Publications
Publications
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- All HBS Web
(659)
- Faculty Publications (152)
- June 2020
- Article
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes... View Details
- May 5, 2020
- Article
Why the Crisis Is Putting Companies at Risk of Losing Female Talent
By: Colleen Ammerman and Boris Groysberg
There has been a massive shift in how work gets done inside many companies and the global pivot to working remotely will likely change how many think about face time and rigid work schedules. Might these changes benefit women? The authors argue that will depend on how... View Details
- Article
Signaling When Nobody Is Watching: A Reputation Heuristics Account of Outrage and Punishment in One-shot Anonymous Interactions
By: Jillian J. Jordan and David G. Rand
Moralistic punishment can confer reputation benefits by signaling trustworthiness to observers. However, why do people punish even when nobody is watching? We argue that people often rely on the heuristic that reputation is typically at stake, such that reputation... View Details
Keywords: Signaling; Morality; Trustworthiness; Anger; Third-party Punishment; Moral Sensibility; Behavior; Trust; Reputation
- November 26, 2019
- Article
Veil-of-Ignorance Reasoning Favors the Greater Good
By: Karen Huang, Joshua D. Greene and Max Bazerman
The “veil of ignorance” is a moral reasoning device designed to promote impartial decision-making by denying decision-makers access to potentially biasing information about who will benefit most or least from the available options. Veil-of-ignorance reasoning was... View Details
- Article
How to Use Heuristics for Differential Privacy
By: Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for which performance can be empirically evaluated. However, privacy guarantees cannot be... View Details
- November–December 2019
- Article
Making Sense of Soft Information: Interpretation Bias and Loan Quality
By: Dennis Campbell, Maria Loumioti and Regina Wittenberg Moerman
We explore whether behavioral biases impede the effective processing and interpretation of soft information in private lending. Taking advantage of the internal reporting system of a large federal credit union, we delineate three important biases likely to affect the... View Details
- 2019
- Working Paper
Veil-of-Ignorance Reasoning Favors the Greater Good
By: Karen Huang, Joshua D. Greene and Max Bazerman
The “veil of ignorance” is a moral reasoning device designed to promote impartial decision-making by denying decision-makers access to potentially biasing information about who will benefit most or least from the available options. Veil-of-ignorance reasoning was... View Details
- 2010
- Article
Fretting About Modest Risks Is a Mistake
By: Matthew Rabin and Max Bazerman
Managers often engage in risk-averse behavior, and economists, decision analysts, and managers treat risk aversion as a preference. In many cases, acting in a risk-averse manner is a mistake, but managers can correct this mistake with greater reflection. This article... View Details
- Article
Choice Architects Reveal a Bias Toward Positivity and Certainty
By: David P. Daniels and Julian Zlatev
Biases influence important decisions, but little is known about whether and how individuals try to exploit others’ biases in strategic interactions. Choice architects—that is, people who present choices to others—must often decide between presenting choice sets with... View Details
- 2019
- Working Paper
Labor Market Shocks and the Demand for Trade Protection: Evidence from Online Surveys
By: Rafael Di Tella and Dani Rodrik
We study preferences for government action in response to layoffs resulting from different types of labor-market shocks. We consider the following shocks: technological change, a demand shift, bad management, and three kinds of international outsourcing. Respondents... View Details
- 2025
- Working Paper
A Preference for Revision Absent Improvement
By: Ximena Garcia-Rada, Leslie K. John, Ed O’Brien and Michael I. Norton
People regularly encounter revised stimuli (e.g., revised versions of products, new editions of
books, tweaked recipes, and technological updates). In principle, a world of constant revision
should benefit people by affording them the most up-to-date offerings. In... View Details
- 2019
- Chapter
Behavioral Economics and Health-Care Markets
By: Amitabh Chandra, Benjamin Handel and Joshua Schwartzstein
This chapter summarizes research in behavioral health economics, focusing on insurance markets and product markets in health care. We argue that the prevalence of choice difficulties and biases leading to mistakes in these markets establish a special place for them in... View Details
- 2019
- Working Paper
Relative Performance Transparency: Effects on Sustainable Choices
By: Ryan W. Buell, Shwetha Mariadassou and Yanchong Zheng
We study how transparency into the levels and changes of relative sustainability performance affects consumer choices. Our work considers two forms of transparency: process transparency, in which customers receive information about the company's sustainability... View Details
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
- January 2019
- Article
The ABCs of Financial Education: Experimental Evidence on Attitudes, Behavior, and Cognitive Biases
By: Fenella Carpena, Shawn A. Cole, Jeremy Shapiro and Bilal Zia
This paper uses a large-scale field experiment in India to study attitudinal, behavioral, and cognitive constraints that can stymie the link between financial education and financial outcomes. The study complements financial education with (1) financial incentives on a... View Details
- November 2018
- Case
frog design
By: Srikant M. Datar and Caitlin N. Bowler
The case follows the genesis and development of Palo, a radical urban communications hub designed to replace payphone booths on Manhattan’s city streets, through a joint venture between frog design and a venture-backed firm LQD WiFi. The case explores the complexity of... View Details
- August 28, 2018
- Article
Maintaining Trust When Agents Can Engage in Self-deception
By: Andres Babino, Hernan A. Makse, Rafael Di Tella and Mariano Sigman
The coexistence of cooperation and selfish instincts is a remarkable characteristic of humans. Psychological research has unveiled the cognitive mechanisms behind self-deception. Two important findings are that a higher ambiguity about others’ social preferences leads... View Details
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
- 2018
- Chapter
Organizational Remedies for Discrimination
By: R. Ely and A. Feldberg
Laws now exist to protect employees from blatant forms of discrimination in hiring and promotion, but workplace discrimination persists in latent forms. These “second-generation” forms of bias arise in workplace structures, practices, and patterns of interaction that... View Details
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details