The Pattern
The wind was flapping a temple flag. Two monks were arguing about it. One said the flag was moving; the other said the wind was moving. Arguing back and forth they could come to no agreement. The Sixth Patriarch said, “It is neither the wind nor the flag that is moving. It is your mind that is moving.” — Zen Koan
“The belief in an external world independent of the perceiving subject is the basis of all natural science.” — Albert Einstein
Does a whirlpool exist in the same way that a rock exists or that energy from the sun exists? For something to exist it either has to have always existed, or there must have been a time prior to its existence. Leaving for a moment the possibility that everything which exists today has always existed, let’s consider that it came into being at some point.
If something came into being, then not only must there have been a point in time prior to its existence, but there must have been a point after which it begins to exist. These two points don’t necessarily need to be the same. If I make dinner — a rare occasion to be sure — then there is a period of time during which I’m in the process of making dinner. Before that period started, my dinner did not exist. After I fished making it, my dinner existed. We could argue over whether existence is therefore better understood as a continuum (does dinner exist while I’m in the process of making it?) but let’s assume for a moment that it’s not. That dinner either exists or it doesn’t. Certainly it’s the case that the rock I am sitting on right now either exists or it doesn’t. I’m going to assume the latter for now.
Let’s talk about creation for a moment. When we say that something was created we often times are implying that there was a creator of some sort. Maybe not a human or deity, but that there was an entity of some sort that had the intention of bringing that thing into existence. Clearly there are things that get created that don’t have an intentional creator. Take for example an oil spill or a planet.
So to recap, we will be talking about the existence (or not) of various things, and we will use the terms “creation” and “created” simply to refer to the notion that for something to exist, there needs to have been a creation process of some sort: human, devine or otherwise.
Consider for a moment a particular pattern in a Persian rug. Or the pattern of the Statue of Liberty. Both have been replicated many times, but there is still the question of whether the pattern itself has existence independent of the physical manifestations that it takes. Let’s not attempt to answer that right now, but rather let’s assume that patterns exist as first class citizens in the universe, and see what that assumption implies.
For a particular pattern to exist, it either always existed, or it was created at some point in time (or during some time period). I think we can all agree that the pattern of the Statue of Liberty was created (that is, it did not always exist). The notion that it has always existed (while ultimately it may have some merit) is too philosophical for our current purposes.
What about ceasing to exist? Is it possible that a pattern, once created, can cease to exist? Again, quite philosophical, but let’s take the harder path and assume that if all instances of a pattern cease to exist in the universe, then the pattern ceases to exist as well. It may come back into existence sometime in the future; it may not.
Now back to that whirlpool. It’s fair to say that there exists a pattern for it that we notice from time to time instantiated elsewhere: other whirlpools of course, but also tornados, hurricanes, eddies in streams, even gravity wells created by stars. We have a technical name for this pattern, we call it a vortex.
Vortices belong to a class of patterns that, once initiated are self-sustaining. They need a flow of energy (such as the pull of gravity) to sustain indefinitely, but the structure of the pattern is such that less energy is required to sustain it than to create it in the first place. The energy is channeled by the pattern in a cycle so that only a fraction is lost during one orbit. The cyclical structure is the key to a self-sustaining pattern.
Not all patterns are self-sustaining of course. Clouds form and dissipate without much in the way of a sustained, coherent pattern. The fact that we can identify individual clouds suggests that there is some degree of self-sustenance. But it’s fair to say that if there were a measure of self-sustenance, clouds would rank lower than vortices.
And if there is a pattern to life, then certainly self-sustenance is one of its properties. And certainly life forms would measure higher on that scale than either clouds or vortices.
Living things have other, related properties as well: self-repair, self-defense, self-replication, self-representation…. This latter property is interesting because it is the basis for (among other things) genes and cognition. Somewhere on the spectrum of self-* properties lies self-awareness and self-consciousness. Maybe not all living things exhibit these higher order self-* properties, but some of us do.
Are there certain patterns that, in addition to being self-sustaining are also self-creating? It’s an interesting question because how would we distinguish self-creation from random chance? Given a long enough time period and random fluctuations of energy and matter, there are bound to appear patterns that just so happen (by pure chance) to be self-sustaining. And if you view the universe as a place where matter and energy interplay in an increasingly random way — which is to say that entropy increases as time goes on — then you could rationally assert that within the universe there are patterns that self-create.
But even if you didn’t want to go that far, there is something very important about self-sustaining patterns no matter how they were created. Namely, that they define a boundary between self and environment that simpler patterns do not. I need to unpack that statement a bit for it to make sense.
How do we know that a pattern is indeed a pattern? Simply because we observe a sameness about some aspect of our reality that persists for some noticeable time. This is a basic definition of pattern. But for us to observe the pattern there must be something doing the observing, namely us. But what if there were nothing doing the observing, would the pattern exist just the same?
Robert Lanza makes a good case against existence without observation. Talking about of the proverbial tree falling in the forrest, he observes,
If someone is nearby, the air puffs physically cause the ear’s tympanic membrane (eardrum) to vibrate, which then stimulates nerves only if the air is pulsing between 20 and 20,000 times a second…. Air that puffs 15 times a second is not intrinsically different from air that pulses 30 times, the the former will never result in a human perception of sound…. [N]erves stimulated by the moving eardrum send electrical signals to a section of the brain, resulting in the cognition of a noise. This experience, then, is inarguably symbiotic. The pulses of air by themselves do not constitute any sort of sound…. [A]n observer, an ear, and a brain are every bit as necessary for the experience of a sound as are the air pulses.
You might be tempted to say that there’s a pattern to the air puffs that are intrinsic, that doesn’t require observation to exist. But that is not so clear if you start digging into the physiology:
…neither electricity nor magnetism have visual properties…. there is nothing inherently visual, nothing bright or colored about that candle flame. Now let these same invisible electromagnetic waves strike a human retina, and if (and only if) the waves each happen to measure between 400 and 700 nanometers in length from crest, then their energy is just right to deliver a stimulus to the 8 million cone-shaped cells in the retina. Each in turn sends an electrical pulse to a neighbor neuron, and up the line this goes, at 250 mph, until it reaches the warm, wet occipital lobe of the brain, in the back of the head. There, a cascading complex of neurons fire from the incoming stimuli, and we subjectively perceive this experience as yellow brightness occurring in a place we have been conditioned to call “the external world.” Other creatures receiving the identical stimulus will experience something altogether different, such as a perception of gray, or even have an entirely dissimilar sensation.
Or none at all, I might add. Lanza goes on….
What about if you touch something? Isn’t it solid? Push on the trunk of the fallen tree and you feel pressure. But this too is a sensation strictly inside your brain and only “projected” to your fingers, whose existence also lies within the mind. Moreover, that sensation of pressure is caused not by any contact with a solid, but by the fact that every atom has negatively charged electrons in its outer shells. As we all know, charges of the same type repel each other, so the bark’s electrons repel yours, and you feel this electrical repulsive force stopping your fingers from penetrating any further. Nothing solid ever meets any other solids when you push on a tree. The atoms in your fingers are each as empty as a vacant football stadium in which a single fly sits on the fifty-yard line. If we needed solids to stop us (rather than energy fields), our fingers could easily penetrate the tree as if we were swiping at fog.
What Lanza is saying, in other words, is that the pattern isn’t somewhere “out there”, rather it exists in our minds. To be sure, the pattern is “symbiotic” requiring both observer and observed. But it’s fair to question how much of the pattern is in our mind verses out there in the world. After all, the pattern of solidity seems pretty discordant with the stadium sized gaps and electrical impulses that physical reality holds.
Probing further still, let’s assume we are talking only about the part of the pattern that is “out there” in the universe, not in our minds. We can ask the question, in the physical manifestation of the pattern, where does it begin and where does it end? What’s not included in the pattern? What can be considered considered external environment or noise?
Let’s take for example a particular rock. That rock is made up of many molecules that have bonded together and remain so for a long period of time. Long enough that we recognize the pattern in nature and say to one another, “rock” without much ambiguity or miscommunication. But it’s also true that the rock was once part of a larger physical structure (a mountain perhaps) in which it would have been difficult, if not impossible, for any observer to distinguish it from its environment. This is problematic because that very same pattern of molecules that we called “rock” existed when the rock was part of the mountain. And at some time in the future our rock will erode and break apart to the point where we all would agree that it’s no longer a rock, let alone our same rock.
It’s a natural human tendency to want to believe that there is something fishy about observer-dependence. We all have this intuitive sense that whether or not there is an observer to notice the rock cleave from the mountain and persist for some millions of years before becoming so much sand, that the rock does indeed exist. That the pattern is real, independent of an observer.
If this intuitive sense is true, then I propose the reason it is true is because of the odd property of patterns that they can be self-sustaining. In the case of the whirlpool, the matter and energy flow in a spiral, which has a large cyclical component to it. This cycle divides the world between the matter/energy that is part of the pattern and that which is outside the pattern.
If you are not convinced then try to come up with a pattern that doesn’t have a cycle of some sort. I think you will come to see that the very essence of a pattern is that which is cyclical about it, that which repeats. After all, without repetition, there is no pattern, there’s just randomness. Randomness. Randomness.
