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(1,272)
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- News (195)
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- Multimedia (11)
- Faculty Publications (308)
Show Results For
- All HBS Web
(1,272)
- People (20)
- News (195)
- Research (692)
- Events (6)
- Multimedia (11)
- Faculty Publications (308)
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- December 2007
- Article
Fair (and Not So Fair) Division
By: John W. Pratt
Drawbacks of existing procedures are illustrated and a method of efficient fair division is proposed that avoids them. Given additive participants' utilities, each item is priced at the geometric mean (or some other function) of its two highest valuations. The... View Details
Pratt, John W. "Fair (and Not So Fair) Division." Journal of Risk and Uncertainty 35, no. 3 (December 2007).
- 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.
- 2008
- Chapter
Allocating Marketing Resources
By: Sunil Gupta and Thomas J. Steenburgh
Companies spend billions of dollars on marketing every year because it is essential to organic growth. Given these large investments, marketing managers have the responsibility to optimally allocate resources and to demonstrate that their investments generate... View Details
Keywords: Investment Return; Resource Allocation; Marketing; Demand and Consumers; Mathematical Methods
Gupta, Sunil, and Thomas J. Steenburgh. "Allocating Marketing Resources." In Marketing Mix Decisions: New Perspectives and Practices, edited by Roger A. Kerin and Rob O'Regan. Chicago, IL: American Marketing Association, 2008.
- 18 Jun 2024
- Research & Ideas
Central Banks Missed Inflation Red Flags. This Pricing Model Could Help.
doctoral student at the University of Chicago. The ‘state’ of the price gap matters Economists generally use two main data models to detect inflation and predict the pace at which retailers raise prices: time dependent and state... View Details
- August 2009 (Revised January 2012)
- Case
Steel Street
The case involves repositioning an old 6-story warehouse in Pittsburgh and many of the issues of rehabilitation and selecting and managing the development team especially in a world of capital market uncertainty. The case also demonstrates the alignment of interests of... View Details
Keywords: Construction; Capital Markets; Financial Management; Investment; Property; Urban Development; Real Estate Industry; Pittsburgh
Segel, Arthur I., William J. Poorvu, Ben Creo, and Justin Seth Ginsburgh. "Steel Street." Harvard Business School Case 210-010, August 2009. (Revised January 2012.)
- January 1988 (Revised February 1991)
- Case
Intercon Japan
Describes the many international sourcing initiatives in a multinational connector manufacturing company from the standpoint of an independent and very successful subsidiary in Japan. Students can explore the conflicts inherent in the situation and thus the more... View Details
Keywords: Business Subsidiaries; Multinational Firms and Management; Supply Chain Management; Manufacturing Industry; Japan
Mishina, Kazuhiro. "Intercon Japan." Harvard Business School Case 688-056, January 1988. (Revised February 1991.)
- Article
Bilateral Contracts
By: Jerry R. Green and Seppo Honkapohja
A mathematical characterization of self-enforcing bilateral contracts is given. Contracts where both parties exercise some control over the quantity traded can sometimes be superior to contracts that rest control entirely with one side. Some qualitative characteristics... View Details
Green, Jerry R., and Seppo Honkapohja. "Bilateral Contracts." Journal of Mathematical Economics 11, no. 2 (1983): 171–187.
- 2007
- Working Paper
Fair (and Not So Fair) Division
By: John W. Pratt
Drawbacks of existing procedures are illustrated and a method of efficient fair division is proposed that avoids them. Given additive participants' utilities, each item is priced at the geometric mean (or some other function) of its two highest valuations. The... View Details
Pratt, John W. "Fair (and Not So Fair) Division." Harvard Business School Working Paper, No. 08-016, September 2007.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- March 1992 (Revised March 1998)
- Case
Adam Opel AG (B)
By: Hugo Uyterhoeven
Should General Motors make a strategic manufacturing investment in East Germany after becoming number one in this market through an aggressive marketing strategy? The proposal, dependent on government assistance and based on a number of uncertain economic assumptions,... View Details
Keywords: Transformation; Investment; Government and Politics; Leadership; Marketing Strategy; Production; Organizational Structure; Strategy; Germany
Uyterhoeven, Hugo. "Adam Opel AG (B)." Harvard Business School Case 392-101, March 1992. (Revised March 1998.)
- August 2015 (Revised January 2017)
- Technical Note
From Correlation to Causation
By: Feng Zhu and Karim R. Lakhani
To make sound business decisions, managers must be comfortable with the concepts of correlation and causation. This background note provides an overview of correlation and causation using examples and explains why the former does not imply the latter. It also describes... View Details
Zhu, Feng, and Karim R. Lakhani. "From Correlation to Causation." Harvard Business School Technical Note 616-009, August 2015. (Revised January 2017.)
- June 2019
- Article
Learning to Become a Taste Expert
By: Kathryn A. Latour and John A. Deighton
Evidence suggests that consumers seek to become more expert about hedonic products to enhance their enjoyment of future consumption occasions. Current approaches to becoming expert center on cultivating an analytic mindset. In the present research the authors explore... View Details
Latour, Kathryn A., and John A. Deighton. "Learning to Become a Taste Expert." Journal of Consumer Research 46, no. 1 (June 2019): 1–19.
- October 2013
- Case
Decision Making at the Top: The All-Star Sports eBusiness Division
By: David A. Garvin and Michael A. Roberto
Describes a senior management team's strategic decision-making process. The division president faces three options for redesigning the process to address several key concerns. The president has extensive quantitative and qualitative data about the process to guide him... View Details
Keywords: Decision Choices and Conditions; Management Teams; Performance Improvement; Planning; Mathematical Methods; Strategy
Garvin, David A., and Michael A. Roberto. "Decision Making at the Top: The All-Star Sports eBusiness Division." Harvard Business School Case 314-010, October 2013.
- Article
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by... View Details
Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
Slack, Dylan, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely... View Details
Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Valuation of Bankrupt Firms
By: S. C. Gilson, E. S. Hotchkiss and R. S. Ruback
This study compares the market value of firms that reorganize in bankruptcy with estimates of value based on management's published cash flow projections. We estimate firm values using models that have been shown in other contexts to generate relatively precise... View Details
Gilson, S. C., E. S. Hotchkiss, and R. S. Ruback. "Valuation of Bankrupt Firms." Review of Financial Studies 13, no. 1 (Spring 2000): 43–74. (Abridged version reprinted in The Journal of Corporate Renewal 13, no. 7 (July 2000))
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a... View Details
Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- Research Summary
Optimal Heteroskedasticity Autocorrelation Consistent Covariance Estimators for GMM Weighting Matrices
This paper considers the optimal bias-variance tradeoff for estimators of the long run covariance matrix used to generate GMM weighting matrices in time series contexts. Minimum MSE HAC estimators do not yield minimum MSE GMM estimators. Instead, achieving... View Details
- 2019
- Article
Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
By: Iavor I Bojinov and Neil Shephard
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal... View Details
Bojinov, Iavor I., and Neil Shephard. "Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading." Journal of the American Statistical Association 114, no. 528 (2019): 1665–1682.
- Research Summary
Mobile web advertising: maximum entropy banner allocation
The worldwide mobile advertising market, currently $3 billion in size, is expected to grow to $20 billion by 2011. Online and mobile advertising employs two main pricing models: pay-per-click (CPC) and pay-per-impression (CPM). To date, most of the... View Details