Mechanism Design for Scheduling with Uncertain Execution Time
Authors: Vincent Conitzer, Angelina Vidali
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study different variations of the Vickrey mechanism that take as input the agents reported distributions and the players realized running times and that output a schedule that minimizes the expected sum of processing times, as well as payments that make it an ex-post equilibrium for the agents to both truthfully report their distributions and exert full effort to complete the task. We devise the Ch PE mechanism, which is uniquely tailored to our problem, and has many desirable properties including: not rewarding agents that fail to finish the task and having non-negative payments. |
| Researcher Affiliation | Academia | Vincent Conitzer Department of Computer Science Duke University Durham, NC 27708, USA conitzer@cs.duke.edu Angelina Vidali Department of Computer Science Duke University Durham, NC 27708, USA vidali@cs.duke.edu |
| Pseudocode | No | The paper contains mathematical derivations and proofs, but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training, hence no information on public dataset availability is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not involve experimental setups, hyperparameters, or training configurations. |