Dynamic Incentive Mechanism Design for COVID-19 Social Distancing
Abstract
As countries enter the endemic phase of COVID-19, people's risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowd-sourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.
Cite
Text
Ho et al. "Dynamic Incentive Mechanism Design for COVID-19 Social Distancing." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21718Markdown
[Ho et al. "Dynamic Incentive Mechanism Design for COVID-19 Social Distancing." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/ho2022aaai-dynamic/) doi:10.1609/AAAI.V36I11.21718BibTeX
@inproceedings{ho2022aaai-dynamic,
title = {{Dynamic Incentive Mechanism Design for COVID-19 Social Distancing}},
author = {Ho, Xuan Rong Zane and Lim, Wei Yang Bryan and Jiang, Hongchao and Ng, Jer Shyuan and Yu, Han and Xiong, Zehui and Niyato, Dusit and Miao, Chunyan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13173-13175},
doi = {10.1609/AAAI.V36I11.21718},
url = {https://mlanthology.org/aaai/2022/ho2022aaai-dynamic/}
}