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 | Conference PDF | Archive PDF | Plain Text | 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. |