When Does Diversity of Agent Preferences Improve Outcomes in Selfish Routing?
Authors: Richard Cole, Thanasis Lianeas, Evdokia Nikolova
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a sharp characterization of the network settings in which diversity always helps, versus those in which it is sometimes harmful. Specifically, we consider routing games, where diversity arises in the way that agents trade-off two criteria (such as time and money, or, in the case of stochastic delays, expectation and variance of delay). Our main contributions are: 1) A participantoriented measure of cost in the presence of agent diversity; 2) A full characterization of those network topologies for which diversity always helps, for all latency functions and demands. |
| Researcher Affiliation | Academia | Richard Cole1, Thanasis Lianeas2 and Evdokia Nikolova3 1 New York University, 2 National Technical University of Athens, 3 University of Texas at Austin |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training. It focuses on mathematical proofs and characterizations. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for validation purposes. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used to run experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |