  Artificial Intelligence based systems may be used as digital nudging
techniques that can steer or coerce users to make decisions not always aligned
with their true interests. When such systems properly address the issues of
Fairness, Accountability, Transparency, and Ethics, then the trust of the user
in the system would just depend on the system's output. The aim of this paper
is to propose a model for exploring how good and bad recommendations affect the
overall trust in an idealized recommender system that issues recommendations
over a resource with limited capacity. The impact of different users attitudes
on trust dynamics is also considered. Using simulations, we ran a large set of
experiments that allowed to observe that: 1) under certain circumstances, all
the users ended accepting the recommendations; and 2) the user attitude
(controlled by a single parameter balancing the gain/loss of trust after a
good/bad recommendation) has a great impact in the trust dynamics.
