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- All HBS Web
(2,449)
- People (7)
- News (511)
- Research (1,436)
- Events (20)
- Multimedia (1)
- Faculty Publications (580)
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- Article
Multi-Echelon Inventory Management Under Short-Term Take-or-Pay Contracts
By: Joel Goh and Evan L. Porteus
We extend the Clark–Scarf serial multi-echelon inventory model to include procuring production inputs under short-term take-or-pay contracts at one or more stages. In each period, each such stage has the option to order/process at two different cost rates; the cheaper... View Details
Keywords: Inventory Management; Multi-echelon Inventory Theory; Karush Lemma; Clark-Scarf Model; Convex Ordering Cost; Advance Commitments; Supply Chain
Goh, Joel, and Evan L. Porteus. "Multi-Echelon Inventory Management Under Short-Term Take-or-Pay Contracts." Production and Operations Management 25, no. 8 (August 2016): 1415–1429. (Finalist for 2014 POMS College of Supply Chain Management Student Paper Award.)
- 2021
- Article
Fair Algorithms for Infinite and Contextual Bandits
By: Matthew Joseph, Michael J Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions... View Details
Joseph, Matthew, Michael J Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Fair Algorithms for Infinite and Contextual Bandits." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
- February 2019 (Revised May 2021)
- Case
Electric Car Wars, 2018
By: John R. Wells and Benjamin Weinstock
Electric cars had long been championed by environmentalists as a superior solution to the internal combustion engine (ICE), but, despite large government incentives and strong pioneering efforts by a few automakers over the years, electric and hybrid cars and light... View Details
Keywords: Electric Vehicle; Electric Vehicles; Electricity; Electric Motors; Electric Power Generation; Electricity Usage; Electricity Distribution; Internal Combustion Vehicle; Auto Manufacturing; Automobile Manufacturing; Automotive Industry; Tesla; General Motors; History; Nissan; Innovation; Batteries; Battery; Subsidies; Government Initiatives; Government Incentives; Political Issues; Energy Generation; Production; Infrastructure; Innovation and Invention; Government Legislation; Global Range; Business History; Auto Industry; China
Wells, John R., and Benjamin Weinstock. "Electric Car Wars, 2018." Harvard Business School Case 719-470, February 2019. (Revised May 2021.)
- January 2017 (Revised October 2023)
- Case
Classtivity: Payal's Pirouette
By: Jeffrey J. Bussgang and Olivia Hull
A few months after launching a new fitness technology product, the small staff of New York startup Classtivity gathers on a Saturday in April 2013 to take stock. With one successful pivot under its belt, Classtivity is finally generating revenue and enthusiasm among... View Details
Keywords: Product Pivot; Boutique Fitness; Fitness Industry; Market Sizing; Consumer Technology; Bundling; Subscription Model; Two-sided Marketplace; ClassPass; Entrepreneurship; Venture Capital; Business Startups; Transition; Customer Focus and Relationships; Technological Innovation; Organizational Change and Adaptation; Customer Value and Value Chain; Marketing Strategy; Failure; Business Strategy; Technology Industry; Health Industry; New York (city, NY)
Bussgang, Jeffrey J., and Olivia Hull. "Classtivity: Payal's Pirouette." Harvard Business School Case 817-002, January 2017. (Revised October 2023.)
- Article
Distributionally Robust Optimization and Its Tractable Approximations
By: Joel Goh and Melvyn Sim
In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more... View Details
Goh, Joel, and Melvyn Sim. "Distributionally Robust Optimization and Its Tractable Approximations." Operations Research 58, no. 4 (pt.1) (July–August 2010): 902–917.
- Forthcoming
- Article
On the Representativeness of Voter Turnout
By: Louis Kaplow and Scott Duke Kominers
Prominent theory research on voting analyzes a variety of models in which expected pivotality drives voters' turnout decisions and hence determines voting outcomes. It is recognized, however, that such work is at odds with Downs's paradox: in practice, many... View Details
Keywords: Voting Behavior; Voting Turnout; Paradox Of Voting; Pivotality; Elections; Model; Theory; Governance Transparency; Government; Democracy; Turnout; Voting; Governance; Government and Politics; Public Sector; Political Elections
Kaplow, Louis, and Scott Duke Kominers. "On the Representativeness of Voter Turnout." Journal of Law & Economics (forthcoming).
- Research Summary
Statistical Methodology
William Simpson is developing methods of inference to use when assumptions of standard models are not met. He has created a hypothesis test to use for ipsative variables that adjusts for the non-zero correlations among variables expected under the null hypothesis. ... View Details
- October 2013
- Article
Ad Revenue and Content Commercialization: Evidence from Blogs
By: Monic Sun and Feng Zhu
Many scholars argue that when incentivized by ad revenue, content providers are more likely to tailor their content to attract "eyeballs," and as a result, popular content may be excessively supplied. We empirically test this prediction by taking advantage of the... View Details
Keywords: Ad-sponsored Business Models; Media Content; Blog; Revenue Sharing; User-generated Content; Platform-based Markets; Blogs; Business Model; Digital Platforms; Commercialization; Digital Marketing
Sun, Monic, and Feng Zhu. "Ad Revenue and Content Commercialization: Evidence from Blogs." Management Science 59, no. 10 (October 2013): 2314–2331.
