Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
Authors: Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in Gen AI. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, USA 2Department of Computer Science, Yale University, USA 3Department of Economics, University of Virginia, USA 4Department of Computer Science and Technology, Tsinghua University 5Department of Computer Science, University of Chicago, USA. |
| Pseudocode | Yes | Algorithm 1 Multi-agent Mirror Descent (MMD) with perfect gradient Algorithm 2 Solving for a targeted PNE of G(1) IN Algorithm 3 PNE checker for G(1) IN Algorithm 4 Solving for an arbitrary PNE of G(1) IN |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper states that parameters for the simulations are 'randomly sampled from uniform distribution U[1, 10]' and does not refer to any publicly available or open dataset. |
| Dataset Splits | No | The paper uses simulations with randomly sampled parameters and does not describe training, validation, or test dataset splits in the conventional sense for machine learning models. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms (e.g., Multi-agent Mirror Descent) and theoretical frameworks, but it does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For G(1) EX and G(1) IN, the default parameters are set to n = 10, α = 1.0, β = 0.5, γ = 0.9, ρ = 1.5, µ = 100, and {ci}n i=1 are randomly sampled from uniform distribution U[1, 10]. For GEX, the default K = 10 and αk, βk, γk, ρk are set to the same values as α, β, γ, ρ. In the subsequent experiments, when we investigate the sensitivity of the PNE on a certain parameter, we use the specified values to replace the default ones. The default T = 1000, η = 0.05, ϵ = 1e 4, x(0) i = (0.1, , 0.1). |