In digital markets comprised of many competing services, each user chooses
between multiple service providers according to their preferences, and the
chosen service makes use of the user data to incrementally improve its model.
The service providers' models influence which service the user will choose at
the next time step, and the user's choice, in return, influences the model
update, leading to a feedback loop. In this paper, we formalize the above
dynamics and develop a simple and efficient decentralized algorithm to locally
minimize the overall user loss. Theoretically, we show that our algorithm
asymptotically converges to stationary points of of the overall loss almost
surely. We also experimentally demonstrate the utility of our algorithm with
real world data.

在由许多竞争服务组成的数字市场中，用户根据自己的偏好在多个服务提供商之间进行选择，所选择的服务利用用户数据来逐步改进其模型。服务提供商的模型影响用户在下一个时间步选择的服务，而用户的选择反过来又影响模型更新，从而形成了一个反馈循环。本文对上述动态进行了形式化，并开发了一个简单高效的分散算法来在本地最小化总体用户损失。理论上，我们证明了我们的算法渐近收敛到整体损失的稳定点。我们还通过真实世界数据实验证明了我们算法的实用性。

当用户选择时从流式数据中学习

Learning from Streaming Data when Users Choose

As AI technology advances rapidly, concerns over the risks of bigness in
digital markets are also growing. The EU's Digital Markets Act (DMA) aims to
address these risks. Still, the current framework may not adequately cover
generative AI systems that could become gateways for AI-based services. This
paper argues for integrating certain AI software as core platform services and
classifying certain developers as gatekeepers under the DMA. We also propose an
assessment of gatekeeper obligations to ensure they cover generative AI
services. As the EU considers generative AI-specific rules and possible DMA
amendments, this paper provides insights towards diversity and openness in
generative AI services.

AI 技术快速发展的同时，对数字市场巨头风险的关注也在增加。欧盟的数字市场法案 (DMA) 旨在应对这些风险，但目前的框架可能还不足以覆盖可能成为 AI 服务入口的生成式 AI 系统。本文主张将某些 AI 软件作为核心平台服务集成，并将某些开发者归为 DMA 下的守门人。我们还提出对守门人义务的评估，以确保其覆盖生成式 AI 服务。随着欧盟考虑生成式 AI 特定规则和 DMA 可能的修正案，本文为生成式 AI 服务的多样性和开放性提供了深入洞察。