Publications
Publications
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(704)
- Faculty Publications (229)
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 2
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 3
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
- October 2022
- Exercise
Shanty Real Estate: Updated Confidential Information for Homebuyer
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
- October 2022
- Exercise
Shanty Real Estate: Updated Confidential Information for iBuyer
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
- 2022
- Article
Nonparametric Subset Scanning for Detection of Heteroscedasticity
By: Charles R. Doss and Edward McFowland III
We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning... View Details
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
- March 2022 (Revised February 2024)
- Case
Applied Intuition: Powering Autonomy
By: Andy Wu, Rocio Wu and Matt Higgins
Applied Intuition, a leader in autonomous vehicle simulation software, has just closed on a $175 million round of Series D financing that values the four-year-old firm at $3.6 billion. With the immediate future secure, CEO Qasar Younis must now chart a strategic course... View Details
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
- March 2022
- Article
Learning to Rank an Assortment of Products
By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue... View Details
- March 2022
- Article
Where to Locate COVID-19 Mass Vaccination Facilities?
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new... View Details
- December 2021
- Article
Seeing Oneself as a Valued Contributor: Social Worth Affirmation Improves Team Information Sharing
By: Julia Lee Cunningham, Francesca Gino, Dan Cable and Bradley Staats
Teams often fail to reach their potential because members’ concerns about being socially accepted prevent them from offering their unique perspectives to the team. Drawing on relational self and self-affirmation theory, we argue that affirmation of team members’ social... View Details
- 2021
- Article
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
By: Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin Feigelis, Daniel M. Bear, Dan Gutfreund, David Cox, Antonio Torralba, James J. DiCarlo, Joshua B. Tenenbaum, Josh H. McDermott and Daniel L.K. Yamins
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include: real-time... View Details
- October 2021
- Article
Changing Gambling Behavior through Experiential Learning
By: Shawn A. Cole, Martin Abel and Bilal Zia
This paper tests experiential learning as a debiasing tool to reduce gambling in South Africa, through a randomized field experiment. The study implements a simple, interactive game that simulates the odds of winning the national lottery through dice rolling.... View Details
- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details
- Article
Joy and Rigor in Behavioral Science
By: Hanne K. Collins, Ashley V. Whillans and Leslie K. John
In the past decade, behavioral science has seen the introduction of beneficial reforms to reduce false positive results. Serving as the motivational backdrop for the present research, we wondered whether these reforms might have unintended negative consequences on... View Details
- May 2021
- Article
Value-Based Healthcare in Urology: A Collaborative Review
By: Chanan Reitblat, Paul A. Bain, Michael E. Porter, David N. Bernstein, Thomas W. Feeley, Markus Graefen, Santosh Iyer, Matthew J. Resnick, C.J. Stimson, Quoc-Dien Trinh and Boris Gershman
Context:
In response to growing concerns over rising costs and major variation in quality, improving value for patients has been proposed as a fundamentally new strategy for how healthcare should be delivered, measured, and... View Details
In response to growing concerns over rising costs and major variation in quality, improving value for patients has been proposed as a fundamentally new strategy for how healthcare should be delivered, measured, and... View Details
- 2021
- Article
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the... View Details
- February 2021
- Technical Note
Probability Distributions
By: Michael Parzen and Paul Hamilton
This technical note introduces students to the concept of random variables, and from there the normal and binomial distributions. After a brief introduction to random variables, the note describes the standard properties of the normal distribution: a single peak, and a... View Details
- January 2021
- Case
The FIRE Savings Calculator
By: Michael Parzen and Paul Hamilton
This case follows Carol Muñoz, a member of the Financial Independence, Retire Early (FIRE) lifestyle movement. At the age of 45, Carol is considering retiring and living off the $1 million she has accumulated. Using Monte Carlo simulation, Carol forecasts the... View Details