Modeling and Control of Sustainable Transitions through Opinion-Behavior Coupling in Heterogeneous Networks

TL;DR

This paper presents a data-driven model of opinion and behavior dynamics in social networks to understand and promote large-scale adoption of electric vehicles, highlighting effective intervention strategies for sustainability transitions.

Contribution

It introduces a coupled opinion-behavior model based on survey data, analyzing control strategies to enhance sustainable adoption in heterogeneous social networks.

Findings

Targeted dissatisfaction reduction policies lead to more stable adoption.

Social influence significantly impacts opinion dynamics and adoption rates.

Model calibration with real data enables realistic simulation of social transitions.

Abstract

Understanding how sustainable behaviors spread within heterogeneous societies requires the integration of behavioral data, social influence mechanisms, and structured approaches to control. In this paper, we propose a data-driven computational framework for coupled opinion-adoption dynamics in social systems. Each node in the multilayer network represents a community characterized by a specific age group and mobility level, derived from large-scale survey data on the predisposition to adopt electric vehicles in Northern Europe. The proposed model captures three mechanisms: behavioral contagion through social and informational diffusion, abandonment driven by dissatisfaction, and feedback between opinions and adoption levels through social influence. Analyzing the equilibrium points of the coupled system allows us to derive the conditions that enable large-scale adoption. We empirically calibrate the model using data to construct synthetic populations and social similarity networks, which we use to explore targeted interventions that promote sustainable transitions. Specifically, the analysis focuses on two types of control strategies: opinion-based policies, which act on the social network layer, and policies that aim to improve experience and reduce dissatisfaction. Simulation results show that the latter ensure more stable and long-term adoption, offering concrete insights for designing effective interventions in sociotechnical transitions toward sustainability.