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

2025-11-14

AI Summary

This study investigates the propagation mechanisms of sustainable behaviors—such as electric vehicle (EV) adoption—in heterogeneous social networks, addressing the core question: How do opinions and behaviors co-evolve to drive large-scale behavioral transitions? Method: We develop a data-driven multilayer network model that innovatively integrates behavior contagion, satisfaction feedback, and bidirectional opinion–behavior coupling dynamics. The model is calibrated using large-scale Nordic EV adoption survey data to synthesize realistic population attributes and sociometric similarity networks. Contribution/Results: Through differential dynamical analysis and agent-based simulations, we demonstrate that interventions targeting experiential improvement (e.g., charging infrastructure, usability) and dissatisfaction mitigation yield more stable and persistent adoption than opinion-based campaigns alone. Our framework provides a quantifiable mechanistic explanation for socio-technical system transitions and enables precise, evidence-based policy evaluation.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Modeling opinion-behavior coupling in heterogeneous networks for sustainable transitions
• Analyzing equilibrium conditions for large-scale adoption of sustainable behaviors
• Evaluating control strategies to ensure stable long-term sustainable adoption

Innovation

• Data-driven framework for opinion-adoption dynamics
• Multilayer network modeling with behavioral mechanisms
• Targeted interventions via opinion and experience policies