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Show Results For
- All HBS Web
(3,013)
- People (14)
- News (648)
- Research (1,553)
- Events (19)
- Multimedia (9)
- Faculty Publications (830)
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- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 08 Sep 2015
- Research & Ideas
Knowledge Transfer: You Can't Learn Surgery By Watching
corporate roles is to improve upon traditional vicarious learning models. ©iStock.com/cacaroot Instead, Myers envisions a model of coactive vicarious learning. “The major shift theoretically is moving from a... View Details
- Article
Algorithms Need Managers, Too
By: Michael Luca, Jon Kleinberg and Sendhil Mullainathan
Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on... View Details
Keywords: Machine Learning; Algorithms; Predictive Analytics; Management; Big Data; Analytics and Data Science
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101.
- 20 Nov 2006
- Research & Ideas
Open Source Science: A New Model for Innovation
In a perfect world, scientists share problems and work together on solutions for the good of society. In the real world, however, that's usually not the case. The main obstacles: competition for publication and intellectual property protection. Is there a View Details
Keywords: by Martha Lagace
- 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).
- 15 Nov 2006
- Research & Ideas
Lessons Not Learned About Innovation
be rediscovered in each managerial generation (about every six years) as a fundamental way to enable new growth. But each generation seems to have forgotten or never learned the mistakes of the past, so we see classic traps repeated over... View Details
Keywords: by Sean Silverthorne
- September 2014
- Article
Advancing Consumer Neuroscience
By: Ale Smidts, Ming Hsu, Alan G. Sanfey, Maarten A. S. Boksem, Richard B. Ebstein, Scott A. Huettel, Joe W. Kable, Uma R. Karmarkar, Shinobu Kitayama, Brian Knutson, Israel Liberzon, Terry Lohrenz, Mirre Stallen and Carolyn Yoon
In the first decade of consumer neuroscience, strong progress has been made in understanding how neuroscience can inform consumer decision making. Here, we sketch the development of this discipline and compare it to that of the adjacent field of neuroeconomics. We... View Details
Keywords: Consumer Neuroscience; Neuroeconomics; Social Neuroscience; Genes; Machine Learning; Meta-analysis; Consumer Behavior; Decision Making; Science
Smidts, Ale, Ming Hsu, Alan G. Sanfey, Maarten A. S. Boksem, Richard B. Ebstein, Scott A. Huettel, Joe W. Kable, Uma R. Karmarkar, Shinobu Kitayama, Brian Knutson, Israel Liberzon, Terry Lohrenz, Mirre Stallen, and Carolyn Yoon. "Advancing Consumer Neuroscience." Marketing Letters 25, no. 3 (September 2014): 257–267.
- Research Summary
Overview
Prithwiraj (Raj) Choudhury is the Lumry Family Associate Professor at the Harvard Business School. He was an Assistant Professor at Wharton prior to joining Harvard. His research is focused on studying the Future of Work, especially the changing Geography of Work. In... View Details
- 15 Jan 2018
- Research & Ideas
A Better Business Model for Fighting Cancer
providing incentives and protecting proprietary information as needed—then leveraging the latest in artificial intelligence and machine learning through entities such as GNS Healthcare and IBM’s Watson to... View Details
- Article
How Real Sales Learning Happens: In the Flow of Work
By: Yuchun Lee, Mark Magnacca and Frank V. Cespedes
Most learning in sales is through peer learning in task-specific contexts, and the effects are cumulative because modeling behavior is a big driver of how salespeople develop. This is very different from the experience in most training seminars, especially if the... View Details
Lee, Yuchun, Mark Magnacca, and Frank V. Cespedes. "How Real Sales Learning Happens: In the Flow of Work." Learning Solutions (February 15, 2021).
- Teaching Interest
Overview
Teaching has been a lifelong passion of mine. As the third generation of academics in my family, I see good teaching as a means to give back and to encourage others to share my passion for discovery. I’ve been very lucky to have many teaching opportunities, both as an... View Details
Keywords: Big Data; Technology Strategy; Machine Learning; Data Science; ＂Marketing Analytics＂; Data Visualization; Analysis; Technological Innovation; Innovation and Invention; Intellectual Property; Corporate Strategy; Software; Information Technology; Entrepreneurship; Marketing; Technology Industry; Information Technology Industry; Green Technology Industry; Computer Industry; Advertising Industry
- 2011
- Working Paper
The Importance of Work Context in Organizational Learning from Error
By: Lucy H. MacPhail and Amy C. Edmondson
This paper examines the implications of work context for learning from errors in organizations. Prior research has shown that attitudes and behaviors related to error vary between groups within organizations but has not investigated or theorized the ways in which... View Details
- Article
Learning Through Noticing: Theory and Evidence from a Field Experiment
By: Rema Hanna, Sendhil Mullainathan and Joshua Schwartzstein
We consider a model of technological learning under which people "learn through noticing": they choose which input dimensions to attend to and subsequently learn about from available data. Using this model, we show how people with a great deal of experience may... View Details
Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein. "Learning Through Noticing: Theory and Evidence from a Field Experiment." Quarterly Journal of Economics 129, no. 3 (August 2014): 1311–1353. (Online Appendix.)
- September 2018 (Revised December 2019)
- Case
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making... View Details
Keywords: Radiology; Machine Learning; X-ray; CT Scan; Medical Technology; Probability; FDA 510(k); Diagnosis; Business Startups; Health Care and Treatment; Information Technology; Applications and Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel
Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- Research Summary
Organisational Learning in Software Requirements Engineering and Management
The current research project addresses the continuing low success rate of software development projects, which has been frequently reported in empirical studies. For example, the 2004 Chaos Report by the Standish Group found that only 29% of 9,236 application... View Details
- 06 Dec 2017
- Working Paper Summaries
Trials and Terminations: Learning from Competitors' R&D Failures
Keywords: by Joshua Lev Krieger
- 17 Jun 2019
- Research & Ideas
What Hospitals Must Learn to Compete
innovation in the industry is coming from entrants that say, “Let’s start with the patient perspective.” Sadun: Absolutely. And hopefully these new models will dispel the idea that there is a trade-off between providing excellent care and... View Details
- March 2003
- Background Note
A Short Note on the AccuFlow Excel Model
By: Jay O. Light
Describes an Excel spreadsheet workbook that facilitates the analysis of AccuFlow, Inc. View Details
Keywords: History; Analytics and Data Science; Cost of Capital; Negotiation; Capital; Business Model; Economic Systems; Machinery and Machining; Leveraged Buyouts; Business Startups; Equity
Light, Jay O. "A Short Note on the AccuFlow Excel Model." Harvard Business School Background Note 203-089, March 2003.
- September 2003 (Revised June 2005)
- Case
Learning from LeapFrog: Creating Educational and Business Value
By: Lynda M. Applegate, Christopher Dede and Susan Saltrick
Explores the success factors leading to one's company's rise to the number three ranking in the aggressively competitive toy industry. LeapFrog has made the strategic decision to exploit its educational model in two industry sectors: consumer toys and educational... View Details
Keywords: Transformation; Decisions; Education; Entrepreneurship; Innovation and Invention; Growth Management; Media; Business and Stakeholder Relations; Research; Value Creation
Applegate, Lynda M., Christopher Dede, and Susan Saltrick. "Learning from LeapFrog: Creating Educational and Business Value." Harvard Business School Case 804-062, September 2003. (Revised June 2005.)