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..
Robust Multi-agent Counterfactual Prediction
Authors: Alexander Peysakhovich, Christian Kroer, Adam Lerer
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply RMAC to classic environments in market design: auctions, school choice, and social choice. and 6 Experiments We now turn to constructing RMAC bounds for classic problems in market design. |
| Researcher Affiliation | Industry | Alexander Peysakhovich Facebook AI Research Christian Kroer Facebook Core Data Science Adam Lerer Facebook AI Research |
| Pseudocode | Yes | Algorithm 1 Revelation Fictitious Play |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | We generate data by first sampling 1000 independent types and their actions from the closed form first-price equilibrium (bid = .5θ), using these actions as D. This indicates synthetic data generation, not the use of a public dataset with access information. |
| Dataset Splits | No | The paper describes generating synthetic data and using it as 'D', but does not specify any explicit train/validation/test splits for this data. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We consider a first-price 2-player auction G with types drawn from [0, 1] uniformly and bids in the interval [0, 1] discretized at intervals of .01. ... We set the domain of possible types to also be equal to [0, 1]. We generate data by first sampling 1000 independent types and their actions from the closed form first-price equilibrium (bid = .5θ), using these actions as D. We then use D to compute ϵ-RMAC predictions for several levels of ϵ. |