Archive for the ‘The Origin of Wealth’ 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

Humanity’s Most Complex Creation

January 31, 2008

Take a look around your house. Take a look at what you are wearing. Take a look out your window.  No matter where you are, from the biggest industrialized city to the smallest rural village, you are surrounded by economic activity and its results.  Twenty-four hours a day, seven days a week, the planet is abuzz with humans designing, organizing, manufacturing, servicing, transporting, communicating, buying, and selling.

The complexity of all this activity is mind-boggling.  Imagine a small rural town, the kind of quiet, simple place you might go to escape the hurly-burly of modern life.  Now imagine that the townspeople have made you their benevolent dictator, but in exchange for your awesome powers, you are responsible for making sure the town is fed, clothed, and sheltered each day.  No one will do anything without your say-so, and therefore each morning you have to create a to-do list for organizing all the town’s economic activities.  You have to write down all the jobs that must get done, all the things that need to get coordinated, and the timing and sequence of everything.  No detail is too small, whether it is making sure that Mrs. Wetherspoon’s flower shop gets her delivery of roses or that Mr. Nultey’s insurance claim for his lumbago is processed.  Even for a small town, it would be an impossibly long  and complex list.

Now think of what a similar to-do list might look like for managing the global economy as a whole.  Think of the trillions of intricately coordinated decisions that must be made every minute of every day around the world to keep the global economy humming.  Yet, there is no one in charge of this to-do list.  There is no benevolent dictator making sure that fish gets from a fisherman in Mozambique to a restaurant in Korea to provide the lunch for a computer worker who makes parts for a PC that a fashion designer in Milan uses to design a suit for an interest-rate futures trader in Chicago.  Yet, extraordinarily, these sorts of things happen every day in a bottom-up, self-organized way.

It is clear that the global economy is orders of magnitude more complex than any other physical or social structure ever built by humankind.

The Origin of Wealth by Eric D. Beinhocker

An explanation for why working group sizes of 6 to 9 people are most effective

January 23, 2008

Some anthropologists have speculated that groups of 6 to 9 people come from our long evolutionary heritage as hunter-gatherers, and that such group sizes made for effective hunting bands.

Evolution tends to be quite efficient over time at finding balances between trade-offs.

So it is likely that working group sizes of 6 to 9 people evolved because they represent a balance between the benefits of scale (a hunting band can get more food per calories expended than a lone individual can), and the diseconomies of complexity. Our ancestors would not have survived for long if hunting groups of thirty people spent hours debating whether they should hunt bison or antelope that day.

The Origin of Wealth by Eric D. Beinhocker

The dramatic effect of “random friends” in connecting you to other people

January 14, 2008

Let’s say we have a population of 1,000 people with 10 friends each and no “random” friends.  That is, everyone’s friends are drawn only from a strictly defined social circle[1].  Then the average degree of separation is 50; in other words, on average it will take 50 hops to get from one randomly selected person to another.  But if we now say that 25% of everyone’s friends are random, that is, drawn from outside their normal social circle[1], then the average degree of separation drops dramatically to 3.6.

[1] Your collection of friends most likely includes people you grew up with, people you went to school with, colleagues from work, people in your profession, and your current neighbors.  These are friends who come from your normal social circles.

[2] In addition to your structured social network, you also have a few random friends, people who are not in your normal social circle, who your have somehow met and become friendly with. For example, it might be someone you got to know while on vacation, or in the waiting room of a doctor’s office.

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.

Humans are relatively good at induction and relatively poor at deduction; computers are just the opposite

December 21, 2007

Induction is reasoning from a limited number of observations toward a general conclusion. A classic example: After observing that 2 or 10 or 1,000 ravens are black, you may decide that all ravens are black.

Another way of thinking about induction is that it is reasoning by pattern recognition – we fill in the gaps of missing information.

With deduction you start with a set of possibilities and reduce it until a smaller subset remains. For example, a murder mystery is an exercise in deduction. Typically, the detective begins with a set of possible suspects — the butler, the maid, the business partner, and the widow. By the end of the story he has reduced this set to only one person: “The victim died in the bathtub but was moved to the bed. Neither woman could have lifted the body, nor could the butler with his war wound. Therefore, the business partner must have committed the crime.”

Humans are relatively good at induction and relatively poor at deduction. Any of us is capable of instantly recognizing a face (an inductive task), yet most of us would have a tough time quickly doing the deductive calculation:

(239.46 x 0.48 + 6.03) / 120.9708

Computers are relatively poor at induction and relatively good at deduction. A simple pocket calculator can quickly and perfectly do the calculation, while it is a very hard programming challenge to get even a powerful computer to accurately recognize a face.

The Origin of Wealth by Eric D. Beinhocker and Logic for Dummies by Mark Zegarelli

Story telling and story listening are important to the way we think

December 20, 2007

Stories are central to our mental processes for understanding, remembering, and communicating.

Plato said: “Those who tell stories rule society.”

The next time you are at a dinner with friends, sit back for a minute and observe the goings-on.  What is everyone doing?  Most of the evening will probably be taken up exchanging stories, funny ones, sad one, stories about friends, stories read in the newspaper, and so on.

Why is story telling and story listening so important to the way we think?

Stories are a way in which we learn.  For example, by reading Shakespeare, we can learn all sorts of useful lessons about love and family relationships.  The best selling business books are often stories of successful individuals or companies; everyone wants to read stories about how Jack Welch or Bill Gates “did it,” hoping to glean patterns of success.

The Origin of Wealth by Eric D. Beinhocker

Brief Tutorial on Dynamic Systems

October 16, 2007

From The Origin of Wealth by Eric D. Beinhocker:

A dynamic system is one that changes over time.

When scientists talk about a system being dynamic, what they mean is that the state of the system at the current moment is a function of the state of the system at the previous moment, and some change in between the two moments.

A simple example of a dynamic system is a bank account. The state of the account, or balance, changes over time. Your balance tomorrow is dependent on your balance today, plus any changes during the intervening day, such as deposits, withdrawals, or interest payments.

Changes in dynamic systems can either be discrete, like a bank account, in which the changes occur at specific points in time (e.g. interest is paid on a particular day), or they may be continuous and smooth, like the orbiting of planets.

A convenient way to describe a dynamic system is in terms of stocks and flows. A stock is an accumulation of something, such as the balance in a bank account. The rate at which a stock changes over time is known as a flow, for example, the rate of money flowing into or out of a bank account.

In an economy the various stocks and flows are connected to each other in complex ways. For example, if the stock of employment fell to a low level, a policy maker might decide to cut interest rates in order to encourage borrowing, which would expand the stock of money available for investment, which would then be used by businesses to invest in new productive capacity, creating demand for employees, thus raising the stock of employment, which finally would feed back to affect future interest rate policy. Such chains of relationships between stock and flows in a dynamic system are known as feedback loops.

Feedback occurs when the output of one part of a system is the input for another, so, for example, A affects B, which affects C, which comes back to affect A again.

Positive feedback occurs when the connections are reinforcing – if I push A, it pushes B even harder, which pushes C even harder, which pushes A harder than my original push, and so on.

Despite the word positive in the phrase, downward spirals are also a form of positive feedback. For example, a drop in consumer confidence can lead to decreased spending, which leads to decreased production, which leads to unemployment, which leads to even lower consumer confidence and thus a further drop in spending, spiraling right down into recession.
The key thing to remember is that positive feedback reinforces, accelerates, or amplifies whatever is happening, whether is it a virtuous cycle of a downward spiral.

The opposite is negative feedback. Negative feedback is a dampening cycle – instead of reinforcing, it pushes in the opposite direction. While positive feedback accelerates change, negative feedback dampens change, controls things, and brings things back in line.

Dynamic systems also have a third ingredient – time delays. You have probably had the experience of taking a shower in an unfamiliar place such as a hotel, turning on the hot water, noticing it isn’t hot enough, turning it up some more, and then it turns scalding, so you turn it down, it is still too hot, so you turn it down some more, then it is freezing, and so on. The problem is that there is a small time delay between your actions on the water knob and the feedback from the shower temperature. The delay causes you to overshoot and oscillate around the desired temperature. Eventually you figure it out and the oscillations get smaller and smaller until you hit the desired temperature. The longer the time delay, however, the harder it is to control the shower and the more oscillations you get.

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

Scientific theories are like maps – there are course-grain maps (and theories) and fine-grain maps (and theories)

September 3, 2007

Scientific theories are like maps.

“Maps are approximate pictures of an underlying reality; a map of Oskaloosa, Iowa is only an approximate representation of the real Oskaloosa.  The only perfect map of Oskaloosa is Oskaloosa itself, which is too big to fit into the glove compartment of your car and thus not very useful.  Just as map makers idealize and leave out certain features of the terrain, scientists simplify and idealize their theories.  What is included or left out will depend on the purpose of the map or theory.  If you are driving across the country, you might just need a course-grained map that shows the major highways.  If, on the other hand, you were going to visit your great-aunt on Ford Avenue in Oskaloosa, you would need a fine-grained map that shows the street grid of Oskaloosa, but not all the highways in the country.”

“The course- and fine-grained maps (and theories) must agree with each other and the observations of the underlying reality.  If a highway map places a river in a particular location, the river must be in the same location on the local map, and must agree with observations of where the river actually is.  One cannot just move roads and rivers around for the purpose of making the maps easier to draw.”

“Science requires different levels of abstraction for different phenomena.  Scientific theories can be big picture and course-grained like a highway map, or fine-grained like a local street map. Both are equally valid; they just need to agree with each other and conform to reality.”

The Origin of Wealth by Eric D. Beinhocker