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- All HBS Web
(12,694)
- People (75)
- News (2,979)
- Research (3,769)
- Events (39)
- Multimedia (347)
- Faculty Publications (2,455)
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- Research Summary
Vicarious Learning in Organizations
To advance the study of how individuals learn through their interactions with others, Professor Myers has adopted a vicarious learning theory lens. Vicarious learning allows individuals to learn from the outcomes of others’ experiences, rather than solely their own... View Details
- 2012
- Dictionary Entry
Learning from Failure
By: Mark D. Cannon and Amy C. Edmondson
Failure is defined as an outcome that deviates from expected and desired results. Learning from failure describes processes and behaviors through which individuals, groups and organizations gain accurate and useful insights from failures and modify future behaviors,... View Details
Cannon, Mark D., and Amy C. Edmondson. "Learning from Failure." In Encyclopedia of the Sciences of Learning, edited by Norbert M. Seel, 1859–1863. New York: Springer, 2012.
- Research Summary
Effective Learning from Failure
Professor Myers examines the traits and characteristics that make people effective at learning from experience—characteristics that are particularly important when they attempt to draw lessons from failure. Results of experiments indicate that individuals learn more... View Details
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- 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).
- 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.
- Teaching Interest
Big Data Analytics and Machine Learning
Big data in the context of marketing, management, and innovation strategy. Machine Learning algorithms and tools.
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- June 2015 (Revised November 2016)
- Case
2012 Obama Campaign: Learning in the Field
By: Leonard A. Schlesinger and Jason Gray
The development and utilization of an intentional Field learning strategy developed for the Obama for President campaign in 2012 following an after action Review calling for it after the 2008 elections View Details
Keywords: Training; Political Campaigns; Learning Organizations; Learning; Political Elections; Organizational Change and Adaptation; United States
Schlesinger, Leonard A., and Jason Gray. "2012 Obama Campaign: Learning in the Field." Harvard Business School Case 315-127, June 2015. (Revised November 2016.)
- 2021
- Working Paper
Deep Learning for Two-Sided Matching
By: Sai Srivatsa Ravindranatha, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers and David Parkes
We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design... View Details
Keywords: Strategy-proofness; Deep Learning; Two-Sided Platforms; Marketplace Matching; Balance and Stability
Srivatsa Ravindranatha, Sai, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers, and David Parkes. "Deep Learning for Two-Sided Matching." Working Paper, July 2021.
- September 2012
- Article
Learning Agility: In Search of Conceptual Clarity and Theoretical Grounding
By: D. Scott DeRue, Susan J. Ashford and Christopher G. Myers
As organizations become more complex and dynamic, individuals' ability to learn from experience becomes more important. Recently, the concept of learning agility has attracted considerable attention from human resource professionals and consultants interested in... View Details
DeRue, D. Scott, Susan J. Ashford, and Christopher G. Myers. "Learning Agility: In Search of Conceptual Clarity and Theoretical Grounding." Industrial and Organizational Psychology: Perspectives on Science and Practice 5, no. 3 (September 2012): 258–279.
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- March 2022
- Article
Learning to Rank an Assortment of Products
By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue... View Details
Keywords: Online Learning; Product Ranking; Assortment Optimization; Learning; Internet and the Web; Product Marketing; Consumer Behavior; E-commerce
Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
- September 2012
- Article
Learning Agility: Many Questions, a Few Answers, and a Path Forward
By: D. Scott DeRue, Susan J. Ashford and Christopher G. Myers
This article responds to and extends the commentaries offered in response to our focal article on learning agility. After summarizing the basic themes in the commentaries, we use this response to clarify points that were unclear in our original article and push back on... View Details
DeRue, D. Scott, Susan J. Ashford, and Christopher G. Myers. "Learning Agility: Many Questions, a Few Answers, and a Path Forward." Industrial and Organizational Psychology: Perspectives on Science and Practice 5, no. 3 (September 2012): 316–322.