The Anthropic Principle suggests that randomness is inherent in the universe, and through randomness (and natural selection) there eventually appear observers (namely us) who notice patterns.
But what if in reality, patterns comes first.
Maybe randomness is the fiction, a model of reality that allows us to get on with things, but isn’t ever purely manifested in the universe.
Maybe everything — even quantum particles and forces — is interconnected in some way, if only indirectly.
And if that were the case, then the way in which everything interconnects might form a pattern.
There are a growing number of scientists who believe this to be the case. That the universe is fundamentally different than we’ve supposed for a while. That rather than being comprised of energy and matter, that the universe at its most basic level is a pattern. And the pattern is… Life.
In this biocentric worldview, the universe and everything in it is a living process, a pattern if you will. Even atoms and molecules are manifestations of this pattern. That which was once considered inert and lifeless, according to biocentrism, is in fact just as alive as you and I are. The trick to seeing the pattern is to view what’s going on at a high enough level of abstraction.
The trick is to forget the idea that there is a distinction between that which exists in the universe and that which we observe.
The trick is to start with the idea that all matter and energy is self-creating.
In a subsequent post I will review the evidence put forth by a couple of biocentric thinkers. If you are with me this far, I think you will be persuaded to look for the pattern.
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Eusociality and a blow to kin selection
A new paper hit the internet today. “The Evolution of Eusociality” by Martin Nowak, Corina Tarnita, and E.O. Wilson re-frames an old evolutionary question and strikes a blow in an increasingly heated debate.
Eusociality is when individual organisms act as a collective reproducing unit. The best-known examples are ants and honeybees, but recently discovered examples include certain beetles, shrimp, and mole rats. Typically all reproduction is done by a single queen, and the rest of the colony exists only to support and protect the queen. Eusociality represents the highest degree of social organization found in nature.
The evolutionary origins of eusociality are something of a puzzle. To transition to eusociality, individuals must give up their own reproductive potential to support that of the queen. This is the ultimate sacrifice, as far as evolution is concerned. If evolution favors those who produce the most offspring, how can it select for actually giving up the chance to reproduce?
The classical answer to this question is kin selection: the idea that cooperative acts can occur between close relatives. Dawkins explained this using the concept of “selfish genes” that promote cooperation with others who have the same gene. One proponent, J.B.S. Haldane, famously said he would jump into a river to save two brothers, or eight cousins.
Ants and honeybees, the two oldest-known examples of eusocial animals, have a special genetic structure in which siblings share 3/4 of their genes, as compared to 1/2 in most sexual reproducers. It seemed reasonable that these close genetic relationships made possible such large-scale organization and extreme altruism.
However, as more eusocial species were discovered, including mammals, this association fell apart. There no longer appears to be any significant relationship between eusociality and relatedness of siblings.
Nowak, Tarnita, and Wilson provide a new model which focuses on the competition between reproductive units, which can be individual or collective. But perhaps more importantly, they thoroughly deconstruct the mathematics underlying kin selection theory.
The big debate in evolutionary theory right now is between those who believe all cooperation can be explained by kin selection (in its more mathematical guise of inclusive fitness theory), and those who believe that the more standard natural selection concept has more explanatory power. This debate has become increasingly heated in recent years.
Backed by rigorous mathematics, the authors argue that
Inclusive fitness theory is not a simplification over the standard approach. It is an alternative accounting method, but one that works only in a very limited domain. Whenever inclusive fitness does work, the results are identical to those of the standard approach. Inclusive fitness theory is an unnecessary detour, which does not provide additional insight or information.
The import of this argument might not be apparent to those not immersed in the field, but this paper could be a turning point in how the evolution of cooperation is understood. Social behavior cannot all be reduced to selfish genes. There are in fact many mechanisms allowing cooperation to evolve. Understanding these mechanisms will continue to be a fascinating question in evolutionary theory.
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Is a new mode of evolution emerging?
Evolutionary theorist Susan Blackmore argues in the New York Times (and elsewhere) that a new form of evolution is emerging, based on the replication of digital information.
This would be the third mode of evolution that we humans are aware of. The first is, obviously, the biological evolution of life. Organisms grow according to DNA blueprints, then produce offspring from copies of these blueprints, perhaps with some variations. Competition between variant copies drives the evolution of life as we know it.
The second mode of evolution is cultural. Ideas spread from person to person, and through this process, whole cultures evolve. Richard Dawkins coined the term “meme” for the units of cultural evolution (i.e. the ideas that “replicate” themselves in people’s minds), analagously to genes in biological evolution. Blackmore is a strong proponent of the meme concept, but there is much debate over the utility of this idea in explaining cultural evolution. In any case, it is clear that there are major differences between how biological and cultural evolution work. Understanding and quantifying these differences is a major project for evolutionary theory, and I hope some day to contribute to this effort.
Blackmore calls her proposed third mode of evolution “technological”, but “digital” might be a more precise term. Every day, millions of files (encoded in binary) are copied from one location to another. Some files are even programmed to copy themselves. But copying isn’t always perfect, and sometimes copies differ slightly from the originals. If these variant copies compete for the ability to reproduce, might we witness a whole new form of evolution in which the “organisms” (which Blackmore calls “temes“) are purely digital?
One reason this idea is compelling to me is it follows a pattern of symbolic representations driving changes in the evolutionary process. Biological evolution took off with the advent of DNA/RNA encoding, in which the characteristics of an organism were recorded in an easy-to-copy format. Written language isn’t necessary for cultural evolution, but it sure helps. It is much easier to copy the blueprints for, say, a motorcycle, and build new motorcycles from the copied blueprints, than it is to build a new motorcycle by observing an existing one. Symbolic languages facilitate the copying process which is essential for evolution.
Binary is one of the most powerful symbolic languages ever, with the potential to encode almost anything. Binary is also extremely easy (for computers) to copy. It is therefore quite appealing to think that the copying of binary files could form the basis of a new evolutionary process. The artificial life community has been experimenting with this idea for several decades, and I am far too ignorant to comment on their successes and challenges.
I will say that, so far, I can’t see much evidence of Blackmore’s teme-based evolution happening outside of simulations. The closest parallel seems to be computer viruses, which can copy themselves from computer to computer and sometimes mutate along the way. But these viruses are all designed by humans, and I don’t know of any that have evolved novel functionality on their own. Viral videos and other internet memes also rely on the copying of digital information. But the decision to copy such memes is made by humans, so this falls within the domain of cultural evolution.
Will we, in the future, see pieces of code that replicate themselves across the internet, compete with each other, and evolve toward increasing complexity? And if so, will we be able to harness this process for good? Or will it be a mere nuisance, like weeds or spam-bots? I’m not yet convinced that this will happen, but these are important questions to ask.
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The Universe Is a Giant Computation Engine
This is my hypothesis based on a relatively consistent diet of physics books over the last couple of decades. Now, this hypothesis is by no means original to me (see here). But it is certainly not the dominant view of and most laypersons are probably unaware it even exists.
We’ve had several previous posts on Emergent Fool that have hinted at this possibility (me here, Rafe here, Plektix here). The latest bit of data that reinforces my belief in the Computation Engine Hypothesis is From Eternity to Here, by Sean Carroll. In this book, Carroll tries to explain the arrow of time using the concept of entropy from the perspective of statistical mechanics.
The basic idea behind entropy is to compute the number of physical microstates (e.g., positions of each molecule of oxygen) that correspond to the same physical macrostate (e.g., the physical distribution of those molecules in a jar). If a macrostate has lots of different corresponding microstates, it’s “ordinary” and has high entropy. If a macrostate, has only a few corresponding microstates, it’s “special” and has low entropy. There are a lot more ways to arrange a set of oxygen molecules so they are uniformly distributed than there are to arrange them in the shape of a duck, so the former has high entropy while the latter has low entropy.
High entropy states occur more frequently than low entropy states. So any interaction tends to increase entropy because transitions to more common states are more likely. Thus the arrow of time is a statistical property of dynamic behavior.
But now there’s a problem. One can apply this same type of analysis to the Universe as a whole (or more precisely, our “observable patch” of the Universe). You see, it has rather low entropy compared to its maximum (which we can calculate using concepts from statistical mechanics). There’s all this orderly clumping of matter into galaxies, solar systems, planets, animals, and humans. And that’s just not very likely. Now, you could try invoking the Anthropic Principle: that we wouldn’t be here to observe the Universe unless it were ordered this way. Sorry, but no. It’s actually much more likely that our brains would materialize out of the ether due to random quantum fluctuations (so called “Boltzmann Brains”).
Carroll has a loophole. What if our Universe (and indeed each Universe in the “Metaverse”) spawns new Universes? Then there is no maximum entropy and the configuration of our observable patch becomes much more likely. Here’s how it might happen. Even a Universe at maximum entropy still undergoes fluctuations, definitely of quantum fields and perhaps of spacetime itself. If a quantum fluctuation to a higher vacuum energy occurred at the same time that a bit of spacetime pinched off, you would get what looks like a new universe undergoing a Big Bang. Astronomically unlikely at any given time and place, but almost certain to happen eventually in a given Universe.
