Archive for the ‘Evolution’ Category

Evolution is not just for biology, it’s a way of creating novelty, knowledge, and growth

February 2, 2008

We are accustomed to thinking of evolution in a biological context, but modern evolutionary theory views evolution as something much more general.

Evolution is an algorithm; it is an all-purpose formula for innovation, a formula that, through its special brand of trial and error, creates new designs and solves difficult problems.

Evolution can perform its tricks not just in the “substrate” of DNA, but in any system that has the right information-processing and information-storage characteristics.

In short, evolution’s simple recipe of “differentiate, select, and amplify” is a type of computer program — a program for creating novelty, knowledge, and growth.  Because evolution is a form of information processing, it can do its order-creating work in realms ranging from computer software to the mind, to human culture, and to the economy.

The Origin of Wealth by Eric D. Beinhocker

Dealing with ideas as squishy as pattern recognition, learning, and analogy making

December 25, 2007

In a previous post I described the differences between induction and deduction: Humans are relatively good at induction and relatively poor at deduction; computers are just the opposite

The discussion of induction vs deduction is quite interesting and relevant to most everyone.  Here are further ideas from the book The Origin of Wealth by Eric Beinhocker:

Deduction only works on very well-defined problems such as chess moves; for deduction to work, the problem cannot have any information missing or ambiguity.  Deduction is thus a powerful method of reasoning, but inherently brittle.

While induction is more error prone, it is also more flexible and better suited for incomplete and ambiguous information that the world throws at us.  It thus makes evolutionary sense that we would be built this way.

Through induction, humans are able to deal with ideas as squishy as pattern recognition, learning, and analogy making.

Note that models of induction featuring pattern recognition and learning have become a staple of computer science research, and many of these models are used in practical applications that range from recognizing the faces of terrorists at airports, to recognizing fraudulent charge patterns on credit cards.

Questions of origins play prominent roles in most sciences. Where do economies come from?

September 7, 2007

Questions of origins play prominent roles in most sciences. It would be difficult to imagine modern cosmology without the Big Bang, or biology without evolution.

“Where do economies come from?”

Traditional economic courses begin with “assume an economy.”

The process of economy formation presents us with a first-class scientific puzzle.

Joshua Epstein and Robert Axtell, researchers at the Brookings Institute, decided to conduct an experiment to see if they could grow an economy from scratch. Like biologists trying to cultivate life in vitro in a petri dish, Epstein and Axtell wanted to see if they could spark economic life in silico, in the simulated world of a computer.

They wanted to go back to the very beginning, to a state of nature, and have a model that included nothing more than people with a few basic abilities, and an environment with some natural resources. They wanted to find out the minimum conditions required to set off a chain reaction of economic activity. What would it take to get the system to start climbing the ladder of increasing economic order?

[My Comment] To make a long story short, Epstein and Axtell created a model they called Sugarscape, and the model was successful in spawning economic activity. The thing of interest to me, however, is the notion described in the preceding paragraph – get back to fundamentals, rethinking how we got to where we are now (this applies not just to economies, but to everything), and considering whether we have arrived at a desirable place.

The Origin of Wealth by Eric D. Beinhocker