Synergistic integration of quantitative sociology and STEM fields for solving critical social dilemma: Recognizing universality in social phenomena: examples of vaccination, migration, and corruption (J7-3156 B)

Project description:

Theories of conspiracy often have immediate effects on economic growth and can trigger even worse long-term economic effects. Democracy seems to be particularly vulnerable when society is divided into comparably sized but highly antagonistic groups. These social phenomena are typically described using tools developed in physics for systems approaching critical points. Here, even a negligible shock can trigger a sudden phase transition that changes the system to a completely different final state.

Human societies are generally complex systems, so understanding whether different social phenomena differ significantly or show universality is crucial. Universality is a property where complex and natural systems have some common properties of a large class of systems that are independent of the dynamic details of these individual systems. In this study, we focus on various social phenomena, such as vaccine hesitancy, migration, and corruption, with an interesting goal of revealing some form of universality or, more precisely, determining whether there is a common fundamental stochastic formalism that governs them. Where did we find the motivation to look for universality in social phenomena? Let’s consider the pandemic, where the majority of the country’s population opposes vaccination. It is crucial to identify the breaking point at which society will transition from the majority opposing vaccination to a new majority strongly accepting vaccination. Similarly, the transition from a more corrupt to a less corrupt state is desirable. Finally, if migration becomes extensive and uncontrolled, potentially dangerous conflicts can arise, and society can transition from an equilibrium state where coexistence between locals and immigrants exists to a completely different equilibrium state characterized by antagonism and severe social conflicts. All the previous examples show a clear two-phase nature. However, the dynamics that control these processes and whether the processes themselves are continuous or sudden are still not well understood. It goes without saying that understanding the origins and preventing adverse scenarios in human societies is crucial.

Quantitative modeling within the project will be based on combined methods of evolutionary game theory and network science. This provides a flexible starting platform as it analyzes the impact of socio-economic incentives on rational actors while the other monitors all possible actor-actor interactions within the population. The proposed research will set new standards for model realism – and thus shape the field for decades to come – by incorporating multiple layers of complexity. It is an interdisciplinary research project, both theoretical and empirical, that involves working in the fields of sociology, economics, and complex systems using network science concepts (collective phenomena), statistical physics (critical points and phase transitions), and evolutionary game theory (cooperative Nash equilibrium).