Aha! Problem solved. But think of the implications. There’s a huge proliferation of Universes. Now, add in the proliferation of different versions of the Universe from from the Many Worlds Interpretation (MWI) of quantum mechanics. Recall that the MWI explains apparently “spooky” quantum behavior by suggesting that the wavefunction does not actually collapse. Instead, every possible value of the wavefunction is realized in a different blob of amplitude, a process known as decoherence. Effectively, any time a quantum particle interacts with a macro objects, it generates a version of the universe for each possible outcome of that interaction.
So at the quantum level, we’ve got all this branching of the Universe every microsecond. Then at the astrophysics level, we’ve got new Universes spawning. Of course, this spawning also obeys the MWI, so you’ve actually got an exponential proliferation of baby Universes. If you squint, this whole process looks like a multi-dimensional forward-chaining computation. Every possibility in this Universe is realized, whole new Universes with slightly different rules get created, and every possibility in them is realized.
Going back to the concept of entropy, it turns out that the Thermodynamic Entropy we can calculate for objects is exactly the same as the Shannon Entropy we can calculate for information. Shannon Entropy measures how unique a piece of information is. Think of it in terms of compression. You can’t compress a file any smaller than its Shannon Entropy will allow. Structured files have low entropy and by encoding their structure, you can compress them more. A random string of bits in a file has maximum entropy, so you can’t compress it at all. Shannon Entropy is a measure of how potentially useful information is. Just like Thermodynamic Entropy is a measure of potential energy.
So there’s already a known equivalence between the physical and informational. Then if you buy into Carroll’s hypothesis and the MWI, it looks like the Metaverse is trying to compute every possible outcome. In fact, it may compute every possible outcome more than once. An infinite number of times if it runs an infinite amount of time. After it runs long enough, someone who could observe the whole Metaverse could actually calculate very precise odds of any outcome given any condition. You’d be statistically omnipotent.
Never bet against a statistically omnipotent being.
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The Apprentice, Round 2
I have been overwhelmed by the quality of applicants for the apprenticeship!
In order to give both you and me a better idea of the fit (which is just as important as your personal skillz) I have five projects for you to complete. To be true to the nature of the job — and so I can learn what I need to about you — I’m not going to provide any further instructions, parameters or clarifications beyond what you see here.
- Apply to attend TEDActive 2011 – if you get accepted and get the job with me, I will pay for you to attend. I will also choose two additional applicants and pay $2000 towards their registration fee. Email your acceptance letter ASAP to the mail link on RafeFurst.com.
- Create a Prezi – my friends at TrustArt.org unearthed the true story of the Statue of Liberty, which is not what the history books tell you. Your task is to create a compelling presentation about how the Statue of Liberty came to be using Prezi.com then email it to liberty@trustart.org.
- Kickstart something good – Create a campaign on kickstarter.com for something you care about. Set the funding deadline to Sept 15. Post a link to your kickstarter in the comments below.
- Write for my blog – Create a post for EmergentFool.com and submit it in the comments below.
- The meta-project – Come up with a project that you believe will best showcase the abilities of the perfect candidate for this apprentice position. Post that project description in the comments below. The one with the most “Likes” will be assigned to all applicants (including you) on Sept 16.
One final thought before you begin: if you look at the above list and are not excited by each of the projects, the apprenticeship is not a good fit for you. However, if you complete any of them, I will add you to the beta community of Possibility Accelerators that my apprentice will help me curate….
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The Apprentice
I’m looking for a budding superstar to be my apprentice. Someone who is eager to change the world but doesn’t exactly know how they are going to do it. At first you will be my shadow, learning everything I know. As soon as possible, you will take on the role of “COO” managing all of my projects. Once you’ve proven capable, if you are passionate about one project, I’ll fund you to take it supernova, but not before you find your replacement.
Here’s the vision. I’ve backed off of the communist stance, and I will pay you a salary. But it will only be enough for you to live modestly for the time being. The real value is in (a) being passionate about what you are doing with your life and (b) the potential to make a lot of money down the road.
If interested, write one paragraph in the comments below to convince me you are right for the job. Instead of references, get your friends to hit the Like button on your comment (gaming this will disqualify you).
Referral Bonus
Anyone who recommends an Apprentice that I hire and makes it past three months will receive $10k in funds to (a) have me donate to the 501.c.3 of their choice or (b) invest in the for-profit social enterprise of their choice under the Presumed Abundance model.
An Apprentice who is self-referring is eligible for the bonus themselves.
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Update on Game-Based High School
I wrote a while back on a high school that uses games as its primary pedagogical tool. NPR’s All Things Considered has a new report on the school. Excerpt:
“In math, we’re traveling around the world,” says sixth-grader Rocco Rose, a student at Quest to Learn and a citizen of Creepytown — an imaginary city where his class learns math and English. The students play travel agents, convert currencies, keep blogs about their travel experiences and budget trips.
Creepytown is structured like a video game that has jumped out of the computer. During their 10-week “missions,” students learn to adapt and improvise.
“The second trimester, Creepytown went broke,” Salen says. “They had … an economic crisis. So the kids worked to figure out … what had gone wrong. And then they proposed the design of a theme park to bring revenue in.”
Systems Thinking
Salen says playing with complex dynamic systems gives kids opportunities to learn.
Students “learn how to solve problems, how to communicate, how to use data, how to begin to predict things that might be coming down the line,” she says.
They also learn something called systems thinking, which Salen says is one of the cornerstones of 21st century literacy. It helps you understand how the behavior of a derivatives trader in Hong Kong affects housing prices in Florida. When a system becomes sufficiently complex, Salen says, you start to get outcomes that are hard to foresee.
“Suddenly you begin to get what’s called emergent behavior, and in emergent behavior, that system, the elements in it, begin to relate to one another in ways that can be unpredictable,” she says.
Hell yeah! If we can give the next generation early experience with complex systems and unintended consequences, there may be hope for the future yet.
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Yes, You Can Save the World with Startups
Dave Lambert pointed me to this new Kauffman Foundation paper by Tim Kane about job creation in the US. Then Will Ambrosini pointed to this Growthology post which reproduces the money diagram from page 5 :
Look carefully. Then think about this statement about US job creation:
The only firms that create jobs on average are brand new ones.
So yes, you can save the world with startups.
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Metaphysics
What fundamental truths exist in the universe?
This question, perhaps above all, is the basis for scientific inquiry. Yet we rarely ask it in this way and we rarely step back to the very basic assumptions we hold about the possible form of answer we might expect. For instance, is matter fundamental? Meaning, if we could not talk about particles and mass, could we understand the universe as well (or better) than we currently do?
Einstein showed that there is an equivalence between matter and energy (E=mc^2), but what does that really mean? Personally, I’m kinda stumped when it comes to understanding energy, and I suspect that many other people are too if they think about it. Then there’s that pesky c^2 part of the equation, which seems even more nebulous. Physics 101 tells us that c is the velocity at which light (a form of energy) travels, and that any velocity squared is acceleration. Also we learn that velocity is distance over time. But now we have even more to ponder because distance is a measure of something called space — anyone got a good definition of that? And time, well, you don’t have to be Einstein to know how relative that can be.
The point I’m making is that we make certain assumptions out of necessity regarding what we accept as fundamental, and then we rarely revisit those assumptions, even when faced with difficulty reconciling observed phenomena with these assumptions. Case in point: what exactly is dark energy and dark matter? The answer is, nobody knows. These are concepts that were made up to allow equations to balance; the “dark” refers in some sense to the fact that we really have no clue. The good news is that, thanks to Einstein, once we figure out what one of them is, we’ll know what the other is :-)
The most difficult part of challenging our assumptions is knowing that we are making assumptions in the first place. Certain assumptions are so ingrained in the culture that they have become metaphysical. That is, they transcend the realm of something we can inquire about and are unintentionally exalted as part of the very nature of reality. In the Western scientific tradition, matter — that is to say material stuff — has been the primary metaphysical assumption since The Enlightenment. But this hasn’t always been so, even in the West; go back and read Plato and Aristotle if you don’t believe me. Furthermore, even today there are thriving societies, economic powerhouses, which have a different relationship to matter and don’t necessarily view it as fundamental.
Until recently physicists scoffed at anyone who deigned to suggest that there was anything in the universe except matter and energy. Space and time were not really thought of as existing “in” the universe so much as defining the boundaries of it. In other words, there is no universally accepted equation that transforms matter into time, and no agreement on how many dimensions (spatial or otherwise) there ultimately are. But I’d like to point out a metaphysical shift that has occurred in the last 30 years or so, coinciding — not so coincidentally I suspect — with the rise of computer technology. And that is the notion that information is fundamental. That the universe is a giant computer and that all the matter and energy we see around us is somehow an artifact of this universal computation.
Now I bring this up not because I believe an informational metaphysics is superior or more true than a material one, but rather to illustrate the cultural relativity of what you might perceive to be self-evident. Because if you received a university education in the U.S. it is very likely that you were not taught to believe information is somehow more fundamental than, say, a photon or an electromagnetic wave. It’s easy for most of us “educated” types to see how information can arise out of matter/energy, but not the other way around.
In the end, whatever metaphysics you adhere to defines the rules of rational inquiry you follow when seeking higher truth. The physical sciences don’t just depend on materialism, they derive from it. Without the notion of a fundamental particle, there would have been no chemistry, no physics. And as any philosopher of science will attest, these two sciences have indelibly shaped inquiry in all of the biological and social sciences as well.
