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..
Walrasian Dynamics in Multi-Unit Markets
Authors: Simina Brânzei, Aris Filos-Ratsikas1812-1819
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that the mechanism converges from any starting profile when we have two buyers. Theorem 8 For n = 2 buyers, the best response dynamic of the ALL-OR-NOTHING mechanism converges to a Nash equilibrium from any initial strategy profile. We present the convergence results for values n = 25 and m = 20 and for two different choices of budgets, either drawn from [1, 50] or from [1, 120], but the mechanism actually always converges to a pure Nash equilibrium from any initial profile, for any choice of n and m that we have considered. We conclude with the following conjecture. Conjecture 1 The best response dynamic of ALL-OR-NOTHING converges to a Nash equilibrium for any initial strategy profile, for any number of buyers. |
| Researcher Affiliation | Academia | Simina Brˆanzei Purdue University Aris Filos-Ratsikas Ecole Polytechnique F ed erale de Lausanne |
| Pseudocode | No | The paper describes theoretical concepts and proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions "randomly generated profiles" for simulations, but does not refer to a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes simulation results using randomly generated profiles, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes theoretical models and simulation results, but it does not provide any specific details about the hardware used for these simulations or experiments. |
| Software Dependencies | No | The paper describes theoretical models and simulation results, but it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions running simulations with "randomly generated profiles" for specific values of n and m and budget ranges, but it does not provide specific hyperparameters, optimizer settings, or a detailed experimental setup section. |