An Asymptotically Optimal VCG Redistribution Mechanism for the Public Project Problem
Authors: Mingyu Guo
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose an asymptotically optimal mechanism, which achieves a worst-case efficiency ratio of 1, under a minor technical assumption: we assume the agents valuations are rational numbers with bounded denominators. We also show that if the agents valuations are drawn from identical and independent distributions, our mechanism s efficiency ratio equals 1 with probability approaching 1 asymptotically. Our results significantly improve on previous results. The best previously known asymptotic worst-case efficiency ratio is 0.102. For non-asymptotic cases, our mechanisms also achieve better ratios than all previous results. The paper focuses on designing mechanisms and proving their properties mathematically (Theorems, Lemmas), with no empirical evaluation on datasets or performance metrics from actual experimental runs. |
| Researcher Affiliation | Academia | Mingyu Guo School of Computer Science, University of Adelaide, Australia mingyu.guo@adelaide.edu.au |
| Pseudocode | No | The paper describes the functions and conditions in prose (e.g., 'If z < 1, then f(a, b, z) = ...') but does not provide a formal pseudocode block or an algorithm listing. |
| Open Source Code | No | The paper does not provide any statements about releasing code for the described mechanisms or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe any empirical training process using a dataset. It discusses agents' valuations as theoretical inputs, not as data for training models. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with data splits such as validation sets. Thus, no validation split information is provided. |
| Hardware Specification | No | The paper focuses on theoretical mechanism design and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical mechanisms and proofs, and does not mention any software implementations or their specific dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, and therefore, no experimental setup details like hyperparameters or training configurations are provided. |