Ridesharing with Driver Location Preferences
Authors: Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, David C. Parkes
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we study settings with constrained supply and asymmetric demand, using simulations to compare the revenue and welfare performance of PARM to existing ridesharing mechanisms. We show that PARM achieves close to first-best revenue and typically outperforms even the best case for preference-oblivious pricing (where strategic behavior hurts efficiency). |
| Researcher Affiliation | Academia | Duncan Rheingans-Yoo1 , Scott Duke Kominers2 , Hongyao Ma3 and David C. Parkes3 1Harvard College 2Harvard Business School and Department of Economics 3Harvard School of Engineering and Applied Sciences {d rheingansyoo@college, kominers@fas, hma@seas, parkes@eecs}.harvard.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper describes simulation results based on a synthetic model with specified parameters (e.g., "We first assume an unbalanced trip flow α00 =α10 = 0.25 and α01 =α11 = 0.75", "Fixing the total demand at location 1 at θ1 = 1000"), rather than using a publicly available dataset. |
| Dataset Splits | No | The paper describes simulations with varying parameters but does not specify training, validation, or test dataset splits in the typical machine learning sense. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the simulations. |
| Software Dependencies | No | The paper describes a theoretical model and its simulation, but it does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In Section 4, "Simulation Results," the paper specifies the parameters used for the simulations, such as "s(0) =s(1) =100. Drivers have outside option w=40, discount factor δ = 0.99, and gain utility I = 0.2W = 0.2w(1 δ) per period from being in their preferred locations. Each rider has value independently drawn U[0, 1]." It also details demand patterns like "α00 =α10 = 0.25 and α01 =α11 = 0.75" and varying demand parameters like "θ1 = 1000, and varying θ0 from 0 to 1000". |