Economics and Electronic Commerce
Google is a global leader in electronic commerce. Not surprisingly, considerable attention is devoted to research in this area. Topics include 1) auction design, 2) advertising effectiveness, 3) statistical methods, 4) forecasting and prediction, 5) survey research, 6) policy analysis and a host of other topics. This research involves interdisciplinary collaboration among computer scientists, economists, statisticians, and analytic marketing researchers both at Google and academic institutions around the world.
A major challenge is solving these problems at very large scales. For example, the advertising market has billions of transactions daily, spread across millions of advertisers, presenting a unique opportunity to test and refine economic principles as applied to a very large number of interacting, self-interested parties with a myriad of objectives. At Google, research is an opportunity to translate direction into practice, influencing how production systems are designed and used.
Recent Publications
Controlling the Conversation
Martino Banchio
Bing Liu
Andres Perlroth
2026
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We study how AI assistants design their conversations. An assistant, with a private objective, guides a user through her search via sequential queries, modeled as partitions of the search space. The user selects the subset with the highest average value. Using a prior-free framework, we minimize the platform's regret relative to an omniscient benchmark.
We provide an optimal randomized policy that achieves bounded regret of $\frac{1}{n-1}$, where $n$ is the number of items. This policy is implemented as a sequence of ``recommendations'', that is, the AI sequentially proposes candidates from the item set until one is accepted.
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Geo experiment is a crucial, privacy-conscious option for measuring media effectiveness. Historically, two primary challenges have hindered the adoption of geo experiments: the high costs required to overcome large variance across geographical regions, and the unreliability of conventional analysis methods under real-world autocorrelation and non-stationary trends. To address these challenges, we introduce Meridian GeoX, Google’s open-source geo experiment framework. As a cornerstone of Google's modern measurement suite, Meridian GeoX is designed to standardize and optimize the end-to-end causal measurement lifecycle. The framework provides a unified suite of methodologies—including data-driven stratified sampling, advanced counterfactual models (Time-Based Regression, Synthetic Control, and Synthetic Difference-in-Differences), and a novel Design-Aware Placebo Inference engine. Extensive empirical benchmarking against industry alternatives demonstrates that Meridian GeoX delivers superior predictive accuracy, lowers Minimum Detectable Effects (MDE), and significantly reduces budget requirements. Furthermore, evaluations demonstrate that the framework's novel placebo inference maintains rigorous control over false positive rates while maximizing sensitivity under challenging conditions. By integrating seamlessly with the Meridian Marketing-Mix Model (MMM), this framework delivers a complete modern measurement solution. Ultimately, Meridian GeoX empowers advertisers with a powerful, cost-efficient, and methodologically robust solution for privacy-safe modern measurement, establishing a new industry standard for causal measurement and media effectiveness.
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Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting
David Autor
Tanya Rodchenko
Joshua Martin
Zanna Iscenko
Scott Strand
David Pearl
Melissa Ferere
NBER (2026)
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Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.
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We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and we have query access to the underlying value distributions. We consider two common query model in the literature: the “sample complexity” model where we can obtain a sample of each item value, and the “pricing query complexity” model where we can set a price to each item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work we give nearly tight sample complexity and pricing query complexity of the problem.
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This paper presents a novel framework for optimizing capacitor selection in electronic design using multi-objective linear and non-linear constrained optimization techniques. We demonstrate the effectiveness of this approach in minimizing cost and board area while meeting critical performance requirements.
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Online Bidding under RoS Constraints without Knowing the Value
Sushant Vijayan
Swati Padmanabhan
The Web Conference (2025)
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We consider the problem of auto-bidding in online advertising from the perspective of a single advertiser. The goal of the advertiser is to maximize their value under the Return-on-Spend (RoS) constraint, with performance measured in terms of \emph{regret} against the optimal offline solution that knows all queries a priori. Importantly, the value of the item is \textit{unknown} to the bidder ahead of time. The goal of the bidder is to quickly identify the optimal bid, while simultaneously satisfying budget and RoS constraints. Using a simple UCB-style algorithm, we provide the first result which achieves optimal regret and constraint violation for this problem.
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