Evolution Isn’t Enough

Though a powerful theory, evolution continues to face strong headwinds from people who question its ability to fully explain the complex structure of the universe.  What’s missing is the ability to completely explain how hierarchy develops.

The universe uses hierarchy to reach high levels of complexity. From the simplest quantum particles to the most complex organisms (humans), there are hierarchical lines of demarcation:

• Quantum matter organized into atomic and elemental matter. 
• Atomic and elemental matter organized into complex molecules. 
• Complex molecules organized into unicellular life.
• Unicellular life organized into multi-cellular life
• Multi-cellular life organized bodies with specialized organs
•  Bodies with specialized organs organized into humans

What differentiates hierarchical levels are the ways the components interact.  Take the example of life.  Living matter and non-living matter are composed of the same chemicals.  Life is formed when non-living components interact in a specific way that we call life.  Explaining how the universe developed requires understanding how the universe builds new interactions.  The “emergent process” (or “emergence”) describes the building of new interactions which complements the evolutionary process.  Evolution is the universal experimental process; consisting of small structural changes. Emergence leads to selection criteria that decide which changes endure and which ones fail.  Selection criteria are derived from emergence, not from evolution.

While we don’t understand how life emerged, we can make educated guesses. At its core, life consists of two major parts - energy transformation and reproduction.  Visualize the primordial soup where membranes (cells) surrounded different chemical mixes.  In some cells, an RNA-like chemical evolved and, in other cells, photosynthesis (energy transformation) evolved.  To make a living thing, these two cells types came together.  The odds of these two cell-types even evolving are small but the chances of these two cell-types evolving within the same cell are infinitesimal.  It is more likely that an emergent process “merged” these two cell types together, creating a living cell.  A living cell is nothing like either of the separate cells.  It isn't magic, but it isn't evolutionary either.

Let’s stay with the life example to discuss how new interactions leads to new selection criteria.

Extending the example above, a living organism uses energy to power its reproductive system.  That energy is produced from sunlight, a limited resource.  Energy efficient organisms succeed because they reproduce more and crowd out less efficient organisms.  A new selection criterion was formed when a new interaction, pairing energy formation with reproduction, required a limited resource. After living things are created, biological evolution, via genetic mutation, takes over. Eventually, a new emergent event creates a new type of interaction – for example, multi-cellular or more specialized organisms.  In this manner a new selection criteria forms and the hierarchy develops.

While it’s tempting to assign emergence to an Intelligent Designer, it’s too early to make that determination.  Throughout history, processes that were deemed too complex to occur “naturally” were assigned to an Intelligent Designer, only to be reversed upon further knowledge. 

Evolution is an incomplete description of how the universe unfolds. Only by pairing the evolutionary process with the emergent process does one get the complete picture. 

Why are dreams so weird?

The topic of why we dream will be dealt with later, but I wanted to talk about why dreams are so strange. One thing that everyone I've spoken with agrees on is that there is never anything out of place in a dream. I've had dreams where I was in college but my classes were in my elementary school building.  People fly all the time in their dreams and it never seems out of place.  Why?  Our brains developed two major functional parts - one for intuitive thinking and one for analytic thinking. The intuitive part is similar to our heart and lungs - it works all the time and takes very low energy to operate.  We recognize familiar places and faces effortlessly because of this part of our brain.  The analytic part of our brain has been shown to use up a great deal of energy; your head can really hurt from thinking too much. Like any organ that uses energy in spurts, it needs to rest.  Because our bodies evolved to rest our large muscles' the analytic portion of our brain rests during that same period of time.  With the analytic part of our brain resting, there is nothing to register nonsense, so anything goes in our dreams.  When the analytic part of our brain kicks in, we wake up, realize we were dreaming, and begin the dream analysis.

What is Dark Matter?

With the mysterious name, Dark Matter, you'd think there was something weird going on.  Despite the name, it really isn't that strange.  All matter is defined by the way they interact; where an interaction is defined by an exchange of something.  Quantum matter has three ways it can interact (the three quantum forces) and the interaction is "carried" by quantum particles.  As you move up the universe's hierarchy, interactions get more complicated.   Living matter introduces new ways of interaction, much more complicated than quantum interactions, but interactions just the same.  Just as life emerged from Earth's primordial "soup", quantum matter emerged from the universe's Dark Matter. Quantum matter is Dark Matter which interacts according to quantum interactions (the  three quantum forces).  Dark matter doesn't interact according to quantum interactions (forces).  How does Dark Matter interact?  We don't know; we need studies to determine Dark Matter interactions.

Howard...

Chapter 7 - Emergence and Science

Chapter 7 - Emergence and Science

Like a rat in a maze

the path before me lies.
And the pattern never alters
until the rat dies.

Patterns, Simon and Garfunkle

Seventh grade science grabbed me and and I was sucked in by Ms. Helen Kicklighter.  (She said we could remember her name as follows – “Don’t kick so hard, kick lighter.”)  I didn’t notice it at the time, but she was truly a nerd.  She had taught my two older brothers and seemed pretty old when I was in seventh grade.  She had been in a car accident years before I was in her class and could not turn her head.  That made for some amusing times when we’d try to get her to quickly look our way.  Rather than turning her head, she had to move her entire body, shuffling her feet as she did.  It seemed funny for a bunch of 12 year olds.  Ms. Kicklighter got me to put together an exhibit for the Dade County Science Fair.  She suggested I do something about the effect of smoking on grades.  It was a pretty daunting research project for a 7^th grader but she was there to make sure it got done.  It was a very scientific project – I put together a questionnaire that I gave to all 7^th and 8^th graders.  Somehow, I was able to get the names of people who indicated they smoked and Ms. Kicklighter was able to get copies of the smoker’s grades.  (I’m pretty sure this was a violation of someone’s civil rights, but things were simpler in the 1970s.)  I put together bar charts using plastic tape on poster board instead of PowerPoint, which was still many years from its invention.  My final results showed that people who said they smokers got lower grades, on average, than people who did not smoke.  I have no idea if the results were statistically valid as I didn’t know about things like sampling error.  I had a conclusion section that postulated why people who smoked might have lower grades but the reality is I had no idea.  People who smoked in 7^th and 8^th grade tended to be rebellious and rebels didn’t do things like homework or studying for tests.  I had found a correlation and we’d like correlations to lead to a cause and effect explanation.  With a cause and effect, we can then determine a pattern and once we have a pattern we can predict the future. 

A Short History of Science

Science grew into the large enterprise we see today because of one simple fact – it does a better job of predicting the future than any other human invention.  People talk about how they get into science to deepen their understanding of a particular subject (Just like people say they get into politics to better the world.)  You don’t get too far in the scientific world if you don’t find patterns.  Without patterns, the world is a scary and unknowable place.  If there were no patterns you’d never go outside because you’d never know what would happen.  Fortunately, the universe is built on a combination that allows for patterns and chaos, which breaks the pattern.  At its heart, science is a methodology for looking at observational results, finding a pattern and using the pattern to predict the future.  We saw in the earlier chapters that most interactions lead to patterns and those patterns allow the universe to have a stable base from which to evolve. 

There are a lot of patterns in the world early on we noticed the pattern of daylight/night time and the seasons.  It took a while for nerds to evolve, taking delight in observing the patterns more closely and starting to see things that others did not.  The closest planets – Venus, Mars, Jupiter and Saturn – were discovered early on and a model of the solar system, with the earth at the center, evolved from there.  Calendars were created as a byproduct of the seasonal patterns.  Sundials were early devices that took advantage of daily patterns.  The relationship between science and religion is documented well in other places so we’ll leave that part of the discussion aside.  It took a while, but eventually science became a separate field of study.  Galileo was not the first scientist but he is credited with promoting the cause more than any one before him.  Early scientists used observations to look for patterns.  Galileo performed experiments (like dropping balls of different weight to see if the fell the same or different speeds) and looked for patterns in the observations.  Once a pattern is observed, a prediction can be made and more experiments can be performed to test the accuracy of the prediction.  One of Galileo’s first experiments was with pendulums.  He noticed and then measured how long it took a pendulum to swing back and forth.  We performed experiments and determined that the length of time it took for a pendulum to complete one cycle (the period of the pendulum) was related only to the length of the pendulum.  He put together a mathematical formula based on some experimental evidence and then used the formula to predict the results of new experiments.  His predictions were spot on and today his discoveries are taught to all new physics students.  Mathematical formulas were a new way of expressing patterns and were a great advance from physical models, like the initial models of the solar system.  Modeling is the way science advances, so the introduction of a new way of modeling allowed science to advance even more quickly.  Let’s look more closely at modeling.

Models are a simplified representation of the real world.  For example, early models of the solar system had the Earth at the center (because everything fell towards Earth, it must be at the center) and the Sun, Moon, planets and stars orbiting the Earth attached to crystalline spheres.  They were spheres because that was the perfect shape and it was a pretty close approximation to the real orbits.  The spheres were crystalline so that the heavenly bodies didn’t fall into the Earth and you could see through the different levels.  For many years this model served us humans well.  There were some tweaks to be made to explain some anomalies in orbits of the planets but all-in-all things worked for hundreds of years.  Ultimately, new and more rigorous observations lead to our current solar system model with the Sun at the center.  It is more accurate than the spherical model and over time has supplanted it.  Now a day, we can’t imagine thinking the Earth is at the center of the universe but when you stop and think about it, it seems to make sense. 

How do these models come about?  Scientists create them and the process they follow is amazingly simple (of course, we’ve learned that simple interactions can lead to complex behavior so don’t assume science is simple).  They look at observations or make observation by conducting experiments and look for patterns.  The scientific method evolved in such a way that scientists create a Null Hypothesis to predict what they think might happen and then conduct experiments to test the accuracy of the Null Hypothesis’ predictions.  Accurate predictions elevate a hypothesis to the lofty state of theory.  Those that are tested and found to predict a wide class of behavior are promoted to Theories.  “Capital T” Theories are few and far between but the Theory of Gravity and the Theory of Relativity are two examples.  As with any human endeavor, the easy problems were solved first.  In the early days of science, it was a hobby for many people and a number of famous scientists were part timers.  For the record, Albert Einstein was a patent clerk when he did some of his seminal work.  Over time, the work got more specialized and people started pursuing science as a career.  Teaching science and research became a way to make a living.  Of course, when a large number of people are part of a profession, you can expect politics to follow.  Before going into that, I’d like to talk a little more about science and the nature of truth.

Science and Truth

George Box was a statistician who made the famous statement, “All models are wrong, some are useful.”  One of the first things a scientist will do is make assumptions that simplify the thing they are studying.  Edward Lorene wanted to study the weather but it was too complicated so he developed an experiment using a hot plate sitting underneath a spinning Bundt pan.  The hot plate was like the sun shining on the equator and the spinning Bundt pan was a 2 dimensional representation of the rotating Earth.  Simplifying a system allows you to see patterns that may be hidden in the larger system.  That simplification comes at a price.  You give up accuracy in order to gain predictability.  These days, the methodology for making observations can be so convoluted that even these observations, the facts of science, can be in dispute.  I’ve read too many science discussions expounding the truth as defined by science and I want to correct that notion.  There is no truth in science!  By its very nature of modeling, it gets things wrong.  We should not confuse usefulness with truth.  Usefulness is pragmatic, it is good enough.  In a confusing and chaotic world, we need to find a way to find our way forward; to get up every day and live in an unknowable world.  We accomplish that by simplifying the world around us.  Scientific studies show we process very little of the input that enter our senses.  We throw away information at an amazing rate because we can only process a limited amount of it.  So even we humans have to be aware of the limitations of our senses in understanding the world around us.  We build models all of the time to help us navigate through our lives and we know we make assumptions that simplify the number of things we need to think about.  For example, marriage is a human invention that allows two people to pledge they will be faithful to one another.  (Among other things.)  Trusting your spouse and having a marriage vow allows you to assume they will not cheat on you.  You can go through your day not worrying if they are cheating on you and focus your attention on other things.  All models are wrong and sometimes the assumptions you made (your spouse is faithful because they said they would be) turn out to be wrong.  We then have to revise our assumptions, change our model and figure out how that affects the relationship to our spouse.  We’ll talk more about this in the chapter on good and evil.

As science has moved into more complicated venues, like psychology and medicine, we see even more clearly how limited the scientific approach becomes.  My 7^th grade science fair project on smoking and grades did not show any causation, only a correlation. (I forgot to mention I got an honorable mention and a $25 savings bond from the South Florida Lung Association – my first indication that science could make you money.)  This is a much weaker statement and isn’t really a prediction.  I couldn’t say that if a student started smoking, their grades would go down.  (That would be a prediction.)  All I could say was that students who admitted they smoked got lower grades than students that didn’t say they smoked. I found a correlation, which is like a pattern, but not as good at predicting the future.  That doesn’t prevent people from acting on these correlations, since in a highly complex world, it seems to be the best we can do. Let’s explore how correlations came about and how they work.

Correlations and Causation

Almost every day, a new study is published that discusses a correlation between foods eaten and rates of certain cancers.   How did we get to this?  Remember in Chapter 5 when I wrote about the study on extra-marital affairs? – “Forget the statistics, I want names and addresses?”  The simplest explanation for how we got here is evolution – specifically, scientific evolution.  (Remember, we need to qualify the word evolution.)  As science evolved as a way to find patterns, it started with the easy problems.  Galileo found the pattern of pendulums using string, weights and his pulse.  The next hundred plus years were dominated mostly by amateurs, with professionals sprinkled in here and there.  Scientists went after problems that were interesting to them or interesting to their bosses and since the tools they had to work with were primitive, they were limited to simple problems.  Scientific evolution took over as people tried different approaches to solving more complicated problems and in the 1800s, Ludwig Boltzmann made a creative leap in solving problems involving large numbers of molecules and atoms.  He realized you could treat them like identical billiard balls and use statistics to combine their individual motion into group motion.  The results were nothing short of amazing.  A box 1 foot on a side has an insanely large number of molecules in it, too many to track individually.  (Remember Avogadro’s number – 6.022 x 10²³?  That is a 6 with 23 zeroes after it and is pretty close to how molecules are in the box.)  The nature of the evolutionary process is to take an emergent property and spread it throughout the environment.  In this case, the emergent property was statistical analysis and scientific evolution took it to other fields of science where it was tried out.  Nuclear physics was able to use it with great effect, since elementary particles are indistinguishable.  As a methodology for dealing with large numbers of things, it worked well and at some point in the 21^st century, it was applied to biological and biochemical problems.  Why?  Mostly because the problems were so complex that no other way to approach a solution was available and a partial solution seemed better than no solution at all.  Looking for a pattern with a large number of human actors is basically a crap shoot.  While as far as we know all elementary particles, atoms and molecules are identical, we know that isn’t true of animals and humans.  The assumption that the actors are identical is violated so any conclusions derived from these assumptions will be less valid.  All models are wrong and if your basic assumption is wrong, you’re bound to be limited in how useful the results will be.  Medical studies are trying to move into a world where they consider the individual differences; DNA testing and genetic sequencing are the first steps.  We’ll need another emergent property (an A-ha! moment) to take us to the next level of pattern recognition in human biology.  Psychology is even further away and there’s more than one reason they call economics the dismal science. 

