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.

Related posts:

1. The Process
2. Non-Dualism
3. Entanglement

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.

Related posts:

1. Evolutionary Game Theory and Archaeology
2. Generalized Evolutionary Theory
3. The Idea of Applied Mathematics

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…

Related posts:

1. Revolutionizing Angel Funding
2. Announcing a new kind of Angel Investment Fund
3. What I'm Working On: Supercharging Innovation

Lee Smolin claims that AP is bad and favors a Cosmological Natural Selection view instead (on grounds of falsifiability).  I believe this is a false dichotomy and that they are really one and the same.  Here’s why:

1. Normally natural selection requires some form of “replication” or it’s not actually natural selection.   But replication is not needed if you start with an infinity of heterogeneous universes.  In other words replication is simulated via the anthropic lens over the life-supporting subset of all possible universes.
2. Replication is a red herring anyway since it presupposes time (or at least well-ordered events).
3. I conjecture that the distribution of universes is unimportant, as long as all possible universes are represented in the multiverse (i.e. the distribution can be random).

It’s worth noting that this is a purely a metaphysical/logical argument and says nothing about specific physics or cosmologies.  One of the things that makes it hard to see why this is true from reading the Smolin/Susskind debate is that they bounce between the logical argument and various proposed, unimportant details (like whether black holes are the replication mechanism in question or not).

More importantly though, we hear scientists call one another “unscientific” whenever they propose an hypothesis that is unfalsifiable.  Here’s why I think that’s problematic:

• Ever since Popper, science has been obsessed with falsifiability, which is really about assuring consistency.
• Godel proved that there are true statements that cannot be proved.
• More specifically he unpacked “truth” into completeness + consistency and showed that we can’t have both simultaneously.
• Due to extant complexity (let alone potential infinity) completeness is out the window.
• If science is only concerned with consistency, then it’s a pointless endeavor; I can sit here all day and generate tautologies that are neither interesting nor useful.
• If science is about truth, then there needs to be a way of expanding the set of discovered tautologies along the completeness dimension as well.
• There are at least three formal logical systems which do that without sacrificing consistency: deduction, induction and abduction.
• Only deduction is formally falsifiable.
• But science relies on induction and many other forms of evidence too (statistical reasoning, clinical trials, simulation, storytelling, etc); this is the “democracy” Smolin himself referrs to in his TED talk.
• The structure of the Anthropic Principle is abduction.  So is the structure of Occam’s Razor.  And depending on who you believe Bayesian inference is either induction or abduction.
• Conjecture: Newton’s Calculus is a formalism based on abduction.
• Conjecture: strong emergence (aka novel emergence) is fundamentally abduction.  This may be why science has such a hard time with it.
• Conjecture: natural selection is fundamentally emergence/abduction.  This may be why Creationists have such a hard time with it.

There is no one true definition of what constitutes “Science.”  We hear reference to the so-called Scientific Method.  Ultimately, the holy Scientific Method is whatever scientists as a whole do; no more and no less.  To say otherwise is ad hominem.  Now I’m not claiming that ad hominem argument shouldn’t be counted as scientific evidence, but anyone who bows before Popper would.  The irony there is that ad hominem is a form of Bayesian inference.  And if you’re keeping score, that means that anyone who claims that you are being unscientific if you don’t forsake all unfalsifiable idols, is themselves committing the sin of inconsistency.  Which by their own logic means they are unscientific too.

To which I respectfully submit, their pants are on fire, hanging from a telephone wire.  And that’s a scientific fact.

Related posts:

1. The Process
2. Science 2.0
3. Non-Dualism

Even among mathematicians, applied math has an odd reputation. Many pure mathematicians (those who spend their time working on purely abstract problems) regard applied math as mere “computation”, as if we were essentially glorified calculators.

But applied mathematics is not about discovering new numbers, nor solving crimes, nor cranking out long calculations (though some of that is involved). At heart, applied math is about creating, refining, and analyzing models.

The “applied” in applied math means that we work on problems that are in some way relevant to the “real world”. However, the real world is a complicated place, and virtually any system you might want to investigate has far too many interactions and unknowns to be understood completely. Imagine, for example, trying to understand the physical properties of a gas by first specifying the mass, volume, and exact location of each of billions of molecules, and then trying to predict where each particle will be in the next instant, and then the instant after that. Even if you were somehow able to do all these calculations, your answer would be valid only for that particular gas in that particular configuration, and would give you little insight into the behavior of gases in general.

