  This paper focuses on devising strategies for control-oriented
decision-making scenarios, in the presence of social and external influences,
e.g. within recommending systems in social contexts. More precisely, we extend
the classical Friedkin and Johnsen model of opinion dynamics to incorporate
random factors, such as variability in individual predisposition, and
uncertainty in social acceptance towards a specific action that a recommending
system aims to promote. Furthermore, we formulate an optimization-based control
problem aimed at fostering the social acceptance of particular actions within
the network. Initially conceptualized as an economic cost minimization, in this
preliminary work, we simplify our problem by reformulating it into an MPC
framework. Through our analysis and numerical simulations, we illustrate the
effectiveness of the proposed methodologies.
