Dynamics in Two-Sided Attention Markets: Objective, Optimization, and Control

2025-09-02

AI Summary

This paper addresses the two-sided attention market formed by platforms, users, and content creators in online content ecosystems, aiming to unify the modeling of their dynamic interactions and heterogeneous evolutionary outcomes. Method: It innovatively formalizes bilateral attention allocation as mirror descent over a non-convex potential function, establishing the first coupled potential function model for such markets. Integrating game-theoretic reasoning, multinomial logit choice models, mirror descent dynamics, and non-convex optimization analysis, the approach designs a family of platform-ranking–induced potential functions to steer system evolution. Contribution/Results: The work yields the first optimization framework capable of uniformly explaining attention-market dynamics under diverse platform mechanisms. It provides novel theoretical guarantees on local convergence for non-convex potential landscapes and lays a rigorous foundation for analyzing content-ecosystem evolution and designing effective intervention policies.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Machine Learning: Learning Preferences or Rankings

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation

Abstract

With most content distributed online and mediated by platforms, there is a pressing need to understand the ecosystem of content creation and consumption. A considerable body of recent work shed light on the one-sided market on creator-platform or user-platform interactions, showing key properties of static (Nash) equilibria and online learning. In this work, we examine the {it two-sided} market including the platform and both users and creators. We design a potential function for the coupled interactions among users, platform and creators. We show that such coupling of creators' best-response dynamics with users' multilogit choices is equivalent to mirror descent on this potential function. Furthermore, a range of platform ranking strategies correspond to a family of potential functions, and the dynamics of two-sided interactions still correspond to mirror descent. We also provide new local convergence result for mirror descent in non-convex functions, which could be of independent interest. Our results provide a theoretical foundation for explaining the diverse outcomes observed in attention markets.

Problem

• Modeling two-sided attention markets dynamics
• Analyzing platform ranking strategies' impact
• Providing convergence guarantees for non-convex optimization

Innovation

• Potential function for coupled interactions
• Mirror descent equivalence for dynamics
• Platform ranking strategies as potential functions