- 2023
- Article
Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms
Lakkaraju, Himabindu, Satyapriya Krishna, and Jiaqi Ma. "Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 17808–17826.
- March 1989 (Revised April 1990)
- Case
Merton Truck Co.
Introduces some of the key concepts in linear programming--problem formulation, relevant costs, shadow prices, and reduced costs. The setting, while artificial, is quite typical: a company manufactures two models of trucks in four manufacturing departments; it must... View Details
Dhebar, Anirudh S. "Merton Truck Co." Harvard Business School Case 189-163, March 1989. (Revised April 1990.)
- June 2023 (Revised November 2023)
- Case
Sober Sidekick
By: Jeffrey J. Bussgang and Kumba Sennaar
Case on the nascent business model of a mobile health IT startup. In particular, should they pivot away from their successful lead generation business model to charging health plans. View Details
Keywords: Entrepreneurship; Venture Capital; Operations; Business Startups; Business Model; Health Industry; United States
Bussgang, Jeffrey J., and Kumba Sennaar. "Sober Sidekick." Harvard Business School Case 823-066, June 2023. (Revised November 2023.)
- 18 Nov 2016
- Conference Presentation
Rawlsian Fairness for Machine Learning
By: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls' notion of "fair equality of opportunity". In the context of a simple model of... View Details
Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), November 18, 2016.
- 2022
- Working Paper
Pricing Power in Advertising Markets: Theory and Evidence
By: Matthew Gentzkow, Jesse M. Shapiro, Frank Yang and Ali Yurukoglu
Existing theories of media competition imply that advertisers will pay a lower price in equilibrium to reach consumers who multi-home across competing outlets. We generalize and extend this theoretical result and test it using data from television and social media... View Details
Gentzkow, Matthew, Jesse M. Shapiro, Frank Yang, and Ali Yurukoglu. "Pricing Power in Advertising Markets: Theory and Evidence." NBER Working Paper Series, No. 30278, July 2022.
- June 2012
- Case
Innovating at AT&T: Partnering to Lead the Broadband Revolution
By: Lynda M. Applegate, Phillip Andrews and Kerry Herman
In 2010, the U.S. retail market value for next-generation non-handset wirelessly-enabled devices was just over $1 billion. By 2011 it had grown 1,141% to $13.2 billion and was forecast to reach $24.7 billion in 2015. At the same time, user demand for data was surging... View Details
Keywords: Innovation ＆ Entrepreneurship; Team Leadership; Emerging Technologies; Business Models; Business To Business; Corporate Vision; Growth Strategy; Corporate Culture; Innovation and Invention; Corporate Entrepreneurship; Partners and Partnerships; Leadership; Mobile and Wireless Technology; Growth and Development Strategy; Globalized Firms and Management; Business Model; Technology Industry; United States
Applegate, Lynda M., Phillip Andrews, and Kerry Herman. "Innovating at AT&T: Partnering to Lead the Broadband Revolution." Harvard Business School Case 812-124, June 2012.
- Teaching Interest
Interpretability and Explainability in Machine Learning
As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these... View Details
- 2016
- Working Paper
Controlling Versus Enabling — Online Appendix
By: Andrei Hagiu and Julian Wright
Section 1 of this online appendix contains the proof of the technical Lemma (Lemma 2) used in the Proof of Lemma 1 in the main paper, which states that Ω* (.) is continuous and differentiable at R*. Section 2 provides the linear example with cost differences between... View Details
Hagiu, Andrei, and Julian Wright. "Controlling Versus Enabling — Online Appendix." Harvard Business School Working Paper, No. 16-004, July 2015. (Revised July 2016.)
- Sep 2007 - 2007
- Conference Presentation
Antecedents of Boundary Spanning in Cross-functional NPD Teams
By: James R. Dillon, Shikhar Sarin and Amy C. Edmondson
Boundary spanning has been shown in prior research to enhance innovativeness and performance of product development teams. In this study, we examine team conditions that foster boundary spanning behavior. We analyze survey data from 207 members of 54 cross-functional... View Details
- February 2024
- Article
Pricing Power in Advertising Markets: Theory and Evidence
By: Matthew Gentzkow, Jesse M. Shapiro, Frank Yang and Ali Yurukoglu
Existing theories of media competition imply that advertisers will pay a lower price in equilibrium to reach consumers who multi-home across competing outlets. We generalize, extend, and test this prediction. We find that television outlets whose viewers watch more... View Details
Gentzkow, Matthew, Jesse M. Shapiro, Frank Yang, and Ali Yurukoglu. "Pricing Power in Advertising Markets: Theory and Evidence." American Economic Review 114, no. 2 (February 2024): 500–533.
- 2024
- Working Paper
Fiscal Policy under Convex Supply Curves
By: Shlok Goyal, Avi Lipton and Borui Niklas Zhu
Recent empirical evidence suggests that supply curves are convex. Supply curve convexity is at odds with conventional Phillips curves, which rely on an infinitely elastic underlying supply curve. This paper explores the effect of supply curve convexity on the... View Details
Keywords: Fiscal Stimulus; Fiscal Policy; Inflation; Inflation and Deflation; Macroeconomics; Policy; Mathematical Methods; United States
Goyal, Shlok, Avi Lipton, and Borui Niklas Zhu. "Fiscal Policy under Convex Supply Curves." Working Paper, August 2024.
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details
Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).