MODELING HUMAN BEHAVIOR IN COMPLEX SOCIAL AND SOCIO-TECHNICAL SYSTEMS

 

We studied social diffusion and diffusion of innovation. In particular, we focused on the important social-psychological role of sensitivity to dynamic norms (i.e., emerging trends at the population level), and we investigated it through experimental studies and analytically-tractable models. Then, we use our model to investigate different intervention policies to facilitate social diffusion:

• Ye M., Zino L., Risselada H., Bolderdijk J. W., Mlakar Z., Fennis B. M., and Cao M., Collective patterns of social diffusion are shaped by individual inertia and trend-seeking. Nature Communications, 12, 5698, 2021

• Zino L., Ye M., and Cao M., On modeling social diffusion under the impact of dynamic norms. Proceedings of the 60th IEEE Conference on Decision and Control, Dec 2021, Austin TX, US

• Zino L., Ye M., and Cao M., Facilitating innovation diffusion in social networks using dynamic norms. PNAS Nexus, 1(5), pgac229, 2022

• Zino L. and Ye M., On incentivizing innovation diffusion in a network of coordinating agents. Proceedings of the 22nd IFAC World Congress, Jul 2023, Yokohama, Japan.

• Gao T., Zino L., Ye M., Effect of network structure and committed minority placement in promoting social diffusion. IEEE Transactions on Computational Social Systems, 11(2), 2326-2339, 2024.

 

For more details on diffusion of social norms and conventions, we suggest to read the following book chapters:

• Zino L. and Cao M., Social Diffusion Dynamics in Cyber-Physical-Human Systems, in Cyber-Physical-Human Systems: Fundamentals and Applications (ed. A. Annaswamy et al.), pp. 43-70, Wiley, 2023

• Ye M. and Zino L., Data-Informed Modeling of the Formation, Persistence, and Evolution of Social Norms and Conventions, in Handbook of Visual, Experimental and Computational Mathematics  (ed. B. Sriraman), Springer, 2025

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We developed a co-evolutionary models to study the interwined evolution of opinions and actions and elucidate important aspects of social systems, such as the emergence of polarization and unpopular but supported norms:​

• Zino L., Ye M., and Cao M., A two-layer model for coevolving opinion dynamics and collective decision-making in complex social systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30, 083107, 2020

• Zino L., Ye M., and Cao M., A Coevolutionary Model for Actions and Opinions in Social Networks. Proceedings of the 59th IEEE Conference on Decision and Control, Dec 2020, Jeju Island, Republic of Korea

• Dehghani Aghbolagh H., Ye M., Zino L., Chen Z., and Cao M., Coevolutionary Dynamics of Actions and Opinions in Social Networks. IEEE Transactions on Automatic Control, 68(12), 7708-7723, 2023

• Raineri R., Como G., Fagnani F., Ye M., and Zino L., On Controlling a Coevolutionary Model of Actions and Opinions. Proceedings of the 63rd IEEE Conference on Decision and Control, 2024.

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We used opinion dynamics to model consumers behavior in energy networks:

• Singh V.K., Zino L., Muinos G., Scherpen J.M.A., and Cucuzzella M., An opinion dynamics approach to model and analyze the behavior of consumers in an energy network. To appear in the Proceedings of the 2025 European Control Conference, 2025.

 

We developed a game-theoretic model with enviromental feedback to study human decision-making concerning enviromental issues:

• Frieswijk K., Zino L., A.S. Morse, and M. Cao, Modeling the Co-evolution of Climate Impact and Population Behavior: A Mean-Field Analysis. Proceedings of the 22nd IFAC World Congress, Jul 2023, Yokohama, Japan.

• Frieswijk K., Zino L., A.S. Morse, and M. Cao, A behavioural-environmental model to study the impact of climate change denial on environmental degradation, Accepted for publication in Physica D, 2025

 

We developed an opinion-dynamics model to study collective risk perception: 

• F. Giardini, D. Vilone, L. Zino, and M. Cao, Homophily in opinion networks affects collective risk perception in heterogeneous populations, ISCRAM Proceedings 21, 2024.

• Zino L., D. Vilone, F. Giardini, and M. Cao, On Modeling Collective Risk Perception via Opinion Dynamics. European Journal of Control, 80(Part A), 101036, 2024.

 

We developed an epidemic-like model to study diffusion of innovation on networks:

• Fagnani F. and Zino L., Diffusion of innovation in large scale graphs: a mean field analysis. Proceedings of the 22nd International Symposium on Mathematical Theory of Networks and Systems, Jul 2016, Minneapolis MN, US

• Fagnani F., and Zino L., Diffusion of innovation in large scale graphs. IEEE Transactions on Network Science and Engineering, 4(2), 100–111, 2017

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We developed a drift-diffusion model for competing information diffusion on networks, accounting for evidence accumulation:

• Corsin J.J., Zino L., and Ye M., An evidence-accumulating drift-diffusion model of competing information spread on networks. Chaos, Solitons & Fractals, 192, 115935, 2025.

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We expanded classical opinion dynamics models beyond the limitations of pairwise and single-hop interactions:

• Raineri R., Zino L., and Proskurnikov A.V., FJ-MM: Friedkin-Johnsen Opinion Dynamics Model with Memory and Multi-Hop Social Influence. To appear in the Proceedings of the 2025 European Control Conference, 2025.

 

We collaborated with researchers and scientists from different fields to develop models of human behavior in different applied contexts, from robot motion to consumers' behavior and pro-enviromantal change:​

• Nakayama S., Krasner E., Zino L., and Porfiri M., Social information and spontaneous emergence of leaders in human groups. Journal of the Royal Society: Interface, 16(151), 2019.

• Hoffmann T., Ye M., Zino L., Cao M., Rauws W., and Bolderdijk J.W., Overcoming Inaction: An Agent-Based Modelling Study of Social Interventions that Promote Systematic Pro-Environmental Change. Journal of Environmental Psychology, 94, 102221, 2024.

• Mlakar Z., Bolderdijk J.W., Risselada H., Fennis B.M., Ye M., Zino L., and Cao M., Social Tipping Games: Experimental Paradigms for Studying Consumer Movements. Journal of the Association for Consumer Research 9(4), 2024.​