Braess’s Paradox of Generative AI
Authors: Boaz Taitler, Omer Ben-Porat
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper initiates the study of Gen AI s long-term social impact resulting from the weakening network effect of human-based platforms like Stack Overflow. First, we study Gen AI s revenue-maximization optimization problem. We develop an approximately optimal solution and show that the optimal solution has a non-cyclic structure. Then, we analyze the social impact, showing that Gen AI could be socially harmful. Specifically, we present an analog to Braess s paradox in which all users would be better off without Gen AI. Finally, we develop necessary and sufficient conditions for a regulator with incomplete information to ensure that Gen AI is socially beneficial. |
| Researcher Affiliation | Academia | Boaz Taitler, Omer Ben-Porat Technion Israel Institute of Technology, Israel EMAIL, EMAIL |
| Pseudocode | No | We defer the full description of the algorithm to the appendix, and only write its formal guarantees. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper develops a theoretical model and illustrates its concepts using a synthetic 'instance' with predefined parameters (Example 1). It does not use or provide access to any real-world public or open datasets for experimentation. |
| Dataset Splits | No | The paper does not use any real-world datasets for experimentation, but rather a theoretical model illustrated with a synthetic 'instance' in Example 1. Therefore, there are no dataset splits (training/validation/test) mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on modeling and analysis. It does not describe any experimental setup involving specific hardware components like GPUs, CPUs, or other computational resources. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software (e.g., programming languages, libraries, frameworks) or their version numbers used for implementing or simulating the described models or algorithms. |
| Experiment Setup | No | The paper is primarily theoretical and does not describe an experimental setup for training machine learning models or running simulations in a manner that would involve hyperparameters or system-level training settings. Example 1 uses specific model parameters (e.g., r = 1, cm = 0.6, ctrain = 0.504, utility functions Rc(t) = 3 - 0.5t, Rs(p) = 1 - p, β = 1, p1 = 1, T = 20) to illustrate theoretical concepts, which are part of the model definition rather than experimental setup details. |