Pure Nash Equilibria in Online Fair Division
Authors: Martin Aleksandrov, Toby Walsh
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our first main result is that no online mechanism is group strategyproof. We then focus on pure Nash equilibria of these two mechanisms. Our second main result is that computing a pure Nash equilibrium is tractable for LIKE and intractable for BALANCED LIKE. Our third main result is that there could be multiple such profiles and counting them is also intractable even when we restrict our attention to equilibria with a specific property (e.g. envy-freeness). |
| Researcher Affiliation | Collaboration | Martin Aleksandrov UNSW Sydney Data61, CSIRO TU Berlin martin.aleksandrov@data61.csiro.au Toby Walsh UNSW Sydney Data61, CSIRO TU Berlin toby.walsh@data61.csiro.au |
| Pseudocode | No | The paper describes theoretical reductions and proof procedures in prose but does not include any clearly labeled pseudocode blocks or formal algorithm representations. |
| Open Source Code | No | The paper does not contain any statements about providing source code or links to code repositories for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on computational complexity proofs for abstract models, not empirical training on datasets. Therefore, it does not mention public datasets or their accessibility for training. |
| Dataset Splits | No | As a theoretical paper, there are no empirical experiments involving data splitting for validation. Thus, no validation split information is provided. |
| Hardware Specification | No | As a theoretical paper, there is no mention of specific hardware (e.g., GPU/CPU models, memory specifications) used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not involve software implementation details or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore it does not provide details about experimental setups, hyperparameters, or training configurations. |