The ultimate validity of any metaphysics — and any scientific models derived from it — is determined by how well it serves us in understanding the world around us, predicting the future we will find ourselves in, and also creating the world we want to find ourselves in. So it is worth examining whether our metaphysical assumptions are serving those purposes from time to time, especially when we find ourselves faced with existential challenges, as we seem to be with greater frequency these days.
Of course the difficulty with metaphysical mind-shift is that our very minds have been shaped by the metaphysics of our culture. It’s not so easy to try alternative metaphysics on like they were a new pair of jeans. If I asked you to accept, for instance, that information was fundamental, and then asked you to derive matter and energy from it, would you know even where to start? Or perhaps information alone isn’t enough, maybe you need to add consciousness. Or maybe the breakthroughs we are looking for will come if we demote the physical and begin with life-itself, as my friends at the Autognomics Institute have done.
One night, not too long ago, I woke up and the following diagram popped into my head:
While I don’t really understand it all myself, I’m trying it on as my metaphysics for 2010 and seeing what happens. A couple of things that are implied by the relationships I will point out.
One is that it is composed of single-color triangles, any one of which can be used as a metaphysics by itself (I guess that makes the whole diagram a metametaphysics, but that’s a separate story). My theory is that, in Pythagorean fashion, if you start with any two vertices you can derive the third. For example, on the purple triangle, if you know enough about the nature of space and energy, you can derive what time is.
Another implication is that concepts close to one another on the diagram are close semantically (e.g. time and event). A corollary of this proximity is that opposing vertices are dualisms, like matter and energy, or dynamic and state.
I realize that thinking in these terms is awkward, but I’m trying to suspend my linguistic and logical predispositions just enough to grok the physical consequences of these metaphysical axioms. I’ll let you know if anything comes of it, and hopefully you will tell me if you have any metaphysical insights of your own.
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The Currency Crisis Is In Your Head
What in the world do I mean by that? Of course I think the dollar and euro are broken, but what’s the alternative? Gold? Maybe, but it won’t last. Tyler Cowen partially touches on information classification in his book, but does he make the link to currencies in their traditional sense? Before I get to why the crisis is in your head, make sure you get what a “currency” really is:
“We are in the lazy habit of thinking that the flow of the currency in a transaction is the one that matters instead of the actual flow of goods, services, resources, knowledge or participation which flows COUNTER to an exchange currency.
Those real-world currents shaped and enabled by currencies are what make them so valuable and powerful.
The currency itself is actually just a flow of information. But there are two reasons we myopically focus our attention on the currency flow as if it is the one that matters:
1) We are big-brained, symbol-using creatures, and it’s much simpler to us to deal with those nice clean symbols than the actual sloppy flows — dollars are easier to account for than time, random quantities of random things and other stuff which is difficult to count (such as the state of relationships), and
2) The REAL flow is an event which happens in a moment and is gone. If you were NOT there to witness the service being performed, the good being exchanged, or the participation of that person, then once that moment has passed, the only consistent way we have of knowing what occurred is the record we keep of the event. We use currencies to keep records of currents.
I believe this is the single MOST CRITICAL CONCEPT for currency practitioners to grasp. It allows us to break out of bad habits of thinking about currencies in very outdated ways (such as believing they have or should have intrinsic value because precious metals were once used as coins). This allows us to see currencies for what they truly are: formal systems which shape, enable and measure currents which allow communities to interact with those currents.
Let me paint a more concrete picture. Imagine being out for a walk in the snow, and you see a set of small animal tracks where it bounded out from under a hedge and crossed a field toward another shrub. Then you see them end in a sudden deep indentation, with some wingtip marks on extending out from either side. These tracks tell a story — a flow of resources and relationships that took place in that field.
Of course, the story itself has passed. All we have left are the tracks. But the tracks can tell quite a lot to the right set of eyes: what types of animals were involved, how long ago it happened, which direction the bird flew off, etc. This is the role that currencies play in our economy. Actual currents of resources and relationships occurred and currencies are the tracks they left behind. The tracks are very informative to the right eyes, so we use them to make business and policy decisions.”
Source: http://newcurrencyfrontiers.com/wagn/Introduction_to_Currencies
The centralized currency of our governments is undergoing a crisis. But that’s simply evidence that the short-sighted hierarchical system of power in our politics and positively reinforcing system of capitalism have become obsolete in light of our electronic technologies. Recommendation and trust network systems unbiased by the speed of light and low cost of competition are exposing the idiocy of godlike decision makers. Do you really think the emperor cannot be naked in this Day and Age?
The point is NOT that these new systems of aggregative expertise and opinion are and will continue to be “better”. Rather we are on the frontier of extracting patterns from behaviors we haven’t been able to capture before, due to the inferior biological, mechanical, and electronic technologies of the past. The new systems will also become obsolete, and definitely at a faster rate. But do you think we have a say? Unless you think we can slow down or stop the pace of quantification of behavior when everything is becoming electronic?
What do you think about the last slide – Wealth a Living Systems Model? OR do you want to wait a few months or years until “60 Minutes” tells you what to think? That IS the question. Find sources of expertise YOU TRUST and trust yourself to move on when YOU judge them to be obsolete.
That’s precisely what MLK meant by this:
“Power properly understood is nothing but the ability to achieve purpose. It is the strength required to bring about social, political and economic change. … What is needed is a realization that power without love is reckless and abusive, and love without power is sentimental and anemic. Power at its best is love implementing the demands of justice, and justice at its best is power correcting everything that stands against love.”
Now don’t make me quote Eminem or Buddha. :) Once you really understand that this means that the power is within you, you will be able to kill goats with a stare.
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Complex Adaptive Systems: Resilience, Robustness, and Evolvability
Annual 2010 AAAI CAS Symposium: November 11-13, Arlington, VA, USA
Covering Natural, Physical, and Social sciences, the symposium should be a good opportunity for interdisciplinary learning and sharing of ideas.
One week left to submit a paper.
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Quantum Reality and the Measurement Paradox
I may be primarily an evolutionary theorist nowadays, but I have many interests, and this summer is proving to be a good time to explore some areas not directly connected to my need to publish. Lately I’ve been doing some reading on quantum mechanics, and what it tells us about reality.
QM is astonishing in both its mathematical elegance and its fundamental counter-intuitiveness. Unfortunately, I think many (including mathematicians) are discouraged from learning about quantum because it is typically presented assuming a deep knowledge of classical mechanics. But in my view, QM isn’t just a theory about physics. It’s a theory about reality and truth, and many of its implications can be understood with no knowledge of physics at all.
The essential feature of quantum reality, and what makes it different from the way we naturally think, is the superposition principle. It says that if A and B are two possible states of something (a photon, a cat, the whole world…), these states can be added to get another possible state, A+B. For example, if a light switch can exist in ON and OFF positions, there must also be a possible state ON+OFF. Subtraction works too: the state ON-OFF must is a valid state as well. To my mathematician friends: we are moving from the set of possibilities {ON, OFF} to the two-dimensional vector space generated by the basis vectors ON and OFF.
It’s important to delineate what is not happening here. ON+OFF does not mean that the switch is stuck somewhere between on and off. It also does not mean that it might be either on or off and we just don’t know which. ON+OFF is a fully-determined state which is neither ON nor OFF, but a superposition of the two.
Of course, no one has ever observed a light switch being ON+OFF. Something happens when we observe these superimposed states, such that we can only ever see the “classical” states ON or OFF.
In the standard (a.k.a. Copenhagen) interpretation of quantum mechanics, when a superimposed state is observed, it “collapses” into one of the classically observable states. In the case of ON+OFF, whenever we look at the switch, it collapses into either an ON or and OFF state, with equal probability. But until we look at it, in remains in the state ON+OFF, which has unique properties making it distinct from either the ON or OFF state.
This interpretation poses a host of logical difficulties. What exactly constitutes an “observation”, and how would a light switch “know” that it is being observed and should therefore jump into an observable state? Many of the best minds in physics believe that observation has something to do with consciousness, but this raises several obvious questions: How is consciousness is defined? What gives it this unique power to induce jumps in physical states?
I’ve recently come across a new interpretation, proposed in 1997 by Cerf and Adami. They suggest that superimposed states do not collapse when observed, but rather the observer becomes entangled with the observed, forming a larger superimposed state.
To illustrate this, let’s turn to Schrodinger’s cat paradox. An atom is prepared in a superposition of two states: one in which the atom will emit a photon and one in which it won’t. This atom is placed in a box with a cat and an apparatus which will release poisonous gas if the photon is emitted (the details of the setup are unimportant). According to the Copenhagen interpretation, the system exists in the superimposed state
(EMIT and DEAD_CAT)+(NOT_EMIT and ALIVE_CAT)
until such point as the box is opened by a conscious observer, whereupon the system “collapses” and the cat becomes either just alive or just dead. (This raises some questions of whether cats count as conscious, but such objections only deepen the underlying paradox).