So when we want to understand a system, we don’t attempt to incorporate every potentially relevant detail. Instead, we model it: we focus on what we believe to be the essential features of the problem and throw out everything else. All models are oversimplifications, but if they are well-constructed, that is, if we have picked the right features to keep and the right ones to discard, they can provide valuable insight into the real-world problem we are studying.

All models incorporate a trade-off, which I’ve (poorly) illustrated here:

We often hear about models on the right end of this spectrum: models of
of large-scale, complex phenomena such as the global climate or economy. These models incorporate many different variables in order to be as accurate as possible in predicting reality. The trade-off is that there is generally less insight to be gained from such models, because cause and effect relationships can be difficult to untangle with so many variables involved.

Mathematicians are more interested in the simple end. Unlike complex models, which can generally only be analyzed through computer simulation, simple models can often be analyzed using a pencil and paper. Though they do not describe reality as accurately as complex models, they illustrate very clearly how and why certain effects lead to certain outcomes. Simple models also have the advantage of generality: the same set of simple features may be present in a wide variety of systems. The more variables and complications you throw in, the more your model becomes tied to the one specific problem you started with.

I’ve written a lot in this blog about the Prisoners’ Dilemma as a model for cooperation. The essence of the model is this: two players each have a choice whether or not to cooperate with the other. If a player decides to cooperate, they pay some cost, and the other player gains some benefit. Of course, cooperation happens in many different forms in human and animal life, and you could study any particular cooperative behavior by tracing its social and/or cognitive basis, as well as its evolutionary origin. But by studying the particularly abstract, simple model that is the Prisoners’ Dilemma, you can gain some insight into the phenomenon of cooperation in general: when and why it evolves, and how it is maintained.

The purpose and method of developing and analyzing models is a strangely absent topic from high school and college math and science classes (a welcome exception is a course I’m currently TAing at Boston University that teaches quantitative reasoning to non-science majors). But given the role that models play in our economy as well as in science, and the catastrophic consequences of their failure, I think that communicating an understanding of the modeling process should be a central goal of science education.

Related posts:

1. The Future of Evolutionary Theory?
2. This Sentence is False
3. Complex Systems Concept Summary

Now lets assume there is some process for picking out universes from the multiverse.  Since there’s no time in the multiverse, the process has no beginning and no end.  It’s like a computer program, but it’s infinitely complex.  Let’s call it The Process.

If The Process is infinitely complex and has no beginning and no end, what can we know about it?  We know that it picks some universes but not others, which effectively creates an “in group” (all those that are picked) and an “out group” (all those that are not).  Of course, both sets are infinite and still have no structure.  But note that all the universes in one group or the other now stand in relation to one another.  That is, they share the property of “in-ness” or “out-ness”, and between the two groups there’s the relationship of “different”.

The Process further divides these sub-multiverses in unknown ways, and this sorting creates other relationships between universes.  You can visualize a network of universes with the connections representing these relationships.  The network is infinite, and if you consider any subset of the network, it’s also infinite.  But these subnetworks are no longer arbitrary, they are networks themselves and networks have structure.  And since a subnetwork by definition shares the same connection relationships as the original network it is a “sub” of, the subnetwork is structurally similar to the network itself.  That is, the network is self-similar, which in mathematical terms means it’s fractal.  Of course this fractal we are talking about is infinite, and so wherever you start, it’s turtles all the way down, and all the way up.

Notice that the process of identifying subnetworks does something interesting, it creates an asymmetry that wasn’t distinguishable before.  For any network N, if you choose a subnetwork, n, then N “contains” n but not vice versa.  This containment relationship can viewed as a network where the connections are arrows, meaning they have directionality, N –> n.  You may have noticed that we just went from talking about a network of universes to a network of networks (of universes), but that’s okay.  Remember the multiverse is infinitely infinite, and we’re just chatting about some arbitrary aspects of it.  There’s lots of other aspects we could talk about instead, but it’s starting to get interesting here, so let’s continue….

