Working Papers

These papers are working drafts of research which often appear in final form in academic journals. The published versions may differ from the working versions provided here.

SSRN Research Paper Series

The Social Science Research Network’s Research Paper Series includes working papers produced by Stanford GSB the Rock Center.

You may search for authors and topics and download copies of the work there.

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Data Tracking under Competition

Kostas Bimpikis, Ilan Morgenstern, Daniela Saban
November102022

We explore the welfare implications of data-tracking technologies that enable firms to collect consumer data and use it for price discrimination. The model we develop centers around two features: competition between firms and consumers’ level of…

Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms

Mohsen Bayati, Junyu Cao, Wanning Chen
November2022

Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from periodically refreshed product catalogs. However, these algorithms face…

Advertising Media and Target Audience Optimization via High-dimensional Bandits

Wenjia Ba, J. Michael Harrison, Harikesh S. Nair
September2022

We present a data-driven algorithm that advertisers can use to automate their digital ad-campaigns at online publishers. The algorithm enables the advertiser to search across available target audiences and ad-media to find the best possible…

Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information

Yulun Wu, Robert A. Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno, Layne C. Price, Luis Voloch, George Karypis
September2022

Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell’s gene…

SystemMatch: Optimizing Preclinical Drug Models to Human Clinical Outcomes via Generative Latent-Space Matching

Scott Gigante, Varsha G. Raghavan, Amanda M. Robinson, Robert A. Barton, Adeeb H. Rahman, Drausin F. Wulsin, Jacques Banchereau, Noam Solomon, Luis Voloch, Fabian J. Theis
May2022

Translating the relevance of preclinical models (in vitro, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The rising abundance of single-cell genomic data from human tumors and…

A General Theory of the Stochastic Linear Bandit and Its Applications

Nima Hamidi, Mohsen Bayati
March2022

Recent growing adoption of experimentation in practice has led to a surge of attention to multiarmed bandits as a technique to reduce the opportunity cost of online experiments. In this setting, a decision-maker sequentially chooses among a set…

Patient-Level Clinical Expertise Enhances Prostate Cancer Recurrence Predictions with Machine Learning

Jacqueline Vallon, Neil Panjwani, Xi Ling, Sushmita Vij, Sandy Srinivas, John Leppert, Mohsen Bayati, Mark K. Buyyounouski
March2022

With rising access to electronic health record data, application of artificial intelligence to create clinical risk prediction models has grown. A key component in designing these models is feature generation. Methods used to generate features…

The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi
March2022

We study a Bayesian k-armed bandit problem in many-armed regime, when k ≥ √ T, with T the time horizon. We first show that subsampling is critical for designing optimal policies. Specifically, the standard UCB…

Equilibria in Repeated Games under No-Regret with Dynamic Benchmarks

Ludovico Crippa, Yonatan Gur, Bar Light
2022

In repeated games, strategies are often evaluated by their ability to guarantee the performance of the single best action that is selected in hindsight, a property referred to as Hannan consistency, or no-regret. However, the effectiveness of the…

Interference and Decision-Making in Marketplace Experimentation

R. Johari, H. Li, I. Liskovich
2022

Work in Progress

Leveraging Consensus Effect to Optimize Ranking in Online Discussion Boards

Gad Allon, Joseph Carlstein, Yonatan Gur
2022

Online discussion platforms (often referred to as discussion boards) are designed for facilitating remote discussions between users. To stimulate engagement (e.g., participation in the discussion), these platforms offer arriving users a ranked…

On the Management of Premade Foods

Jae-Hyuck Park, Dan A. Iancu, Erica Plambeck
2022

This paper examines a grocery retailer’s management of a premade food product. The retailer’s goal is to maximize a weighted sum of direct profit and customer welfare. Multiple items of the product are produced in batches and displayed for sale.…

Pay-as-Bid Procurement Mechanisms for Differentiated Products

J. Choi, Daniela Saban
2022

Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which the platform chooses an assortment of suppliers that balances the tradeoff between two objectives: providing enough…

Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach

Ramesh Johari, Bar Light, Gabriel Weintraub
2022

Online platforms collect rich information about participants, and then share this information back with participants to improve market outcomes. In this paper we study the following information disclosure problem of a two-sided market: how much…

Learning New Auction Format by Bidders in Internet Display Ad Auctions

Shumpei Goke, Ralph Mastromonaco, Sam Seljan
October2021

We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel data set on internet display ad auctions that exploits a staggered adoption by…

Private Genetic Genealogy Search

Mine Su Erturk, Kuang Xu
June282021

Genetic genealogy search has emerged as a powerful technique for identifying individuals by leveraging their genetic information and a genealogical network. The current practice relies on searching within a pre-constructed database containing…

Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Practitioners

Hannah Li, Geng Zhao, Ramesh Johari, Gabriel Weintraub
April2021

Two-sided marketplace platforms often run experiments to test the effect of an intervention before launching it platform-wide. A typical approach is to randomize individuals into the treatment group, which receives the intervention, and the…

Low-Acuity Patients Delay High-Acuity Patients in an Emergency Department

Mohsen Bayati, Michael Aratow, Danqi Luo, Erica Plambeck
2021

This paper provides evidence that the arrival of an additional low-acuity patient substantially increases the wait time to start of treatment for high-acuity patients, contradicting the long-standing prior conclusion in the…

The Social Divide of Social Distancing: Lockdowns in Santiago, Chile During the Covid-19 Pandemic

Aldo Carranza, Marcel Goic, Eduardo Lara, Marcelo Olivares, Gabriel Weintraub, Julio Covarrubia, Cristian Escobedo, Natalia Jara, Leonardo J. Basso
2021

Shelter-in-place and lockdowns have been some of the main non-pharmaceutical interventions that governments around the globe have implemented to contain the COVID-19 pandemic. In this paper we study the impact of such interventions in the…

Asymptotically Optimal Control of a Centralized Dynamic Matching Market with General Utilities

Jose H. Blanchet, Martin I. Reiman, Virag Shah, Lawrence M. Wein, Linjia Wu
December72020

We consider a matching market where buyers and sellers arrive according to independent Poisson processes at the same rate and independently abandon the market if not matched after an exponential amount of time with the same mean. In this…