Fractal Thinking Perfectly Fits Communication Theory.

The Three Ages of the History of Communication Models

The history of communication models can be divided into three “Ages:”

• Age 1 – Linear Communication Models
  • It starts with Aristotle and goes through Shannon-Weaver. One-Way – Source to Receiver.
  • Shannon added “Noise” to the Model.
• Age 2 – Interactive Communication Models
  • Added “Synchronous” back-and-forth communication.
  • Added “Feedback to the Model
  • Allowed for more than 1 iteration.
• Age 3 – Iterative Communication Models
  • Adds “Fractal Thinking” to the Model.
  • Adds Shared Intentionality

The Mathematical Theory of Communication

In 1949, a brilliant theorist named Claude Shannon wrote his groundbreaking work, “The Mathematical Theory of Communication (Shannon & Weaver, 1949)

In his theory, Shannon built on the work of Charles Babbage and others to suggest that information can be measured by “the logarithm of the number of possible choices.” (Shannon Weaver, 1949, Page 8)

This work brought us three major ideas.

1. Three Levels of Communication Problems
   Level A – How accurately can the information be transmitted – The Technical Problem
   Level B – How precisely does the information convey the desired meaning – The Semantic Problem
   Level C – How effectively does the information influence behavior in the desired way – The Effectiveness Problem.
2. Standard Model of Information Communication
3. The idea is that information relates not so much to what the information does say, but rather to what the information could say.
   If there are only 2 answers to a question, A or B. Then an answer of A would have 100% value.  Because if you know A is the answer, then you know B is not the answer.
   However, if there are more than 2 answers to a question, then an answer of A has less value.

Using the Electron as Information

While both the electric motor and electric light were important, the more interesting use of the electron is its ability to transmit information.[9]  One of the first uses of the electron as an information tool is Morse Code.  We then got the phone, Radio, and TV.  And we see the results of our domestication of the electron in the Internet.

Using the Electron as a Computer

The first programmable machine is credited to Joseph Jacquard in the early 1800’s.  He invented a loom that used punched wooden cards to weave complex designs.[10]

The next leap forward was from Charles Baddage.  In 1822 he came up with the idea of a steam-driven calculating machine.

It would take a century for the next step forward.  Alan Turing was able to figure out how we could use our ability to control electrons to do computing work. (Turing, 1937).

Social Networking

Humans were born to connect.  And humans have practiced social networking from the beginning.  Families, armies, companies, unions, and churches are all examples of social networks.

In the beginning, there was only one kind of social networking, face-to-face.  When we invented writing we could enhance our social networking by mitigating it through a medium – clay tablets, parchment, or paper.  The problem with this kind of medium is that it is slow and has limited bandwidth.

The domestication of the electron changed the way we social network dramatically. Now our social networking can happen at the speed of light and we have a lot of bandwidth to play with.

Collective Intelligence

Before I begin to talk about Collective Intelligence, let me describe intelligence as – the practical application of observation, analysis, memory, creativity, judgment, and wisdom.

Recent innovations have given us a lot more tools to increase our observations, analysis, and memory.  Thus increasing our ability to add new knowledge to the community.

Fractals

“Fractals” is the most recent innovation brought on by the domestication of the Electron.

It is a natural evolution of the path humans have been taking from the beginning of time.

A fractal is a never-ending pattern that repeats itself at different scales. This property is called “Self-Similarity.”

An interesting aspect of fractals is that they are the same at any scale.  You can zoom in and find the same shapes forever.

Surprisingly fractals are very easy to make. A fractal is simply an iteration of the same process again and again.  For example Z=Z² + C iterated millions of times would be a fractal equation.

Natural Fractals – Branching

There are many examples of natural fractals.

Look around you and you’ll see the tiny branching of our blood vessels and neurons to the branching of trees, lightning bolts, and river networks. These are all fractals.

Independent of scale, what you see is simply a repeating branching process.

Geometric Fractals

Geometric fractals are constructed by repeating a simple process.

The Sierpinski Triangle is made by repeatedly removing the middle triangle from the prior generation.

Algebraic Fractals

Fractals can also be built by repeatedly calculating a simple equation over and over. Algebraic fractals were discovered until the computer made it possible to perform millions of iterations of the same formula.  The best know Algebraic Fractal is the Mandelbrot Set.  The Mandelbrot Set uses the equation Z=Z² +C.

Cognitive Systems

No one would talk much in society if they knew how often they misunderstood others. – Johann Wolfgang Von Goethe

Better to keep your mouth shut and be thought a fool than to open it and remove all doubt. — Mark Twain

We have two ears and one mouth so that we can listen twice as much as we speak.” Epictetu

In order to maximize information in political systems, we have to fully understand how our brains process information.

Fortunately, the domestication of the electron gave us tools to better understand how we think and how it affects our political systems.

Selective Perception, Confirmation Bias, and Motivated Reasoning

Selective perception, confirmation bias, and motivated reasoning are natural cognitive processes.  Unfortunately, they are the cognitive processes that hinder one’s ability to make good decisions.

The use of Fractal Thinking and Collective intelligence are intended to help community overcome these barriers to effective public policy.

Fractal Communication Model

In the 1960s, Benoit Mandelbrot developed a cohesive “fractal theory.”

Fractals provide a predictive model of iterative stability.

Here is the Mandelbrot Set formula:

f(c) = Z²+ c

Here is the result of that formula iterated over many times.

By using “Feedback” as the “f(c)” function, the efficiency of communication as the “Z²” variable, and the initial starting point of the communication as the “c” variable, Communication fits nicely into Fractal thinking.

Here is the formula.

Fractal Communication Formula

f(e) = eC((t+i)-n) + Starting Point

f = Feedback, Fractal, Function
e = Efficiency as measured by the distance to the Endpoint (or Goal)
C = Communication = ((T+S)-N) – ((Technical Value of the Communication + Information Value of the Communication) – Noise)

In other words, this would be:

“Fractals” describe the chances of reaching our goals.

Reaching our Goals is dependent on our Communication Efficiency. 

Our Communication Efficiency is based on our ability to maximize communication’s technical and informational aspects and minimize the noise in our communication. (Which, by the way, is exactly what Shannon did with his groundbreaking 1948 paper.)

Here is the result.

In a perfect world, every communication act – “iteration” – would move us closer to the end goal. However, we don’t live in a perfect world. Therefore, we need to check our position after every iteration.

After every iteration, the question should be: Have we moved closer to the goal?

This movement could be “positive or “negative.” With positive communication, the iteration moves us closer to the goal. With negative communication, the iteration moves us further from the goal.

Communication, as a thing, is measured by the interaction between technical communication, Informational communication, and Noise.

• Technical Communication
  • Measured by “Bandwidth.” The maximum amount of information possible.
  • The ability of the receiver to “physically” connect to the sender.
  • The degree of “shared codes.”
• Informational Communication
  • The effectiveness of communication to affect change in a desired way.
• Noise
  • Anything that reduces effective communication.
  • Static in radio systems is an example.

The “Fractal” formula for Communication applies the communication thinking developed by Claude Shannon in his work on “The Mathematical Theory of Communication” and iterates it over time.

Over time certain combinations of communication can be stable over time in the sense that the parties reached their goals.