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Show Results For
- All HBS Web
(601)
- People (1)
- News (94)
- Research (413)
- Events (5)
- Multimedia (1)
- Faculty Publications (217)
- 03 Jun 2019
- Working Paper Summaries
Memory and Representativeness
- 27 Feb 2019
- News
Privacy in the Digital Age: An Interview with Leslie John
- Winter 2017
- Article
Why Big Data Isn't Enough
By: Sen Chai and Willy C. Shih
There is a growing belief that sophisticated algorithms can explore huge databases and find relationships independent of any preconceived hypotheses. But in businesses that involve scientific research and technological innovation, this approach is misguided and... View Details
Keywords: Big Data; Science-based; Science; Scientific Research; Data Analytics; Data Science; Data-driven Management; Data Scientists; Technological Innovation; Analytics and Data Science; Mathematical Methods; Theory
Chai, Sen, and Willy C. Shih. "Why Big Data Isn't Enough." Art. 58227. MIT Sloan Management Review 58, no. 2 (Winter 2017): 57–61.
- Spring 2016
- Article
The Billion Prices Project: Using Online Prices for Inflation Measurement and Research
By: Alberto Cavallo and Roberto Rigobon
New data-gathering techniques, often referred to as “Big Data” have the potential to improve statistics and empirical research in economics. In this paper we describe our work with online data at the Billion Prices Project at MIT and discuss key lessons for both... View Details
Keywords: Billion Prices Project; Online Scraped Data; Online Price Index; Economics; Research; Price; Analytics and Data Science
Cavallo, Alberto, and Roberto Rigobon. "The Billion Prices Project: Using Online Prices for Inflation Measurement and Research." Journal of Economic Perspectives 30, no. 2 (Spring 2016): 151–178.
- August 2020
- Article
Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation
By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic... View Details
Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.
Don’t Focus on the Most Expressive Face in the Audience
Research has shown that when speaking in front of a group, people’s attention tends to gets stuck on the most emotional faces, causing them to overestimate the group’s average emotional state. In this piece, the authors share two additional findings: First, the... View Details
- 26 Apr 2017
- Working Paper Summaries
Assessing the Quality of Quality Assessment: The Role of Scheduling
- January 2004
- Background Note
Why Developers Don't Understand Why Consumers Don't Buy
Looks at the psychological biases developers bring to the new product development process. Identifies three reasons why developers may do a poor job of identifying the demand for an innovative, new concept or product: (1) the self-selection bias, (2) differing initial... View Details
- 28 Sep 2017
- News
Why Venture Capitalists Aren’t Funding The Businesses We Need
- September 2023
- Exercise
Irrationality in Action: Decision-Making Exercise
By: Alison Wood Brooks, Michael I. Norton and Oliver Hauser
This teaching exercise highlights the obstacle of biases in decision-making, allowing students to generate examples of potentially poor decision-making rooted in abundant and unwanted bias. This exercise has two parts: a pre-class, online survey in which students... View Details
Brooks, Alison Wood, Michael I. Norton, and Oliver Hauser. "Irrationality in Action: Decision-Making Exercise." Harvard Business School Exercise 924-007, September 2023.
- November 30, 2020
- Editorial
Don't Focus on the Most Expressive Face in the Audience
By: Amit Goldenberg and Erika Weisz
Research has shown that when speaking in front of a group, people’s attention tends to gets stuck on the most emotional faces, causing them to overestimate the group’s average emotional state. In this piece, the authors share two additional findings: First, the larger... View Details
Goldenberg, Amit, and Erika Weisz. "Don't Focus on the Most Expressive Face in the Audience." Harvard Business Review (website) (November 30, 2020).
- December 2014
- Article
Team Reflexivity as an Antidote to Team Information Processing Failures
By: M. C. Schippers, A. C. Edmondson and M. A. West
This article proposes that team reflexivity—a deliberate process of discussing team goals, processes, or outcomes—can function as an antidote to team-level biases and errors in decision making. We build on prior work conceptualizing teams as information-processing... View Details
Keywords: Team Reflexivity; Team Information-processesing Failures; Team Regulatory Processes; Team Learning; Groups and Teams; Knowledge Management
Schippers, M. C., A. C. Edmondson, and M. A. West. "Team Reflexivity as an Antidote to Team Information Processing Failures." Small Group Research 45, no. 6 (December 2014): 731–769.
- TeachingInterests
Behavioral Economics and Applications in Markets (Econ 970, Spring 2013 and 2014)
Second-year undergraduate course introducing students to academic research in the field of behavioral economics. The course covers key models of time-inconsistent preferences, overconfidence, social preferences, and projection bias. The students are introduced to... View Details
- 28 Aug 2008
- Working Paper Summaries
How Can Decision Making Be Improved?
- 12 PM – 1 PM EDT, 16 Sep 2014
- Career Events
Returning to Work After a Break
As a "relauncher" herself, Carol Cohen (MBA 1985) understands the challenges of returning to work after multi-year career breaks. She has also engaged with hundreds of hiring managers to understand their biases and the risk they associate with hiring people who are... View Details
- April 3, 2024
- Article
How Automakers Can Address Resistance to Self-Driving Cars
By: Stuti Agarwal, Julian De Freitas and Carey K. Morewedge
Research involving multiple experiments found that consumers have biased views of their driving abilities relative to those of other drivers and automated vehicles. These findings have implications for the adoption of partly or fully automated vehicles, which one day... View Details
Keywords: Technology Adoption; Consumer Behavior; Government Legislation; Prejudice and Bias; Auto Industry; Technology Industry
Agarwal, Stuti, Julian De Freitas, and Carey K. Morewedge. "How Automakers Can Address Resistance to Self-Driving Cars." Harvard Business Review (website) (April 3, 2024).
- Research Summary
Overview
Given the difficulty of directly debiasing cognitive and social biases, Ariella's research focuses on how environments can be structured to reduce biased behaviors and outcomes. Ariella is currently pursuing two main strands of research: the first is a focus on... View Details
Samuel G. Hanson
Samuel G. Hanson is the William L. White Professor of Business Administration at Harvard Business School, a Research Associate at the National Bureau of Economic Research, and a Faculty Affiliate of the Harvard Economics department. He teaches Finance 1... View Details