Now there are some places where the assumption that humans are all alike is more valid – like in the e-commerce field – and we see those models are more useful.  It is especially important that you understand the assumptions that are made at the beginning of any scientific modeling exercise.  Sometimes, the assumptions are so off-base that they have more in common with make-believe than science.  As long as there is emergence, there will be places where the old models fail – like in the economic models of the 2000s.  The effect of emergence is so large, why isn’t there more science published on emergence?  Where’s the science of emergence?

How do forces emerge?

Just to be clear.  No one has any good ideas how new forces emerge.  The history of science has been based on the assumption that all forces can be derived from the three quantum forces and gravity.  I’d like to talk about some of the conditions needed for a force to emerge and some details of how a new force might emerge.  Chaotic behavior is needed for a property to emerge.  Nothing will ever emerge from the classical world and their attractors.  A necessary condition for emergence is strange attractors.  But chaotic behavior isn’t enough.  There needs to be a high level of interaction in order under some constraints to obtain emergence.  In fact, I’ve come to look at emergence as developing from chaotic behavior under constraint.  Think of an explosion as an uncontrolled event where the energy literally blows things apart.  Emergence has the level of intensity of an explosion, but instead of flying apart, it is focused inwardly and there is a transition to a new level of interaction.  For example, a nuclear explosion uses the power of the strong nuclear force to generate a lot of energy that spreads out quickly.  The sun uses the same strong nuclear force but, under the constraint of gravity, gradually produces energy that is released much, much more slowly.  This controlled energy produces new, heavier elements from the basic building blocks of electrons, protons and neutrons.  These elements are then spread out throughout the galaxy when the sun finally explodes, only to be brought together by gravity into planets where they can be used to create ever more complex properties – like life.

One physical characteristic that seems to be related to emergence is phase transitions.  We all know that water comes in three phases – vapor, liquid and solid (ice).  Scientists have spent a lot of time studying the process where water vapor liquefies and water freezes.  It is a most complicated scenario and one that even today is not fully understood.  (So it isn’t farfetched to think that since we don’t understand phase transitions that we don’t understand emergence.)  The transition from solid to liquid has some of the properties of emergence, in that liquid water has different ways to interact than ice, but there is more to emergence. 

We really should discuss entropy at this point, because it is related to emergence.

Entropy is a measure of organization.  Larger values of entropy mean a system is more random (less organized).  So your car is a highly organized piece of equipment.  Someone had to put a lot of energy to build your car.  A car will not organize itself out the parts all by itself.  But it is how you apply the energy that makes a car “emerge” out of the individual parts.  If you just took an explosive, put it into the pile of parts and lit the fuse you would certainly put energy into the system, but you would blow it apart.  That same energy, applied in a directed way, can create an automobile.  The car has less entropy than the parts that went into it. 

Left to its own devices, your car’s entropy increases and the level of organization decreases.  Eventually, you are left with a rusted pile of junk where the car used to be. Things fall apart unless you do something (which requires energy) to maintain and organize them.    Even worse, the universe is put together in such a way that entropy always increases.  The only way you can decrease entropy is to increase it somewhere else.  We see that on Earth.  The Sun is increasing its entropy by burning as a “controlled” nuclear reactor.  The heat from this reaction warms the Earth and allowed life to emerge (decreasing entropy).  Our gain is the Sun’s loss and it is always the case, there are winners (entropy decreases) and losers (entropy increases).  In fact, if you hearken back to our discussion in Chapter 1 on hierarchy you’ll notice that as the level or organization increases in the universe, the total amount of organized material decreases.  There are more quantum particles than solid matter.  There is more inert matter than living matter.  There is more un-intelligent life on the Earth than intelligent life.  As you organize things more and more, you must leave behind more and more un-organized detritus to balance out the scale.  So there is a limit to how organized physical systems can become and I suspect we’ve reached that level on Earth. The emergent process appears to have reached a physical limit in the origin of human intelligence.  It isn’t because the physical part of the planet earth has stopped the emergent process, but the emergence of intelligence endowed us with the ability to create things with ideas much more quickly and free us from slowness of physical emergence. 

In addition to physical entropy, entropy can also correspond to information.  Once intelligent creatures created the concept of information, we were freed from physical constraints.  While there is only so much physical mass in the universe, information has no limits to the things that can emerge.  Information and it associated properties of language (spoken and written) free us from physical limitations.  Information still follows the U-ROC, things change by interacting.  Information is just something that forms that basis of interaction between intelligent objects.  Technology allows us to increase the speed of information interaction (we can talk on phones where the information travels at the speed of light rather than the speed of sound) and the range of interaction (we can talk to anyone in the world now and are not limited to just talking to people near us).  It is worth noting that while technology can increase the speed and range of information interactions, it has no effect on the quality of the interaction.  I think this is a basic limitation of technology.  Technology can improve productivity but it does not necessarily improve the quality of life.  Remember when people said we’d eventually have a 20 hour work week because technological advances would make us more productive and we’d have more leisure time?  That didn’t happen because in a capitalistic society, you need to maximize profits so the increased productivity allowed us to produce more in the 40 hour work week, not work 20 hours.  Economic evolution trumps technological evolution sp the work week remains at 40 hours, we just get more done.  It is effects like this that make us question the definition of progress.

Good and Evil - Addendum - Heaven and Hell

Chapter 6A – Heaven and Hell

I meant to address the notion of heaven and hell in Chapter 6 but published before I remembered. I just finished a Wired article in the February Issue – You Can’t Beat the Devil – discussing how hell is always better portrayed in movies and books than heaven. The author, Chris Suellentrop, brings up Dante’s Inferno as a classic example. I couldn’t agree more. Historically hell seems more interesting than heaven. With the discussion of good and evil in Chapter 6, we saw that evil evolves from interactions based on lies (either partial or full). I claimed that if everyone told the full truth all of the time, there would be no evil. Now a world with no evil should be heaven, right? Given that description, we see that heaven is nothing close to the worlds portrayed in books and movies. In a world without evil, we’d still play football and get injured. We’d still be able to swear and drink; we’d be happy and we’d be sad. None of our daily lives would be changed by the lack of evil, except we’d be able to trust everyone all of the time. We’d not need to spend any energy sorting through people’s words to see if there is some nuance, or fear that we’d be taken in by a Ponzi pyramid scheme. We’d still have murder, and people would still get mad.

