Robust Multi-agent Counterfactual Prediction
Authors: Alexander Peysakhovich, Christian Kroer, Adam Lerer
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |