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- Faculty Publications (347)
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- All HBS Web (991)
- Faculty Publications (347)
- 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.
- 1995
- Chapter
Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability
By: S. B. White, M. H. Bazerman and M. A. Neale
White, S. B., M. H. Bazerman, and M. A. Neale. "Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability." In Research on Negotiation in Organizations, edited by R. J. Bies, R. Lewicki, and B. Sheppard. Greenwich, CT: JAI Press, 1995.
- 2020
- Conference Presentation
A Performance-optimized Limb Detection Model Selectively Predicts Behavioral Responses Based on Movement Similarity
By: X. Zhao, J. De Freitas, L. Tarhan and G. A. Alvarez
- 2013
- Working Paper
Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs
By: Juan Alcacer, Wilbur Chung, Ashton Hawk and Goncalo Pacheco-de-Almeida
Although Strategy research aims to understand how firm actions have differential effects on performance, most empirical research estimates the average effects of these actions across firms. This paper promotes Random Coefficients Models (RCMs) as an ideal empirical... View Details
Alcacer, Juan, Wilbur Chung, Ashton Hawk, and Goncalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs." Harvard Business School Working Paper, No. 14-022, September 2013.
- 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.)
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- 2009
- Case
What People Want (and How to Predict It)
By: Thomas H. Davenport and Jeanne G. Harris
Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics -- data, statistics, predictive modeling -- to determine the likely success of their offerings. Instead, companies relied on the brilliance of... View Details
Keywords: Product Development; Creativity; Customer Satisfaction; Forecasting and Prediction; Markets; Business Model; Publishing Industry; Motion Pictures and Video Industry
Davenport, Thomas H., and Jeanne G. Harris. "What People Want (and How to Predict It)." 2009.
- June 2021
- Article
From Predictions to Prescriptions: A Data-driven Response to COVID-19
By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at... View Details
Keywords: COVID-19; Health Pandemics; AI and Machine Learning; Forecasting and Prediction; Analytics and Data Science
Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
- 2017
- Working Paper
Knowledge Flows within Multinationals—Estimating Relative Influence of Headquarters and Host Context Using a Gravity Model
By: Prithwiraj Choudhury, Mike Horia Teodorescu and Tarun Khanna
From the perspective of a multinational subsidiary, we employ the classic gravity equation in economics to model and compare knowledge flows to the subsidiary from the MNC headquarters and from the host country context. We also generalize traditional economics gravity... View Details
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details
Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 13 Sep 2016
- News
The Hard Truth About Business Model Innovation
- Article
A Choice Prediction Competition for Market Entry Games: An Introduction
By: Ido Erev, Eyal Ert and Alvin E. Roth
A choice prediction competition is organized that focuses on decisions from experience in market entry games (http://sites.google.com/site/gpredcomp/ and http://www.mdpi.com/si/games/predict-behavior/). The competition is based on two experiments: An estimation... View Details
Keywords: Experience and Expertise; Decision Choices and Conditions; Forecasting and Prediction; Learning; Market Entry and Exit; Game Theory; Behavior; Competition
Erev, Ido, Eyal Ert, and Alvin E. Roth. "A Choice Prediction Competition for Market Entry Games: An Introduction." Special Issue on Predicting Behavior in Games. Games 1, no. 2 (June 2010): 117–136.
- January 2010
- Journal Article
A Choice Prediction Competition: Choices from Experience and from Description
By: Ido Erev, Eyal Ert, Alvin E. Roth, Ernan E. Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Steward, Robert West and Christian Lebiere
Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: one-shot decisions from description (decisions under risk), one-shot decisions from experience, and repeated decisions from experience. Each competition was based... View Details
Keywords: Experience and Expertise; Decision Choices and Conditions; Forecasting and Prediction; Mathematical Methods; Risk and Uncertainty; Competition
Erev, Ido, Eyal Ert, Alvin E. Roth, Ernan E. Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Steward, Robert West, and Christian Lebiere. "A Choice Prediction Competition: Choices from Experience and from Description." Special Issue on Decisions from Experience. Journal of Behavioral Decision Making 23, no. 1 (January 2010).
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models. View Details
- 18 Sep 2019
- Working Paper Summaries
Using Models to Persuade
Keywords: by Joshua Schwartzstein and Adi Sunderam
- 29 Aug 2013
- Working Paper Summaries
X-CAPM: An Extrapolative Capital Asset Pricing Model
- June 2023
- Article
When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
As machine learning (ML) models are increasingly being employed to assist human decision
makers, it becomes critical to provide these decision makers with relevant inputs which can
help them decide if and how to incorporate model predictions into their decision... View Details
McGrath, Sean, Parth Mehta, Alexandra Zytek, Isaac Lage, and Himabindu Lakkaraju. "When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making." Transactions on Machine Learning Research (TMLR) (June 2023).