Social theory that the 'enemy of my enemy is my friend' is proven by physics

“The enemy of my enemy is my friend.” We’ve all heard this classic adage known as social balance theory. It speaks to a fundamental aspect of how we humans navigate our social world. Sure, sometimes it’s messy, but now we know for certain, thanks to a fascinating new study, that there’s a physics logic underlying this social theory and our other social interactions.

What is social balance theory?

Back in the 1940s, a psychologist named Fritz Heider proposed Social Balance Theory. This theory suggests that we have a deep-seated preference for consistency and predictability in our relationships. We feel most comfortable in “balanced” social situations where the feelings between people align.

This could mean everyone genuinely liking each other, or, if dislikes are involved, there’s a clear understanding of those negative connections (like disliking the same person as someone else).

When this balance is disrupted, such as when close friends have a falling out or you find yourself disliking someone your friend admires, it creates a sense of unease and social tension.

This tension stems from our desire for cognitive consistency – we want our understanding of our social world to make sense. Inconsistent or unpredictable relationships make that difficult, leading to discomfort.

The challenge of proving the theory

Social scientists have spent years attempting to validate Heider’s Social Balance Theory. However, this effort has proven challenging for a number of reasons. Firstly, social networks themselves are inherently intricate. Individual personalities play a significant role in how connections form and develop.

Secondly, social circles aren’t all-encompassing. People often have limited connections within a broader network, meaning they might not be aware of all the relationships between the people they know. Early attempts at modeling social balance often treated relationships as overly simplistic.

These models frequently assigned positive or negative values to connections at random, failing to account for the complexities of real-world interactions. This shortcoming made it difficult to definitively determine if the patterns observed in social networks aligned with the predictions of Heider’s theory.

Physics proving the social theory

Researchers at Northwestern University have a developed a new approach using physics that provides strong evidence for the validity of Social Balance Theory at a large scale.

However, the key to their success wasn’t necessarily a new theory. Instead, it involved a fresh perspective on established mathematical tools and their application to social networks.

The Northwestern team took a unique approach by analyzing four extensive collections of real-world interactions across various contexts. These datasets provided a rich tapestry of human behavior:

• News site comments: Discussions and reactions to articles on a social news platform, revealing how users agreed or clashed over current events and trending topics.
• Political debates: Exchanges and recorded interactions between politicians, showcasing alliances, disagreements, and the dynamics of power.
• Bitcoin exchanges: Trading activity within the world of cryptocurrency, demonstrating trust, skepticism, and competition within the financial marketplace.
• Product reviews: User opinions and critiques about different products, highlighting likes, dislikes, and the spectrum of consumer sentiment.

Crucially, the datasets didn’t just record positive interactions. They also captured expressions of dislike, disagreement, and negative sentiment. By including both positive (“I agree!” or “Great product!”) and negative (“This is terrible!” or “I disagree!”) responses, the researchers were able to create a more nuanced and realistic model of social networks. This comprehensive approach was vital in finally validating Social Balance Theory on a broader scale.

The power of constraints in physics and social theory

Two major constraints were involved in the study:

Constraint 1

Human social circles tend to have limits. We don’t personally know or meaningfully interact with every single person on Earth. Realistically, our connections form within specific communities and contexts (work, hobbies, school, etc.).

Incorporating this constraint into the model is crucial. It prevents unrealistic scenarios where every individual (“node”) within the network has the potential to form connections with everyone else. By limiting interaction possibilities, the model more accurately mirrors the way real-world social networks function.

Constraint 2

People have wildly different personalities. Some of us are extroverted, actively seeking new connections and thriving on social engagement. Others are more introverted, preferring a smaller circle of close friends and less frequent interaction.

Acknowledging this variation in social behavior is essential. It ensures the model doesn’t assume everyone has the same capacity or desire for interaction.

Social butterflies are naturally assigned more connections (both positive and negative), while those less outgoing have fewer overall links. This reflects the diverse social landscape found in real life.

The confirmation

By incorporating these realistic constraints, the researchers observed a remarkable pattern. The results definitively demonstrated that real-world social networks strongly adhere to the principles of Social Balance Theory, including the “enemy of my enemy” concept. This validates our intuitive understanding that we strive for harmony and consistency in our social environments.

The far-reaching implications of this study are particularly exciting. This model has the potential to provide a powerful framework for understanding the dynamics of any system where relationships and interactions play a key role.

Broader implications of physics proving social theory

The human brain is arguably one of the most complex networks in existence. This model could be applied to map how vast numbers of neurons form connections.

Researchers could then study how the balance (or imbalance) between excitatory and inhibitory signals influences brain activity, potentially shedding light on learning, memory, and even neurological disorders.

Moreover, drug interactions can be difficult to predict. Applying a similar framework could help doctors understand how different medications interact, both positively and negatively.

This could lead to more effective treatment strategies, optimizing the benefits while minimizing potential side effects for patients taking multiple prescriptions.

Social balance principles can even be applied to understand complex social phenomena on a broader scale. Researchers might model how political opinions spread or how extremist views gain traction within certain groups. This could potentially lead to the development of strategies for combating polarization and promoting greater social cohesion.

Study significance

This research highlights the interconnected nature of different systems. The tools developed to investigate social networks may provide the key to unlocking mysteries in fields as diverse as neuroscience and pharmacology.

The next time you’re caught in the middle of a friend feud or feel drawn to someone because you share a common dislike, remember: there’s a good reason for it. Science shows that your feelings have roots in our deep-seated desire for social harmony.

Of course, the real world is always a bit messier than theory, but at least now we know that the old saying has some mathematical weight behind it.

The study is published in the journal Science Advances.

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