Strategyproofness and Monotone Allocation of Auction in Social Networks
Authors: Yuhang Guo, Dong Hao, Bin Li, Mingyu Xiao, Bakh Khoussainov
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1: Results of DNA-MU in Figure 1 with different r D Allocation Payment r D = {H} {B, F, C} A(0), B(0), F(5), C(4), D(0), H(0) r D = {A, B, D} A(4), B(0), F(0), C(0), D(6), H(0) Table 2: Results of DNA-MU-R in Figure 1 with different r D Allocation Payment r D = {H} {B, F, C} A(0), B(0), F(4), C(1), D(0), H(0) r D = {A, B, F} A(4), B(0), F(4), C(0), D(0), H(0) These tables present specific numerical outcomes for different scenarios and mechanisms, indicating an empirical evaluation of the proposed methods on a concrete example. |
| Researcher Affiliation | Academia | 1SCSE, University of Electronic Science and Technology of China 2AI-HSS, University of Electronic Science and Technology of China 3SCSE, Nanjing University of Science and Technology 4 University of New South Wales EMAIL, EMAIL, EMAIL, EMAIL, EMAIL All listed affiliations are universities (University of Electronic Science and Technology of China, Nanjing University of Science and Technology, University of New South Wales) and email domains are academic (.edu.cn, .edu.au). |
| Pseudocode | Yes | Algorithm 1 DNA-MU Mechanism Input: G = (N {s}, E), θ, K; Output: Allocation f, payment p; ... Algorithm 2 k-approximation for Combinatorial Auction with Single-minded Bidders Input: θ = {(vi, S i )}i N, W = ; Output: Allocation f; ... Algorithm 3 Allocation Rule of NSA Mechanism Input: G = (N {s}, E), θ, K; Output: Allocation f; |
| Open Source Code | No | Results lacking full proofs are proven in the appendix1. 1Full version is available at: https://drive.google.com/file/d/ 1F82c Dn2si-c Qqorw VMQd Otsi KGGjl Pj C/view?usp=drive link While a link to a "full version" is provided, it does not explicitly state that source code for the methodology is available at this link. It primarily refers to full proofs in the appendix. |
| Open Datasets | No | No concrete access information (link, DOI, repository, or citation) is provided for any publicly available or open dataset. The paper illustrates concepts using a social network example with 6 agents (Figure 1 and 2), which appears to be a small, illustrative instance rather than a formal dataset. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or methodology) is provided. The paper primarily uses small, illustrative examples for its analysis rather than formal datasets requiring splits for experimentation. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its analyses or simulations. The focus is on theoretical mechanism design and small-scale illustrative examples. |
| Software Dependencies | No | The paper does not mention any specific software components or libraries with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4) that would be needed to replicate the presented analysis or simulations. The focus is on the algorithmic and theoretical aspects of auction design. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. The work focuses on theoretical characterization of mechanisms, using small-scale examples to demonstrate properties rather than large-scale experiments with such parameters. |