Seminars / Conferences

In this talk, I will present a series of theoretical and applied work in competitive pricing. A retailer following a competition-based dynamic-pricing strategy tracks competitors’ price changes and then must decide whether and how to respond. The answers require modeling of consumer decisions, unbiased measures of self- and cross-price elasticity as well as competitor impacts. I will discuss how we achieve them through a combination of consumer modeling, experimentation, causal inference, as well as high-dimensional statistics. I will highlight two implementations that each led to 10-20 percent revenue and profit improvement through close collaborations with leading US and international retailers, and a theoretical development that scales up choice models through high dimensional regularization.

Fulfillment by Amazon (FBA) is Amazon’s third party marketplace for e-commerce sellers. In the past decade, FBA has grown rapidly — growth that has only accelerated during the pandemic. By most measures, FBA is now larger than Amazon’s own retail business. This has created a significant challenge for capacity management, since third party sellers have considerable autonomy over how much inventory they send to Amazon. In this talk, we provide an overview of the FBA capacity management problem and describe an innovative market-based mechanism (auction) for capacity recently launched in North America. The auction uses a novel combination of securities and ex-post inspection. It admits a simple bidding strategy, implicitly scores sellers on a combination of sales and profit, and extracts minimal (often zero) revenue from sellers.

Supply chain risks, despite being a well-developed theoretical concept, are difficult to quantify, limiting the scope of empirical research. I develop a firm-level measure of supply chain risk exposure from a novel source of unstructured data—managers’ discussions of supply chain-related topics during earnings conference calls and Q&A sessions—using textual analysis techniques including seeded word embedding and bag-of-words-based content analysis. I validate the measure by showing that (1) it exhibits intuitive variations over time and across firms, and successfully captures both systematic supply chain risk events and supply risks in firms’ routine operations; (2) the measure indeed captures exposure to risks as it significantly correlates with contemporaneous and next-quarter stock return volatility, and (3) this relation remains significant after controlling for well-known aggregate risk measures such as the VIX and economic uncertainty indices. I then conduct a series of tests to demonstrate that the measure is indicative of risk that is specifically related to supply chains. (4) Consistent with theoretical predictions, firms facing higher supply chain risks have higher inventory buffers, with the majority of the inventory buildup concentrated in raw materials and intermediate inputs. High-risk firms also significantly increase their cash holding in lieu of investments and receive significantly lower trade credit from suppliers. (5) A placebo measure that only tabulates risk words does not correlate with any supply chain risk buffers. Moreover, (6) during well-known risk episodes such as the Tohoku earthquake, firms with higher ex-ante risk exposure have worse performance. These results indicate that the text-based measure provides a credible quantification of firm-level exposure to supply chain risks, and can thus be reliably utilized as outcome or explanatory variables in empirical supply chain research

: The use of artificial intelligence (AI) in healthcare is rapidly increasing, with over 500 medical AI systems having received FDA approval by 2022. While AI is unlikely to replace physicians, it is becoming plausible that physicians who use AI will replace those who don’t. Integrating AI into healthcare delivery is not a simple task and requires rethinking current workflows. In this talk, I will discuss ongoing efforts to understand and overcome barriers to the appropriate use of AI in clinical practice. Additionally, I will discuss the potential for AI to improve clinician productivity, increase access to care, and reduce disparities through studies conducted both in the United States and in developing countries. I will propose a research agenda for business scholars to consider as we continue to develop principles for the purposeful design of AI-augmented healthcare systems.

Which companies should we invest in? Who should we hire or promote? Which products should we launch? To make effective decisions, managers need to learn how different cues (e.g., the features of a product) relate to desirable business outcomes (e.g., its performance in a target market). Learning these contingencies is difficult, as many exogenous factors (e.g., the competitors’ actions, the general trend of the economy…) affect outcomes independently of the managers’ actions. Oddly, decision-makers typically report a high degree of confidence in their ability to understand business cues and drive favorable outcomes.

We highlight a possible factor driving this contradiction: some exogenous factors can paradoxically foster an illusion of learning. Specifically, we discuss the role of positive market trends. From the observation that decision-makers tend to explore sets of cues that they believe will produce favorable outcomes (a “positive test strategy”), we hypothesized that positive exogenous trends would reinforce the participants’ beliefs, regardless of their accuracy. We present a simulated model of contingency learning, and the results of five behavioral experiments, in support of this hypothesis. We conclude by discussing connections between our findings and existing research on overconfidence and illusory learning.

We study the interplay between inventory replenishment policies and information sharing in the context of a two-tier supply chain with a single supplier and a single retailer serving an iid Gaussian market demand. We investigate how the retailer’s inventory policy impacts the supply chain’s cumulative expected long-term average inventory costs C in two extreme information sharing cases: (a) full information sharing and (b) no information sharing. To find the retailer’s inventory policy that minimizes C, we formulate an infinite-dimensional optimization problem whose decision variables are the MA(infinity) coefficients that characterize a stationary ordering policy. Under full information sharing, the optimization problem admits a simple solution and the optimal policy is given by an MA(1) process. On the other hand, to solve the optimization problem under no information sharing, we reformulate the optimization from its time domain formulation to an equivalent z-transform formulation in which the decision variables correspond to elements of the Hardy space H2. This alternative representation allows us to use a number of results from H2 theory to compute the optimal value of C and characterize a sequence of epsilon-optimal inventory policies under some mild technical conditions.  By comparing the optimal solution under full information sharing and no information sharing we derive a number of important practical takeaways. For instance, we show that there is value in information sharing if and only if the retailer’s optimal policy under full information sharing is not invertible with respect to the sequence of demand shocks. Furthermore, we derive a fundamental mathematical identity that reveals the value of information sharing by exploiting the canonical Smirnov-Beurling inner-outer factorization of the retailer’s orders when viewed as an element of H2. We also show that the value of information sharing can grow unboundedly when the cumulative supply chain costs are dominated by the supplier’s inventory costs.

