Convergence of Learning Dynamics in Information Retrieval Games
Authors: Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz1780-1787
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that under the probability ranking principle (PRP), which forms the basis of the current state-of-the-art ranking methods, any better-response learning dynamics converges to a pure Nash equilibrium. We also show that other ranking methods induce a strategic environment under which such a convergence may not occur. Our main result proves that under the PRP, any better-response learning dynamics converges to a pure Nash equilibrium. |
| Researcher Affiliation | Academia | Omer Ben-Porat, Itay Rosenberg, Tennenholtz Technion Israel Institute of Technology Haifa 32000 Israel {omerbp@campus, itayrose@campus, moshet@ie}.technion.ac.il |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes theoretical work and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper describes theoretical work and does not use datasets for validation. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any hardware specifications. |
| Software Dependencies | No | The paper describes theoretical work and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not detail any experimental setup or hyperparameters. |