A No Free Lunch Theorem for Human-AI Collaboration
Authors: Kenny Peng, Nikhil Garg, Jon Kleinberg
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result answers this question, mostly in the negative. In fact, Theorem 1 shows that it is difficult to ensure a much lower bar: producing binary classifications that are always at least as accurate as the worst individual agent. [...] To prove Theorem 1 (which we do in Section 4) |
| Researcher Affiliation | Academia | 1Cornell Tech 2Cornell University |
| Pseudocode | No | The paper contains mathematical definitions, propositions, and proofs. There are no structured pseudocode or algorithm blocks explicitly labeled or formatted as such. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper defines a theoretical 'collaboration setting' for its proofs but does not use or provide access information for any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper does not conduct empirical experiments or use specific datasets; therefore, no dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments; therefore, no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments; therefore, no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments; therefore, no experimental setup details or hyperparameters are provided. |