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- Article
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
By: Katrina Ligett, Seth Neel, Aaron Leon Roth, Bo Waggoner and Steven Wu
Traditional approaches to differential privacy assume a fixed privacy requirement ϵ for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint. As differential privacy is increasingly deployed in practical settings, it... View Details
Ligett, Katrina, Seth Neel, Aaron Leon Roth, Bo Waggoner, and Steven Wu. "Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM." Journal of Privacy and Confidentiality 9, no. 2 (2019).
- July 1985
- Background Note
Measuring Forecast Accuracy
Keywords: Forecasting and Prediction
Schleifer, Arthur, Jr. "Measuring Forecast Accuracy." Harvard Business School Background Note 186-027, July 1985.
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 2007
- Chapter
Team Emotion Recognition Accuracy and Team Performance
By: H. A. Elfenbein, J. T. Polzer and N. Ambady
Elfenbein, H. A., J. T. Polzer, and N. Ambady. "Team Emotion Recognition Accuracy and Team Performance." Chap. 4 in Research on Emotions in Organizations. Vol. 3, edited by N. M. Ashkanasy, W. J. Zerbe, and C. E.J. Härtel, 87–119. Amsterdam: Elsevier, 2007.
- November 2018
- Article
Worthy of Swift Trust? How Brief Interpersonal Contact Affects Trust Accuracy
By: Oliver Schilke and Laura Huang
Organizational scholars have long underscored the positive consequences of trust, yet trust can also have dysfunctional effects if it is not placed wisely. Though much research has examined conditions that increase individuals’ tendencies to trust others, we know very... View Details
Schilke, Oliver, and Laura Huang. "Worthy of Swift Trust? How Brief Interpersonal Contact Affects Trust Accuracy." Journal of Applied Psychology 103, no. 11 (November 2018): 1181–1197.
- 2003
- Article
Do Investors Respond to Analysts’ Forecast Revisions As If Forecast Accuracy Is All That Matters?
By: Michael B. Clement and Senyo Tse
Prior research suggests that investors' response to analyst forecast revisions increases with the analyst's forecast accuracy. We extend this research by examining whether investors appear to extract all of the information that analyst characteristics provide about... View Details
Clement, Michael B., and Senyo Tse. "Do Investors Respond to Analysts’ Forecast Revisions As If Forecast Accuracy Is All That Matters?" Accounting Review 78, no. 1 (2003): 227–249.
- Article
Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
By: Edward Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to... View Details
Keywords: User-generated Content; Operations; Tournaments; Policy-making; Machine Learning; Online Platforms; Analytics and Data Science; Mathematical Methods; City; Infrastructure; Business Processes; Government and Politics
Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118.
- May 2006
- Article
Detection Defection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models
By: Scott Neslin, Sunil Gupta, Wagner Kamakura, Junxiang Lu and Charlotte Mason
Neslin, Scott, Sunil Gupta, Wagner Kamakura, Junxiang Lu, and Charlotte Mason. "Detection Defection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models." Journal of Marketing Research (JMR) 43, no. 2 (May 2006): 204–211.
- 2019
- Working Paper
Moral Mind Reading: Meta-Perceptive and Motive-Judgment Accuracy Across Moral Motives
By: J. Lees, L. Young and A. Waytz
- Article
Economics at the FTC: Physician Acquisitions, Standard Essential Patents, and Accuracy of Credit Reporting
By: Julie Carlson, Leemore S. Dafny, Beth Freeborn, Pauline Ippolito and Brett Wendling
Carlson, Julie, Leemore S. Dafny, Beth Freeborn, Pauline Ippolito, and Brett Wendling. "Economics at the FTC: Physician Acquisitions, Standard Essential Patents, and Accuracy of Credit Reporting." Review of Industrial Organization 43, no. 4 (December 2013): 303–326.
- Article
Learning and Equilibrium as Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games
By: Ido Erev, Alvin E. Roth, R. Slonim and Greg Barron
Erev, Ido, Alvin E. Roth, R. Slonim, and Greg Barron. "Learning and Equilibrium as Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games." Special Issue on Behavioral Game Theory. Economic Theory 33, no. 1 (October 2007): 29–51.
