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.
Finally, let us note that there is also a related draft @cite_13 by the present authors, which explores the notion of @math -extreme interval signalling and optimisation over truthful interval signals.
@cite_13
1898644197
We consider a multi-agent system where, at every time instant, many users choose to use one of multiple resources, whose performance depends on the number of concurrent users. In addition, a central agent has up-to-date knowledge of the congestion across all resources. What signals should the central agent broadcast to the individual users in order to reduce the total congestion? We model users with varying levels of aversion to risk, and a heterogeneous population of users over these levels. We consider signaling schemes that communicate for each resource an interval of possible congestion values, instead of scalar values. We show how to optimise over these intervals with respect to the social cost, under constraints that these intervals be consistent with past observations. Moreover, under mild assumptions, the resulting distribution of users across resources converges.