# Online Learning in a Creator Economy

arXiv: https://arxiv.org/abs/2305.11381 · HTML: https://mlsanity.com/paper/2305.11381/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 22 of 22)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Bandits and Experts in Metric Spaces](https://mlsanity.com/paper/1312.1277/) (2013)
- [Who Leads and Who Follows in Strategic Classification?](https://mlsanity.com/paper/2106.12529/) (2021)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [Regret Minimization with Performative Feedback](https://mlsanity.com/paper/2202.00628/) (2022)
- [The Sample Complexity of Online Contract Design](https://mlsanity.com/paper/2211.05732/) (2022)
- [Simple versus Optimal Contracts](https://mlsanity.com/paper/1808.03713/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses](https://mlsanity.com/paper/2107.05762/) (2021)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Contracts under Moral Hazard and Adverse Selection](https://mlsanity.com/paper/2010.06742/) (2020)
- [The Complexity of Contracts](https://mlsanity.com/paper/2002.12034/) (2020)
- [Principal-Agent Hypothesis Testing](https://mlsanity.com/paper/2205.06812/) (2022)
- [Learning Approximately Optimal Contracts](https://mlsanity.com/paper/1811.06736/) (2018)
- [Bayesian Agency: Linear versus Tractable Contracts](https://mlsanity.com/paper/2106.00319/) (2021)
- [Designing Menus of Contracts Efficiently: The Power of Randomization](https://mlsanity.com/paper/2202.10966/) (2022)
- [Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments](https://mlsanity.com/paper/2208.11040/) (2022)
- [Learning Strategy-Aware Linear Classifiers](https://mlsanity.com/paper/1911.04004/) (2019)

## Built on by (most cited first, 8 of 8)

- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Matching of Users and Creators in Two-Sided Markets with Departures](https://mlsanity.com/paper/2401.00313/) (2023)
- [Contractual Reinforcement Learning: Pulling Arms with Invisible Hands](https://mlsanity.com/paper/2407.01458/) (2024)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents](https://mlsanity.com/paper/2412.16318/) (2024)
- [Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits](https://mlsanity.com/paper/2604.08643/) (2026)
- [Content Creation with Spillovers: An Incentive Design Approach](https://mlsanity.com/paper/2603.14372/) (2026)

## Abstract (arXiv)

The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the content creator under a principal-agent model through contracts to encourage better content. Additionally, the platform interacts with the users to recommend new content, receive an evaluation, and ultimately profit from the content, which can be modeled as a recommender system. Our study aims to explore how the platform can jointly optimize the contract and recommender system to maximize the utility in an online learning fashion. We primarily analyze and compare two families of contracts: return-based contracts and feature-based contracts. Return-based contracts pay the content creator a fraction of the reward the platform gains. In contrast, feature-based contracts pay the content creator based on the quality or features of the content, regardless of the reward the platform receives. We show that under smoothness assumptions, the joint optimization of return-based contracts and recommendation policy provides a regret Theta(T2/3). For the feature-based contract, we introduce a definition of intrinsic dimension d to characterize the hardness of learning the contract and provide an upper bound on the regret O(T(d+1)/(d+2)). The upper bound is tight for the linear family.