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Selecting Compliant Agents for Opt-in Micro-Tolling (2019)
Josiah Hanna
,
Guni Sharon
, Stephen Boyles, and
Peter Stone
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.
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Citation:
In
Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI)
, Honolulu, HI, January 2019.
Bibtex:
@inproceedings{AAAI19-Hanna, title={Selecting Compliant Agents for Opt-in Micro-Tolling}, author={Josiah Hanna and Guni Sharon and Stephen Boyles and Peter Stone}, booktitle={Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI)}, month={January}, address={Honolulu, HI}, url="http://www.cs.utexas.edu/users/ai-lab?AAAI19-Hanna", year={2019} }
People
Josiah Hanna
Ph.D. Student
jphanna [at] cs utexas edu
Guni Sharon
Postdoctoral Fellow
gunisharon [at] gmail com
Peter Stone
Faculty
pstone [at] cs utexas edu
Areas of Interest
Autonomous Traffic Management
Labs
Learning Agents