Seminars 2021-2022

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How do you measure the value of a commodity that transacts at a price of zero from an economic standpoint? This study examines the potential for and extent of omission and misattribution in standard approaches to economic accounting with regards to open source software, an unpriced commodity in the digital economy. The study is the first to follow usage and upgrading of unpriced software over a long period of time. It finds evidence that software updates mislead analyses of sources of firm productivity and identifies several mechanisms that create issues for mismeasurement. To illustrate these mechanisms, this study closely examines one asset that plays a critical role in the digital economic activity, web server software. We analyze the largest dataset ever compiled on web server use in the United States and link it to disaggregated information on over 200,000 medium to large organizations in the United States between 2001 and 2018. In our sample, we find that the omission of economic value created by web server software is substantial and that this omission indicates there is over $4.5 billion dollars of mismeasurement of server software across organizations in the United States. This mismeasurement varies by organization age, geography, industry and size. We also find that dynamic behavior, such as improvements of server technology and entry of new products, further exacerbates economic mismeasurement.

We propose a simple explanation for price rigidity in perishable groceries: inventory record inaccuracy (IRI). We build our argument in two steps. First, we tailor Gallego and Van Ryzin’s (1994) revenue management model to perishable groceries by adding an inventory waste process, deteriorating product quality, and menu costs. Comparing the model’s optimal time-dependent pricing policy (i.e., when prices do not condition on real-time inventories – which corresponds to IRI) with its optimal state-dependent policy (i.e., when prices do condition on real-time inventories – which corresponds to no IRI), we demonstrate that IRI can explain about 90% of the menu costs required to rationalize any given level of price rigidity. Moreover, based on the model, we predict that reducing menu costs from 10% of marginal cost to 1% would increase a representative product’s price-updating frequency without IRI by at least three times more than it would with IRI. Second, we test our prediction with data from two case studies: a U.K. grocery store that installed electronic shelf labels (ESLs) – a technology that reduces physical menu costs – and an E.U. grocery store that installed both ESLs and GS1 barcodes – the latter being a technology that reduces IRI. Consistent with our prediction, the technology upgrade increased price-updating frequency by 55% in the first case study and by 853% in the second study.

Marketplace platforms use experiments (also known as “A/B tests”) as a method for making data-driven decisions about which changes to make on the platform. When platforms consider introducing a new feature, they often first run an experiment to test the feature on a subset of users and then use this data to decide whether to launch the feature platform-wide. However, it is well documented that estimates of the treatment effect arising from these experiments may be biased due to the presence of interference, due to substitution effects on the demand and supply sides of the market.

In this work, we develop an analytical framework to study experimental design in two-sided marketplaces. We develop a stochastic market model and associated mean field limit to capture dynamics in such experiments. Notably, we use our model to show how the bias of commonly used experimental designs and associated estimators depend on market balance. We also propose novel experimental designs that reduce bias for a wide range of market balance regimes. Finally, we discuss a simpler model to study the bias-variance trade-off among different experimental choices. Overall, our results yield insights on experimental design for practitioners.

Based on joint work with Ramesh Johari, Hannah Li, Inessa Liskovich, and Geng Zhao.

The past years have seen the development and deployment of machine-learning algorithms to estimate personalized treatment-assignment policies from randomized controlled trials. Yet such algorithms for the assignment of treatment typically optimize expected outcomes without taking into account that treatment assignments are frequently subject to hypothesis testing. In this article, we explicitly take significance testing of the effect of treatment-assignment policies into account, and consider assignments that optimize the probability of finding a subset of individuals with a statistically significant positive treatment effect. We provide an efficient implementation using decision trees, and demonstrate its gain over selecting subsets based on positive (estimated) treatment effects. Compared to standard tree-based regression and classification tools, this approach tends to yield substantially higher power in detecting subgroups with positive treatment effects.

Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g. blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Due to lack of updated information, existing sample transportation (ST) systems typically operate fixed schedules which do not account for demand variability. We present an innovative approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones and integrate it with a novel Multi-Stage version of the Dynamic Multi-Period Vehicle Routing Problem to generate daily routes in response to this updated information. The Optimized Sample Transportation (OST) system which comprises two components: a novel data sharing platform to monitor incoming sample volumes at healthcare facilities, and an optimization-based solution approach to the problem of routing and scheduling courier trips in a multi-stage transportation system. Our solution approach performs well in a range of numerical experiments. We implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Based on analysis of implementation data describing over 20,000 samples and results transported during July-October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Results from our implementation demonstrate the practical feasibility of our approach for improving centralized ST operations in Malawi and its broader applicability to other resource-limited settings, particularly in sub-Saharan Africa.

https://mitsloan.mit.edu/faculty/directory/jonas-oddur-jonasson

Despite acknowledging the importance of events, organizational scholars rarely explore the influence of broader societal events on employee experiences and behaviors at work. Recognizing the importance of major societal occurrences, Leigh and Melwani (2019) introduced a theory of mega-threats – large scale identity relevant negative occurrences that receive significant media attention – that begins to explain the impact of major societal events on individuals and organizations. In this talk, I will introduce new theory and present results from multiple studies that explains the psychological consequences of mega-threats – namely embodied threat – for individuals that share identity group membership with those targeted and/or harmed by mega-threats. I then demonstrate that this embodied threat spills over into the workplace, leading racial minority employees to engage in a process of emotional and cognitive suppression that I characterize as identity labor. Finally, I demonstrate that this process of identity labor has detrimental effects on employees and ultimately organizations, by leading employees to engage in higher levels of avoidance behaviors in the days following a mega-threat. Taken together, this work yields important theoretical and practical implications about the significant influence that societal events have on employees at work.

https://www.fuqua.duke.edu/faculty/angelica-leigh

We propose a new diffusion-asymptotic analysis for sequentially randomized experiments, including those that arise in solving multi-armed bandit problems. In an experiment with n time steps, we let the mean reward gaps between actions scale to the order 1/\sqrt{n} so as to preserve the difficulty of the learning task as n grows. In this regime, we show that the behavior of a class of sequentially randomized Markov experiments converges to a diffusion limit, given as the solution of a stochastic differential equation. The diffusion limit thus enables us to derive a refined, instance-specific characterization of the stochastic dynamics of adaptive experiments. As an application of this framework, we use the diffusion limit to obtain several new insights on the regret and belief evolution of Thompson sampling. We show that a version of Thompson sampling with an asymptotically uninformative prior variance achieves nearly-optimal instance-specific regret scaling when the reward gaps are relatively large. We also demonstrate that, in this regime, the posterior beliefs underlying Thompson sampling are highly unstable over time.

Bio:  Kuang Xu is an Associate Professor of Operations, Information and Technology at Stanford Graduate School of Business, and Associate Professor by courtesy with the Electrical Engineering Department, Stanford University. Born in Suzhou, China, he received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology. His research focuses on understanding fundamental properties and design principles of large-scale stochastic systems using tools from probability theory and optimization, with applications in queueing networks, privacy and machine learning. He is a recipient of the First Place in the INFORMS George E. Nicholson Student Paper Competition (2011), the Best Paper Award, as well as the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS (2013), and the ACM SIGMETRICS Rising Star Research Award (2020). He currently serves as an Associate Editor for Operations Research and Management Science.

http://web.stanford.edu/~kuangxu/

Network structure can affect when, where, and how widely new ideas, products, and behaviors are adopted. Classic work in the social sciences has emphasized that “long ties” provide access to novel and advantageous information. In our empirical work, we show how particular life events (migration, education) are associated with forming long ties and how having long ties is associated with beneficial economic outcomes. Counties in the United States with more long ties have higher incomes, lower unemployment, and more economic mobility, even after adjusting for other measures of social connections.

These stylized facts are consistent with some models of contagion. In widely-used models of biological contagion, interventions that randomly rewire edges (generally making them “longer”) accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which individuals adopt if and only if the number of adopting neighbors exceeds a threshold. Recent work has argued that highly clustered, rather than random, networks facilitate spread of these “complex contagions”. Here we show that minor modifications to this model, which make it more realistic, reverse this result: we allow very rare below-threshold adoption, i.e., rarely adoption occurs when there is only one adopting neighbor. In a version of “small world” networks, allowing adoptions below threshold to occur with order 1/√n probability — even only along some “short” cycle edges — is enough to ensure that random rewiring accelerates spread. Hypothetical interventions that randomly rewire existing edges or add random edges (versus adding “short”, triad-closing edges) in hundreds of empirical social networks reduce time to spread.

In summary, we emphasize the outsized role of long ties in the spread of valuable information and behaviors, even when those behaviors spread via threshold-based contagions.

     This is joint work based on two papers: one on threshold-based contagions with Elchanan Mossel, M. Amin Rahimian, Subhabrata Sen, and one on formation of long ties and economic outcomes with Eaman Jahani and Michael Bailey.