Somewhere in the fractal multiverse network of networks described by The Process there is a subnetwork (actually an infinite number of them) where the structure is like this: each universe is connected to by only one other universe but connects to an infinite number.  Let’s call this structure, Time, and note that there are an infinity of subnetworks of the network which have this Time structure.  Unless stated otherwise, I’ll be talking from now on about networks with Time structure.

Remember though, the multiverse itself has no structure; The Process overlays structure on top of it and thereby allows us to know about things like Time.

Now let’s start using the words network, system, particle, entity, agent and universe interchangeably, so we can say things like “time network”, “temporal system”, “particles over time”, and “A causes B” to refer to roughly the same thing.  I realize that by overloading these terms I’m jacking into (and hopefully hijacking) your intuition about what these words mean, but that’s my intent.  Hopefully you’ll continue playing along by my rules and try not to project what you already know onto this alternative cosmology.

When we use the words network, system, particle, entity and agent, you might wonder whether we are talking about a universe or a multiverse.  The answer is Yes.   Remember, the multiverse is infinitely infinite and self-similar, so in some sense we can say it contains itself.  We have a hard time with infinity so this concept is mind-boggling, but if you follow the logic, hopefully you’ll accept this paradox as true.  So lets just use the word universe from now on and forget about multiverses.  And to not get confused, let’s refer to what we used to think of as the Universe as the known universe instead.  The known universe is where you live (or more precisely where you think you live) along with everyone and everything you know about or can imagine.

The known universe is expanding the more you learn about it.  The known universe is temporal.  And as we know from Einstein, it must therefore also be spatial — remember it’s not space and time but rather the spacetime continuum.  The known universe consists of particles (i.e. matter) and therefore — also thanks to Einstein — it consists of energy.  Time, space, matter and energy here may or may not be totally in sync with our intuitions of them, but just suppose they are the same thing and that our intuition is slightly biased by our particular experiences in life and could use adjustment.

We haven’t really talked explicitly about laws of nature, fundamental constants, invariant equations or even mathematics.  And I kinda jumped the gun when bringing Einstein into the equation (so to speak).  But it’s really hard to follow a line of thought without some sort of logical paradigm, some structure of thought.  In the end it doesn’t really matter what I’m saying, what you’re hearing, or whether any of this is “true”.  I’m just telling you a story, and hopefully it’s amusing enough for you to finish reading.

Originally we talked about The Process, which is infinitely complex and which describes all sorts of possible realities.  The known universe is one of those possibilities, one in which we see structure and patterns, order and complexity all around us.  Somewhere “out there” there may be portions of the multiverse (whoops, I said I wasn’t going to use that term anymore, sorry) where it’s still appears, unstructured and thus unknowable.  But let’s come back to the known universe and the “knowable” universe.

Because of the fact that we are here in the known universe thinking and talking about it, and not in some unknown or unknowable part, the non-random patterns that we see may look to us like universal laws (E=mc^2, the second law of thermodynamics, etc.)  Well, we know that even these laws are not truly universal, they apply to only certain scopes.   For example, “relativistic but not classical or quantum realms”, or “closed systems but not open systems.”  String theorists are looking for universal laws, but so far none have been found.  But let’s just grant them that they will eventually find some (or one).  How would we be able to distinguish between a true Law and just a pattern that is very very persistent over all known scopes?

How about we stop using the word “law” and instead replace it with the word “principle” to suggest that it may really just be a pattern that we see in the known universe.  And as the known universe expands via our increase in knowledge/understanding/awareness, we might find exceptions to the pattern.  After all, that’s what’s happened to every “law” ever considered in the history of science so far, and why should that pattern stop?  (Sorry, my paradox detector just went off, let me reset it….)

Coming back to principles, there’s one that emerged from the last few paragraphs, did you notice it?  Cosmologists call it the Anthropic Principle, which is the notion that the universe appears ordered in the particular way that it does with these nifty laws and constants because of the very cosmic coincidence that we are here observing it!  In other words, we live (and can only live) in the known universe, by definition.  And we wouldn’t be “here” and able to “notice” anything if we were in some unknowable part.  That’s a pretty trippy concept, but one that many physicists take very seriously.  It’s the same kind of argument as for why we haven’t been contacted by aliens yet: there’s a decent chance we are the most advanced intelligence out there and we’ll have to wait for others to catch up so we can communicate.  It’s also the reason that your keys are always in the last place you look.

