When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure

Authors: Hongbo Li, Lingjie Duan

AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Besides the worst-case performance, we further examine our mechanism s average-case performance by using extensive simulations.
Researcher Affiliation Academia Hongbo Li, Lingjie Duan Singapore University of Technology and Design hongbo li@mymail.sutd.edu.sg, lingjie duan@sutd.edu.sg
Pseudocode No The paper describes its models and mechanisms mathematically and textually but does not include any pseudocode or algorithm blocks.
Open Source Code Yes Due to the page limit, we move the lengthy proofs of the paper to our supplementary material and online technical report (Li and Duan 2022), and also provide code here. 1https://github.com/redglassli/Congestion-games-SID
Open Datasets No The paper describes a dynamic congestion game model and uses simulations. It does not refer to the use of a publicly available or open dataset for training, but rather a simulated environment.
Dataset Splits No The paper does not mention traditional training/validation/test splits, as the evaluation is based on simulations of a defined model rather than empirical data from a dataset.
Hardware Specification No The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the simulations.
Software Dependencies No The paper does not provide specific software dependencies or version numbers used for running the simulations.
Experiment Setup Yes Figure 3: Average inefficiency ratios γ(m) under myopic policy in (17) and γ(SID) under our selective information disclosure. We vary risky path number N in set {2, 3, 4, 5}. We set α = 0.99, αL = 0, ℓ= 1, p H = 0.8, p L = 0.2, q HH = 0.99, q LL = 0.99 here, and we change αH = 2 and αH = 5 to make comparison. At initial time t = 0, we let ℓ0(0) = 100, ℓi(0) = 105 and xi(0) = 0.5 for any path.