@inproceedings{Hanna_Selecting_2019,
	title={{Selecting} {Compliant} {Agents} {for} {Opt-in} {Microtolling}},
	author={Hanna, Josiah P. and Sharon, Guni and Boyles, Stephen D. and Stone, Peter},
	year={2019},
	month={January},
	booktitle={Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI)},
	abstract={
		This paper examines the impact of tolls on social welfare in the context of a transportation network in which only a portion of the agents are subject to tolls. More specifically, this paper addresses the question: which subset of agents provides the most system benefit if they are compliant with an approximate marginal cost tolling scheme? Since previous work suggests this problem is NP-hard, we examine a heuristic approach. Our experimental results on three real-world traffic scenarios suggest that evaluating the marginal impact of a given agent serves as a particularly strong heuristic for selecting an agent to be compliant. Results from using this heuristic for selecting 7.6% of the agents to be compliant achieved an increase of up to 10.9% in social welfare over not tolling at all. The presented heuristic approach and conclusions can help practitioners target specific agents to participate in an opt-in tolling scheme.
	},
}