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

TL;DR

This work develops a data-driven, coupled opinion-adoption framework to model sustainable transitions on a heterogeneous social network. By integrating mobility-age heterogeneity, survey-based population synthesis, and a similarity-based social network, the model interlinks diffusion, abandonment, and feedback between opinions and adoption. Through equilibrium and stability analysis, and extensive simulations, it shows that dissatisfaction-reduction policies produce more stable, long-term adoption than opinion-shaping alone, and that centrality-guided interventions can further enhance outcomes. The approach provides a tractable, data-informed tool for designing effective, evidence-based strategies to promote sociotechnical transitions toward sustainability.

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.

Figures (10)

• Figure 1: Schematic representation of the three-state adoption model. Individuals can transition between compartments through the indicated flows, describing the dynamics of adoption, abandonment, and potential re-adoption.
• Figure 2: Determination of the optimal number of clusters for socio-demographic data. Left: Elbow method based on Within-Cluster Sum of Squares (WCSS). Right: Silhouette coefficient analysis. Both methods indicate $k=5$ as the most appropriate clustering configuration.
• Figure 3: 3D projection of socio-demographic clusters obtained via Principal Component Analysis (PCA). The five clusters identified by the K-Means algorithm are shown, highlighting the separation and cohesion of groups based on age, education, and political orientation.
• Figure 4: Distribution of self-reported annual travel distances by age group in five countries. The $x$-axis represents age groups, the $y$-axis indicates annual distance traveled (km), and the $z$-axis shows the fraction of respondents in each group.
• Figure 5: Heatmap of the social interaction matrix $W$ for Germany. Color intensity indicates the strength of opinion alignment between mobility-age communities, with lighter shades representing stronger social proximity.

Theorems & Definitions (3)

• Proposition 1
• Theorem 1
• proof : Proof of Theorem \ref{['theo:stability']}