Publications

September 2017
Article

It Doesn't Hurt to Ask: Question-asking Increases Liking

By: K. Huang, M. Yeomans, A.W. Brooks, J. Minson and F. Gino

Conversation is a fundamental human experience, one that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational... View Details

Keywords: Question-asking; Liking; Responsiveness; Conversation; Natural Language Processing; Interpersonal Communication; Behavior

Read Now

2010
Chapter

Deferred Acceptance Algorithms: History, Theory, Practice

By: Alvin E. Roth

The deferred acceptance algorithm proposed by Gale and Shapley (1962) has had a profound influence on market design, both directly, by being adapted into practical matching mechanisms, and indirectly, by raising new theoretical questions. Deferred acceptance algorithms... View Details

Keywords: Labor; Market Design; Marketplace Matching; Failure; Mathematical Methods

Jeremy Yang is an Assistant Professor of Business Administration in the Marketing Unit at Harvard Business School. He teaches Marketing in the MBA required curriculum. He develops data products for... View Details

Keywords: advertising; media; entertainment; information; consumer products

December 2019
Article

Costly Concessions: An Empirical Framework for Matching with Imperfectly Transferable Utility

By: Alfred Galichon, Scott Duke Kominers and Simon Weber

We introduce an empirical framework for models of matching with imperfectly transferable utility and unobserved heterogeneity in tastes. Our framework allows us to characterize matching equilibrium in a flexible way that includes as special cases the classic fully- and... View Details

Keywords: Sorting; Matching; Marriage Market; Intrahousehold Allocation; Imperfectly Transferable Utility; Marketplace Matching; Mathematical Methods

SSRN

Forthcoming
Article

Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation

By: Maya Balakrishnan, Kris Ferreira and Jordan Tong

Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details

Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing

10 Jul 2024
Video

Inequality in the Digital Age | An Interview with Kalinda Ukanwa from the University of Southern California

February 2011 (Revised February 2012)
Case

Online Marketing at Big Skinny

By: Benjamin Edelman and Scott Duke Kominers

Describes a wallet maker's application of seven Internet marketing technologies: display ads, algorithmic search, sponsored search, social media, interactive content, online distributors, and A/B testing. Provides concise introductions to the key features of each... View Details

Keywords: Advertising Campaigns; Digital Marketing; Resource Allocation; Marketing Strategy; Performance Evaluation; Internet and the Web; Retail Industry

Educators

Article

Vungle Inc. Improves Monetization Using Big-Data Analytics

By: Bert De Reyck, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin and Andrew Kritzer

The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry,... View Details

Keywords: Big Data; Monetization; Data and Data Sets; Advertising; Mobile Technology; Customization and Personalization; Performance Improvement

2021
Working Paper

First Law of Motion: Influencer Video Advertising on TikTok

By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang

This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details

Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology

SSRN

29 Aug 2013
News

Computer Scientists Figured Out How To Execute The Perfect Reddit Submission

14 Nov 2016
News

Why Big Data Isn’t Enough

Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers

1. Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to... View Details

17 Jan 2020
News

Review: Competing in the Digital Age

September 2020 (Revised February 2024)
Teaching Note

Artea (A), (B), (C), and (D): Designing Targeting Strategies

By: Eva Ascarza and Ayelet Israeli

Teaching Note for HBS No. 521-021,521-022,521-037,521-043. 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... View Details

Keywords: Targeted Advertising; Targeting; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

Research Summary

Overview

By: Himabindu Lakkaraju

I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:

1. How to build... View Details

Article

Adaptive Machine Unlearning

By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites

Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details

Keywords: Machine Learning; AI and Machine Learning