Remember the Anthropic Principle because it’s really useful.  It has the same logical structure as Darwinian evolution and other “emergent” phenomena.  Is this Generalized Anthropic Principal (GAP) a universal/fundamental one?  Who knows.  Probably not.  We anthropic agents are so self-absorbed.

Another principle that emerges from our cosmology is Coherence.  Because of The Process, birds of a feather flock together.  Actually, The Process defines which birds are of which feather, so this is a tautology, though it’s fun to think of it as “like attracts like”.  But we know that really it’s just co-incidence: the birds exist at the same Time.  Using the analogy of birds, we can ask whether these coincident birds are different birds or the same bird.  But it’s a silly question because the answer is Yes.  Think of quantum coherence, if you like.

Now let’s say we are talking about particles and not birds, and instead of Coherence we’ll say Gravity.  Isn’t it the same thing?  We talk about stars and planets and other astral bodies as if they were coherent entities, but If there were no gravity, would those entities exist?  Or let’s talk about the Cooperation of the cells in your body; without it, would you exist?  We’ve all heard about the “law of attraction” from The Secret, isn’t it the same thing?  You imagine the future you want, and that acts as a beacon guiding you in every decision you make, every micro-decision, every unconscious action until at some point you find yourself living in the future you imagined.  Coherence, cooperation, attraction, unity.  Same thing.

Here’s a secret: there is no Process.  Or if you prefer, The Process is completely random.  Yet that doesn’t change anything I’ve said above.  Think of it this way: in an infinite series of random numbers, all patterns appear eventually, right?  So “somewhere” in the infinite randomness, The Process “produces” the structure I’ve been talking about.  Or maybe the fact that we’re anthropically talking about it produces the structure.  We are The Process.  Or more generally, we humans are part of The Process.  The Process is the universe.

Related posts:

1. Why Falsifiability is Insufficient for Scientific Reasoning
2. Notes from TED
3. Synthesis of Complexity Theory

What if you wanted to start your own sovereign nation in a tucked away corner of earth somewhere?  Problem is, every piece of land more than a few feet above sea level is already claimed by governments, private individuals or commercial interests.  Enter, the high seas.  Turns out there’s nothing stopping you from going out to international waters, building a platform, giant boat or floating something-or-other and starting over with government, completely from first principles.  Patri and Seastesd.org are committed to helping you do just that.  And before you go assuming that the best form for your utopian flotilla must be some form of democracy (social or otherwise), consider all the unsolvable problems that face even your very favorite “government service provider” today.

So with that in mind, I’ve invented a new form of government that I’m putting in the public domain for any would-be seasteaders, guerilla factions or velvet revolutionaries to use as they see fit.  Don’t thank me now, just send postcards from time to time.

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Valuestan

“Having a nice life… Wish you were here!”

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Principle 1: Values First

• Rather than assert that there are such things as Inalienable Rights (or even Rights at all), recognize that there exist a set of  Shared Values which can be explicitly stated.  It is the Shared Value Statement (SVS) around which the State is organized.
• To be a Citizen you must uphold and abide by the SVS.  You may renounce Citizenship at any time.
• The SVS may be amended (process TBD by founders; process subject to amendment by Citizens).  It is understood that any amendment is likely to turn some Citizens into non-Citizens.
• Any non-Citizen who is visiting or residing in the State is to be treated — and act — AS IF they were a Citizen.

Example values: Empathy; Discipline; Group Harmony; Consensus; Individualism; Personal Freedom; Happiness; Respect; Gratitude; Absolute Truth; Relative Truth; Parsimony; Efficiency; Sustainability; Education; Personal Improvement; Democracy; Meritocracy; Marketocracy; Autocracy; Theocracy; Ends Before Means; Means Before Ends; Aesthetic Beauty; Entertainment; ad infinitum.

It’s clear that Values are soft, not hard like Rights.  And that any particular set of Values will, to a greater or lesser extent, conflict.  The SVS is an unordered finite set.  Relative significance of Values is unspecified by the SVS and can only be known by inference from practical implementation via Principles 2 and 3.

