Publications

04 Oct 2019
Working Paper Summaries

Soul and Machine (Learning)

Keywords: by Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano et al.

2020
Conference Presentation

Active World Model Learning with Progress-driven Exploration

By: K-H Kim, M. Sano, J. De Freitas, N. Haber and D. L. K. Yamins

2025
Working Paper

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli

Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details

Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science

SSRN

November 2007
Article

A Model of Consumer Learning for Service Quality and Usage

By: Raghuram Iyengar, Asim Ansari and Sunil Gupta

In many services, e.g., the wireless service industry, consumers choose a service plan based on their expected consumption. In such situations, consumers experience two forms of uncertainty. First, consumers may be uncertain about the quality of their service provider... View Details

Keywords: Experience and Expertise; Customer Value and Value Chain; Learning; Price; Knowledge Use and Leverage; Marketing Strategy; Consumer Behavior; Service Delivery; Quality; Risk and Uncertainty; Service Industry

April 2024
Article

A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification

By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson

Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details

Keywords: Health Disorders; Health Testing and Trials; AI and Machine Learning; Health Industry

26 Feb 2018
Working Paper Summaries

Different Strokes for Different Folks: Experimental Evidence on Complementarities Between Human Capital and Machine Learning

Keywords: by Prithwiraj Choudhury, Evan Starr, and Rajshree Agarwal; Information Technology

April 2018 (Revised February 2019)
Supplement

Improving Worker Safety in the Era of Machine Learning (B)

By: Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson

Supplements the (A) case. View Details

October 2023
Article

Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

By: Matthew S. Johnson, David I. Levine and Michael W. Toffel

We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details

Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States

January 2015 (Revised April 2015)
Case

Zeal: Launching Personalized and Social Learning

By: John J-H Kim and Christine S. An

Set in 2014, this case follows John Danner and his team at Zeal as they consider their product development strategy. In February 2013, serial entrepreneurs John Danner and Sanjay Noronha co-found Zeal, an education technology start up providing a web-based, mobile... View Details

Keywords: Entrepreneurship; Education Technology; MVP; Product Development; Product Market Fit; Monetization Strategy; SaaS Business Models; Education; Personalized Learning

Educators

July 2006
Article

Dynamic Mixed Duopoly: A Model Motivated by Linux vs. Windows

By: Ramon Casadesus-Masanell and Pankaj Ghemawat

This paper analyzes a dynamic mixed duopoly in which a profit-maximizing competitor interacts with a competitor that prices at zero (or marginal cost), with the cumulation of output affecting their relative positions over time. The modeling effort is motivated by... View Details

October 2018
Article

The Operational Value of Social Media Information

By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details

Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media

2023
Working Paper

Auditing Predictive Models for Intersectional Biases

By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill

Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details

Keywords: Predictive Models; Bias; AI and Machine Learning

February 2018 (Revised March 2018)
Case

Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)

By: Lauren Cohen, Christopher Malloy and William Powley

This case examines the intersection of two firms (Cogent Labs—a machine learning software firm in Tokyo; and Google, the technology infrastructure giant) attempting to exploit the benefits of artificial intelligence and machine learning in the financial services... View Details

Keywords: Technological Innovation; Finance; Growth and Development Strategy; Business Model; Applications and Software; Infrastructure; Technology Industry; Financial Services Industry

Educators

2025
Article

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

By: Kosuke Imai and Michael Lingzhi Li

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details

Keywords: AI and Machine Learning; Mathematical Methods; Analytics and Data Science

2022
Working Paper

Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments

By: Fanglin Chen, Xiao Liu, Davide Proserpio and Isamar Troncoso

We propose a method, Product2Vec, based on representation learning, that can automatically learn latent product attributes that drive consumer choices, to study product-level competition when the number of products is large. We demonstrate Product2Vec’s... View Details

Keywords: Consumer Choice; Consumer Behavior; Competition; Product Marketing

SSRN

May 2022
Case

AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services

By: Karim R. Lakhani, Shane Greenstein and Kerry Herman

Educators

November 25, 2016
Article

How to Tell If Machine Learning Can Solve Your Business Problem

By: Anastassia Fedyk

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Article

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the... View Details

Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning

2021
Working Paper

Deep Learning for Two-Sided Matching

By: Sai Srivatsa Ravindranatha, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers and David Parkes

We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design... View Details

Keywords: Strategy-proofness; Deep Learning; Two-Sided Platforms; Marketplace Matching; Balance and Stability