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Show Results For
- All HBS Web
(11,286)
- People (74)
- News (2,859)
- Research (3,804)
- Events (45)
- Multimedia (239)
- Faculty Publications (2,367)
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- June 10, 2022
- Article
What Top Executives Can Learn from Junior Employees
Having reached the pinnacle of their careers, many top executives think their learning days are over. Their role, as they see it, is to make pronouncements, define strategy and impart to others the benefits of their vast experience—that is, to tell the employees below... View Details
Kanter, Rosabeth Moss. "What Top Executives Can Learn from Junior Employees." Wall Street Journal (online) (June 10, 2022).
- September 2021
- Article
Trials and Terminations: Learning from Competitors' R&D Failures
I analyze project continuation decisions where firms may resolve uncertainty through news about competitors' research and development (R&D) failures, as well as through their own results. I examine the trade-offs and interactions between product-market competition and... View Details
Krieger, Joshua L. "Trials and Terminations: Learning from Competitors' R&D Failures." Management Science 67, no. 9 (September 2021).
- 05 Feb 2015
- Research & Ideas
How New BofA Executives Learn its ’Deep Smarts’
Editor's note: How does an organization hold on to its wealth of accumulated knowledge when the knowledge-holders depart? It's a very real dilemma made even more critical as Baby Boomers begin their mass exit into retirement. The new book... View Details
- 2015
- Chapter
Entrepreneurial Creativity: The Role of Learning Processes and Work Environment Supports
By: Michele Rigolizzo and Teresa M. Amabile
Rigolizzo, Michele, and Teresa M. Amabile. "Entrepreneurial Creativity: The Role of Learning Processes and Work Environment Supports." Chap. 4 in The Oxford Handbook of Creativity, Innovation, and Entrepreneurship, edited by Christina E. Shalley, Michael A. Hitt, and Jing Zhou, 61–78. Oxford University Press, 2015.
- October 2022 (Revised December 2022)
- Case
SMART: AI and Machine Learning for Wildlife Conservation
By: Brian Trelstad and Bonnie Yining Cao
Spatial Monitoring and Reporting Tool (SMART), a set of software and analytical tools designed for the purpose of wildlife conservation, had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching and contributing to wildlife... View Details
Keywords: Business and Government Relations; Emerging Markets; Technology Adoption; Strategy; Management; Ethics; Social Enterprise; AI and Machine Learning; Analytics and Data Science; Natural Environment; Technology Industry; Cambodia; United States; Africa
Trelstad, Brian, and Bonnie Yining Cao. "SMART: AI and Machine Learning for Wildlife Conservation." Harvard Business School Case 323-036, October 2022. (Revised December 2022.)
- 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
- April 2011
- Case
Kay Sunderland: Making the Grade at Attain Learning
By: Linda A. Hill and Heather Beckham
Kay Sunderland is an account director at Attain Learning Inc., a business training solutions company. In January 2011, one of Attain's most important clients, Juan Nunez of Gramen Equipment Company, contacts Sunderland with a request: Nunez would like Attain content... View Details
Keywords: Communication; Interpersonal Relations; Personal Strategy ＆ Style; Creativity; Conflict; Interdepartmental Relations; Talent Management; Management Style; Interpersonal Communication; Talent and Talent Management; Relationships; Conflict and Resolution; Communication Strategy; Power and Influence; Service Industry
Hill, Linda A., and Heather Beckham. "Kay Sunderland: Making the Grade at Attain Learning." Harvard Business School Brief Case 114-289, April 2011.
- 25 Sep 2015
- Working Paper Summaries
Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning
- 2007
- Working Paper
What Have We Learned From Market Design?
By: Alvin E. Roth
This essay discusses some things we have learned about markets, in the process of designing marketplaces to fix market failures. To work well, marketplaces have to provide thickness, i.e. they need to attract a large enough proportion of the potential participants in... View Details
Roth, Alvin E. "What Have We Learned From Market Design?" NBER Working Paper Series, No. 13530, October 2007.
- 2013
- Working Paper
Learning from Double-Digit Growth Experiences
By: Eric D. Werker
This extended memorandum identifies episodes of sustained double-digit growth in real GDP, defined as a compound annual growth rate of 10 percent or more over a period of 8 years or longer. Using a measure of real GDP reported in the World Development Indicators, we... View Details
Werker, Eric D. "Learning from Double-Digit Growth Experiences." International Growth Centre Working Paper, April 2013.
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
- March 2017 (Revised March 2018)
- Case
Connections Education: Shifting the Paradigm?
By: John J-H Kim and Aldo Sesia
The online virtual learning (K-12) industry in 2017 remains an industry moving fast with many different players and stakeholders. While online virtual learning is beginning to make its way into school districts, it is far from being mainstream and a long way from full... View Details
Keywords: K-12; Online Learning; Virtual Learning; Blended Learning; Education; Learning; Strategy; Online Technology; Education Industry; United States
Kim, John J-H, and Aldo Sesia. "Connections Education: Shifting the Paradigm?" Harvard Business School Case 317-051, March 2017. (Revised March 2018.)
- May 2022
- Case
AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services." Harvard Business School Case 622-060, May 2022.
- May 2022 (Revised July 2022)
- Supplement
AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-087, May 2022. (Revised July 2022.)
- May 2022
- Supplement
AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-086, May 2022.
- Article
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
- April 2011
- Article
What Can We Learn from 'Great Negotiations'?
What can one legitimately learn-analytically and/or prescriptively-from detailed historical case studies of "great negotiations," chosen more for their salience than their analytic characteristics or comparability? Taking a number of such cases compiled by Stanton... View Details
Keywords: Learning; International Relations; History; Agreements and Arrangements; Negotiation Process; Conflict and Resolution
Sebenius, James K. "What Can We Learn from 'Great Negotiations'?" Negotiation Journal 27, no. 2 (April 2011).
- January–February 2022
- Article
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
How does a knowledge worker’s level of domain experience affect their algorithm-augmented work performance? We propose and test theoretical predictions that domain experience has countervailing effects on algorithm-augmented performance: on one hand, domain experience... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Organization Science 33, no. 1 (January–February 2022): 149–169. ("Best PhD Student Paper" at SMS conference 2020.)
- November 2023
- Case
Open Source Machine Learning at Google
Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
- Mar 2020
- Conference Presentation
A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details
Jung, Christopher, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. "A New Analysis of Differential Privacy's Generalization Guarantees." Paper presented at the 11th Innovations in Theoretical Computer Science Conference, Seattle, March 2020.