- 2006
- Working Paper
Learning and Equilibrium As Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games
By: Ido Erev, Alvin E. Roth, Robert L. Slonim and Greg Barron
- Article
(Too) Optimistic about Optimism: The Belief that Optimism Improves Performance.
By: Elizabeth R. Tenney, Jennifer M. Logg and Don A Moore
A series of experiments investigated why people value optimism and whether they are right to do so. In Experiments 1A and 1B, participants prescribed more optimism for someone implementing decisions than for someone deliberating, indicating that people prescribe... View Details
Keywords: Optimism; Bias; Accuracy; Decision Phase; Performance; Attitudes; Performance Improvement; Perception; Outcome or Result
Tenney, Elizabeth R., Jennifer M. Logg, and Don A Moore. "(Too) Optimistic about Optimism: The Belief that Optimism Improves Performance." Journal of Personality and Social Psychology 108, no. 3 (March 2015): 377–399. (lead article.)
- 2018
- Working Paper
Algorithm Appreciation: People Prefer Algorithmic to Human Judgment
By: Jennifer M. Logg, Julia A. Minson and Don A. Moore
Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think... View Details
Keywords: Algorithms; Accuracy; Advice Taking; Forecasting; Theory Of Machine; Mathematical Methods; Decision Making; Forecasting and Prediction; Trust
Logg, Jennifer M., Julia A. Minson, and Don A. Moore. "Algorithm Appreciation: People Prefer Algorithmic to Human Judgment." Harvard Business School Working Paper, No. 17-086, March 2017. (Revised April 2018.)
- September 2020
- Article
Analyst Forecast Bundling
By: Michael Drake, Peter Joos, Joseph Pacelli and Brady Twedt
Changing economic conditions over the past two decades have created incentives for sell-side analysts to both provide their institutional clients tiered services and to streamline their written research process. One manifestation of these changes is an increased... View Details
Keywords: Analysts; Earnings Forecasts; Forecast Accuracy; Forecast Bundling; Business Earnings; Forecasting and Prediction
Drake, Michael, Peter Joos, Joseph Pacelli, and Brady Twedt. "Analyst Forecast Bundling." Management Science 66, no. 9 (September 2020): 4024–4046.
- Article
Emotional Acknowledgment: How Verbalizing Others' Emotions Fosters Interpersonal Trust
By: Alisa Yu, Justin M. Berg and Julian Zlatev
People often respond to others’ emotions using verbal acknowledgment (e.g., “You seem upset”). Yet, little is known about the relational benefits and risks of acknowledging others’ emotions in the workplace. We draw upon Costly Signaling Theory to posit how emotional... View Details
Keywords: Emotion; Costly Signaling; Interpersonal Trust; Emotional Valence; Interpersonal Relationships; Empathic Accuracy; Emotions; Relationships; Trust; Interpersonal Communication
Yu, Alisa, Justin M. Berg, and Julian Zlatev. "Emotional Acknowledgment: How Verbalizing Others' Emotions Fosters Interpersonal Trust." Organizational Behavior and Human Decision Processes 164 (May 2021): 116–135.
- 2025
- Working Paper
Government-Brokerage Analysts and Market Stabilization: Evidence from China
By: Sheng Cao, Xianjie He, Charles C.Y. Wang and Huifang Yin
We show analysts at government-controlled brokerage firms serve as a market stabilization tool
in China. Using earnings forecasts from 2005–2019, we find government-brokerage analysts
issue relatively more optimistic—yet less accurate and timely—forecasts during... View Details
Keywords: Sell-side Analysts; Forecast Optimism; Forecast Accuracy; Government Incentives; Market Stabilization; Government Ownership; Coordinated Economies; Stocks; Forecasting and Prediction; Business and Government Relations; Emerging Markets
Cao, Sheng, Xianjie He, Charles C.Y. Wang, and Huifang Yin. "Government-Brokerage Analysts and Market Stabilization: Evidence from China." Harvard Business School Working Paper, No. 18-095, March 2018. (Revised March 2025.)