It is up to the Citizenship to determine what values belong in their SVS.  Some sets of values will be inherently more stable than others, and some are simply not viable.  But it is a category error to suggest that some SVSs or states are more moral than others.  Morality is internal to the State and relative to Shared Values.

. . .

Principle 2: Positive Incentives Before Laws

• Where possible, formal positive incentives (economic, social and otherwise) will be used to shape individual action.
• Where such incentives are impractical or undesirable, formal laws may be created.
• Laws trump incentives and should be used sparingly.
• The entire set of formal incentives and laws (i.e. the Formal Code) is meant to embody and prioritize the SVS.

How the FC is arrived at, amended and implemented will vary from state to state, and is to be in accordance with the SVS.  If Democracy is part of the SVS you would presume to see some form of voting mechanism.  If Democracy is not on the SVS but Consensus is, you might expect the FC to be determined by a jury-like process.  And so on.

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Principle 3: Practical Wisdom

It is recognized that Principles 1 and 2 are not enough by themselves to create a good society.  To wit: our loss of moral wisdom.

Therefore, it is the Responsibility…

• …of each Citizen to be a moral exemplar always and embody practical wisdom
• …of the State to celebrate moral heroes and create a culture of moral action

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Principle 4: Non-Human Agents

It is recognized by the State that there are non-human agents that exist in the world, some of which exist in the State, and that they do not necessarily have the same motivations or moral capacity as humans.

Examples of non-human agents include: corporations, governments (the State, other sovereign states, local governmental bodies within the State), military systems, market systems, exogenous non-state actors (e.g. terrorist groups), religions, cults, sociotechnical complexes (e.g. military-industrial complex, academia-medical-regulatory complex, DDoS attacks, crowdsources), technological agents (e.g. software viruses, robots).  New forms of non-human actors are emerging at an accelerating rate, and are largely unpredictable.  So-called “artificial intelligences” are of particular interest and concern.

Non-human agents are good at responding to incentives, but not good at responding to laws or moral intuition.  The proper treatment of non-human agents — including and especially the State itself — is recognized as important, especially as it pertains to the legal system.

The treatment of non-human agents as Citizens in Fact may be a threat to a good society.  For instance:

• Should Corporations be treated as Persons (as they are in the U.S. legal system for many purposes) ?
• Should the State or local governments be party to lawsuits?
• Should there be a para-governmental system designed to protect humans inside the State? the State itself? the SVS? humans not in the State?  humanity as a whole?
• What should be done about non-human agents which threaten the State from (at least partially) inside (e.g. military-industrial complex) ?

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Related posts:

1. Financial Crisis Act I: Government Meddling
2. Notes from TED
3. Crowdsourcing Election Verification

In thinking about this I am reminded about a duet of innovators who spoke at the Pop!Tech conference last year about scaling.  Both Bunker Roy and Paul Polack have some profound lessons to teach us about scalability.  You will learn these lessons by watching Roy talk about Barefoot College and by watching Polack talk about his Out of Poverty approach (also see BarefootCollege.org and PaulPolack.com).  But despite all of the incredible wisdom to be gleaned from observing how Roy and Polack achieve scale, I’ve been wondering about how their success can be translated to other realms.

Replicators

In creating a general theory of scalability, I think there is a key conceptual anchor from Susan Backmore’s TED talk on the third replicator.  Now, I have to pause here because as simple and great as the universal Darwinism principle is, I know from conversations that many people have a really hard viewing evolution in non-biological systems as anything more than a good metaphor.  It’s hard for most people to see that “true evolution” — the kind that Darwin was talking about — is actually what is happening in these non-biological systems.  I will address this in detail in a later post, but ask that you indulge me for the time being so that we can talk about replicators.

When we talk about scaling sociotechnical systems, really we’re talking about one of two things: either growing the original system to handle “more”, or replicating the original system (or enabling it to replicate itself) with appropriate variation for the new context.  Growth models are the more familiar and comforting to governments and policy makers for reasons that should be obvious to anyone who has noticed how scared these types get when faced with systems that scale via replicators.  Formal organizations (corporations, non-profits, governments, anything with a legal structure or formal set of rules) are growers; networks of cooperating agents (open source software, social change movements, revolutionaries, anything that is formed in a grass-roots / bottom-up manner) are replicators.