In the Cerf and Adami interpretation, there is no collapse, only entanglement. When we observe the contents of the box, we ourselves become entangled with this system. We become part of the resulting superimposed state:
(EMIT and DEAD_CAT and WE_SEE_DEAD_CAT)
+ (NOT_EMIT and ALIVE_CAT and WE_SEE_ALIVE_CAT)
Of course, we still only see the cat as being either dead or alive, not both. But according to Cerf and Adami, this is only because the state EMIT+NOT_EMIT of the atom is unobservable to us. Of the full superimposed state, we can only see the parts pertaining to the cat and to the observer. Observing only part of the system, it appears to us that the cat is either alive or dead. Anyone else observing the cat would see it to be in the same state that we do, but this is only because the second observer is just as entangled as we are. The cat is still superimposed between alive and dead, and if we could see the whole system, we’d realize that we ourselves are superimposed between seeing it alive and seeing it dead.
From a mathematical point of view, Cerf and Adami’s proposal neatly resolves the paradox of observation and state collapse. However, it raises far more troubling questions of its own, which the authors do not begin to explore.
Think of a decision you made today. It’s not unreasonable to think that there are quantum processes in our brain whose outcomes affect our decisions (this view is advanced by my friend Bob Doyle). Let’s say that there was a certain quantum state in your brain whose collapse into one of two states (in the Copenhagen interpretation) tilted your decision one way or the other.
If this is true, then in Cerf and Adami’s interpretation, we actually exist in a superposition of realities: one in which your decision went one way and one in which it went the other. You can only see one of these realities, and everyone you’ve encountered since has become entangled with you and therefore sees the same reality that you do. But the alternate reality is playing itself out, Sliding Doors-style, superimposed on top of our own.
Furthermore, due to quantum interference, any actions taken in this reality can affect any of the superimposed other realities. And conversely, anything your alternate-reality twin does in his or her reality can affect the reality you and I see.
I tend to believe Cerf and Adami’s idea, because millenia of physics research have shown us that the mathematically elegant solution is usually the right one. But this means our universe is weirder than we can possibly imagine.
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The Long Tail of Innovation in an Information Economy
Once an innovation ecosystem has successfully created the structure to consistently tackle the big billion dollar opportunities, the system possesses the energy to begin evolving greater complexity, and squeezing out more efficiency. This efficiency will be realized by evolving the ecosystem to support the long tail of opportunities, which is exactly what the startup ecosystem is doing now.
Information age innovation ecosystems start by optimizing to hit the home run and can tolerate extremely high failure rates because the opportunity is so big. This is where venture money operates. Then the industry matures and seed and angel capital becomes more prevalent to support hitting multimillion dollar markets with greater consistency. I suspect for each industry an 80/20 power law applies: 80% of the wealth will be captured by a small number of billion dollar companies a la Google, Microsoft, Apple, Oracle, Facebook etc. but 80% of the opportunities (and 20% of the wealth) exist down the long tail. Just past the head of the power law exists collaboration products like Basecamp and publishing platforms Wordpress. Further down the long tail exist products like Etherpad and Disqus. Farther still are where all the little tools and widgets that help us do day-to-day tasks just a little bit better, such as the popular iPhone App Shazam. In Wordpress’ case it was a critical last piece to opening up blogging and self-publishing, which has transformed the way society shares information. Having a computer, the Internet, a rich web browser, and a really smart but not technically savvy person was not enough. We needed a simple publishing platform to crack the nut for self publishing.
So we need to create the proper surrounding ecology to make sure the long tail of innovation thrives. Without the right mix of capital, community, information and tools tuned for operating at this stage, the system will not come close to realizing all the opportunities that exist down the far end of the tail.
The incentives are strong for the individual, because even playing down the far end of the long tail is very lucrative and rewarding compared to an entry level corporate job, because you are working on something that matters, have passionate users and the potential to make millions. But the opportunity cost may be very high for those who are choosing between attempting to tackle billion dollar opportunities and opportunities down the long tail. So there are challenges in getting the top talent to tackle niche problems.
But I think it’s very important we figure how to tackle these problems as it’s a flourishing long tail of innovation that will both streamline and build resiliency into our systems. The long tail of products fill the many potholes that will enable us to run smoothly.
For example, I have a few friends who have a number of great ideas about how to solve email for in demand people who are always overloaded. This is a very important problem, that if solved could unlock a lot of productivity for society, because the influential can now more effectively communicate, delegate and make things happen. But the market is simply not that big. So how do talented entrepreneurs justify working on important niche projects vs. bigger problems they could sink their teeth into?
I’m not sure. Maybe they can treat it as a side project and let someone else do the scaling. Maybe as the entrepreneurial ecosystem gets more efficient entrepreneurs who were failing before, or people who weren’t even entrepreneurs before are now able to solve these problems.
Regardless of what the solution ends up being, it’s important we end up getting the incentives right so somebody is attacking these multimillion (but not billion) dollar niche opportunities. I think this last 20% may end up being what enables humanity’s solutions to finally outpace our problems. Achieving this escape velocity will be the difference in allowing the next stage of humanity to unfold.
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Why Maximizing the Efficiency of the Startup Ecosystem Is Essential for Society’s Transition to an Information Economy
Right now the tech industry is the most innovative industry on the planet. Its success is in large part due to it being the first information age innovation ecosystem, which has implications for the future of the world, as we transition to an information economy.
This emerging information age is just the latest epochal shift of organized society as we have already progressed through tribal, agrarian and industrial societies. Now, on the cusp of this transition, the tech industry is pioneering the movement forward. (For more on this transition see Arthur Brock’s fantastic Prezi). Many of the methodologies and organizational structures that enhance a startup’s success will be relevant not just for startups but universal to the entire information age, because much of what works for tech startups will apply to emerging industries in the information economy. This is because innovation ecosystems that operate in the information age, are likely to operate in very similar ways, regardless of sector. The tech industry is just the first to inhabit space in the information economy and therefore is a harbinger for future industries.
The startup ecosystem is now creating the blueprint for the future of the information economy because much of the startup ecosystem replicated across emerging industries in sectors diverse as social change, health, biotech, molecular manufacturing and government, as soon as these industries begin their transition into the information age. Since the startup ecosystem will be replicated, it’s important to begin focusing on maximizing the efficiency now, both to increase the output of startups today, and to figure out how a more complete system works, so that we transfer a more stable, well-understood system. And since efficiencies realized in the tech ecosystem now will cascade over into the emerging industries, every further increase in efficiency will be amplified enormously and echo for generations, as it affects not just current startups, but all future copies of information age innovation ecosystems. The cheapest and therefore by definition, best place to experiment with improving innovation ecosystems for the entire information age is right here, right now in startup world. We must attempt to make our information age innovation ecosystems as robust as possible, because they represent the foundation of the future of the world’s economy.
If companies in these emerging industries want to maximize the scalability and impact of their solutions they will not only to need imitate the structure of the startup ecosystem but they will also need to draw heavily on the rules of the information economy that startups have begun to uncover. Their war chest will need to include tools and methods such as: social networks, crowdsourcing, the cloud, virtual collaboration, lean methodology, metrics and conversion funnels, customer development, rapid iteration, handling uncertainty and many other ideas now fundamental to a startup’s success.
You can already see some early signals of people and organizations in other sectors achieving great success by cross fertilizing principles and methdology from the startup world.
The Obama campaign changed political campaigns forever with their revolutionary level of citizen engagement. This was achieved by drawing heavily on Silicon Valley credo, with the campaign spearheaded by Chris Hughes, one of the co-founders of Facebook.
Kiva brought microfinance to the masses and has raised and distributed in an unprecedented amount of money by bridging the social sector with the operating principles of a Silicon Valley startup.
Government 2.0 is essentially an experiment asking the question, what happens if we mix Sillicon Valley with Government on a larger scale than campaigns, and use the power of data, transparency and API’s to increase the effectiveness of government? Health Care, Biotech and a slew of other industries are asking similar questions.
Hello Health is attempting to turn one aspect of the health care industry upside down by cutting insurance companies out of the doctor-patient relationship, simply by applying a few Silicon Valley startup principles to health care.
The tech industry has already changed the world, but as new industries adopt similar organizational principles society will experience multiplicative networks effects that will be utterly mind blowing. When people talk about accelerating change and the singularity and you don’t know what to expect, this it: when Silicon Valley leaves the valley and sweeps across the other industries of the world and transforms them into information age innovation ecosystems.
Information Age Innovation EcosystemsIf you look at the startup ecosystem’s output compared to all other industries over the last 30 years, you might dismiss it as an anomaly that will fade with time. But the industry’s incredibly fast wealth creation is not hype that will peter out, it’s a sign of what’s to come. The tech industry is just is the first of many information age innovation ecosystems, that will also be able to create a flurry of progress at an exponential rate.
The first requirement for an innovation ecosystem is that the core practice of an industry becomes an information technology. This is key because its information technology’s inherent scalability and replicability that enables exponential progress. The second critical requirement for an industry to become an innovation ecosystems is a large number of people freely experimenting. In the startup ecosystem, this was triggered by the personal computer and the mass amateurization of computing it allowed. When using a computer was incredibly complex and expensive the industry had a huge bottle neck. When that barrier was broken down and costs fell far enough that anyone could experiment in their bedroom or garage, the creativity of the masses was unleashed and amazing breakthroughs began to happen. That was the birth of the startup ecosystem.
