Incentivizing High-Quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium
Authors: Yingce Xia, Tao Qin, Nenghai Yu, Tie-Yan Liu
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the existence of pure Nash equilibrium (PNE) for the mechanisms used in Internet services (e.g., online reviews and question-answering websites) to incentivize users to generate high-quality content. ... We prove the existence of PNE for some mechanisms and the non-existence for some other mechanisms. We also discuss how to find a PNE (if exists) through either a constructive way or a search algorithm. |
| Researcher Affiliation | Collaboration | Yingce Xia , Tao Qin , Nenghai Yu and Tie-Yan Liu Key Laboratory of Electromagnetic Space Information of CAS, USTC, Hefei, Anhui, 230027, P.R. China Microsoft Research, Building 2, No. 5 Danling Street, Haidian District, Beijing, 100080, P.R.China yingce.xia@gmail.com, {taoqin,tyliu}@microsoft.com, ynh@ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1 Algorithm to find a PNE of the original game from an induced local game Input: q = (q1, q2, qn) where q1 q2 qn; Output: x(n) = (x(n) 1 , x(n) 2 , , x(n) n ) 1: Calculate the yni i [n] with Eqn. (9). If none of yni is smaller than zero or larger than qi, x(n) yn; verify whether it can be extended to a PNE of the original game by Lemma 4; if so, return x(n). 2: for m 1 : n do 3: Calculate xnim i {m + 1, , n} with Eqn. (13). x(n) i qi i {1, , m}, x(n) i xnim i {m + 1, , n} if they are all feasible; 4: if x(n) is a local PNE (verified by Lemma 5) then 5: Verify whether x(n) can be extended to a PNE of the original game and return it if so; 6: end if 7: end for |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. It references an extended version on arXiv, but this is not a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and model analysis, therefore no datasets are used for training and no concrete access information for a public dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits, so no information on training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithms, and therefore does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical models and mathematical proofs, not experimental setups with hyperparameters or training configurations. |