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- Faculty Publications (230)
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- All HBS Web (302)
- Faculty Publications (230)
- Forthcoming
- Article
Branch-and-Price for Prescriptive Contagion Analytics
By: Alexandre Jacquillat, Michael Lingzhi Li, Martin Ramé and Kai Wang
Contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision maker allocates shared resources across multiple segments of a population, each governed by... View Details
Jacquillat, Alexandre, Michael Lingzhi Li, Martin Ramé, and Kai Wang. "Branch-and-Price for Prescriptive Contagion Analytics." Operations Research (forthcoming). (Pre-published online March 13, 2024.)
- 2007
- Working Paper
Optimal Reserve Management and Sovereign Debt
By: Laura Alfaro and Fabio Kanczuk
Most models currently used to determine optimal foreign reserve holdings take the level of international debt as given. However, given the sovereign's willingness-to-pay incentive problems, reserve accumulation may reduce sustainable debt levels. In addition, assuming... View Details
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- May 2020
- Article
Identifying Sources of Inefficiency in Health Care
By: Amitabh Chandra and Douglas O. Staiger
In medicine, the reasons for variation in treatment rates across hospitals serving similar patients are not well understood. Some interpret this variation as unwarranted and push standardization of care as a way of reducing allocative inefficiency. However, an... View Details
Keywords: Health Care and Treatment; Performance Efficiency; Performance Productivity; Mathematical Methods
Chandra, Amitabh, and Douglas O. Staiger. "Identifying Sources of Inefficiency in Health Care." Quarterly Journal of Economics 135, no. 2 (May 2020): 785–843.
- 2022
- Article
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
By: Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach and Himabindu Lakkaraju
As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across all subgroups of a population. For instance, it... View Details
Dai, Jessica, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach, and Himabindu Lakkaraju. "Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 203–214.
- 2011
- Chapter
An Exploration of the Japanese Slowdown during the 1990s
By: Diego A. Comin
Why was the 1990s a lost decade for Japan? How is it possible that the Japanese economy stagnated for a decade if none of the shocks that arguably hit the economy seemed to have persisted for much more than three years or so? In this paper I show that the endogenous... View Details
Keywords: Economic Slowdown and Stagnation; Performance Productivity; Mathematical Methods; Research and Development; Technology Adoption; Japan
Comin, Diego A. "An Exploration of the Japanese Slowdown during the 1990s." In Japan's Bubble, Deflation, and Long-term Stagnation, edited by Koichi Hamada, Anil Kashyap, and David Weinstein. MIT Press, 2011.
- 2010
- Working Paper
Substitution Patterns of the Random Coefficients Logit
By: Thomas J. Steenburgh and Andrew Ainslie
Previous research suggests that the random coefficients logit is a highly flexible model that overcomes the problems of the homogeneous logit by allowing for differences in tastes across individuals. The purpose of this paper is to show that this is not true. We prove... View Details
Steenburgh, Thomas J., and Andrew Ainslie. "Substitution Patterns of the Random Coefficients Logit." Harvard Business School Working Paper, No. 10-053, January 2010.
- 2016
- Working Paper
Algorithmic Foundations for Business Strategy
By: Mihnea Moldoveanu
I introduce algorithmic and meta-algorithmic models for the study of strategic problem solving, aimed at illuminating the processes and procedures by which strategic managers and firms deal with complex problems. These models allow us to explore the relationship... View Details
Moldoveanu, Mihnea. "Algorithmic Foundations for Business Strategy." Harvard Business School Working Paper, No. 17-036, October 2016.
- May–June 2018
- Article
Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations
By: Joel Goh, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh and David Moore
Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecision in the elements of the transition matrix. In this paper,... View Details
Keywords: Markov Chains; Cost Effectiveness; Medical Innovations; Colorectal Cancer; Health Care and Treatment; Cost vs Benefits; Innovation and Invention; Mathematical Methods; Health Industry
Goh, Joel, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh, and David Moore. "Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations." Operations Research 66, no. 3 (May–June 2018): 697–715. (Winner, 2014 INFORMS Health Applications Society Pierskalla Award & Finalist, 2014 INFORMS George E. Nicholson student paper competition.)
- 2010
- Working Paper
Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans
By: Doug J. Chung, Thomas J. Steenburgh and K. Sudhir
We estimate a dynamic structural model of sales force response to a bonus based compensation plan. The paper has two main methodological innovations: First, we implement empirically the method proposed by Arcidiacono and Miller (2010) to accommodate unobserved latent... View Details
Keywords: Compensation and Benefits; Performance Productivity; Mathematical Methods; Salesforce Management; Motivation and Incentives
Chung, Doug J., Thomas J. Steenburgh, and K. Sudhir. "Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans." Harvard Business School Working Paper, No. 11-041, October 2010.
- 2010
- Working Paper
The Unbundling of Advertising Agency Services: An Economic Analysis
By: Mohammad Arzaghi, Ernst R. Berndt, James C. Davis and Alvin J. Silk
We address a longstanding puzzle surrounding the unbundling of services occurring over several decades in the U.S. advertising agency industry: What accounts for the shift from bundling to unbundling of services and the slow pace of change? Using Evans and Salinger's... View Details
Keywords: Advertising; Change; Forecasting and Prediction; Cost; Price; Analytics and Data Science; Surveys; Marketing Strategy; Media; Service Operations; Agency Theory; Mathematical Methods; Advertising Industry; United States
Arzaghi, Mohammad, Ernst R. Berndt, James C. Davis, and Alvin J. Silk. "The Unbundling of Advertising Agency Services: An Economic Analysis." Harvard Business School Working Paper, No. 11-039, September 2010.
- March 2022
- Article
Where to Locate COVID-19 Mass Vaccination Facilities?
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new... View Details
Keywords: Vaccines; COVID-19; Health Care and Treatment; Health Pandemics; Performance Effectiveness; Analytics and Data Science; Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li, and Alessandro Previero. "Where to Locate COVID-19 Mass Vaccination Facilities?" Naval Research Logistics Quarterly 69, no. 2 (March 2022): 179–200.
- Article
Moment-to-moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing
By: Thales S. Teixeira, Michel Wedel and Rik Pieters
We develop a conceptual framework for understanding the impact that branding activity (the audio-visual representation of brands) and consumers' dispersion of attention have on their moment-to-moment avoidance decisions during television advertising. It formalizes this... View Details
Keywords: Advertising; Decision Choices and Conditions; Television Entertainment; Brands and Branding; Consumer Behavior; Mathematical Methods
Teixeira, Thales S., Michel Wedel, and Rik Pieters. "Moment-to-moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing." Marketing Science 29, no. 5 (September–October 2010): 783–804. (Lead Article.)
- 26 Apr 2023
- In Practice
Is AI Coming for Your Job?
users may have additional knowledge or context that the AI doesn’t (e.g. that the AI hasn’t been trained on, propriety knowledge, a better understanding of the specific task at hand, etc.). Another risk with these generative AI models is... View Details
- 2011
- Working Paper
Free to Punish? The American Dream and the Harsh Treatment of Criminals
By: Rafael Di Tella and Juan Dubra
We describe the evolution of selective aspects of punishment in the U.S. over the period 1980-2004. We note that imprisonment increased around 1980, a period that coincides with the "Reagan revolution" in economic matters. We build an economic model where beliefs about... View Details
Keywords: Crime and Corruption; Economy; Moral Sensibility; Mathematical Methods; Opportunities; Behavior; United States
Di Tella, Rafael, and Juan Dubra. "Free to Punish? The American Dream and the Harsh Treatment of Criminals." NBER Working Paper Series, No. 17309, August 2011.
- Article
Matching in Networks with Bilateral Contracts: Corrigendum
By: John William Hatfield, Ravi Jagadeesan and Scott Duke Kominers
Hatfield and Kominers (2012) introduced a model of matching in networks with bilateral contracts and showed that stable outcomes exist in supply chains when firms' preferences over contracts are fully substitutable. Hatfield and Kominers (2012) also asserted that in... View Details
Hatfield, John William, Ravi Jagadeesan, and Scott Duke Kominers. "Matching in Networks with Bilateral Contracts: Corrigendum." American Economic Journal: Microeconomics 12, no. 3 (August 2020): 277–285.
- June 2008
- Article
Minimally Acceptable Altruism and the Ultimatum Game
By: Julio J. Rotemberg
I suppose that people react with anger when others show themselves not to be minimally altruistic. With heterogeneous agents, this can account for the experimental results of ultimatum and dictator games. Moreover, it can account for the surprisingly large fraction of... View Details
Rotemberg, Julio J. "Minimally Acceptable Altruism and the Ultimatum Game." Journal of Economic Behavior & Organization 66, nos. 3-4 (June 2008).
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- Forthcoming
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression Under Sparsity." Operations Research (forthcoming). (Pre-published online March 27, 2024.)
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208.