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(2,277)
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- May 2024
- Teaching Note
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie
Teaching Note for HBS Case 724-383. The case has 3 important teaching purposes: First, what are the advantages and disadvantages of imitation? (e.g., Should AI21 imitate OpenAI with a chatbot?) Second, what are the advantages and disadvantages of keeping new technology... View Details
- September–October 2024
- Article
The Crowdless Future? Generative AI and Creative Problem-Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
Keywords: Large Language Models; Generative Ai; Crowdsourcing; AI and Machine Learning; Creativity; Technological Innovation
Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem-Solving." Organization Science 35, no. 5 (September–October 2024): 1589–1607.
- Article
Temporary General Equilibrium in a Sequential Trading Model with Spot and Futures Transactions
By: Jerry R. Green
The existence of an equilibrium is proven for a two-period model in which there are spot transactions and futures transactions in the first period and spot markets in the second period. Prices at that date are viewed with subjective uncertainty by all traders. This... View Details
Green, Jerry R. "Temporary General Equilibrium in a Sequential Trading Model with Spot and Futures Transactions." Econometrica 41, no. 6 (November 1973): 1103–1123.
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- 2024
- Working Paper
The Crowdless Future? Generative AI and Creative Problem Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
Keywords: Large Language Models; Crowdsourcing; Generative Ai; Creative Problem-solving; Organizational Search; AI-in-the-loop; Prompt Engineering; AI and Machine Learning; Innovation and Invention
Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised July 2024.)
- March 1999 (Revised February 2000)
- Case
Patient Care Delivery Model at the Massachusetts General Hospital, The
By: Amy C. Edmondson, Richard M.J. Bohmer and Emily Heaphy
Examines the implementation of a new patient care delivery model at Massachusetts General Hospital. Uses clinical and financial data to examine different choices for staffing non-physician health care professionals and to understand the challenges of managing change... View Details
Keywords: Change Management; Service Delivery; Health Care and Treatment; Health Industry; Massachusetts
Edmondson, Amy C., Richard M.J. Bohmer, and Emily Heaphy. "Patient Care Delivery Model at the Massachusetts General Hospital, The." Harvard Business School Case 699-154, March 1999. (Revised February 2000.)
- 2020
- Working Paper
A General Theory of Identification
By: Iavor Bojinov and Guillaume Basse
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree
on a definition in the context of parametric statistical models — roughly, a parameter θ in a model
P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective.... View Details
Bojinov, Iavor, and Guillaume Basse. "A General Theory of Identification." Harvard Business School Working Paper, No. 20-086, February 2020.
- 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).
- 1995
- Chapter
Dynamic General Equilibrium Models with Imperfectly Competitive Product Markets
By: Julio J. Rotemberg and Michael Woodford
- August 1976
- Article
A Model of Economic Growth With Altruism Between Generations
By: Elon Kohlberg
Kohlberg, Elon. "A Model of Economic Growth With Altruism Between Generations." Journal of Economic Theory 13, no. 1 (August 1976): 1–13.
- June 2015
- Supplement
Generating Higher Value at IBM (A): EPS Forecasting Model
By: Benjamin C. Esty and Scott Mayfield
This case analyzes IBM's financial performance and its capital allocation decisions over a 10-year period from 2004-2013, during which IBM returned more than $140B to shareholders through a combination of dividends and share repurchases. During this time, CEO Sam... View Details
- 2024
- Working Paper
Scaling Core Earnings Measurement with Large Language Models
By: Matthew Shaffer and Charles CY Wang
We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details
Keywords: Large Language Models; AI and Machine Learning; Accounting; Profit; Corporate Disclosure; Analytics and Data Science; Measurement and Metrics
Shaffer, Matthew, and Charles CY Wang. "Scaling Core Earnings Measurement with Large Language Models." Working Paper, November 2024.
- 2025
- Working Paper
Generative AI Use by Capital Market Information Intermediaries: Evidence from Seeking Alpha
By: Mark Bradshaw, Chenyang Ma, Benjamin Yost and Yuan Zou
We study the use of generative AI for firm-specific financial analysis on the Seeking Alpha platform. We find that, after the initial launch of ChatGPT in November 2022, the share of AI-generated articles rose sharply to 13.4% of all articles, then declined in late... View Details
Keywords: Generative Ai; Seeking Alpha; Equity Research; Large Language Models; Gpt; AI and Machine Learning; Information Publishing; Financial Markets
Bradshaw, Mark, Chenyang Ma, Benjamin Yost, and Yuan Zou. "Generative AI Use by Capital Market Information Intermediaries: Evidence from Seeking Alpha." Harvard Business School Working Paper, No. 25-055, April 2025.
- January 2014 (Revised December 2014)
- Case
GenapSys: Business Models for the Genome
By: Richard G. Hamermesh, Joseph B. Fuller and Matthew Preble
GenapSys, a California-based startup, was soon to release a new DNA sequencer that the company's founder, Hesaam Esfandyarpour, believed was truly revolutionary. The sequencer would be substantially less expensive—potentially costing just a few thousand dollars—and... View Details
Keywords: DNA Sequencing; Life Sciences; Business Model; Innovation ＆ Entrepreneurship; Health Care and Treatment; Genetics; Business Strategy; Biotechnology Industry; Pharmaceutical Industry; Technology Industry; Health Industry; Medical Devices and Supplies Industry; United States
Hamermesh, Richard G., Joseph B. Fuller, and Matthew Preble. "GenapSys: Business Models for the Genome." Harvard Business School Case 814-050, January 2014. (Revised December 2014.)
- Article
Ideation with Generative AI—In Consumer Research and Beyond
By: Julian De Freitas, G. Nave and Stefano Puntoni
The use of large language models (LLMs) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing... View Details
- 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).
- January 2016 (Revised November 2018)
- Case
Match Next: Next Generation Middle School?
By: John J-H Kim and Daniel Goldberg
This case is set in 2015 as a team at Match Education, a high performing charter middle school in Boston, explores new staffing and technology approaches in their quest to obtain what they term "jaw dropping" results. The team hopes to test and model for other schools... View Details
Keywords: General Management; K-12; Charter Schools; Public Schools; Edtech; Education; Information Technology; Management; Public Sector; Entrepreneurship; Education Industry; Boston
Kim, John J-H, and Daniel Goldberg. "Match Next: Next Generation Middle School?" Harvard Business School Case 316-138, January 2016. (Revised November 2018.)
- Article
Faithful and Customizable Explanations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details
Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- April 2000
- Teaching Note
Patient Care Delivery Model at the Massachusetts General Hospital, The TN
By: Amy C. Edmondson, Richard M.J. Bohmer and Emily Heaphy
Teaching Note for (9-699-154). View Details