Bridging the Gap Between Sciences

The traditional notion of incompatibility between social and natural sciences has historically hindered a deep understanding of social interaction. Contrary to this tradition, contemporary science has accumulated compelling evidence that social interaction is conditioned by biological, psychological, economic, and evolutionary factors.

An adequate understanding of the complexity of social systems requires a multidisciplinary approach that integrates insights from distinct theoretical and methodological frameworks.

Computational Social Science

At PoliCICS, we model social phenomena using a diverse array of advanced techniques, making extensive use of the digital footprint left by human interactions. Our methodological approach includes:

• Price Theory and Game Theory
• Social Network Analysis
• Agent-Based Modeling (ABM)
• Applied Data Science

Based on the foregoing, we seek to contribute to an integrated understanding of the mechanisms underlying emergent phenomena such as cooperation, social stratification, paradigm shifts in knowledge systems, and the emergence of rules and institutions resulting from the interaction between social beings.

Political Behavior in the Digital Age

Political behavior examines the actions, opinions, and decisions of individuals and groups within the political context. With the rise of the digital age, data science and formal modeling have become essential tools for analyzing and predicting patterns in such behavior, allowing for a deeper and more precise analysis of political dynamics.

This discipline allows us to:

1. Analyze motivations: Unravel the drivers behind the political behavior of individuals and groups using advanced data science techniques.
2. Understand public opinion: Study the formation and evolution of public opinion through formal modeling and the analysis of large datasets.
3. Examine multifactorial influences: Investigate the influence of social, psychological, and economic factors on political behavior using analytical and computational tools.

Relevance for Complexity Sciences

The relevance of political behavior for social complexity sciences lies in the fact that it is affected by multifactorial interactions at the individual, social, and institutional levels.

The use of data science and formal modeling allows for the unraveling and prediction of these complex interactions. Through the analysis of large datasets and advanced modeling, it is possible to obtain insights that were traditionally inaccessible, providing a more detailed and predictive understanding of political dynamics.

Incorporating political behavior, with an emphasis on data science and formal modeling, into a research program in social complexity sciences not only allows for a better understanding of political dynamics but also equips researchers with crucial skills for political analysis in the digital age.