Computational Social Science: The Road to Building Robust Societies

Introduction

For those unfamiliar, Computational Social Science (CSS) fuses traditional social sciences with computational methods like AI, ML, network analysis, and agent based simulation. At the heart of this intersection lies an ambitious goal: decode societal structures and behaviors to improve policy-making and societal outcomes.

CSS stems on the researchers ability to retrieve and process enormous amounts of societal data, transforming it into cohesive patterns that provide a clear depiction of communal systems. The hope for any nation state is to use this deeper understanding to create efficient and harmonious societal blueprints.

Some ways we use CSS today include:

• Corporations and governments analyzing public sentiment on social media,

• Gathering data on individual behavior patterns and societal trends,

• Epidemic modeling using cell-phone traces,

• Assessing public response to different policies or events -- during election campaigns, these CSS-powered evaluations can help strategically shape political messaging and engage citizens more effectively,

• Predicing consumer behavior,

• In urban planning, CSS helps make cities more responsive to residents' needs. Through digitally mapping public spaces and analyzing data on population density, foot traffic, and citywide behavior patterns, urban designers can create improved community spaces that foster interaction and well-being.

Our research institute is attempting to use CSS for making the process of conflict resolution more effective. By using advanced computational models, we can simulate societal behaviors, uncovering patterns that predict conflict and unrest. This data-driven approach can provide helpful insights for peacekeeping organizations, enabling them to identify potential hotspots and preemptively mediate disputes. We're still a long ways out to truly scale these approaches, much of the issue has been a lack of robust data to build accurate models, which is another focus of our work.

Some of the key techniques used in CSS for those that are unfamiliar include the following.

Agent-based modeling

Agent-based modeling involves creating computational models that simulate the behavior and interactions of individual agents within a social system. These agents can represent individuals, organizations, or even entire nations. By creating rules and decision-making processes, agent-based models can help researchers understand how macro-level social phenomena emerge from the interactions of individual agents.

Network Analysis

Another important principle of CSS is network analysis, which focuses on studying the structure and dynamics of social networks. Social networks are composed of nodes (representing individuals or entities) and edges (representing relationships or interactions between them). By analyzing network properties, such as centrality, clustering, and diffusion, CSS researchers can gain insights into how information, influence, and resources flow within social systems.

ML & NLP

CSS also emphasizes the use of computational methods, such as machine learning and natural language processing, to analyze and extract meaningful patterns from large-scale social data. These methods enable researchers to uncover hidden patterns, detect trends, and make predictions about social phenomena. By combining computational techniques with social theories, CSS provides a powerful toolkit for understanding and shaping the dynamics of societies.

Social Media Analytics

Drawing inspiration from the initiatives like the MIT Media Cloud at the Civic Media Lab, the study also extends its analysis into the vast realm of social media. For instance, examining Twitter exchanges between Israelis and Palestinians provides a treasure trove of data. By dissecting the language and the reactions evoked by different posts, the research aims to quantitatively assess how social media interactions can either bridge the chasm or widen the gulf between communities.

A Lack of Data

The inputs to CSS models are both essential for building more accurate simulations, as well as inherently difficult to capture. Most academic research, especially in psychology, sociology, and anthropology, is quite poor at dealing with complex systems, which human dynamics always present. Therefore, trying to conduct studies that are replicable, as well as collect high quality data in real-time throughout, has proven to be an extremely difficult challenge.

Additionally, results that might seem promising in the short run, can have drastically different outcomes just a few years later. For the American left, Mitt Romney's loss to Obama in 2012 presented itself as an enormous win. Four years later when Trump was elected, it became clear that in retrospect Romney being elected to office would have likely prevented Republicans from winning the election in 2016, especially with a leader as controversial as Trump. What may seem like either a positive or negative in the short run can turn out to have the opposite effect years or decades later.

Subjective data, most often via surveys, continues to be the norm in this field, leaving more objective measures such as neural response, biometric data, and action-based measurement behind. This continues to contribute to social science fields oftentimes being more "fluffy" than calculated and provides fewer opportunities to implement effective, resilient solutions.

These problems run the gamut. We have centuries of data on protests, yet we still don't know whether they're effective or not. Dozens of research institutes focusing on sex work legalization and we're still lost when it comes to policy decisions. Not always because of "prude" non-progressive policy-makers, more often due to the complexity of phenomena like the expansion and substitution effect.

Even when we have good leaders, the poor data, research, and methodologies available cause bad decision making. Until we have more objective, replicable, and affordable data sources it is unlikely any movement, including the New Societies community, will be able to do much to truly improve the lives of citizens.

Future Echoes: The Promise of Computational Social Science

As we edge closer to the reality of crowdsourced nations, the insights garnered from such in-depth studies are not mere academic pursuits; they are the compass by which new nations can navigate the complex waters of societal dynamics. The meticulous analysis of interpersonal and digital interactions, coupled with the power of Computational Social Science, holds the promise of sculpting nations that are resilient, harmonious, and reflective of the collective aspirations of their citizens. Through these endeavors, we inch closer to a world where the establishment of new nations is not a walk into the unknown, but a journey mapped with the insights and understanding of the collective consciousness that binds communities together.