Markovian Interference in Experiments
Authors: Vivek Farias, Andrew Li, Tianyi Peng, Andrew Zheng
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation includes a set of experiments on a city-scale ride-hailing simulator. |
| Researcher Affiliation | Academia | Vivek Farias Sloan School of Management Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL Andrew A. Li Tepper School of Business Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Tianyi Peng Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL Andrew Zheng Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL |
| Pseudocode | No | No pseudocode or algorithm block is present in the provided text. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Riders and drivers are generated according to real world data, based on [1]; this yields 300k requests and 7k unique drivers per real day. [1] TLC Trip Record Data TLC. https://www1.nyc.gov/site/tlc/about/tlc-trip-recorddata.page. |
| Dataset Splits | No | The paper describes running experiments over simulated trajectories but does not specify explicit train/validation/test splits for a fixed dataset. The data for experiments is generated dynamically. |
| Hardware Specification | No | The paper states "See Appendix." regarding compute and resources used, indicating that the hardware specifications are not detailed in the provided main text. |
| Software Dependencies | No | No specific software dependencies with version numbers are listed in the provided text. |
| Experiment Setup | Yes | Our MDP setup exactly replicates that of [24], with N = 5000, λ = 1, µ = 1; see the appendix for further details. We run all estimators over 100 separate trajectories of length t = 104N of the above MDP initialized in its stationary distribution. Each estimator was run over 50 independent simulator trajectories, each over 3 105 requests. The DQ and OPE estimators shared a common linear approximation architecture with basis functions that count the number of drivers at every occupancy level. |