Signalling and obfuscation for congestion control

TL;DR

This paper proposes new signalling schemes that introduce uncertainty to reduce congestion social costs in smart city applications, ensuring efficient communication and convergence of population dynamics.

Contribution

It introduces novel signalling strategies that leverage uncertainty to improve social welfare in congestion scenarios, with efficient implementation and convergence guarantees.

Findings

Reduced social cost of congestion through uncertainty-based signalling

Efficient signalling schemes with low communication and computation overhead

Convergence of population dynamics under proposed signalling methods

Abstract

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.