I believe the birth of future innovation ecosystems, will occur the moment information technology can be used in the garage. Currently the startup ecosystem is the only scalable garage industry around, but imagine the creativity that will be unleashed as the costs fall far enough to allow other industries to enter garage territory. As soon as the garage threshold for biotech is crossed, an ecosystem similar to the startup ecosystem will begin to emerge. There will be firms dedicated to investing capital at various stages of the lifecycle of the company, communities of practice will emerge, formal conferences and informal meetups will spring up everywhere, databases of knowledge will be abundant, and open source infrastructure will be created that gives people even more leverage. Imagine people playing with atoms just as easily as they play with bits. Imagine biotech companies being born out of bedrooms and garages. The moment biotech becomes a fully functioning garage industry, with an efficient supporting ecosystem, the world is in for a crazy ride.
Again, this evolutionary process will apply not just to biotech but every industry with potential in an information economy. The 4 pillars of any innovation ecosystem I believe are Capital, Community, Information, and Tools.
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Maximizing the Efficiency of the Startup Ecosystem
Over the last 3 decades the technology entrepreneurship sector has been the primary sector driving economic growth. The sector initiated the information economy and has given life to thousands of innovative companies, four of which are ten of the biggest companies in the United States, including Google, Apple, Microsoft and IBM. By any metric the sector has been wildly successful, but it’s possible to make the ecosystem even more efficient and realize an even greater number of opportunities.
Currently, projects that succeed are squeezing through a very tight bottle neck, and only the right combination of personality, skill and luck can breakthrough. Better infrastructure widens that bottle neck, so potential impact can be realized at a greater rate.
This post will look at how we can increase efficiency in the entrepreneurship ecosystem, but the significance of this effort may extend beyond the tech sector to the future of the information economy. My post on that topic is here.
Systems Perspective on EntrepreneurshipTo increase efficiency further requires looking at the entrepreneurship ecosystem as a system in order to find the holes and the greatest points of leverage. But before we focus on improving the efficiency of system we need to understand the difference between the primary and secondary causes that drive innovation. There is nearly unanimous agreement that the most important players in the startup ecosystem are the founders and CEO’s who start from nothing and go on to create and control billion dollar markets. Society can’t stop glorifying entrepreneurs like Steve Jobs, Bill Gates and Richard Branson. But while they deserve immense praise and adulation the impact they are able to have has more to do with the surrounding innovation ecosystem than their individual ability and vision.
Something new and innovative can only be created and scaled if a confluence of forces come together— market, team, systems that allow you to find your team, capital, advice, cost of production, cost of distribution and culture (whether people are ready for it) etc. Breakthroughs are the result of way more than an individual with insight; they emerge from the last iteration of the system, building on top of existing tools and a huge history of knowledge.
Products and companies do make a huge impact but their success has more to do with the state and incentives of the system than the entrepreneur. It’s not the company that changes the world, it’s the system that creates the right incentives to make the creation of world changing breakthroughs extremely probable.
As the startup ecosystem has been fine tuned it has made the existence of certain products and companies almost inevitable, because the system exerts so much pressure to make opportunities come about once the timing is right. In Apple’s and Microsoft’s case there was immense pressure exerted on bringing about computer hardware and software companies.
If you are looking to bet on an individual company than a great entrepreneur is certainly the centerpiece. But if you want to maximize the innovation of a certain sector then you must look at the system, in this case the startup ecosystem. From the system perspective you just want a need to be filled and you don’t care who fills it. Thus the individual matters less, because there’s enough talent out there that if the incentives are strong enough someone will capitalize. But taking the system perspective far from trivializes the entrepreneur. In fact, talent development, which must have a very humanistic lens to be effective, is a critical part of an efficient startup ecosystem because potential talent needs to be actualized at a high rate.
The startup ecosystem is already extremely effective, just look around at the mark it has already left on the world in only a few decades, but if we want to make the system even more efficient and increase both the quality and quantity of innovative breakthroughs and great companies, we must identify places where friction is reducing the possibility of successful ventures and create solutions to remove this friction.
How The Startup Ecosystem Works NowIf we look at the rise of 3 of Silicon Valley’s fastest growing companies: Google, Facebook, and Twitter, in the context of the startup ecosystem it’s possible to see the existence of a category leader as almost inevitable, and the eventual winner as extremely unlikely. This matters because as long as someone can seize the opportunity and fill the market need, the battle from a macroeconomic perspective has already been won, it’s just a matter of which individual player will earn the spoils and how long they can maintain relevance before a new competitor overtakes them or the market becomes mature and commoditized.
Google, Facebook and Twitter now each dominate a category: Search, Social Networks and Microblogging, but there was plenty of competition and uncertainty at one point (remember Altavista, Yahoo, Myspace and Friendster? With better execution these companies could have also won). Once these categories were identified either consciously or by accident, the startup ecosystem was effective enough to support the formation of many teams, supplying them with capital, services, people, and advisors in hopes of capitalizing on a billion dollar market opportunity. And the team that executed the best won. The individual winner was unpredictable but the system was good enough to make sure someone won. That humans’ evolved a system that can create many competitors and then naturally select the winner based on merit or “fitness” is a tremendous accomplishment for this industry and for the world, and it is what makes tech the most innovative industry on the planet.
The success of these companies had a lot more to do the size of the market, the timing for when it was ready to be capitalized on and the resources in the startup ecosystem that supported effective scaling than the founders or the idea. The markets they operated in were big enough that inevitably an industry giant would emerge who would be able to use the lucrativeness of the market to generate a runaway positive feedback loop up until saturation, using their momentum to continually take market share and capture the best talent.
This evolutionary competitive process continues even after an industry giant has saturated a market, because there are always new markets emerging. While it would be very hard for Facebook and Google to screw up and concede supremacy in their primary markets, it is probable a new company will beat them in the new markets that they try to extend to that fall outside of their core competencies. (For more on why the market is the most important factor for a startup’s success check out this Marc Andressen’s post).
In summary, once the timing and conditions are ripe there will be enough people trying to tackle the clear billion dollar markets that somebody will get the execution right. The startup ecosystem is that good at providing all the puzzle pieces to make sure this happens!
Future billion dollar companies will ride trends such as the move to the cloud, mobile information, the real-time web, extreme personalization, and new kinds of data enabled by smaller and cheaper sensors.
The Evolving Startup EcosystemThe tech industry has cracked the nut for how to tackle billion dollar market opportunities, making it better than any other industry at capitalizing on opportunity. But there is still a lot of room for growth. Increasing the effectiveness of the startup ecosystem matters as long as their are markets to be filled. The faster we can fill unmet with greater effectiveness the better off the world will be.
The tech ecosystem is now well tuned to hit the home run in billion dollar markets, but there is a shift happening as people began to realize there’s more opportunity and less risk to be had in aiming for singles and doubles, and hitting them consistently. The home runs of the previous era have created a new playing field and there is now a wealth of opportunities on the long tail with all kinds of business and consumer needs waiting to be met.
As the information economy has developed and become more complex, an increasing number of lucrative niches now make market sense to pursue. Whereas previously the opportunities either weren’t there (you couldn’t have a million dollar facebook app before facebook) or the costs were too high to have certain opportunities make sense (startups needed venture capitalists and VC’s only wanted to play in markets bigger than 100 million). But in recent years startups have become disentangled from their dependence on VC’s as the costs of starting a startup have continued to fall due to cheaper hardware and services moving to the cloud. This is driving a growing seed stage ecology where the primary actors are startup accelerators, angel investors and seed stage venture capitalists.
The focus now is on startups attacking smaller opportunities (though still in the 10’s of millions) with less investment capital. There will be an abundance of lucrative, unserved niches for startups to tackle. This coincides well with a number of trends:
- Science will be injected into the art of running a startup
Structure and methodology will be experimented with to increase the success rate of startups and startups will fail less because of self-destruction and more because of getting beat by competitors. As the overall number of startups in the ecosystem increase over the next few years, many of the startups in big markets will fail due to competitors, but in the huge number of opportunities in smaller markets startups will be more dispersed and there will be few direct competitors. In these markets startups that use a more scientific approach should be able to figure out how to hit the 10-100 million dollar markets with great consistency.
This consistency will enable the funding ecosystem to make sense for these smaller opportunities. When many investments are made in these smaller markets (<500 Million) the venture community’s approach of haphazardly throwing money at many petri dishes won’t work, because the upside potential is capped. Home run hitters can afford to strike out a lot, singles hitters can’t.
Even startups that lose to competitors in niche markets will find it easier to pivot, than pack it in and start from scratch, because the farther down the long tail you go the closer the adjacent verticals.
A fractal tree is a good analogy for why it’s easier to pivot in <100 million niches vs. billion dollar markets, if you consider the thickness of the branch equal to the size of the market. The core branches are very far apart. If your startup is set up to tackle a billion dollar opportunity it’s hard to pivot all the way over to another one, or shift gears and attack a smaller opportunity. But if you follow the analogy and you’re a startup attacking a niche as the tree branches further away from the trunk the twigs become closer together. The larger branches are too far away from each other to pivot from one to the other, but the small branches just require a little back tracking and a slight change in direction.
