Multi-Unit Bilateral Trade
Authors: Matthias Gerstgrasser, Paul W. Goldberg, Bart de Keijzer, Philip Lazos, Alexander Skopalik1973-1980
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterise the set of dominant strategy incentive compatible (DSIC), strongly budget balanced (SBB), and ex-post individually rational (IR) mechanisms for the multi-unit bilateral trade setting. ... For increasing submodular valuation functions, we show the existence of a deterministic 2-approximation mechanism and a randomised e/(1 e) approximation mechanism, matching the best known bounds for the single-item setting. |
| Researcher Affiliation | Academia | 1,2,4Department of Computer Science, University of Oxford 3School of Computer Science and Electronic Engineering, University of Essex 5Department of Applied Mathematics, University of Twente |
| Pseudocode | No | The paper presents theoretical proofs and theorems, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper discusses valuation functions drawn from probability distributions in a Bayesian setting, but does not refer to or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper does not describe any dataset splits (training, validation, test) as it is a theoretical work and does not involve empirical evaluation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |