Social Networks

Complexity Science

Complexity Science is the study of complex systems—economies, ecologies, organisms, etc.

What makes a complex system complex?

Abstractly, a system is built out of components.

In a simple system, components don't influence each other, so the behavior of the system is just the sum or average of the behaviors of its components.

In a complex system components do influence each other. As waves of influence spread from component to component they interfere with each other, sometimes creating interesting patterns. A influences B influences C influences A.

Like a simple system, the behavior of the system depends on the behavior of its components, but unlike a simple system, the behavior of the components depends on the behavior of the system. It's difficult to untangle this part-whole feedback loop, but it's easy to simulate.

Emergence, when 1 + 1 = 3

Sometimes the behavior of a complex system exhibits interesting or mysterious patterns that can't be reduced to the behaviors of its components. It seems as though the system has a life of its own. This is referred to as emergent behavior or synergy.

Sometimes emergent behavior can appear to be driven by some goal-oriented central control such as a conductor, executive, or dispatcher. When this behavior emerges spontaneously, without the benefit of centralized control, we say that the system is self-organizing.

Examples of emergence include: flocking, evolution, consciousness, culture, etc.

There are negative examples, too: segregation, traffic jams, recessions, etc.

Forerunners of Complexity Science

Biology

Ecology, Evolution, Immunology

Mathematics

Dynamical Systems, Mathematical Logic

Computer Science

Information Theory, Neural Nets, Cybernetics, O(f), genetic programming, Network Science

Business, Economics, Social Sciences, History

Social Networking Concepts

Social networks are complex systems. The study of social networks is a field of complexity science that is also related to Network Science.

A social network is a collection of linked, interacting agents.

An agent is an abstraction of a person, organism, peer, or robot.

Agents have properties and behavior.

They can observe and interact with other agents and the environment. Examples of interaction include fighting, mating, and bargaining.

Agents are goal-driven. They perpetually execute a control-loop of the form:

while not goal-reached? [
   select-neighbor
   interact-with neighbor
]

Mobile agents, like shoppers in a virtual mall, can move around in their environment, while stationary agents, like clerks in the virtual mall, have to stay put.

A relationship between a pair of agents is called a link. Examples of links include: friends, enemies, siblings. Links can be directed (e.g. supervises) on undirected (e.g. sibling).

 

Examples of social networks include: bee hives, corporations, flocks, clubs, nations, eco-systems, and FaceBook.

Artificial social networks can also be simulated and studied using computer models.