Digital Good Exchange
Authors: Wenyi Fang, Pingzhong Tang, Song Zuo
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We put forward game theoretical models tailored for digital good exchange. In the first part of the paper, we first consider a natural class of games where agents can choose either a subset of other participants items or no participation at all. It turns out that this class of games can be modeled as a variant of congestion games. We prove that it is in general NP-complete to determine whether there exists a non-trivial pure Nash equilibrium... In the second part of the paper, we investigate digital good exchange from a mechanism design perspective. |
| Researcher Affiliation | Academia | Wenyi Fang and Pingzhong Tang and Song Zuo Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Calculate Ki satisfying (2). input : Ki, ki0 for all i0 2 Ki output: Ki ;; sort i0 2 Ki in descending order of ki0; for sorted i0 2 Ki do if ki0 > |Ki |; else |
| Open Source Code | No | The information is insufficient. The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The information is insufficient. The paper is theoretical and does not describe experiments involving dataset training. |
| Dataset Splits | No | The information is insufficient. The paper is theoretical and does not describe experiments involving validation sets. |
| Hardware Specification | No | The information is insufficient. The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The information is insufficient. The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The information is insufficient. The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |