“Behind all processes which allow us to make buildings live, there is a single common process…but though this method is precise, it cannot be used mechanically. Indeed, it turns out, in the end, that what this method does is simply free us from all method…It is a process which lies deep in us: and only needs to be released. But these are not mechanical rules. They require the nature of the designer, and of the learner, to be considered. This makes things always changing and different.”

“We find out that we already know how to make buildings live, but that the power has been frozen in us: that we have it, but are afraid to use it: that we are crippled by our fears, and crippled by the methods and the images which we use to overcome these fears. But we learn too, that this capacity in us is not accessible, until we first go through the discipline which teaches us to let go of our fears. It is instead a process which lies deep in us: and only needs to be released. It is a process which brings order out of nothing but ourselves; it cannot be attained, but it will happen of its own accord, if we will only let it.”

“At the human level of complexity, then, there is a distinction between systems which are true to their ‘inner nature,’ and those which aren’t. Not all of us are equally true to our inner nature, or equally real, or equally whole. And exactly the same is true in those larger systems, outside us, which we call our world. Not all parts of the world are equally true to themselves, equally real, equally whole.”

For information on Christopher Alexander and his work, visit these web pages:

See what impact Alexander has had on music producer Brian Eno.

Get a glimpse into the use of pattern languages in computer programming.

An Pattern Language website.

For more information, check out these noteworthy books on architecture:

How Buildings Learn–This book offers an excellent example of architecture that grows and even “learns.” A fun read!

The Architecture Pack–This title is as wonderful an educational product as it is a primer on architecture.

The ABCs of Architecture–This book presents a concise, accessible introduction to the field.

Edison worked incessantly, often for 20 hours a day. Believe it or not, throughout history, this is the key ingredient found in the most consistently successful individuals.

Edison found his schooling utterly “repulsive.” Everything was forced on him. He found it impossible to observe and learn the processes of nature simply by description, or to comprehend the alphabet and arithmetic just by rote. To Edison, it was always necessary to discover with his own eyes–to “do things” or “make things” himself–in order to learn. Edison said that to see for himself, to test things himself, “for one instant, was better than learning about something I had never seen for two hours…” What Edison realized is that to really learn something, people need hands-on experience. They need to be able to build knowledge for themselves.

Edison clearly sensed that invention and discovery are dependent on the total accumulation of knowledge, including information that temporarily gets forgotten. He was forever collecting curious and miscellaneous facts, and squirreling them away in his memory. Edison stressed the important role chance, or accident, plays in discovery. In other words, he found no fact too trivial, no piece of information too irrelevant, no idea too outlandish. It’s as if he were perpetually conducting a brainstorming session with himself.

Edison wrote the book on perseverance, saying, “The trouble with other inventors is that they try a few things and quit. I never quit until I get what I want!”

Edison knew the value of learning from his mistakes. To find the right material for the filament of the light bulb, he went through countless materials almost randomly until he found one that worked.

Source: Edison: A Biography, by Matthew Josephson

1. Systems Thinking–Senge defines this as examining the patterns that connect the larger system. This kind of thinking is intuitive. In fact, children pick it up almost instantly.

2. Personal Mastery–The key to this trait is to continually clarify and deepen our personal vision, focus our energies, develop patience, and see reality objectively.

3. Shared Vision–This occurs when a group collectively develops a “picture of the future.” Shared vision is the sum of the personal visions of all participating individuals. It is not something a person can learn by rote; instead, it stems from a deep-seated belief.

4. Team Learning–The group IQ is higher than that of the individual. Just like in complex systems, unexpected results that are greater than the sum of their parts will emerge. However, people have to communicate with each other for team learning to take effect.

5. Mental Models–Senge attest that we must continually question the deeply ingrained assumptions, generalizations, and perceptions that influence how we comprehend and react to the world. Once we understand our biases, we can begin to examine and deconstruct them.

Researchers are also exploring whether parallel processors can serve as models for how the brain functions. Parallel processors are computers that excel at pattern recognition, or inductive thinking. Parallel processors that can handle many instructions at once are called neural networks (or nets). Neural nets excel at inductive tasks, such as pattern recognition, for which many commercial applications are now being developed.

It’s possible these researchers will conclude that the brain is not a linear tool, as originally suggested by the serial model, but that the parallel model of processing information more closely represents how the mind works. Maybe the ultimate model of the human brain would be one that combines both the serial and parallel analogies.

Complexity
Neural Networks

Systems Theory
The Theories of Thomas Edison
Architecture

A complex system is emergent. In an emergent system, smaller parts comprise a larger system. This larger system has properties the smaller units lack. For example, the brain is made up of individual neurons that, when functioning together, are capable of tasks no single neuron can perform alone. The new properties only emerge when the neurons work together.

A complex system is unpredictable.

A complex system contains many iterations and feedback/feedforward loops.

In a complex system, decision-making is decentralized.

Learning is a typically a “complex” activity. Most learning systems contain a number of separate parts that must work together for learning to occur. For example, a typical learning system consists of students, a teacher, a content focus, and resources. This system operates according to a fixed plan–the students follow the teacher’s “rules.”

Learning environments

A learning environment can be emergent. Working together, a group of learners can collectively build their knowledge of a topic, for instance, the phases of the moon. To do so, each learner might research a particular lunar phase, then share what he or she has learned with the rest of the group. This way, the group amasses a body of knowledge that no one person could have acquired alone.

A learning environment can be unpredictable. An exploration of the phases of the moon could result in the group considering whether planets also have phases.

A learning environment can contain many iterations and feedback/feedforward loops. People learn by trial and error–in other words, they learn from their mistakes.

Decision-making in a learning environment can be decentralized. Groups can really thrive when students control the learning process, rather than the instructor.

It’s quite possible that learning occurs best on the “edge of chaos,” where order and chaos meet. To see for yourself, check out these two resources:

Kevin Kelley’s outstanding book, Out of Control, examines how we can use biological theories to help us construct complex systems.

If you want to experience complexity in action, try “building” your own system with SimCity. This software offers a fantastic way to learn about urban planning, while viewing many of the principles of complexity in action. Plus, it’s a blast!