I am not here to argue that either type of system is dispensable, indeed they are both essential.  I will leave it as an unproven conjecture that we are at a point in history wherein the ecology of sociotechnical systems is dominated by growers that are straining and stretching to the edges of their dynamic range.  Societal edifices are crumbling under their own weight, and are thus vulnerable to subversion by an algal bloom of replicators in their midst.  For those that want the argument and evidence, go read The Chaos Point by the grandfather of complex systems theory, Ervin László.

And I will leave alone in this theory of scalability the entire grower side of the equation.  It’s been systematized and refined since at least the days of Machiavelli;  we know it today as management science.  Instead I want to suggest that there is lacking an entire half of the formalization project for a unified theory of scale, and that’s a formal model for scaling via replication.  The reason this formalism has eluded us for so long is the same reason Darwinian evolution is so hotly contested: it requires a fundamentally different way of thinking than the Western analytic tradition is based on.  That’s not to say that the complex systems paradigm is not scientific, just that the scientific method as it exists today has not yet incorporated the bottom-up, emergent calculus required to be complete.

The first question we must ask is what exactly is being replicated, and only then we can ask how that replication is achieved.  Blackmore names three classes of replicators which I would like to refine by pointing out (as she does) that these are really self-replicators.  In her TED talk she observes that biological self-replicators exist (i.e. what we normally refer to as “life”), that mental self-replicators do indeed exist (though most people don’t take this notion seriously enough yet), and that technological self-replicators are in the process of being born.  If we think about it though, it is easy to see that certain forms of this third replicator already exist: computer viruses, bot nets (e.g. as are used in DDoS attacks), digital agents in artificial life simulations and genetic algorithm systems, and others.  What Blackmore was hinting at with the her more restrictive definition of technological self-replicator is one in which the artifact being replicated has a physical form (as opposed to digital information form).

I must digress here for a moment to point out that it is a red herring to try to neatly circumscribe the system being replicated (the “artifact” or agent) from its environment.  In reality there is no such thing as a true self-replicator; there are always some resources or information that is outside the self-replicator that is required for replication to occur.  Neither the chicken nor the egg can recreate itself.  And if you (rightly) view the chicken/egg system as the thing self-replicating, you only need observe that food is also essential (as are many other things) for replication to occur.  Given this truth in the realm of biology, is it really so far fetched to view digital cameras self-replicating technological agents, that is replicators of the third kind?  Sure they require humans, manufacturing processes and other technology from their environment to replicate, but I’ll reiterate that there are no biological life forms either that are entirely self-replicating.  (This blog post puts an even finer point on it all, if you are still not convinced).

Principles

The scaling brilliance of Bunker Roy and Paul Polack was hard-won, after many years of solving specific problems at the bottom.  It was only after gaining a deep understanding all of the interacting subsystems was it possible for each of them to engineer an overall system that was scalable via replication.  Looking at various attempts to scale sociotechnical systems, both successful and unsuccessful, a pattern starts to emerge of the key principles and dynamics.  Here are a few:

• Counterintuitive: Brilliant solutions are only obvious in retrospect.  Crazy. Crazy. Crazy. Obvious.
• Self-Replicators: It is important to identify the parts of the system that are — or that can be made to be — self-replicating.
• Fecundity: Digital information replicators are more easily replicable than mental constructs (i.e. memes), which are in turn more easily replicable than organizations of humans.
• Mutation: The more fecund the replicator, the easier it is to co-opt for ulterior motives, and the more likely it is that random variation will throw the overall system off course.
• Environment: It is easy to mistakenly believe that a prospective environment is suitable for replication when it’s not.
• Side-effects: With any complex dynamic process there are always side-effects. If ignored, this usually leads to collateral damage, but on the flip side there is usually an opportunity to accomplish other goals and turn side-effects into new benefits.

In thinking about how to engineer a system to bring solar electric installations to rural villages around the world, it is counterintuitive to think that poor, illiterate grandmothers (with no formal education and very little social standing in their village) could learn to be solar engineers.  To further think that they could be taught by illiterate trainers (who don’t speak the same language) is crazy.  Until Bunker Roy proved it was possible.

