Incentivizing Reliability in Demand-Side Response
Authors: Hongyao Ma, Valentin Robu, Na Li, David C. Parkes
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an experimental evaluation with a wide range of parameter values, we show that the mechanisms achieve close to the first best (i.e. assuming the mechanism knows agent types and can choose the most reliable ones) with regard to the number of agents who are selected and asked to prepare. We show in this section via simulation that the direct and indirect mechanisms have good performance, comparing with the best possible outcome (in a world without private information) as well as the spot auction. |
| Researcher Affiliation | Academia | Hongyao Ma Harvard University hma@seas.harvard.edu Valentin Robu Heriot Watt University V.Robu@hw.ac.uk Na Li Harvard University nali@seas.harvard.edu David C. Parkes Harvard University parkes@eecs.harvard.edu |
| Pseudocode | No | The paper describes mechanisms and mathematical models but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for simulations based on uniform distributions (e.g., 'vi U[0, 2], ci U[0, 2], pi U[0, 1]') rather than using an existing publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulation experiments with randomly generated data ('average number of selected agents over 1000 economies', 'computed over 1 million economies'), but does not specify training, validation, or test dataset splits as it's not a machine learning model trained on a fixed dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running its simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or solvers used for the simulations. |
| Experiment Setup | Yes | Let the total number of agents be n = 500 and the types be iid from the distributions: vi U[0, 2], ci U[0, 2], pi U[0, 1]. With varying τ from 0.9 to 0.999, the average number of selected agents over 1000 economies are as shown in Figure 3(a). Fixing τ = 0.98, the effect of varying reward R is as shown in Figure 3(b). computed over 1 million economies. |