This talk presents evidence from two field experiments to shed light on the effects of non-monetary incentives and how they interact with the social relational context. The settings are Wikipedia and healthcare. In one case, the non-monetary incentive had positive and long-lasting effects. In the other case, the non-monetary incentive backfired, significantly reducing people’s well-being. The talk presents an overarching theory to reconcile these findings. It lays out a framework and an agenda for future experiments on incentives and social relationships, with implications for incentive and organizational design.

A carbon offset represents one unit of reduction in greenhouse gas emissions that can be used to compensate for emissions that occur elsewhere. Companies can purchase voluntary carbon offsets under two delivery terms. The first is prompt delivery, where a seller first undertakes the investment, then yield uncertainty realizes, based on which the seller determines the price for carbon offsets as a recourse action. The second is forward delivery, where a buyer orders a certain quantity of offsets before the seller invests. Therefore, there is quantity risk due to yield uncertainty because the seller may generate fewer offsets than the buyer’s order. Motivated by the importance of choosing the right delivery term for buyers, in this paper, we investigate the economic and environmental implications of these two delivery terms. Our results offer several managerial insights: A buyer should choose prompt delivery for projects with low or high investment costs, but forward delivery for projects with an intermediate investment cost. The delivery term preferred by the buyer typically leads to a higher quantity of delivered offsets, enabling the buyer to offset more of its emissions. However, the buyer may prefer a delivery term that leads to a lower investment by the seller, and thus a lower overall emissions reduction. Accordingly, environmental groups’ preference between delivery terms may not be aligned with that of a buyer.

Professor Mullainathan’ current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. He recently co-authored Scarcity: “Why Having too Little Means so Much and writes regularly for the New York Times”. Additionally, his research has appeared in a variety of publications including the Quarterly Journal of Economics, Science, American Economic Review, Psychological Science, the British Medical Journal, and Management Science.

Professor Hemant K. Bhargava is an academic leader in economic modeling and analysis of technology-based business and markets. His research focuses on decision analytics and how the distinctive characteristics of technology goods influences specific elements of operations, marketing, and competitive strategy, and the implications it holds for competitive markets and technology-related policy.  He has examined deeply these issues in specific industries including platform businesses, information and telecommunications industries, healthcare, media and entertainment, and electric vehicles. Bhargava has published extensively in the top journals Management Science, Operations Research, Marketing Science, Journal of Marketing Research, Information Systems Research, INFORMS Journal on Computing, and Production and Operations Management.

Antonio (Toni) Moreno is the Sicupira Family Associate Professor in the Technology and Operations Management Unit. He teaches courses related to technology and operations management in the MBA, executive, and doctoral programs. Before joining HBS, he was an associate professor in the Kellogg School of Management.

Professor Moreno’s research studies the digital transformation of operations, and he is particularly interested in the transformation of retail and service industries. Professor Moreno’s work has appeared in journals such as Management Science, Marketing Science, Manufacturing & Service Operations Management, Information Systems Research, and Sloan Management Review, and has been covered by several media outlets. He received the Wickham Skinner Early-Career Research Accomplishments Award from the Production and Operations Management Society.

Professor Moreno earned degrees in electrical engineering and industrial engineering from Technical University of Catalonia. He has an MA in statistics and a PhD in operations and information management from the Wharton School of the University of Pennsylvania.

Professor Gupta’s research focuses on data-driven decision-making and optimization, particularly in settings where data are scarce. Such settings are common in applications that rely on personalization (like precision healthcare) and real-time decision-making (like risk management). Consequently, his research spans a wide variety of areas including revenue management, education, healthcare, and artificial intelligence. Professor Gupta has received a number of recognitions for his work, including the Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research, the Pierskalla Best Paper Prize, the Jagdish Sheth Impact of Research on Practice Award.

Professor Sahin’s research interests include pricing, revenue and marketplace analytics, consumer choice models and strategic capacity management. Some of her recent research projects include analysis of pricing strategies with consumer search cost, ancillary service pricing, identifying consumer biases and heuristics for sequential decision making in retail. Professor Sahin has published papers in prestigious academic journals including Management Science, Operations Research, Manufacturing and Service Operations Management, among others. She is currently the Department Editor of revenue management and pricing area of Decision Sciences Journal.

Professor Pope is a behavioral economist that researches a variety of topics at the intersection of economics and psychology. He has published work in prestigious journals within economics, psychology, and management. His research primarily uses observational data and studies how psychological biases play out in important economic markets. Examples include left-digit bias and projection bias in car markets and present bias in housing markets. Prior to joining Chicago Booth faculty in 2010, Professor Pope was on the faculty at the Wharton School at the University of Pennsylvania. He earned a PhD in economics from UC Berkeley in 2007 and a BA in economics from Brigham Young University in 2002.

Michael leads economics at Microsoft. His Office of the Chief Economist has a team of economists and data scientists; in addition, economists across the company who report into product groups are hired and evaluated with the help of Michael and his team. The Office of the Chief Economist engages primarily in three areas: (1) Demand Forecasting – to optimize capacity planning/supply chain management and to identify top trends, (2) Product Pricing and Discounting (e.g. Azure Services, Surface, Windows) and (3) Market Design – to optimize the effectiveness of marketplaces. The team engages in a variety of other topics such as antitrust issues and zero-carbon energy. The team also helps the Microsoft sales organization engage with governments and our largest customers. Michael drives key topics and themes that need cross-company Economics representation.