Cowles Foundation for Research in Economics
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Center for Algorithms, Data, and Market Design at Yale (CADMY)

CADMY is an innovative research center working at the intersection of computer science, economics, and data science. The Center aims to support Yale faculty and students with their research in relevant areas and will serve as a platform to host visiting faculty and postdoctoral fellows, promoting ongoing academic engagement and advancement.

With the arrival of the Internet, including rapid increases in the capacity to transmit, communicate and process data and information, algorithms and data have become central objects of interest in computer science, data science, and economics. Data and digital information have become essential for the allocation and distribution of services and commodities worldwide, which includes the design of markets and resource allocation mechanisms.

From traffic navigation apps to social networks, algorithms and data have become essential. Even with the arrival of large language models that build algorithms on massive data sets, these developments in artificial intelligence have only recently accelerated. The question of how to collect, aggregate, and disseminate data among diverse individuals in a decentralized society is critical for the functioning of democracy, as well as fair and efficient markets.

CADMY’s goal is to initiate and support research and teaching around the fundamental questions that arise at the intersection of computer science, data science, economics, and computational social sciences. CADMY aims to support Yale faculty and students with their research in relevant areas and will serve as a platform to host visiting faculty and postdoctoral fellows, promoting ongoing academic engagement and advancement.  

For more information about CADMY and research areas, please visit cadmy.yale.edu.

CADMY Website

Research Briefs

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Latest Publications

Menu Pricing of Large Language Models
Discussion Paper
March 2026
CFDP 2502
Author(s) Dirk Bergemann, Alessandro Bonatti & Alex Smolin
Abstract

We develop a framework for the optimal pricing and product design of LLMs in which a provider sells menus of token budgets to users who differ in their valuations across a continuum of tasks. Under a homogeneous production technology, we show that users’ high-dimensional type profiles are summarized by a scalar index, reducing the seller’s problem to one-dimensional screening. The optimal mechanism takes the form of committed-spend contracts: buyers pay for a budget that they allocate across token classes priced at marginal cost. We extend the analysis to environments with multiple differentiated models and to competition between a proprietary leader and an open-source fringe, showing that competitive pressure reshapes both the intensive and extensive margins of compute provision. Each element of our theory (token-budget menus, maximum- and minimum-spend plans, multi-model versioning, and linear API pricing) has a direct counterpart in the observed pricing practices of providers such as Anthropic, OpenAI, and GitHub.

Digital Ecosystems And Data Regulation
Discussion Paper
February 2026
CFDP 2426R1
Author(s) Andrew Rhodes, Jidong Zhou & Junjie Zhou
Abstract

This paper develops a framework in which a multiproduct ecosystem competes
with multiple single-product firms in both price and innovation. The ecosystem
can use data from one product to improve the quality of its other products.
We use the framework to study three regulatory policies aimed at leveling the
playing field. Restricting the ecosystem’s cross-product data usage, or forcing it
to share data with single-product firms, benefits those firms and induces them to
innovate more. However, these policies also dampen the ecosystem’s incentive to
collect data and innovate, potentially raising prices. Consumers are better off only
when single-product firms are sufficiently good at innovating. Facilitating data
exchange between single-product firms via a data cooperative can backfire and
harm them, because it induces the ecosystem to price more aggressively. For both
the data-sharing and data-cooperative policies, there exist data-compensation
schemes such that consumers are better off compared to no regulation.