctor they have seen most frequently over the past two years, while there is no operationally meaningful difference in consultation duration. The data shows that the productivity benefit of care continuity is larger for older patients, patients with multiple chronic conditions, and patients with mental health conditions. We estimate that the total consultation demand in our sample could have fallen by up to 5.2% had all practices offered continuity of care at the level of the top decile of practices while prioritizing patients expected to yield the largest productivity benefits. We discuss operational and strategic implications of these findings for primary care practices and for third-party payers.
Tuesday, October 18, 2022
Seminar held in JMHH 355
Presenter: John Lazarev
Title:Quantifying Delay Propagation in Airline Networks” (joint with Liyu Dou and Jakub Kastl)
We develop a framework for quantifying delay propagation in airline networks. Using a large comprehensive data set on actual delays and a model-selection algorithm (elastic net) we estimate a weighted directed graph of delay propagation for each major airline in the US. We use these estimates to decompose the airline performance into “luck” and “ability.” We find that luck may explain about 38% of the performance difference between Delta and American in our data. We further use these estimates to describe how network topology and other airline network characteristics (such as aircraft fleet heterogeneity) affect the expected delays. Finally, we propose a model of aircraft scheduler who decides which flights to delay and by how much. We then use the estimated model to evaluate counterfactual scenarios of investments in airport infrastructure in terms of their impact on delays.
Tuesday, September 27, 2022
Seminar held in JMHH 350
Presenter: Marshall Van Alstyne
Title: Free Speech, Platforms & The Fake News Problem
How should a platform or a society address the problem of fake news? The spread of misinformation is ancient, complex, yet ubiquitous in media concerning elections, vaccinations, and global climate policy. After examining key attributes of “fake news” and of current solutions, this article presents design tradeoffs for curbing fake news. The challenges are not restricted to truth or to scale alone. Surprisingly, there exist boundary cases when a just society is better served by a mechanism that allows lies to pass, even as there are alternate boundary cases when a just society should put friction on truth. Harm reflects an interplay of lies, decision error, scale, and externalities. Using mechanism design, this article then proposes three tiers of solutions: (1) those that are legal and business model compatible, so firms should adopt them (2) those that are legal but not business model compatible, so firms need compulsion to adopt them, and (3) those that require changes to bad law.
The first set of solutions, grounded in choice architecture, seek to alter information sets available to those affected by misinformation. By enabling transparency into not simply the content and sources but also the distribution and destination, the system provides effective means for counter narratives that are infeasible under current transparency proposals.
The second set of solutions, based in externality economics, considers how to protect free speech while updating Section 230. Revisions have faced two main critiques: one, that holding platforms actionable for false speech would cause them to take down user speech, and two, that ambiguity in individual messages makes judgement of false speech infeasible at scale. Whistleblower testimony before congress emphasized platform amplification of content in pursuit of engagement. A targeted solution, therefore, can separate original speech from amplified speech, generously protecting the former while reverse amplifying the latter. The posting and even discovery of false speech is protected even as amplification is unprotected. The second element uses scale as an advantage. Rather than vet every message, the system takes only statistical samples. The Central Limit Theorem guarantees that establishing the presence of misinformation in amplified speech is feasible to any level of desired accuracy simply by taking larger samples.
The third set of solutions imports insights of antitrust jurisprudence into free speech jurisprudence. The paradox of antitrust before 1978 was that legal decisions, intended to protect consumers and free markets, artificially raised prices by protecting inefficient firms from consequences of competition. Free speech rulings vigorously protect speakers on the basis of enabling a free market of ideas. Overzealous protection of those promoting false facts, however, prevents the market from clearing itself. No government intervention is required. Rather, it simply needs to step aside in such cases as WASHLITE v Fox News, where numerous false stories that covid is no worse than flu and that vaccines do not work have been causally implicated in thousands of unnecessary deaths. The free speech paradox is that legal decisions intended to protect citizens and free idea markets artificially raise harms and protect those pushing false claims from consequences of acting on those claims.
Thursday, September 22, 2022
Seminar held in JMHH 350
Presenter: Spyros Zoumpoulis
Title: Quantifying the Benefits of Targeting for Pandemic Response
Problem definition: To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Since targeting is potentially contentious, rigorously quantifying its benefits is critical for designing effective and equitable pandemic control policies.
Methodology/results: We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: age of different groups and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation focused on the Île-de-France region of France, based on commonly available public data. We find that targeted policies generate substantial complementarities that lead to Pareto improvements, reducing the number of deaths and the economic losses, as well as the time in confinement for each age group. Optimized dual-targeted policies are interpretable: by fitting decision trees to our raw policy’s decisions across many problem instances, we find that a feature corresponding to the ratio of marginal economic value prorated by social contacts is highly salient in explaining the confinements that any group – activity pair experiences. We also propose coarser and thus more practical targeting mechanisms that avoid explicitly enforcing differential confinements across age groups, while still capturing some of the benefits of dual targeting.
Implications: Given that some amount of targeting of activities and age groups is already in place in real-world pandemic responses, our framework highlights the significant benefits in explicitly and transparently modelling targeting and identifying the interventions that rigorously optimize overall societal welfare.
Tuesday, September 20, 2022
Seminar held in JMHH 350
Presenter: David Rand
Title: Harnessing Partisan Animosity to Solve the Misinformation Problem
When discussing the ills afflicting social media, there is a great deal of concern about the role played by partisan animosity. Among other negative consequences, dislike of counter-partisans – and political motivations more generally – have been suggested to promote belief in, and sharing of, misinformation. While partisanship may be part of the misinformation problem, here I will argue that political motivations are also essential for one of the only possibilities for identifying (and therefore combatting) misinformation at scale – the wisdom of crowds. While professional fact-checkers play a critical role in countering misinformation, they are relatively few in number, and cannot possibly keep up with the vast amount of content posted on social media every day. Recent work has suggested that it is possible to supplement professional fact-checking by harnessing the wisdom of crowds: the ratings of fairly small politically-balanced groups of laypeople can generate high levels of agreement with professional fact-checkers. A central challenge for conducting crowd-based evaluations at scale, however, is the problem of encouraging participation – why should people bother to flag misleading content? In this talk, I will argue that not wanting people to be exposed to posts by counter-partisans helps to solve this participation problem by motivating people to flag. Although extreme partisans would flag all counter-partisan content as misleading regardless of its actual truth value, such extreme partisans are rare. A much larger group of people care somewhat about both truth and partisanship, such that they would only be sufficiently motivated to flag when content is both misleading and counter-partisan. For these people, the partisan motivation is needed to drive participation – without any partisan motive they would flag nothing. I will present experimental and observational social media data supporting this account.
Thursday, September 8, 2022
Seminar held in JMHH 540
Presenter: Jan Van Mieghem
Title: Smart Logistics, Sourcing and ESG
We discuss how smart logistics can facilitate environmental aspirations to combat climate change.
Some smart logistics utilize dual sourcing models to increase slower carbon-friendly transport modes while remaining responsive.
We review analytic solutions to this classic model and compare them with recent deep learning solutions.
October 6-7, 2022
Since 2006, the Workshop for Empirical Research in Operations Management brings together a community of scholars with a passion for empirical research in Operations. The purpose of the Workshop is to exchange research ideas, share experiences in the publication process, discuss methodological issues, and grow together as a group of colleagues with a common research interest.
October 16th, 2022
2022 Informs Reception
Location: JW Marriott Indianapolis – 10 S. West Street, Indianapolis, IN 46204
Sunday, October 16th
7:00 – 9:00 pm