Now you could say that I’ve redefined heaven and you are correct. I think the onus on others to tell me why a world without evil is not heaven. The heaven conjured up in movies and books are not just a world without evil. It is devoid of the very thing that allows the world to exist – hope. They claim heaven is a place where everything is perfect which, by definition, means nothing changes. I understand that people grow tired of the constant “rat race” that is the evolutionary process that is part and parcel of the universe. By its very design, the universe devises new, emergent properties and then evolution take over and spreads the new property throughout the environment. That is the only way things work in our universe so what people are calling heaven is not a world without evil, it is a completely different place that has very little in common with our current world. Just as the opposite of hate is not love (it is indifference); the opposite of hell is not heaven. Heaven is a make believe place that could never exist in our universe.

We all grow tired of having to expend energy to keep ourselves evolving. Every now and then we need to take a break and gather together all of the changes we’ve experienced into a new, stable base. Every one of us has a different tolerance for that stable base (remember the drop and pool model) but all of us are programmed to eventually move to a new set of challenges and face a new “drop” of evolution.

Perhaps the biggest harm religions have done is persuade us that the drop and pool of the universe – rapid change followed by consolidation – is not natural and a prolonged period of consolidation is a desirable place to be. It isn’t – it is the very definition of death. As long as you interact with others (and yourself) you will change and you cannot change that and remain alive. Not interacting is just another definition of death.

So I’m not surprised that the portrayal of heaven is so boring and un-natural. It is a make believe place that cannot exist in the universe and one it we would not be at peace, we’d be dead.

Good and Evil

Chapter 6 – Good and Evil

You ask me if I love you
And I choke on my reply
I'd rather hurt you honestly
Than mislead you with a lie
And who am I to judge you
On what you say or do?
I'm only just beginning to see the real you

Sometimes When We Touch, Dan Hill

My buddy Gene, remember him from the 1^st Chapter, and I have known each other since 2^nd grade. He moved to Hialeah to live with his aunt and two sisters in a two bedroom duplex. Gene’s aunt kept a tight reign over him, Gene was not allowed to have anyone over to the house when she wasn’t there. Gene also couldn’t go over to anyone’s house after school, condemning him to an afternoon of boredom. I couldn’t let that happen so I made it point to go over to Gene’s house pretty much every day after school. We made up a number of games to play, most of which were related to baseball. We played baseball in his backyard with a plastic lemon and a stick for a bat. The lawn chair was set up behind home plate to act as the umpire and anything hit over the roof was a home run. One day, I was looking in Gene’s refrigerator/freezer when I saw a button that read “defrost.” Curiosity got the better of me and I pressed it to see what it did. It clicked down and as mightily as I tried, I couldn’t get the button to pop back out. I heard Gene coming into the kitchen so I closed the door and said nothing; hoping against hope that nothing would happen. Gene’s aunt came home about 5:30 and went to the fridge to get her after work beer and saw an enormous puddle of water. She yelled for Gene and asked what had happened. Poor Gene had absolutely no idea what had gone on but when his aunt told him he pushed the defrost button, he said he must have pressed it by accident. He was punished, mildly as I remember, and the next day he asked me what I was thinking. I said I wasn’t thinking at all and in spite of the deception, we’ve remained friends for over 40 years. I’ve apologized to him a number of times about the incident and have never put him in that sort of position ever again.

We all fail to tell the truth and we all sometimes say things that we know are not 100% factual. This is the genesis of our discussion of good and evil. If everyone told the truth all of the time, there could be no evil. There would be a lot of hurt feelings, but no evil. Rest assured, the universe can cope with lying and, in some ways, good and evil are basic parts of the universe. No need for a Garden of Eden to bring evil into the world; we humans are pretty good at doing that without any outside help. Let’s get into it a bit.

Origin of Evil - Awareness

First off, realize that humans are incredibly complex creations and are the product of amazing emergence. We haven’t spoken a great deal about awareness, how we perceive the world around us, and the topic is too great to cover in detail now. Suffice it to say that the way we perceive our world is built up from the interactions we have with the physical world as reported to us through our senses. We take the various inputs and build our view of the world. Perception is reality - the interactions between us and the outside world, coupled with our own internal interactions form our world view. The reason it is hard to pin down awareness is the same reason scientist can’t agree on a definition of life. It are the interactions that form the emergent property we call awareness. We can look into the brain as long as we want and we’ll never find the one place where awareness takes place. This dispersed awareness allows us to function in a degraded fashion if part of our brain no longer works, or some of our senses fail. We still have awareness, it is just not the same awareness we’d have if all of our senses worked. Since science has not embraced the notion of emergence, we’re currently stuck in a reductionist mode of research. This will pass.

The complexity of our brain and the resulting awareness means we can interact with ourselves. We no longer need external stimuli to generate thoughts and ideas. We generate them ourselves. This self-interaction is a big part of what makes up our self-image and will plays a part in the discussion of good and evil. The awareness evolutionary process, under the influence of human awareness – remember you need to qualify the term evolution to describe which selection criteria is involved, leads to a wide range of self-images from people who are outgoing to inward turning folks. For now, let’s focus on the majority of folks who are relatively well adjusted and are not self delusional. They process the data sent to them by their senses in what we would term a “normal” fashion. There are those who are delusional and seem to have problems integrating sensory data into a reality that matches the external world. That’s a topic for a later discussion as I want to focus on how good and evil come into being in the absence of any “abnormal” situations.

A lot of words have been written on whether or not people have free will. In our discussions, we’re touched on the emergence of human awareness and intelligence. Again, in the world of emergence, hard definitions seem to elude our grasp and we find it easy to find examples that defy explanation. Given the uncertainty, I’ll assume that most people exhibit what we call free will. The exact nature of free will can be maddeningly difficult to pin down because it isn’t a thing, but a set of interactions. The various parts of our brain interact and assemble our view of the world. Since there isn’t a single place where reality exists in our brain, there is no place where we find free will. Go along with me for now as it seems like a lot of people exhibit free will. Self interaction is a valid an interaction as any other interaction and is part of the reason evil evolves. Let’s start at the beginning.

Evil Evolves

I claim that children are born without the knowledge of good and evil. After all we’ve talked about emergence and how simple interactions can lead to complicated behavior; it should come as no surprise that I believe evil evolves. It is difficult to define what you mean by evil. Again, this should come as no surprise since we’ve seen that scientists cannot define life or species. Emergent properties and the associated evolution seem to be defined by our inability to figure them out completely. We certainly know that some things are definitely living and some things are not but there are grey areas that defy definition. The same thing happens with good and evil. I’ll avoid a definition of good and evil as I don’t think that is necessary in this discussion.

As children we observe and interpret our observations. It seems reasonable to associate these observations with facts about our surroundings. We build up a fact database and as we interact with our world we very quickly find out about consistency in the physical world. Gravity is one of the first physical things we find out about and we fight it from the beginning. As we interact with others, our physical body and our own thoughts we are shaped as a person. At some point in our development, we realize that there are things that are not facts. Make believe is a very big part of our coming of age. Once we have an understanding of facts and make believe, we are in a position to learn about lies. A lie is where part of the interaction is intentionally not entirely factual. That means there are two parts to a lie, in my definition. There not only needs to be some part of the facts left out or the entire interaction is make believe but there needs to be an intentional piece to it. That’s why I needed to talk about free will and awareness before venturing into good and evil. If no one ever lied, there would be no evil in the world. There might be a lot of other things – like hurt feelings and broken hearts – but there would be no evil. (Again, we’re not talking about the self delusional folks – that is a separate discussion.)

