Optimal policy design for decision problems under social influence

TL;DR

This paper extends opinion dynamics models to include randomness and personalized policies, proposing optimal control strategies to influence social acceptance efficiently.

Contribution

It introduces a novel extension of the Friedkin-Johnsen model with stochastic elements and develops optimal nudging strategies for social influence.

Findings

Model captures variability in individual opinions due to external factors

Optimal policies effectively increase social acceptance in simulations

Personalized nudging strategies outperform uniform approaches

Abstract

This paper focuses on describing the impact of policy actions on individuals' opinions in the presence of social and external influences toward proposing preliminary nudging strategies to achieve a cost-effectiveness trade-off. To this end, we extend the classical Friedkin and Johnsen model of opinion dynamics to incorporate random factors, such as variability in individual predispositions due to uncontrolled events (e.g., modeling the impact of the weather on daily mobility choices), and describe the impact of personalized policies. Furthermore, we formulate an optimal control problem aimed at fostering the social acceptance of particular actions/choices within the network. Through our analysis and numerical simulations, we illustrate the features of the proposed model in the absence of nudging and the effectiveness of the proposed (optimal) nudging strategies.