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.
Behavioral Generative Agents for Energy Operations
Problem definition: Accurately modeling consumer behavior in energy operations is challenging due to uncertainty, behavioral heterogeneity, and limited empirical data-particularly in low-frequency, high-impact events. While generative AI trained…
Scaling Clinician-Grade Feature Generation from Clinical Notes with Multi-Agent Language Models
Developing accurate clinical prediction models is often bottlenecked by the difficulty of generating meaningful predictive features from unstructured data. While electronic health records (EHRs) contain rich narrative information, extracting a…
What Would it Cost to End Extreme Poverty?
We study poverty minimization via direct transfers, framing this as a statistical learning problem while retaining the information constraints faced by real-world programs. Using nationally representative household consumption surveys from 23…
Simulating and Experimenting with Social Media Mobilization Using LLM Agents
Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Building on the landmark 61-million-person Facebook experiment, we…
The Oversight Game: Learning to Cooperatively Balance an AI: Agent’s Safety and Autonomy
As increasingly capable agents are deployed, a central safety challenge is how to retain meaningful human control without modifying the underlying system. We study a minimal control interface in which an agent chooses whether to act autonomously…
Who to Offer, and When: Redesigning Feeding America's Real-Time Donation Tool
In collaboration with Feeding America, we aim to redesign Real-Time—a tool on its food sourcing and rescue platform, MealConnect—that facilitates the connection of ad-hoc, time-sensitive food donations to local agencies (e.g., meal programs)…
Tariffs and Supply Chain Diversification under Scale Economies
The recent elimination of the United States de minimis exemption for import tariffs has been reported to have a significant impact on ultra-fresh fashion companies such as Shein and Temu. This paper develops a game-theoretic model to investigate…
On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture patterns the end user requires. For example, a model may predict a…
Conformal Arbitrage: Risk-Controlled Balancing of Competing Objectives in Language Models
Modern language-model deployments must often balance competing objectives—for example, helpfulness versus harmlessness, cost versus accuracy, and reward versus safety. We introduce Conformal Arbitrage, a post-hoc framework that…
Policy Brief: Generative Artificial Intelligence: Opportunities for the Future of Work in Chile
We study the impact of Generative Artificial Intelligence (GenAI) in Chile, focusing on opportunities for task acceleration-specifically, the reduction of execution time for tasks within the hundred most common jobs in the country, corresponding…
Price Experimentation and Interference
In this paper, we examine biases arising in A/B tests where firms modify a continuous parameter, such as price, to estimate the global treatment effect of a given performance metric, such as profit. These biases emerge in canonical experimental…
Switchback Price Experiments with Forward-Looking Demand
We consider a retailer running a switchback experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices that consist of a reference price and a few discounted…
When Does Interference Matter? Decision-Making in Platform Experiments
This paper investigates decision-making in A/B experiments for online platforms and marketplaces. In such settings, due to constraints on inventory, A/B experiments typically lead to biased estimators because of interference; this phenomenon has…
Aligning Model Properties via Conformal Risk Control
AI model alignment is crucial due to inadvertent biases in training data and the underspecified machine learning pipeline, where models with excellent test metrics may not meet end-user requirements. While post-training alignment via human…
Designing Algorithmic Recommendations to Achieve Human–AI Complementarity
Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly model their effect on human decisions. This discrepancy between the…
Transitional Market Dynamics in Complex Environments
This paper presents a new approach to modeling transitional dynamics in dynamic models of imperfect competition, a crucial yet often neglected aspect of empirical models in industrial organization that seek to understand market responses to…
Do Mergers and Acquisitions Improve Efficiency? Evidence from Power Plants
Using rich data on hourly physical productivity and thousands of ownership changes from U.S. power plants, we study the effects of acquisitions on efficiency and underlying mechanisms. We find a 2% average increase in efficiency for acquired…
Minimax-Regret Sample Selection in Randomized Experiments
Randomized controlled trials are often run in settings with many subpopulations that may have differential benefits from the treatment being evaluated. We consider the problem of sample selection, i.e., whom to enroll in a randomized trial, such…
Bidders’ Responses to Auction Format Change in Internet Display Advertising Auctions
We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel dataset on internet display advertising auctions that exploits a staggered adoption by…
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and handcrafted rules. We propose rank-weighted average treatment effect (RATE) metrics as…