Back to our young child, growing up and realizing there are facts and make believe. We find out pretty quickly that people who tell us facts can be trusted. Most of us quickly develop trust in our parents and we find stability in the trust. We need stability in order to grow and people who develop in this atmosphere are what we term well adjusted. At some point in our lives all of us came across a situation where we did something that was “wrong” in our parent’s terms and we were punished. We didn’t like the punishment and at some point in the future we might try to make something up to avoid the punishment. Our initial attempts (based on some of the things my children tried) are pretty lame and our parents see through them. At some point, we find out how to make up something that is close enough to the facts that they are accepted. We have learned that we may be able to avoid punishment by making something up – or leaving something out. It should be pointed out that most people tell the truth most of the time. If everyone lied all of the time, the world would be so chaotic that nothing could happen and we’d dissolve into anarchy and death. Nature learned early on (even before there were humans) that working together, it can accomplish things that cannot be accomplished alone. Cooperation is necessary for survival and cooperation requires trust and trust is based on the facts.

Of course, since most people tell the truth most of the time we make it possible for someone who is good at make believe to gain a competitive advantage. Let me state that greed and fear are the root of all evil. I haven’t thought about it exhaustively, but every lie that has been told by me or to me was either to avoid punishment (fear) or get something that wasn’t deserved (greed). So the biblical quote,”Love of money is the root of all evil” is partially right. Greed and fear is the root of all evil. Let’s go back to our blossoming child.

Their notion of good and evil is inherently tied up in how they learn from their experimenting with facts and make believe. As with any evolutionary process, there are a wide range of differences but most people end up telling few lies. There are very few people that lie all of the time, they are eventually shunned and without trust they become isolated (or imprisoned). It is the path from mostly telling the truth the lying I’d like to explore. In the late 1960’s Carol King wrote a song entitled, “Tapestry” where she used a tapestry as an analogy of the interactions in her life. I like this imagery in dealing with good and evil so I’d like to incorporate it into our discussion. If we look at our interactions as trailing a thread behind them, then it isn’t too hard to see that our interactions do indeed build of a sort of tapestry. However, we only have a thread in our interactions if they are based on facts. Lies lead to interactions without thread. There is nothing behind the interaction so it leaves a “hole” in the tapestry.

We all lie and leave holes in our tapestry. Most of the holes start out small – like a little white lie to keep from hurting someone’s feelings. Most of the holes stay small and they don’t affect the overall strength of the tapestry; they are easy to cover up and do0n’t spread. Our world evolves away from the hole and it is lost in the fabric of the cosmos with no one the wiser. Some holes become strange attractors and they don’t get left behind.

An Example

Let’s say I had made plans to go to the movies this coming Friday with my girlfriend Anne. On Tuesday, I got to talking to Marie, who was very pretty and someone I had wanted to date, and she said she’d really like to go out with me - what was I doing on Friday? Making a split second decision, I said I’d love to go out with her on Friday. So far, I’ve not gotten myself into too much trouble, but I need to figure out how to be in two places at the same time. Friday afternoon I make the decision to call Anne and tell her I’m really sick and we can’t go out. She said she hoped I felt better and we’d see each other over the weekend, when I felt better. I pick up Marie and we go to the local movie house and as I’m standing in line to buy the tickets, Anne and her girl friend show up. The hole in the tapestry Anne and I had put together blew up and the tapestry was rent asunder. I could have tried to patch up the hole and tried to gain Anne’s trust but she wasn’t ready for that and we never dated again. (Dating Marie didn’t last very long either.)

Evil doesn’t scale

That was a very simplistic example of a hole but it doesn’t take much to see that some of us make holes that become strange attractors. Ponzi pyramid schemes are a great example of a hole that becomes a strange attractor. While some Ponzi schemes start out crooked, most of them start out as well intentioned interactions based on facts. If I am investment councilor, I need money to invest. I promise I’ll make a good return on your investment. If the investment returns I promised don’t come to pass I am faced with a choice. Tell the truth and take the chance that you’ll pull your money from me or find someone new to invest their money with me, take their money and pay you the promised return. Once you make the decision to take the money from a new investor and give it to an older investor, you’ve created a strange attractor and guarantee the hole will grow. As the hole grows in size, you need to expend more and more energy to find new investors to give you enough money to pay all of the original investors. At some point, there is not enough money in the world to cover your payments and the hole is discovered and your tapestry turns into shreds.

In the same way, evil evolves out of lies that are covered up. Small lies (the white ones) usually don’t grow in size. There are ones that grow (and we don’t always know which ones will grow and which ones will not) and the fear (or greed) surrounding them make the perpetrators do things we call evil. As bigger the hole, the more energy you need to put into repairing the hole. All of your might is focused on stitching around the hole and keeping it together. Since there is a finite amount of energy in the world, the hole will eventually rend the tapestry. Evil is not scalable, since it is built on lies (omissions) which leave holes in the tapestry of your life.

Lies that start with good intentions can stay bounded and not grow so the “evil” is small and can be ignored. It is the lies that keep growing that need to be covered up; that’s the big problem. Most evil starts with good intentions and it is the intentions that differentiate between types of evil. Evil is an evolutionary process; can we call it moral evolution? You start out with a white lie with every intention to not hurt someone’s feelings. If that lie never gets passed on through interactions (remember that if there are no further interactions around the lie it will not grow) and the lie stays small. However, if that lie gets passed on (through interactions) it might get passed on to someone who knows it is a lie and they may come back to you and ask why you are perpetrating a lie. Now you have reached a crossroads. You can fess up and say you lied, explain why you lied and the lie goes away. You may need to go back to the person you originally lied to in order to completely heal the hole; thereby breaking the lying feedback loop. If you decide that there’s too much invested in the perpetrating the lie (fear or greed is driving you) you need to come up with a new lie to cover up the original lie. You have now officially started down the moral evolutionary process with a negative (because it is based on fear or greed) feedback loop. I claim that your intentions have now changed and you are no longer lying to keep from hurting someone else’s feelings but are now lying to cover yourself. Once you have made that change of intentions, you are on the proverbial “slippery slope” that leads to an unpredictable place.

What are the outcomes? They are as many outcomes to this as there to any evolutionary process - unlimited.

Some people catch themselves early in the feedback loop, swallow their pride and make an attempt to uncover the lies. These people practice “godly” sorrow for they came to the conclusion on their own that they needed to correct the lie.

There are some who can stop the trip down the slippery slope by containing it. If only a few people are involved, you can probably put together a set of lies that will “fill the hole with spackle” that may be ugly, but it works. These folks feel no sorrow and the positive reinforcement that they can cover up a lie gives them the confidence to do it again.

At some point, lies spin out of control and start requiring an enormous amount of energy and time to keep the hole covered. Bernie Madoff was able to swindle thousands of people out of millions of dollars for many, many years. The amount of time and energy he spent covering things up was incredible but you know what? Eventually he got caught and they will always get caught. At some point, the amount of energy needed to cover the hole will exceed the total amount of energy in the universe and things will come crashing down. I’d like to explore two things about these types of people.

1. In order to pull off a really big lie and perpetrate serious evil requires accomplices and dupes. Accomplices are paid to come along for the ride and are part of the evil. Dupes are people who are told a lie but are lead to believe it to be a fact. A lot of early investors of Bernie Madoff got the promised returns on their investments and they thought Bernie was being factual. They told their friends – officially becoming Madoff dupes. In college, I read the book, “They Thought They Were Free” about the German people during the 1930s and 40s. in order to foist he incredible evil that grew out of the Nazi movement, the party needed a lot of accomplices and a lot of dupes. Why? You typically have to pay accomplices more than dupes because accomplices know you are lying and could turn on you.

2. People who are caught generally show “earthly” sorrow. They are sorry they got caught. Some time people change their ways – Michael Vick has told us he is no longer interested in dog fighting and appears genially ready to make amends. The problem with earthly sorrow is that you are never sure when the person is really sorry and when they just got caught.

Summary

Evil is an evolutionary process that grows from lies. Sometimes, a lie starts out evil in that the person originates the lie out of fear or greed. Those lies are generated by internal interactions – someone hurt your feelings and you want to get back at them. Even if they started out with the best intentions, fear or greed will turn their good intentions into some self-serving beast and the evil begins to grow. How do you stop evil? The good news is that because of the scalability issues, evil will always be found out and good eventually triumphs. By good, I mean factual interactions with people; they are truthful. The only reason evil can survive in the world is because most people are truthful and that makes them trustworthy. A perfect human would be someone who never intentionally lied. They could not do evil. They could hurt people’s feelings, get angry and yell but they would not be evil. It is pretty clear that no one living on the earth is perfect. We all learn at an early age that we can lie and it almost incumbent upon us to experiment with lying to see what it does. Psychology has a long way to go to figure out what some humans to find lying abhorrent and find ways to avoid it most of the time and some have little or no problem lying whenever it suits the situation.

In the meantime, rest assured that evil has its day but good will triumph in the long run. You need to be on guard that you do not become an evil dupe. While perpetuating an evil innocently absolves you of some guilt, being in the critical path of evil (when it is eventually found out) still hurts a great deal. There are few evil people, but when you find them – avoid them like the plague. Interacting with them is the best way to become a dupe or accomplice. If you think being a dupe hurts, being an accomplice hurts even more as you are as guilty as the principles but you more than likely didn’t get as much benefit from the lies. One lie doesn’t make you an evil person. Realizing when you made a lie into an evolutionary, negative feedback loop and correcting it without outside prompting will allow you to regain trust much more quickly than hiding it.

Chapter 5 - The Shape of Evolution

Chapter 5 – The Shape of Evolution

All freshmen at Guilford College were required to take an inter-disciplinary class that was called Being Human in the 20^th Century. (It had recently undergone a name change from Man in the 20^th Century and eventually became The First Year Experience.) JR Boyd, the head of the Mathematics Department referred to them as “a bunch of damn circle classes.” That they were; we read a book, sat around in a circle discussing them and then wrote a paper on some aspect of the book. We read pieces like They Thought They Were Free: The Germans 1933 - 1945 by Milton Mayer and Flannery O’Connor’s Everything that Rises Must Converge. I wrote a paper on the biological origin of the soul. As a college freshman, I couldn’t see that there was some miraculous event that caused the soul to spring into existence. I don’t have a copy of the paper anymore but I remember it was not well received by anyone who read or heard about it, especially the teaching assistant who graded it. Now I better understand the hesitation. My paper lead one to believe that by some unknown evolutionary process that human awareness and intelligence (what we call the soul) came into being. Millions of words have been written about the evolutionary process and still people, not creationist, but educated folks, believe there was something missing. That something was emergence. Now we’re in a position to talk about how emergence and evolution work together to build the hierarchy of the universe.

Just to review, an emergent property is a new way of interacting that comes into existence in some as yet unknown process. There are some other terms that can be substituted for this, force and selection criteria are two most used. Every new emergent property leads to a new evolutionary process that “pushes” it into the environment, exploring every nook and cranny. You cannot have a little bit of life. Once life emerged, biological evolution took it to every corner of the earth. Once gravity emerged, gravitational evolution took it to every corner of the universe. The selection criteria set up how the universe selects winners and losers. In a very real sense, evolution is a game. (I suspect that is one of the reasons humans invent games. They are ways to simplify the universe into a much easier to understand pieces.) The universe has a finite amount of energy and matter in it. That implies there is always going to be a competition when the new selection criterion, through the evolutionary process, tries different combinations to see what works. There is no predetermined path; the changes (like the mutations in biological evolution) are random. The selection criteria works as the decision maker and some changes survive and some changes become extinct. In addition, once a change is selected for survival, the evolutionary process goes to work to change (and improve) it over time. One way to explore this continuous change and improvement is to look at what happens as a result of the process – taking a snapshot of the process along the way and look for patterns. A common technique is to look at how the evolutionary process distributes the emergent property in the new landscape.

Distributions

People like to put things into groups. At an early age, we learned to sort toys by color or shape. Putting things into piles comes naturally to us and I think that it’s because that’s how the universe works. The evolutionary process makes small changes that are amplified over time. The new emergent property takes hold, spreading it across the environment. Since some of the changes are small, grouping things into larger buckets can help us better see the effects. The most popular distribution – because of its predictability – is the normal distribution. Let’s look at an example of a characteristic that exhibits a normal distribution.

Height Distribution

If you were to measure the height of every male aged 45- 54 in the United States and plot it in a frequency chart it would look like this.

This data came from the 2000 US Census. Along the bottom are the heights and the vertical line is how many people you measured with that height. The average height is between 5’8” and 5’9”. Very few middle aged men measure more than 6’4” and few are shorter than 5’. This familiar “bell-shaped” curve, another name for the normal distribution, is used in many fields – medicine, economics and college entrance test analysis. Normal distributions work best in places where the extremes between the highs and lows are small. In the case of height, the maximum difference between the tallest male and shortest male is less than 4 feet. There are physical limits to how tall a human can grow - gravity puts an upper limit on how tall you can make a human - there are no 12 foot humans. Whenever you have characteristics that have a fairly small range of values – height, weight, IQ – you’ll find the normal distribution. Statisticians work with normal distributions because they explain a lot of phenomenon and help in predicting outcomes. Most medical studies these days are based on normal distributions. When a study says something along the lines of - drinking a glass of red wine every day lowers your chances of cancer by 10% - it is depending on the normal distribution to make these pronouncements. (In a later chapter, we’ll talk about the limitations of these sorts of pronouncements. What exactly does a 10% reduction in risk of getting a certain kind of cancer mean? I remember a comic who did a bit about the Masters and Johnson sex study that said stated 60% of all married women engaged in extra-marital affairs. “Forget the statistics,” he said, “I want names and addresses!” The use of statistics is a necessity in modern clinical studies, but most folks fail to appreciate the statistics – they want the names and addresses of the people who avoid cancer by drinking red wine.)

Population Density

Now let’s look at something that doesn’t have the limited range of values associated with height or weight. The US Census data includes information about population density by state. Here we take the population of a state and divide it by the square mileage of the state to get the population density. While there is an upper limit on how high the population density could go, it turns out that for all practical purposes, it is infinite – since upper limit of population density is many thousands of times larger than the lower limit. (In comparison, the tallest human is not even three times taller than the shortest person.) Having a larger range of values to work with makes an incredible difference in how the distribution looks. Let’s start by looking at the raw data.

Along the bottom of the graph is an entry for each state (and the District of Columbia) and the vertical axis is the density. This is nothing like a normal distribution. It is more like a hockey stick lying on the ground. The scale in the vertical direction goes from 1 person per square mile (Alaska) to over 8,000 people per square mile (the District of Columbia). When the difference between the lowest values and highest values are so great, we can use some mathematics to make the picture easier to visualize. Logarithms are used to scale the vertical graph and make it easier to see how things relate to one another. Let’s look at a table of logarithms to see how they work.

