Synergistic Integration of Quantitative Sociology and STEM Fields to Address a Critical Societal Dilemma: Identifying Universality in Social Phenomena: The Cases of Vaccination, Migration, and Corruption

Short Project Description

Conspiracy theories often generate immediate impacts on economic growth and can trigger even more severe long-term economic consequences. Democracy appears particularly vulnerable when a society is fragmented into groups of comparable size but high antagonism. These social phenomena are typically described by tools developed in physics for systems approaching tipping points. In such scenarios, even a negligible shock can trigger a sudden phase transition, causing the system to shift into a completely different final state.

Human societies are fundamentally complex systems; hence, understanding whether different social phenomena vary drastically or exhibit universality is crucial. Universality is a property where complex and natural systems share certain common characteristics across a large class of systems, independent of the dynamic details of those individual systems. This research focuses on various social phenomena—such as anti-vaccination movements, migration, and corruption—with the compelling goal of uncovering a form of universality, or more specifically, determining whether a common underlying stochastic formalism governs them.

Our motivation for seeking universality in social phenomena stems from real-world scenarios. Consider a pandemic where a majority of a country’s population opposes vaccination. It is highly critical to identify the tipping point at which society will transition from an anti-vaccination majority to a new majority that strongly embraces vaccination. A transition from a highly corrupt phase to a less corrupt phase in a country is similarly desirable. Lastly, if migration becomes extensive and uncontrolled, potentially dangerous conflicts may arise, shifting society from an equilibrium of peaceful coexistence between locals and immigrants to an entirely different equilibrium characterized by antagonism and severe social conflicts. All these examples clearly illustrate a two-phase nature. However, the dynamics controlling these processes—and whether the processes themselves are continuous or abrupt—remain poorly understood. Needless to say, understanding the origins and preventing unfavorable scenarios in human societies is of paramount importance.

Quantitative modeling within the project will rely on combined methods of evolutionary game theory and network science. This provides a flexible starting platform, as the former analyzes the impact of socio-economic incentives on rational actors, while the latter monitors all possible actor-actor interactions within a population. The proposed research will set new standards for model realism—shaping the field for decades to come—by integrating multiple layers of complexity. It is an interdisciplinary research project, both theoretical and empirical, operating across the expert fields of sociology, economics, and complex systems using concepts from network science (collective phenomena), statistical physics (tipping points and phase transitions), and evolutionary game theory (cooperative Nash equilibrium).

Results / Activities

The project is implemented through 4 work packages (WPs), representing the phases of the project:

WP1 – Consolidation of the existing platform for modeling migration processes: We begin with a review of existing models of migration processes, identifying those best suited to examine the current crisis in Europe. We will expand them (initially via a computational approach only) by adding new elements tailored to our core problem.

WP2 – Collection of available and acquisition of new empirical data: Within this WP, we will examine the non-computational aspects of the problem and construct an overview of current social science approaches to the topic. We will primarily rely on various immigration theories and conducted survey research.

WP3 – Collection of available and creation of new neuroscientific results / experiments: Recent findings in neuroscience indicate that political attitudes have a corresponding place in brain structure. These insights will be integrated into our modeling frameworks alongside results from new experiments using EEG methods (conducted by our partner at FAMNIT) aimed at identifying aspects not yet covered by existing research.

WP4 – Fusion of all findings into a universal interdisciplinary model: In this final WP, we will synthesize the insights gained from previous work packages into a universal model of immigration and integration that merges knowledge from various disciplines. Its final form will not be exclusively computational but will incorporate other dimensions that go beyond the current state of the art. The input data of our model will include known micro and macro parameters regarding specific immigration/integration processes, and the output will provide a prediction of social dynamics within the host society.