Markovian Interference in Experiments
Authors: Vivek Farias, Andrew Li, Tianyi Peng, Andrew Zheng
NeurIPS 2022 | Conference 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 vivekf@mit.edu Andrew A. Li Tepper School of Business Carnegie Mellon University Pittsburgh, PA 15213 aali1@cmu.edu Tianyi Peng Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA 02139 tianyi@mit.edu Andrew Zheng Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139 atz@mit.edu |
| 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. |