- 2000
- Book
Learning in Action: A Guide to Putting the Learning Organization to Work
By: David A. Garvin
Keywords: Market Intelligence; Learning Organizations; After-Action Reviews; Experimentation; Learning
Garvin, David A. Learning in Action: A Guide to Putting the Learning Organization to Work. Boston: Harvard Business School Press, 2000.
- September 2017
- Article
The Advocacy Trap: When Legitimacy Building Inhibits Organizational Learning
By: Tiona Zuzul and Amy C. Edmondson
This paper describes a relationship between legitimacy building and learning for a new firm in a nascent industry. Through a longitudinal study of a new firm in the nascent smart city industry, we found that the firm failed to make progress on important internal... View Details
Keywords: Organizational Learning; Advocacy; Organizations; Learning; Organizational Culture; Entrepreneurship
Zuzul, Tiona, and Amy C. Edmondson. "The Advocacy Trap: When Legitimacy Building Inhibits Organizational Learning." Academy of Management Discoveries 3, no. 3 (September 2017): 302–321.
- October 2021
- Article
Changing Gambling Behavior through Experiential Learning
By: Shawn A. Cole, Martin Abel and Bilal Zia
This paper tests experiential learning as a debiasing tool to reduce gambling in South Africa, through a randomized field experiment. The study implements a simple, interactive game that simulates the odds of winning the national lottery through dice rolling.... View Details
Keywords: Debiasing; Experiential Learning; Behavioral Economics; Financial Education; Learning; Games, Gaming, and Gambling; Behavior; Decision Making
Cole, Shawn A., Martin Abel, and Bilal Zia. "Changing Gambling Behavior through Experiential Learning." World Bank Economic Review 35, no. 3 (October 2021): 745–763.
- April 2023
- Article
Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below
By: Ting Zhang, Dan Wang and Adam D. Galinsky
Although mentorship is vital for individual success, potential mentors often view it as a costly burden. To understand what motivates mentors to overcome this barrier and more fully engage with their mentees, we introduce a new construct, learning direction, which... View Details
Keywords: Mentoring; Learning Direction; Interpersonal Communication; Learning; Leadership Development
Zhang, Ting, Dan Wang, and Adam D. Galinsky. "Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below." Academy of Management Journal 66, no. 2 (April 2023): 604–637.
- Article
From Orientation to Behavior: The Interplay Between Learning Orientation, Open-mindedness, and Psychological Safety in Team Learning
By: Jean-François Harvey, Kevin J. Johnson, Kathryn S. Roloff and Amy C. Edmondson
Do teams with motivation to learn actually engage in the behaviors that produce learning? Though team learning orientation has been found to be positively related to team learning, we know little about how and when it actually fosters team learning. It is obviously not... View Details
Keywords: Emergent States; Goal Orientation; Open-mindedness; Psychological Safety; Team Learning; Teams; Groups and Teams; Learning; Goals and Objectives
Harvey, Jean-François, Kevin J. Johnson, Kathryn S. Roloff, and Amy C. Edmondson. "From Orientation to Behavior: The Interplay Between Learning Orientation, Open-mindedness, and Psychological Safety in Team Learning." Human Relations 72, no. 11 (November 2019): 1726–1751.
- 2017
- Working Paper
Machine Learning Methods for Strategy Research
By: Mike Horia Teodorescu
Numerous applications of machine learning have gained acceptance in the field of strategy and management research only during the last few years. Established uses span such diverse problems as strategic foreign investments, strategic resource allocation, systemic risk... View Details
Keywords: Machine Learning; Natural Language Processing; Classification; Decision Trees; Strategic Decisions; Strategy; Research; Information Technology
Teodorescu, Mike Horia. "Machine Learning Methods for Strategy Research." Harvard Business School Working Paper, No. 18-011, August 2017. (Revised October 2017.)