Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Unit Bilateral Trade
Authors: Matthias Gerstgrasser, Paul W. Goldberg, Bart de Keijzer, Philip Lazos, Alexander Skopalik1973-1980
AAAI 2019 | Venue PDF | 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. |