Creating a startup where the goal is to make something people want will still be a chaotic, iterative process but it’s possible to induce predictability and stability into chaotic systems.
- The potential for more collaboration horizontally and vertically across markets to create a more seamless experiences for the customer and more leverage for the startup. (I’ve started exploring this process, naming it the lego model)
- An increased demand for entrepreneurs due to clear ~10 million dollar opportunities just waiting to be tackled. This demand in the ecosystem for entrepreneurs coincides perfectly with changing cultural values about work, which will drive huge increase in the number of people pursuing entrepreneurship. And that in combination with a more entrepreneur friendly ecosystem evolving, will unleash a new golden age of entrepreneurship.
Here are two good posts on the changing seed stage ecology: Dave Mcclure’s presentation on the evolution of the startup ecosystem and Nathaniel Whittemore’s take on the seed stage ecology in the social sector..
New Efficiencies in the Startup EcosystemThe startup ecosystem is certainly past its infancy, but it is still evolving rapidly and there are many more efficiencies to be unlocked that increase the success of startups further and support the long tail of innovation. Here’s my opinion on where we have opportunities to improve the system:
- Talent development
- Better conversion rate of people with ideas for companies, to entrepreneurs actually starting companies
- Pushing world’s brightest to choose entrepreneurship over other industries (college students starting companies instead of becoming an investment banker. Creating incentives for experienced execs to take risks starting something new instead of languishing in the rungs of the corporate ladder)
- Reducing friction in team formation, and better “deal flow” by interacting with more potential co-founders
- Aggregating startup services and service providers in order to remove distractions and allow startup teams to focus fully on the new innovation they’re trying to create
- Networks becoming more efficient in sharing assets (knowledge, people, code, strategy)
- More fluid and less cumbersome funding rounds, all the way from idea to scalable profitability
- Collaboration amongst startups to attack new verticals and interlink their advances to create networked impact— where success exists behind an activation energy only realizable by coordinated efforts of multiple startups
- Connecting entrepreneurs to the people and information at the time they need to support maximization of potential— time and energy will consistently be put in highest leverage places
- Better filters by injecting personalization and social graph into many tools
- Systems that use psychology and persuasion to nudge people to act in their own long term self interest, mitigating human kind’s insidious propensity for short term thinking
And what I’m personally targeting right now with Founders First: accelerated just in time learning.
Finally, a few projects and trends I think are very important:
Rise of startup accelerators and therefore an emerging market for post-startup accelerators and pre-startup accelerators. (I’m working on the post startup accelerator phase with Founders First. See all the new startup accelerators here and many of the companies here)
Venture Hacks Angel List and Startup List to reduce friction in the funding of startups.
Right Side Capital Management— A new kind of investment fund trying to dramatically increase deal flow to 100-200 investments a year. This will support faster expansion into niches.
Related posts:
The Safety Net
The following story is true, I’ve just changed the names and told it in parable form. The material numbers and circumstances are roughly accurate, and Alice is a friend of mine who may tell the story herself on video here soon…
A True StoryAlice was feeling particularly poor at a certain time in her life and because of this she was under a lot of stress. Her friend, Bob, was a billionaire many times over and he disliked seeing his friend in pain and so he wrote her a blank check and said, “Alice, whatever amount you cash this for, it will relieve me of the burden of figuring out what to do with it. Will you do me the favor of accepting this gift?” Alice was stunned because she knew she could have cashed the check for $30 Million and Bob would not have missed it at all. And she knew Bob was sincere in what he was saying.
Alice was overwhelmed with Bob’s kindness, but instantly relieved of the stress. It was a big decision though, how much to take, and Alice didn’t want to make it hastily. She put the check in her safety deposit box at the bank so she could sleep on it. But she still hadn’t figured out the answer the next day and so the check remained there. This went on for a week, eventually a month. Bob didn’t mention the check and while Alice thought about it every day, the amount of time she did so diminished. Life has a way of crowding out unnecessary thoughts, and despite Alice’s financial difficulties, there were other realities that needed more attention. Alice knew she’d get to it soon enough and wanted to choose the right amount of money to accept, both because it was important for her future, but also out of respect for Bob’s gift.
Twenty years later, Alice was very successful financially (and otherwise) and Bob asked her to meet him. Bob was under some stress because a relative of his had fallen on hard times and was in desperate need of cash. Bob knew his cousin was too proud to ask Bob for help and was thinking of how he could give the money to his cousin anonymously without the cousin figuring it out.
The amount of money to give was an important detail, but Bob was stumped, so he asked Alice’s advice. ”Alice, I hope you don’t take this the wrong way, but my memory isn’t so good anymore and it was a long time ago that I gave you that blank check, but I can’t for the life of me remember how much you cashed it for. Would you mind telling me so I have some idea of what an appropriate amount would be for my cousin?”
Alice smiled, “Bob, I’m happy to tell you, but first I need you to understand that what you gave me that day was the best gift that anyone has ever given me, for which I am eternally grateful. And because your gift has allowed me to become successful and realize my dreams, I’ve been giving similar gifts to those around me who I know will be similarly helped by it. Also, I think it’s time I return what you gave to me that day, after all it’s been 20 years….”
Bob began to protest; he didn’t want the money back, he just wanted to know what amount to give to his cousin, but Alice ignored him and reached into her wallet and came out with a check. ”Here’s the check you gave me that day. I never cashed it. I don’t know what the right number of dollars is for your cousin, but the right amount for me was zero.”
Angels and DevilsWhile true, this story is also an archetype. It appears in many forms we are all familiar with: The Wizard of Oz; music lyrics; the Bible; and so on. What I like about the version above is that it’s particularly relevant in these times of financial hardship and uncertainty about the future. It helps us realize that sometimes what we think we need most is really an illusion, preventing us from seeing how we can be living right now the life we want for ourselves in the future.
Quite often in American society, the illusion happens to be money or time. But it’s different for all of us. For many it’s an immature egoic construct — an emotional mechanism that served our needs as a child (e.g. I have to clean up my room for my parent to smile and play with me), but which fails to mature and keep pace with our lives.
One form of emotional immaturity is overgeneralization — we unconsciously apply childhood mechanisms to situations which are more nuanced. Just because I had to achieve a clean room for my parent to reward me with one particular outward demonstration of love, doesn’t mean that I now must achieve career success to deserve the love of my spouse and friends. Yet so often, that’s what it boils down to, doesn’t it? Or if not exactly that, some variant in which parent, spouse, friend is replaced by society, church, God, child, and so on.
These immature emotional constructs become scripts which we memorize as a child and we shorten into mantras that go through our minds subconsciously thousands of times a day, even as we get older. Cleaning my room yields affection, but I wanted to play with my toys instead, so I got scolded and it made me feel bad, and maybe that means I am a bad person and I don’t deserve love, and if I want to not be bad and thus deserving of love I need to do something that I don’t want to do or that I fear, and so I don’t do it, which brings me back to that feeling bad about myself, and so on. Ultimately, it just becomes mantras: I’m a bad person; I don’t deserve love; I’m afraid of failing; etc.
Mantras are the emotional version of the music that gets stuck in our heads and reverberates. They color how we feel in the moment and how we perceive the world around us. They can be devils or angels, depending on their form. They lead to self-fulfilling prophecies. If we believe we need more money, then indeed, we need more money. But the devil is always in the details. It’s not the money that’s the problem, it’s the conflict between money and “more”. Money is finite, more never ends. The illusion is complete and we become blinded to the more nuanced reality.
The Safety NetWe are born with infinite possibility to create the lives that we want and it is only through the scripts, mantras and illusions we spin that we become distanced in time, space, and emotionally from most of them. We are all both angels and devils, the distinction itself being just an illusion we create. And just as we can become reconnected to the world of abundance by presuming it exists, we can protect ourselves from becoming fallen angels with a safety net.
The key is, remembering that the safety net is equally as real as the illusion. The yin does not exist without the yang. The need for more money does not exist without the perception that what we need is different than what we already have.
Alice shared her story with me two months ago, and I have been telling it to many people since as my way of paying it forward. If you feel inspired by her story, please pay it forward by sharing a safety net story of your own in the comments. It doesn’t matter how trivial you feel it is, others will be inspired by it who you won’t ever know about.
Alternatively, if you know someone who needs inspiration, tell them the safety net story they need to hear most, right now, whatever it is.
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The Myth of Time
Once again, Japanese “lost decades” or massive exodus into virtual worlds? Here’s the conclusion, but I highly recommend the entire post (it may be clear as mud, but you’re gonna get dirty down the rabbit hole):
“Whereas we think we are talking to each other, we are all simply connecting our bodies into a massive network, creating a massive DNA calculator, with hosts and endpoints consisting of carbon and flesh. The human species in the post-information society is a Turing state- machine capable of either turning on or off the screen. Communication on the macro scale becomes the number of screens transmitting light at any given time. It is a modality of code consisting of blinking UV and visible light emanating from the surface of terrestrial earth.
The input data either stimulates the cortical nervous action of turning on a screen or not. The earth pulses at varied rates, possibly radiating at some strange cosmic level a gaia morse-code linking schuman resonance to galactic exchange between Urantia/Earth and her surrounding celestial spheres. Humanity as a whole becomes a massive empirical experiement in systems analysis, an Einthoven fantasy. What ideas, images, and events trigger the most ebullient patterns of UV splashing emitted from the surface of earth outwards towards the cosmos? Thus the shortening and dampening of words and phrases towards being images and signs analytically flashed across millions of screens occurs as a facilitator, easing and enabling more flashing of screens as the earth uses humanity as path towards cosmic connectivity.
Post-information society wages an imperial war against the present, for the psychological focus attuned through focusing attention to the present moment, what is to be observed through hearing and sight clears the cognitive space relished by multinational market interests craving to insert their laboratory-designed mass produced branded self sculptures. Without time, there is no need for accumulation. Accumulation is a temporal-anxiety effect, and hence the endgame technology, the death of event and time is the destruction of the system from which its myth unfolded from” http://www.chadscoville.com/post/450781965/3-15-10-tetralocks
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The Future of Evolutionary Theory?
Well… it’s been quite a month. This April I (a) successfully defended my PhD thesis, and (b) won a Templeton Foundation fellowship to work with Martin Nowak at Harvard for two years. For those who don’t know him, Nowak is one of the world’s top researchers in abstract evolutionary theory. Working with him will be a tremendous challenge and opportunity.
So how to respond to this challenge? My vision for the next two years is to begin laying out a new mathematical approach to the study of evolution. Allow me to explain.
Currently, the field of evolutionary theory revolves around the study of models. As I discussed a few posts ago, a model takes a real-world situation and reduces it to those features that are considered essential. The model can then be analyzed mathematically, and hopefully the results tell you something useful about the original real-world problem.
Models are powerful tools for understanding the world, but they have a fundamental limitation: they always depend crucially on the particular simplifying assumptions made at the model’s inception. A different set of simplifying assumptions might yield completely different conclusions, and it’s often unclear which model is more relevant to the natural world.
This problem is ubiquitous in mathematical biology: a paper might devote pages and pages of mathematical analysis to understanding one particular model, but if that model were changed just slightly, all that analysis would suddenly be invalid. The question in my mind is always “What insight do we gain from our mathematics?” All the technical derivation in the world is of limited value unless it can help us reach broader conclusions.
My vision is to shift the focus of evolutionary research from models to theories. A theory, like a model, rests on certain fundamental assumptions, but in the case of a theory these assumptions are so broad as to apply to any system in question. For example, a theory might specify “Individuals interact, reproduce, and die in some manner”, whereas a model would have to specify the particular manner in which this occurs. So a single theory can encompass many (even infinitely many) models. It’s like the difference between saying “3+4=4+3″ versus “x+y=y+x for any real numbers x and y”. Moving from models to theories is a leap forward in abstraction, generality, and power.
Shifting to theories also changes the kinds of conclusions you can reach. Models produce predictions: specific outcomes that would occur if reality indeed conformed to the assumptions of the model. Theories produce theorems: general statements that apply to any system of the type in question. A theorem won’t tell you exactly what will happen, but it can characterize of the space of possibilities. And that’s what I think is needed in evolutionary theory: a general understanding of what can or cannot result from evolution, and how this depends on the certain features of an evolutionary process.
So that’s my research agenda in a nutshell. I’m extremely excited to see where this leads, and I’m looking forward to sharing more in the future.
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Simulating Angel Investment: Kevin’s Remix
Jeff Miller has done a couple of nice posts on “A Simulation of Angel Investing” here and here. I think it’s terrific that Jeff actually asked the question and tried to answer it with simulation. However, his answer of 20 is way too low because of two key oversimplifications. Using a more sophisticated methodology, I’ll show that a better answer is 100 to 150.
You may recall that Saving the World with Startups explained the “why” of RSCM. Our goal is to increase the number of technology startups. In some sense, this post describes the “how”. Well, at least part of it. One of the biggest barriers to getting a company off the ground is finding working capital. Ergo, we need to figure out how to facilitate investments in startups. More precisely, we need to promote seed-stage investments because those are what help founders initially launch their companies.
The ideal solution would be an investment vehicle that can turn huge chunks of money into digestible seed-stage bites with a return that induces plenty of investors to participate. But here are some slightly scary statistics. 50% of all seed-stage startups fail and returns come disproportionately from the top 10%. As all you poker players in the audience will note, you’re making big bets with high variance. The natural question is, “How many bets should you place?”
To answer this question, I’ve built several generations of seed-stage investing simulations for RSCM. My models are rather complicated because we wanted to evaluate a bunch of secondary questions such as whether it’s better to do follow on investments, what happens if the balance between seed and Series A valuations changes, and what happens in cases where a startup does poorly initially but then takes off. Therefore, I actually had to model the startup lifecycle round by round and the mechanics became very complex. (If you’re not a quant, you can stop reading now. Things are going to get real geeky real fast).
However, a simplified single-round version of my model will illustrate the missing pieces of Jeff’s model. The first is what diversification means. He focuses on the risk of total loss and the chances of not getting at least one “hit”. In my opinion, the question you really want to ask is what the probability is that you’ll under-perform the market by more than a given amount. For example, what’s the probability that you’ll under-perform by more than 25%? The logic here is that you invest in an asset class because of the overall return of that asset class, so you want to know the chances that you’ll realize returns in that ballpark.
The second key oversimplification is that Jeff uses a discrete probability distribution of returns. If you’ve read Taleb’s The Black Swan, you know this is a mistake because at least some seed-stage outcomes probably follow a Pareto distribution. The key characteristic of this distribution is that regions of extreme outcomes are self similar. So not only do the top 10% of companies represent a disproportionate share of the returns, the top 10% of the top 10% represent a disproportionate share of those returns. And so on. And so on. 20 investments may be enough to get you a fair share of the top 10%, but not enough to get you a fair share of the top 1%.
So here’s my simplified model, which roughly follows Jeff’s qualitative taxonomy:
- 50% failures: the company utterly fails. The investor gets 0 money returned.
- 20% break even: the company achieves some limited success and the money returned follows a lognormal distribution with a minimum of 0, a, mean of 1, and a standard deviation of 1. So an average outcome is 1.0x and 1 standard deviation above is 2.0x.
- 20% decent: the company achieves substantial success and the money returned follows a lognormal distribution with a minimum of 2, a mean of 4, and a standard deviation of 4. So the minimum outcome is 2x, the mean outcome is 4x, and 1 standard deviation above is 8x.
- 10% homeruns: the company achieves massive success and the money returned follows a Pareto distribution with a location of 10 and an index of 1.5. So the minimum outcome is 10x and the mean outcome is 30x.
Now, we can compute the expected value of an investment as .50*0 + .2*1 + .2*4 + .1 *30 = 4.0. The data I’ve seen puts the average hold time for successful angel investments at 6 years, so this would imply an IRR of about 26%. This is in line with the available research on angel returns (RSCM has a summary of this research here).
I ran a simulation with these parameters using Oracle’s Crystal Ball, producing an overall return distribution for a run of 100K trials. Here’s the excess distribution plot (the probability that the money returned will exceed a given multiple), truncated at 50x for some semblance of readability:
The return across the entire simulation was 4.05x (very close to the analytically expected return of 4.0x). The maximum return was 8,361x (think Andy Bechtolsheim’s $100K investment in Google which was eventually worth about $1B). The top 10% accounted for 77% of the total return. The top 1% accounted for 35%. The top .1% accounted for 17%. We can already see that a portfolio of 20 will be insufficient.
The source file is here. Most of you probably don’t have Crystal Ball so this will look like a pretty useless Excel file to you. However, I set up the run to output a TXT file with the results of each trial (for some reason, WordPress thinks CSV files are a security risk but thinks TXT are OK). It’s just a list of the 100K different returns generated by the model. Anyone can analyze this with standard charting tools or import the data for use by his own code.
I’ve also got another Excel file with a macro that generates 10K random portfolios of a given size from the trial data (because WordPress doesn’t like macro-enabled Excel files either, you’ll have to install the macro yourself). I’ve run it for portfolio sizes from 10 to 200 in intervals of 10. After sorting the portfolio returns at the specified size, the macro calculates the probability of hitting 75% of the market return by seeing what percentage of the portfolio returns are greater than 3.0. Here’s a chart of those probabilities:
[Edited 5/14 in response to suggestion from AN]. As you can see, 20 investments isn’t nearly enough if you’re a fund investing other people’s money. Worse than a coin flip that you’ll hit 75% of the market return. In fact, in my simulated portfolio data, there’s about a 7% chance that you’ll lose money with a portfolio of 20 investments. Personally, I’d say you want a fund to be in the 100 to 150 investment range. But it’s different for individual investors putting in their own money. I’d say you want to hit at least a 50% chance of realizing 75% of the market return, which would be 30 investments. Now, if you think you think you have some forecasting skill and less than 50% of your seed investments will fail and/or more than 10% will be homeruns, 20 may be plenty.
Of course, if you accept the thesis that 100-150 is the right range for a fully diversified fund-like portfolio, you may now be asking yourself how making that many seed-stage investments is logistically possible. The challenge is actually worse than that. Due to vintage risk, you probably want to make 100-150 investments per year or at least every few years. But that’s a story for another day…
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