Signaled queueing
Laura Brink, Robert Shorten, et al.
AAMAS 2015
We aim to reduce the social cost of congestion in many smart city applications. In our model of congestion, agents interact over limited resources after receiving signals from a central agent that observes the state of congestion in real time. Under natural models of agent populations, we develop new signalling schemes and show that by introducing a non-trivial amount of uncertainty in the signals, we reduce the social cost of congestion, i.e., improve social welfare. The signalling schemes are efficient in terms of both communication and computation, and are consistent with past observations of the congestion. Moreover, the resulting population dynamics converge under reasonable assumptions.
Laura Brink, Robert Shorten, et al.
AAMAS 2015
Sébastien Gerchinovitz, Jia Yuan Yu
Theoretical Computer Science
Yassine Lassoued, Julien Monteil, et al.
ITSC 2017
Jie Liu, Jakub Marecek, et al.
PSCC 2018