Number	Logarithm
1	0
10	1
100	2
1000	3
10000	4
10000	5
1000000	6

Logarithms “count” the amount of tens places in a number. While the numbers in the chart go from 1 to a million, the logarithm goes from 0 to 6. We turn large differences into small differences. Let’s look at the graph above using the logarithm of the population density instead of the actual population density.

Now the vertical scale goes from 0 to almost 4. That’s because the logarithm of 1 (which is the population density of Alaska) is 0 and the logarithm of 8,300 (the population density of the District of Columbia) is almost 4. Don’t be mislead, however, the raw data is the same and the population density still spans a scale of 1 to almost 10,000. (We call it 4 orders of magnitude, where an order of magnitude is 10 times as much. Between 1 and 10,000 are 4 orders of magnitude.) The logarithm makes it easier to see any patterns in the data. Now, just like the height data, we want to turn this data into a distribution by grouping states into buckets of population density. (The height data was already put into height buckets by the census bureau.) We’ll group the states by population density of single digits (1 – 10, 10 – 100, etc.) to form our buckets. We’ll make one more change in the graph to show the pattern more visibly. Instead of showing the number of states in each bucket – as we did for height- we’ll plot the logarithm of the number. Again, we use logarithms to turn large changes into smaller ones. The distribution of population density, graphed by the logarithm of the number of states in each bucket is below.

Here we see a downward sloping line from upper left to lower right. Because we are using logarithms in both the vertical and horizontal direction, for each step down and to the left we get 10 times fewer states with 10 times more population density. This curve happens so much it has been given its own name –a power curve. There are a relatively large number of states with low population densities and only one state with a really large population density. As you move downward and to the left on a power curve you get 10 times fewer things (in this case states) but each one is ten times more (in this case dense). Power curves manifest themselves in all sorts of places and I’d like to show a few examples.

Web Links – There are tens of millions of web sites with very few links. Small business, personal web pages all fall into this realm. There are thousands of websites (large businesses and government sites) with millions of links. There are hundreds of web sites with hundreds of millions of links – Google, Microsoft, etc.

Net Worth – There are billions of poor people in the world – defined as income under $100 a year. There are fewer than 100 people in the world with net worth over 10 billion dollars.

Actor’s Annual Income – Very few actors earn $20 million per picture, but there are a dozen or so that garner big paydays. They don’t have to audition for any part and “live the dream.” There are tens of thousands of actors that make less than the minimum wage, who tramp from audition to audition hoping for a break. Again, there are a lot of people making very little money and a few making tens of thousands of times more money.

Professional Athletes – See above.

In a normal distribution, the average clumps around the middle hump of the distribution (5’ 8” in the height distribution). In the power curve, the largest group is at the far left of the curve and as you move towards he right, the number drops off dramatically and the impact goes up dramatically. In a normal distribution, the largest values of the distribution are still relatively close to the average value – a very short person, say 3 feet tall, which is within 5 feet or so of the average height. In a power curve, the impact grows by orders of magnitude and the low values of net worth are nowhere near the average. That makes for an incredible draw for the struggling actors – or athletes or entrepreneurs or gamblers. They have to believe that if they can just hang in there and keep plugging along they’ll make it into the upper echelons of their profession. That very few make it is of little consequence because just knowing that someone can make it gives hope. The fact that every story of success is different (you should be thinking about chaos and the sensitivity of initial conditions here) means there is no one way to the top.

So far, we’ve focused on static distributions – looking at height distributions for a given year or population density for a given year. Distributions show you how the attribute looks at a given point in time. The distributions shown below came from the 2000 US census and the graphs would be different if we used data from previous census. In the early days of the United States, there were many fewer people and the population density was not nearly so spread out. I haven’t graphed things too far back, but I could believe that early on in the history of our country, population density followed more of a normal distribution. The evolutionary process “stretches” out the attribute and gives a competitive advantage to the outliers on the top end – the rich get richer and the poor get poorer, as the saying goes. Jared Diamond’s ”Germs, Guns and Steel” gives a number of historical accounts where a selection criteria emerged and led to an new evolutionary process that benefited some groups more than others.

We should try to better understand how the power curve works over time.

Evolution and the Power Curve

Any evolutionary process leads towards a power curve because as various mutations compete, some small set gain a competitive advantage over the rest and start accumulating more than their fair share of whatever resource is the source of competition and selection. In a December 17, 2010 NY Times Magazine article – A Physicist Solves the City – Geoffrey West’s work on population density is discussed. He has collected data that he says shows higher density leads to greater interaction which leads to more productivity. Although West’s work was done on cities, the same ideas apply to states. (We’ve already discussed how interactions are the basis of change so having more density leads to more change which leads to even more change, setting up a positive feedback loop, should seem familiar.) As a state increases its population density, perhaps because the weather was better, it experiences an increase in productivity which leads to more jobs and better pay rates which gives it a more competitive advantage over other states. Of course, having a small-sized state to begin with helps in this situation but that isn’t the driving force. Once a mutation gets selected, the feedback loop starts and when the tipping point is reached, the competition quickly falls behind. Malcolm Gladwell’s book, “Outliers” discusses this phenomenon and the story of junior hockey players is illustrative. The competitive advantage was how old a young player was relative to his peers. Since they are grouped by age, based on an entire year, players could be a much as a year older than their teammates. At the age of 10, that’s a big difference and is significant enough to give them a big leg up on becoming an elite junior hockey player. A December 25, 2010 excerpt from Eduardo Porter’s book, “The Price of Everything” discusses the capitalistic evolutionary process and its effect on the top pay for everyone from professional athletes to musical artists to bankers.

All of these writers are describing the same effect – an evolutionary process tied to an emergent selection criteria lead to the power curve. When there is a limiting factor (like gravity in the case of height or weight) you get a normal distribution. The large outliers we see at the ends of the power curve – the large number of very small at the upper left and the small number of very large at the lower right - get cut off and folded back into the middle of the power curve. This is described as the regression towards the mean and is an integral part of attributes that follow the normal distribution. Absent any external limitations, these outliers will not regress towards the mean and will spread out under evolutionary pressure and lead to a power curve. In fact, it is possible to see that some distributions start from normal distributions and as the evolutionary process plays out, the distribution spreads and more of the resource controlled by the process gets concentrated in the hands of the elite. Porter shows examples in the banking industry where before the Great Depression, limited regulation lead to a wider spread in income and post depression regulation lead to a more limited income spread throughout the banking industry. We’ll look at what happened in the 2000s when regulations were relaxed and the normal distribution was allowed to spread, via economic evolution, to a power curve.

To be complete, there are other types of interactions that can lead to power curves so just because you see a power curve does not mean that an evolutionary process is directly behind it. Let’s look at a one example where power curves are not tied directly to an evolutionary process.

