Publications
Publications
Show Results For
- All HBS Web
(60)
- Faculty Publications (10)
Page 1 of 10
Results
- October 2024
- Article
Sampling Bias in Entrepreneurial Experiments
By: Ruiqing Cao, Rembrand Koning and Ramana Nanda
Using data from a prominent online platform for launching new digital products, we document that ‘sampling bias’—defined as the difference between a startup’s target customer base and the actual sample on which early ‘beta tests’ are conducted—has a systematic and... View Details
- July 2024
- Article
Acceptance of Automated Vehicles Is Lower for Self than Others
By: Stuti Agarwal, Julian De Freitas, Anya Ragnhildstveit and Carey K. Morewedge
Road traffic accidents are the leading cause of death worldwide for people aged 2–59. Nearly all deaths are due to human error. Automated vehicles could reduce mortality risks, traffic congestion, and air pollution of human-driven vehicles. However, their adoption... View Details
- 2023
- Working Paper
How People Use Statistics
By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis... View Details
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... 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
- Article
Default Neglect in Attempts at Social Influence
By: Julian Zlatev, David P. Daniels, Hajin Kim and Margaret A. Neale
Current theories suggest that people understand how to exploit common biases to influence others. However, these predictions have received little empirical attention. We consider a widely studied bias with special policy relevance: the default effect, which is the... View Details
- 2017
- Working Paper
Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments
By: Daniel J. Benjamin, Don A. Moore and Matthew Rabin
This paper describes results of a pair of incentivized experiments on biases in judgments about random samples. Consistent with the Law of Small Numbers (LSN), participants exaggerated the likelihood that short sequences and random subsets of coin flips would be... View Details
- March 2011
- Supplement
BioPasteur: Instructions for the group discussion
By: Giovanni Gavetti and Francesca Gino
The purpose of this exercise is to let students experience a few biases that can be deleterious to strategic decision-making. In particular, students are induced to fall into a confirmatory trap, and to experience other biases such as anchoring and sampling bias.... View Details
- March 2011 (Revised April 2011)
- Exercise
The Future of BioPasteur
By: Giovanni Gavetti and Francesca Gino
The purpose of this exercise is to let students experience a few biases that can be deleterious to strategic decision-making. In particular, students are induced to fall into a confirmatory trap, and to experience other biases such as anchoring and sampling bias.... View Details
- March 2011
- Supplement
The Future of BioPasteur -- Supplement
By: Giovanni Gavetti and Francesca Gino
The purpose of this exercise is to let students experience a few biases that can be deleterious to strategic decision-making. In particular, students are induced to fall into a confirmatory trap, and to experience other biases such as anchoring and sampling bias.... View Details