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- All HBS Web
(647)
- News (142)
- Research (416)
- Events (13)
- Multimedia (11)
- Faculty Publications (291)
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- 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.)
- 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.
- May 2023
- Article
Competition in Pricing Algorithms
By: Zach Y. Brown and Alexander J. MacKay
We document new facts about pricing technology using high-frequency data, and we examine the implications for competition. Some online retailers employ technology that allows for more frequent price changes and automated responses to price changes by rivals. Motivated... View Details
Keywords: Pricing Algorithms; Pricing Frequency; Commitment; Online Competition; Price; Information Technology; Competition
Brown, Zach Y., and Alexander J. MacKay. "Competition in Pricing Algorithms." American Economic Journal: Microeconomics 15, no. 2 (May 2023): 109–156.
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- April 2024
- Article
Decision Authority and the Returns to Algorithms
By: Hyunjin Kim, Edward L. Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
We evaluate a pilot in an Inspections Department to explore the returns to a pair of algorithms that varied in their sophistication. We find that both algorithms provided substantial prediction gains, suggesting that even simple data may be helpful. However, these... View Details
Keywords: Algorithmic Aversion; Algorithmic Decision Making; Algorithms; Public Entrepreneurship; Govenment; Local Government; Crowdsourcing; Crowdsourcing Contests; Inspection; Principal-agent Theory; Government Administration; Decision Making; Public Administration Industry; United States
Kim, Hyunjin, Edward L. Glaeser, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Decision Authority and the Returns to Algorithms." Strategic Management Journal 45, no. 4 (April 2024): 619–648.
- 2021
- Conference Presentation
An Algorithmic Framework for Fairness Elicitation
By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.... View Details
Jung, Christopher, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton, and Zhiwei Steven Wu. "An Algorithmic Framework for Fairness Elicitation." Paper presented at the 2nd Symposium on Foundations of Responsible Computing (FORC), 2021.
- 2019
- Article
Fair Algorithms for Learning in Allocation Problems
By: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth and Zachary Schutzman
Settings such as lending and policing can be modeled by a centralized agent allocating a scarce resource (e.g. loans or police officers) amongst several groups, in order to maximize some objective (e.g. loans given that are repaid, or criminals that are apprehended).... View Details
Elzayn, Hadi, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth, and Zachary Schutzman. "Fair Algorithms for Learning in Allocation Problems." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 170–179.
- September 2020 (Revised July 2022)
- Exercise
Artea (C): Potential Discrimination through Algorithmic Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Targeting; Algorithmic Bias; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (C): Potential Discrimination through Algorithmic Targeting." Harvard Business School Exercise 521-037, September 2020. (Revised July 2022.)
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details
Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 2023
- Working Paper
Algorithm Failures and Consumers' Response: Evidence from Zillow
By: Isamar Troncoso, Runshan Fu, Nikhil Malik and Davide Proserpio
In November 2021, Zillow announced the closure of its iBuyer business. Popular media largely attributed this to a failure of its proprietary forecasting algorithm. We study the response of consumers to Zillow’s iBuyer business closure. We show that after the iBuyer... View Details
Keywords: Algorithmic Pricing; Price; Forecasting and Prediction; Consumer Behavior; Real Estate Industry
Troncoso, Isamar, Runshan Fu, Nikhil Malik, and Davide Proserpio. "Algorithm Failures and Consumers' Response: Evidence from Zillow." Working Paper, July 2023.
- 2022
- Working Paper
Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces
By: Santiago Gallino, Nil Karacaoglu and Antonio Moreno
Most online sales worldwide take place in marketplaces that connect sellers and buyers. The presence of numerous third-party sellers leads to a proliferation of listings for each product, making it difficult for customers to choose between the available options. Online... View Details
Keywords: Algorithms; E-commerce; Sales; Digital Marketing; Internet and the Web; Customer Satisfaction
Gallino, Santiago, Nil Karacaoglu, and Antonio Moreno. "Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces." Working Paper, September 2022.
- 2021
- Article
Fair Algorithms for Infinite and Contextual Bandits
By: Matthew Joseph, Michael J Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions... View Details
Joseph, Matthew, Michael J Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Fair Algorithms for Infinite and Contextual Bandits." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
- September 2020 (Revised July 2022)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. (Revised July 2022.)
- January 2020
- Case
Lunchclub: Algorithmic Networking
By: Scott Duke Kominers and George Gonzalez
Algorithmic networking startup Lunchclub coordinates in-person meetings between professionals who would have been unlikely to meet. The company faces marketplace design, growth, and monetization challenges: The executive team has to refine Lunchclub's marketplace... View Details
Keywords: Monetization Strategy; Networking; Business Startups; Marketplace Matching; Market Design; Growth and Development Strategy; Information Industry
Kominers, Scott Duke, and George Gonzalez. "Lunchclub: Algorithmic Networking." Harvard Business School Case 820-051, January 2020.
- September 2020 (Revised July 2022)
- Teaching Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
Teaching Note for HBS No. 521-020. This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and... View Details
- March 2024
- Case
Unintended Consequences of Algorithmic Personalization
By: Eva Ascarza and Ayelet Israeli
“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details
Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Customization and Personalization; Technology Industry; Retail Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Unintended Consequences of Algorithmic Personalization." Harvard Business School Case 524-052, March 2024.
- November 2023
- Article
Algorithmic Mechanism Design with Investment
By: Mohammad Akbarpour, Scott Duke Kominers, Kevin Michael Li, Shengwu Li and Paul Milgrom
We study the investment incentives created by truthful mechanisms that allocate resources using approximation algorithms. Some approximation algorithms guarantee nearly 100% of the optimal welfare, but have only a zero guarantee when one bidder can invest before... View Details
Akbarpour, Mohammad, Scott Duke Kominers, Kevin Michael Li, Shengwu Li, and Paul Milgrom. "Algorithmic Mechanism Design with Investment." Econometrica 91, no. 6 (November 2023): 1969–2003.
- 15 Nov 2016
- Working Paper Summaries
Algorithmic Foundations for Business Strategy
Keywords: by Mihnea Moldoveanu
- 2016
- Working Paper
Algorithmic Foundations for Business Strategy
By: Mihnea Moldoveanu
I introduce algorithmic and meta-algorithmic models for the study of strategic problem solving, aimed at illuminating the processes and procedures by which strategic managers and firms deal with complex problems. These models allow us to explore the relationship... View Details
Moldoveanu, Mihnea. "Algorithmic Foundations for Business Strategy." Harvard Business School Working Paper, No. 17-036, October 2016.