Earthquakes – The familiar Richter scale for earthquakes uses a logarithm of the severity, so we’re already half way to a power curve. If you look at earthquake data over any period of time you’ll see a lot of little earthquakes and very few powerful earthquakes. It follows a power curve not because of evolution, but because it is the outcome of two closely matched, but powerful, forces working against each other. Earthquakes are formed as tectonic plates move past each other. There are many different ways they can slide past but let’s focus on a strike slip fault where the two plates slide along side of each other horizontally– like the infamous San Andreas Fault in California. The two plates – called the Pacific on the west and North American on the east - slide past each other at a slow rate of speed. These plates are massive so the forces between them are incredibly large. Most of the time, the place where the plates meet is like sandpaper so the plates slip past each other with a few little hitches. These small hitches represent small earthquakes. Every so often, a large outcropping in one or both of the plates catch on each other, like in the figure below, and the plates get hung up. The entire plate continues to move and the pressure between the plates builds up until it is finally large enough to overcome the resistance to movement formed by the outcroppings. The two plates explode past each other as the part of the fault that was “hung up” on the outcropping catches up to the rest of the plate. The distance covered as the plates realign themselves is directly related to the severity of the earthquake. During the 1906 San Francisco earthquake, these two plates moved approximately 21 feet and the quake’s intensity was estimated at 8.25 on the Richter scale. This is one of the largest earthquakes measured along the San Andreas Fault. A good question to ask is - why are there so few large earthquakes? It is directly related to the distribution on large outcroppings in the Pacific and North American plates. Now we see where an evolutionary process was involved. As the earth cooled from its initial state, the magma condensed in to rocks. If we plotted a graph of rock distribution by size, we’d find the familiar power curve as a result of the evolutionary process. There would be a lot of little rocks and a few large rocks embedded in the tectonic plates. Why? Large rocks get chewed up by the grinding action of the plates, turning them into smaller rocks. Initially there were probably more large rocks as the magma cooled, but over the millions of years of grinding the large rocks get turned into small rocks. Without a way to generate more large rocks, over time we see few large rocks and numerous smaller rocks. So even though earthquake distributions are not directly related to an evolutionary process, there is an evolutionary process as a secondary effect.

Normal Distributions and Power Curves in the Economy - In 2008, there was another power curve related event – the Recession – that illustrates the shape of the evolutionary curve and what happens when you misapply normal distribution predictions to power curve events. First off, any time you use a normal distribution to predict the outcome of an evolutionary process, you will eventually get it wrong. And not just a little bit wrong, you’ll eventually get it so wrong that you should lose your job. Why do people use normal distributions to make predictions for evolutionary processes? Remember those strange attractors? They give the appearance of a pattern and sometimes, the normal data fits the model when it is near a strange attractor. They get it right enough that they are lulled into a false sense of complacency. Then the chaotic system makes a move to another strange attractor, the model gets it very wrong for a while. If the system settles down into a new strange attractor, it goes back into looking like a pattern and we go back to our old models.

Prior to 2008, we were in a pattern governed by the strange attractor that was dominated by fact that average home prices increased every year. In its February 2009 issue, Wired magazine wrote an article “The Formula That Killed Wall Street” which discusses the formula used by banks to model risk more “accurately;” it is based on normal distributions. In the years leading up to 2008, the assumption of increasing home prices across most of the United States was true. Sure there were times and places (1980s in Texas and 1990s in San Diego) where home prices dropped but that didn’t affect the rest of the US. In a very real sense in decade preceding 2008 became a Ponzi Pyramid scheme based on real estate. Mortgage originators got paid fees to sign people up for mortgages, banks got fees for closing costs, ratings agencies got fees for ranking pools of mortgages and investment banks got fees for selling pools of mortgages (all of them with AAA ratings – imagine that!) to various investment groups looking for more interest for low risk. Ponzi schemes require dupes on the front end to bring in money and dupes on the back end to tell their friends what a great investment they found. You’ll notice the feedback loop similar to what we described in Chapter two, interactions that feed on themselves. As the pool of credit worthy people dried up, the mortgage originators started signing up for more and more riskier people to lend money to. The government was telling everyone that home ownership was a national right and it was imperative that we do everything we could to get more home owners. The loan originators were more than willing to sign up more and since the ratings agencies published their ranking models (which assumed housing prices would continue to rise) investment banks could continue to put together AAA rated pools of what turned out to be toxic mortgages and sell them as investment grade instruments. Some people noticed that this was a self-fulfilling prophecy – assuming housing prices would tend to rise across the US lead to poor credit risk people getting mortgages they could never pay back. These toxic mortgages were “magically” turned into investment grade instruments (of mass destruction) and sold to the masses. The folks who started doubting the models started placing bets that the mortgage market would fall. They believed that the market would eventually move from the current strange attractor (housing prices rising) to a new strange attractor (housing prices fall). The fact that some of those groups (like Goldman Sachs) were set to make money on both sides of the house – fees for selling the toxic investments and insurance collection when they failed – is painful. In 2010, Goldman Sachs settled with the SEC (without admitting fault, it’s good to not admit fault as it keeps one’s ass out of jail in a criminal prosecution) for $500 million in just one of the many mortgage deals they sold. Even more fascinating, Goldman thought there was a good chance that the people who were given these mortgages wouldn’t be able to pay them so they took out insurance with AIG to protect them in the (likely) event the economy went to hell in a hand basket. When AIG didn’t have enough money to pay the claims, the US government stepped in and paid Goldman Sachs (and others) 100 cents on the dollar (via a $80 billion bailout of AIG). It’s good to be king. [Aside: Goldman and some other banks deemed too big to fail were able to keep the money they made on the run up and kept in business to continue to make more money in the aftermath. In a strict evolutionary system, these leeches would have been allowed to fail and all of them would have gone bankrupt. A purely capitalistic society is too much for us to stomach so we have introduced various flavors of socialism to make the system more humane.] [Aside #2: The housing bubble was not an exclusively US event but I limited this discussion to the US situation.]

In the summer of 2008, Ben Bernake made a statement that the foreclosure problems were under control. That is because the models said things were under control. Every model used by the banks and mortgage risk analysts assumed a normal (also known as Gaussian) distribution. However, the economy is controlled by capitalism, which is the selection criterion for the US economy. By definition then, the US economy is chaotic and doesn’t follow a normal distribution. It was just a matter of time before the models got it wrong and boy did they get it wrong. The financial meltdown of 2008 started in just 35 counties, spread mostly between the states of California, Florida, Nevada and my home county of Fulton in the state of Georgia. As we found out, in a chaotic system (all evolutionary systems are chaotic) a small change can lead to large changes. When gasoline went to $4.50 a gallon, people in those 35 counties couldn’t drive to work and make their mortgage payments – especially as their introductory interest rates doubled or tripled their payments. That was the tipping point. These 35 counties started showing an increase in foreclosures and in a short amount of time, the entire economy was frozen. The transition from housing prices always rising to falling took a breath taking short amount of time – roughly 3 months after Ben Bernake made his infamous statement, Lehman Brothers declared bankruptcy. A strange attractor (rising housing prices) that had gone on for many decades turned around in a few months. That’s the way the transitions go. They happen very quickly and no one (even an intelligent designer) can predict when they will turn.

That’s why people can’t model the transition between strange attractors. It is not only too hard but I suspect there is no pattern to model. That transition is almost pure chaotic behavior and any small change can lead, very quickly, to large variances. If we were able to run the clock back and look at the financial meltdown of 2008 the number of interactions are so large that a change of one of them would have lead to another pathway and the timing would have been different. But the end result, a recession, would have been the same. We’ll explore the relationship between science, strange attractors, chaos and predictions in a later chapter.