Abstract: Statistical physics researchers have validated the axiom “the enemy of my enemy is my friend”. The researchers implemented complex network models to verify Heider’s theory of social equilibrium, which suggests that humans innately seek harmonious relationships in social networks.
His style effectively targets interpersonal awareness and individual positivity, offering a more accurate mirror image of social interactions. This study not only confirms long-standing social theories, but also offers avenues to address issues such as political polarization.
Reflexes:
Source: Northwestern University
Most people have heard the famous word “the enemy of my enemy is my friend. “
Now, researchers at Northwestern University have used statistical physics to verify the theory of this axiom.
It will be published May 3 in the journal Science Advances.
In the 1940s, Austrian psychologist Fritz Heider proposed the theory of social equilibrium, which explains how human beings naturally try to fit in in their social circles.
According to the theory, four rules (enemy of enemy is friend, friend of friend is friend, friend of enemy is enemy, and finally enemy of friend is enemy) lead to a balance of relationships.
Although countless studies have attempted to verify this theory of social networks and mathematics, their efforts have failed, as networks deviate from perfectly balanced relationships. Therefore, the real question is whether social networks are more balanced than expected based on a good enough network model.
Most network models were oversimplified to fully capture the complexities of human relationships and social equilibrium, generating inconsistent effects on whether the observed deviations from the network model’s expectations are consistent with social equilibrium theory.
The Northwestern team, however, managed to integrate the two key elements that make Heider’s social system work. In real life, not everyone knows each other, and some people are more positive than others.
Researchers have long known that each and every thing influences social bonds, but existing styles can only explain one thing at a time. By incorporating the two limitations simultaneously, the network style received through the researchers in spite of any and all things showed the theory pointed out. , some 80 years after it was first proposed through Heider.
This useful new framework could help researchers better understand social dynamics, adding up political polarization and foreign relations, as well as any formula that includes a combination of positive and negative interactions, such as neural networks or drug combinations.
“We thought this social instinct worked, but we didn’t know why it worked,” said István Kovács, lead author of the Northwestern study.
“All we needed was to understand the math. If you look at the literature, there are many studies on this theory, but there is no agreement among them. For decades, we continue to misunderstand it.
“The explanation is that genuine life is complicated. We learned that we had to take into account those two limitations simultaneously: who knows who and that some people are simply friendlier than others.
“Regardless, we can conclude that social media is in line with the expectations that were formed 80 years ago,” added Bingjie Hao, first author of the study.
“Our effects also have broad programs for long-term use. Our mathematics allows us to incorporate constraints into the personal connections and tastes of the other entities in the system. This will be useful for modeling other systems beyond social media.
Kovács is an assistant professor of physics and astronomy in Northwestern’s Weinberg College of Arts and Sciences. Hao is a postdoctoral researcher in his lab.
What is Social Equilibrium Theory?
Using teams of 3 people, Heider’s theory of social equilibrium maintains the assumption that humans try to identify comfortable and harmonious relationships. In balanced relationships, everyone loves each other.
Or, if a user doesn’t like two other people, those two are friends. Unbalanced relationships exist when the other three people don’t like the other, or one user loves two other people who don’t love each other, leading to anxiety and tension.
The success of those foiled systems led to the 2021 Nobel Prize in Physics awarded to Italian theoretical physicist Giorgio Parisi, who shared the prize with weather modelers Syukuro Manabe and Klaus Hasselmann.
“It’s very much in line with social intuition,” Kovács said. “You can see how this would lead to excessive polarization, which we’re seeing today in terms of political polarization. If everyone you love doesn’t love those you don’t love, it affects two parties that hate each other. “
However, it has been difficult to bring together in a large-scale knowledge directory only friends and also enemies. With the advent of great knowledge in the early 2000s, researchers tried to see if that knowledge signed on social media could verify Heider’s theory. To test Heider’s rules, the Americans serve as nodes. The edges that connect the nodes constitute the relationships between Americans.
