How to Strategize Human Content Creation in the Era of GenAI?

2024-06-07

arXiv.org

AI Summary

This paper investigates content strategy optimization for human creators in the generative AI (GenAI) era, focusing on their dynamic competition with GenAI on content platforms. It introduces, for the first time, a feedback mechanism modeling how GenAI’s capabilities evolve dependent on human-generated content, distinguishing between time-sensitive (e.g., news, trending music) and time-invariant (e.g., historical facts) topics. Theoretically, it proves that computing the optimal strategy in time-sensitive settings is NP-hard under the RET H assumption; accordingly, it proposes a cyclic algorithm with a guaranteed 1/2 approximation ratio. For time-invariant settings, it derives a polynomial-time solvable long-term optimal strategy. The framework is grounded in dynamic game-theoretic modeling, computational complexity analysis, and numerical simulations. Results demonstrate that the proposed methods significantly outperform multiple baseline strategies in terms of creator utility.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative AI (GenAI) will have significant impact on content creation platforms. In this paper, we study the dynamic competition between a GenAI and a human contributor. Unlike the human, the GenAI's content only improves when more contents are created by the human over time; however, GenAI has the advantage of generating content at a lower cost. We study the algorithmic problem in this dynamic competition model about how the human contributor can maximize her utility when competing against the GenAI for content generation over a set of topics. In time-sensitive content domains (e.g., news or pop music creation) where contents' value diminishes over time, we show that there is no polynomial time algorithm for finding the human's optimal (dynamic) strategy, unless the randomized exponential time hypothesis is false. Fortunately, we are able to design a polynomial time algorithm that naturally cycles between myopically optimizing over a short time window and pausing and provably guarantees an approximation ratio of $frac{1}{2}$. We then turn to time-insensitive content domains where contents do not lose their value (e.g., contents on history facts). Interestingly, we show that this setting permits a polynomial time algorithm that maximizes the human's utility in the long run. Finally, we conduct simulations that demonstrate the advantage of our algorithms in comparison to a collection of baselines.

Problem

• Dynamic competition between GenAI and human content creators.
• Maximizing human utility in time-sensitive content domains.
• Designing algorithms for optimal human strategy against GenAI.

Innovation

• Polynomial time algorithm for dynamic strategy optimization
• Approximation ratio guarantee of 1/2 for time-sensitive content
• Polynomial time algorithm for time-insensitive content utility maximization