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..
Scalable Equilibrium Computation in Multi-agent Influence Games on Networks
Authors: Fotini Christia, Michael Curry, Constantinos Daskalakis, Erik Demaine, John P. Dickerson, MohammadTaghi Hajiaghayi, Adam Hesterberg, Marina Knittel, Aidan Milliff5277-5285
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we test our model by computing equilibria using mirror descent for the two-agent case on random graphs. and We implement and validate this approach on families of random graphs. |
| Researcher Affiliation | Academia | 1 Massachusetts Institute of Technology 2 University of Maryland, College Park |
| Pseudocode | No | The paper describes algorithms and methods using mathematical notation and prose, but does not include any clearly labeled Pseudocode or Algorithm blocks. |
| Open Source Code | No | The paper states that Our work can be implemented using only open source tools, and relies mainly on well-known Python libraries like the Num Py stack and Network X but does not provide a specific link or explicit statement of code release for the methodology described. |
| Open Datasets | Yes | We experiment with computing equilibrium strategies on a number of random graphs from the Barabasi-Albert and Watts-Strogatz families (as implemented in Network X) |
| Dataset Splits | No | The paper experiments with random graphs from the Barabasi-Albert and Watts-Strogatz families but does not specify any explicit training, validation, or test dataset splits in terms of percentages or sample counts. |
| Hardware Specification | Yes | the experiments in this paper were run on a laptop. |
| Software Dependencies | No | relies mainly on well-known Python libraries like the Num Py stack and Network X |
| Experiment Setup | Yes | Experiments ran for 10,000 iterations with step size 3 × 10−4. |