If the nodes are not friends, then the border between them is assigned a negative (or hostile) value. If the nodes are friendly, then the edge is marked with a positive (or friendly) value. In previous models, edges were randomly assigned positive or negative values, without respecting any of the constraints. None of those studies capture, as they should, the realities of social media.
Succeeding Within Constraints
To explore the problem, Kovács and Hao drew on four large-scale, publicly signed networking knowledge sets in the past selected by social scientists, adding knowledge from (1) user-rated comments on the social news site Slashdot; (2) exchanges between members of Congress in the House; (3) interactions between Bitcoin traders; and (4) product reviews from the Epinions customer review site.
In their network model, Kovács and Hao do not in fact assign negative or positive random values to edges. In order for the interaction of one and both to be random, both nodes deserve to have an equal chance of meeting each other. .
However, in real life, not everyone knows everyone on a social network. For example, a user may never meet their friend’s friend, who lives on the other side of the world.
To make their style more realistic, Kovács and Hao distributed positive or negative values in a statistical style that described the probability of assigning positive or negative symptoms to existing interactions. This kept the random (though random) values within the constraints of the network topology.
In addition to who knows who, the team took into account that some people in life are friendlier than others. Other friendly people are more likely to have more positive and less hostile interactions.
By introducing those two constraints, the style showed that large-scale social networks consistently align with Heider’s theory of social equilibrium. The style also highlighted patterns beyond 3 nodes. This shows that social equilibrium theory applies to larger graphites, involving 4 or more nodes. .
“We now know that you have to take those two limitations into account,” Kovács said. “Without it, you can’t put the right mechanisms in place. It sounds complicated, but it’s pretty undeniable math. “
Information on polarization and beyond
Kovács and Hao are currently exploring several long-term directions for this work. In a forward-looking direction, the new design could be used only to explore interventions to reduce political polarization. But the researchers say the design could better understand systems beyond social groups and bonds. between friends.
“We can simply take a look at the excitatory and inhibitory connections between neurons in the brain or the interactions that represent other combinations of drugs to treat the disease,” Kovács said.
“The study of social networks is an ideal playground to explore, but our main interest is to go beyond the study of interactions between friends and take a look at other complex networks. “
Author: Amanda Morris Source: Northwestern University Contact: Amanda Morris – Northwestern University Image: Image credited to Neuroscience News
Original research: open access. The proper randomization of networks is to assess social equilibrium” through István Kovács et al. Scientific breakthroughs
Abstract
Proper randomization of networks is to assess social equilibrium.
Social bonds, positive or negative, lead to models of signed networks, which are the subject of equilibrium theory. For example, a strong equilibrium introduces cycles with an even number of negative edges.
The statistical significance of these models is assessed by comparisons with null models. However, the effects of the signed networks remain controversial. Here we show that even if a network has a strong equilibrium through construction, existing null models would possibly fail to identify it.
Our effects imply that matching the personal tastes of degree to sign of nodes is a critical step, as is preserving the topology of the network in the null model. As a solution, we propose the STP null model, which integrates the two constraints into a maximum entropy framework.
STP randomization leads to qualitatively different results, and maximum social networks consistently demonstrate a balance between three- and four-node models.
Based on our results, we provide a prospective wiring mechanism for the observed signed models and describe other STP randomization programs.
Your email address will be published. Required fields are indicated *
Comment*
Name*
Email*
Website
Notify me of new comments via email.
Notify me of new items via email.
Neuroscience News Sitemap Graduate and Undergraduate Programs in Neuroscience Free MOOCs in Neuroscience About Contact Us Privacy Policy Send Neuroscience News Subscribe to Emails
Neuroscience Research Psychology News Brain Cancer Research Alzheimer’s Disease Parkinson’s Disease News Autism/ASD News Neurotechnology News Artificial Intelligence News Robotics News
Neuroscience News is an online scientific journal that offers loose readings of study articles on neuroscience, neurology, psychology, synthetic intelligence, neurotechnology, robotics, deep learning, neurosurgery, intellectual fitness, and more.