An MDP-Based Approach to Online Mechanism Design
Abstract
Online mechanism design (MD) considers the problem of provid- ing incentives to implement desired system-wide outcomes in sys- tems with self-interested agents that arrive and depart dynami- cally. Agents can choose to misrepresent their arrival and depar- ture times, in addition to information about their value for di(cid:11)erent outcomes. We consider the problem of maximizing the total long- term value of the system despite the self-interest of agents. The online MD problem induces a Markov Decision Process (MDP), which when solved can be used to implement optimal policies in a truth-revealing Bayesian-Nash equilibrium.
Cite
Text
Parkes and Singh. "An MDP-Based Approach to Online Mechanism Design." Neural Information Processing Systems, 2003.Markdown
[Parkes and Singh. "An MDP-Based Approach to Online Mechanism Design." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/parkes2003neurips-mdpbased/)BibTeX
@inproceedings{parkes2003neurips-mdpbased,
title = {{An MDP-Based Approach to Online Mechanism Design}},
author = {Parkes, David C. and Singh, Satinder P.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {791-798},
url = {https://mlanthology.org/neurips/2003/parkes2003neurips-mdpbased/}
}