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(3,768)
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- 2005
- Working Paper
Additive Rules for the Quasi-linear Bargaining Problem
By: Christopher P. Chambers and Jerry R. Green
We study the class of additive rules for the quasi-linear bargaining problem introduced by Green. We provide a characterization of the class of all rules that are e¢ cient, translation invariant, additive, and continuous. We present several subfamilies of rules: the... View Details
Keywords: Econometric Models
Chambers, Christopher P., and Jerry R. Green. "Additive Rules for the Quasi-linear Bargaining Problem." Working Paper, January 2005.
- 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
- 2018
- Working Paper
Bayesian Ensembles of Binary-Event Forecasts: When Is It Appropriate to Extremize or Anti-Extremize?
By: Kenneth C. Lichtendahl Jr., Yael Grushka-Cockayne, Victor Richmond R. Jose and Robert L. Winkler
Many organizations face critical decisions that rely on forecasts of binary events. In these situations, organizations often gather forecasts from multiple experts or models and average those forecasts to produce a single aggregate forecast. Because the average... View Details
Keywords: Forecast Aggregation; Linear Opinion Pool; Generalized Additive Model; Generalized Linear Model; Stacking.; Forecasting and Prediction
Lichtendahl, Kenneth C., Jr., Yael Grushka-Cockayne, Victor Richmond R. Jose, and Robert L. Winkler. "Bayesian Ensembles of Binary-Event Forecasts: When Is It Appropriate to Extremize or Anti-Extremize?" Harvard Business School Working Paper, No. 19-041, October 2018.
- 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.)
- August 2012 (Revised February 2021)
- Case
Hub and Spoke, HealthCare Global and Additional Focused Factory Models for Cancer Care
By: Regina E. Herzlinger, Amit Ghorawat, Meera Krishnan and Naiyya Saggi
This case compares and contrasts four different models for delivering cancer care in India and the US. Students are asked to select the best model in its alignment with the Six Forces in those two countries and Africa, to which one of the models is considering... View Details
Keywords: Cancer Care Services; Focused Factories For Cancer Care; Hub And Spoke Cancer Care; Cancer Care In The U.S.; Cancer Care In Africa; Cancer Care In India; Health Care and Treatment; Business Model; Six Sigma; Health Disorders; Health Industry; United States; India; Africa
Herzlinger, Regina E., Amit Ghorawat, Meera Krishnan, and Naiyya Saggi. "Hub and Spoke, HealthCare Global and Additional Focused Factory Models for Cancer Care." Harvard Business School Case 313-030, August 2012. (Revised February 2021.)
- 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 2012
- Article
Discrete Choice Cannot Generate Demand That Is Additively Separable in Own Price
By: Sonia Jaffe and Scott Duke Kominers
We show that in a unit demand discrete choice framework with at least three goods, demand cannot be additively separable in own price. This result sharpens the analogous result of Jaffe and Weyl (2010) in the case of linear demand and has implications for testing of... View Details
Keywords: Discrete Choice; Unit Demand; Separable Demand; Linear Demand; Demand and Consumers; Market Design; Mathematical Methods; Economics
Jaffe, Sonia, and Scott Duke Kominers. "Discrete Choice Cannot Generate Demand That Is Additively Separable in Own Price." Economics Letters 116, no. 1 (July 2012): 129–132.
- 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.
- 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
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).
- 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
- 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.
- 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).
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).