  This paper considers the problem of steering the aggregative behavior of a
population of noncooperative price-taking agents towards a desired behavior.
Different from conventional pricing schemes where the price is fully available
for design, we consider the scenario where a system regulator broadcasts a
price prediction signal that can be different from the actual price incurred by
the agents. The resulting reliability issues are taken into account by
including trust dynamics in our model, implying that the agents will not
blindly follow the signal sent by the regulator, but rather follow it based on
the history of its accuracy, i.e, its deviation from the actual price. We
present several nudge mechanisms to generate suitable price prediction signals
that are able to steer the aggregative behavior of the agents to stationary as
well as temporal desired aggregative behaviors. We provide analytical
convergence guarantees for the resulting multi-components models. In
particular, we prove that the proposed nudge mechanisms earn and maintain full
trust of the agents, and the aggregative behavior converges to the desired one.
The analytical results are complemented by a numerical case study of
coordinated charging of plug-in electric vehicles.