Microcredit was crazy too, until Muhammad Yunus proved that it wasn’t, and then it was obvious.  So obvious in fact that it became a viral meme and has spread all over the world.  The concept of microcredit is a very fecund self-replicator.  Unfortunately, the practice of microcredit in many places has ignored the nuances of different environmental contexts and unintended side-effects.  Add to that a high mutation rate: the model being tweaked to confer greater benefit to lenders (at the expense of borrowers); the introduction of middlemen who screw up the incentive structure and unwritten social contracts; etc.  The net effect has been that in some areas microcredit has been a net negative to the economy, and especially negative to the borrowers, whom the model was originally designed to help most.

Polack’s franchise model (an indeed all franchise models) are inherently replicators.  They are also good self-replicators because customers and other locals get exposure to the idea of becoming an entrepreneur themselves. And some of them end up as franchisees.  That is replication.  But to move from solving one problem (e.g. clean drinking water) to solving a very different one (e.g. locally available energy), new technologies that are also “radically affordable” have to be created on a regular basis.  And this type of innovation does not self-replicate.  So Polack created an entirely separate institution, the non-profit R&D lab, specifically to tackle the problem of replicating franchises (i.e. going from an electrochlorinator franchise to a solar concentrator franchise).

Applications

With this nascent framework in mind, I’d like to invite you to evaluate some of the social ventures that I encountered at The Feast (and a few of my favorites from Pop!Tech last year) and see if you can predict how scalable their model will be based on the replicator principles above.  And in cases where they have achieved some amount of scale (like charity: water and frontlineSMS), can you explain their success using the theory?

• SHE (Feast talk Session #3 starting 0:46:30)
• charity: water (Feast talk Session #4 starting 0:01:30)
• frontlineSMS (Feast talk Session #3 starting 0:25:45)
• Yellow Brick Road (Feast talk Session #1 0:31:37)
• 9th Ward Field of Dreams (Feast talk Session #4 starting 0:59:20)
• Change.org (Kitchen)
• International Transparency Solutions (Kitchen)
• Madecasse (Kitchen)
• Parent Earth (Kitchen)
• Srina (Kitchen)
• TrustArt (Kitchen)
• VotaVox (Kitchen)
• Global Citizen Year (Pop!Tech)
• Vittana (Pop!Tech)

I would love to hear your thoughts, both on the specifics of these ventures, and on the theory of scaling through replication.

Related posts:

1. Alfred Hubler on Stabilizing CAS
2. Autocatalytic Systems
3. Daniel Nocera’s Gift

“Reconsolidation research has helped foster a growing sense that the flexibility of memory might be functional—an advantage rather than a bug in the brain. Reconsolidation might be how we update our store of knowledge, by making old memories malleable in response to new information. “When you encounter a familiar experience, you are remembering the original memory at the same time, and ?the new experience somehow gets blended in,” says Jonathan Lee of the University of Birmingham in England, who recently found evidence for this effect in animals. “That is essentially what reconsolidation is.” The evident purpose of episodic memory, after all, is to store facts in the hope of anticipating what might happen next. From the perspective of survival, constructive memory is an asset. It allows you to pull together scraps of information to simulate the future on the fly.”

 Imagination seems to require memory to be bendable.  Is the brain running monte carlo simulations?:

“Put another way, memory and imagination are two sides of the same coin. Like memory, imagination allows you to put yourself in a time and place other than the one we actually occupy. This isn’t just a clever analogy: In recent neuroimaging studies, Harvard psychologist Daniel Schacter has shown that remembering and imagining mobilize many of the same brain circuits. “When people are instructed to imagine events that might happen in their personal future and then to remember actual events in the past, we find extensive and very striking overlap in areas of brain activation,” he says. Other researchers have found that people with severe amnesia lose their ability to imagine. Without memory, they can barely picture the future at all.”

Wonder where dejavu’s and dreaming come in :)

 
via Traderfeed (Discover)

Related posts:

1. The Conflict Between Complex Systems and Reductionism
2. Foldit
3. Go Forth and Reify

First up is a provocative post by the ever-interesting Scott Sumner.  Rafe in particular should read it because Sumner starts from one of Rafe’s favorite premisse that “laws” of nature are purely cognitive constructs.  We should measure them by their usefulness and not ascribe to them any independent existence. So Newton’s laws of motion are useful in certain contexts.  Einstein’s are useful in others.  But neither are ground truth.  Moreover, we will never find ground truth.  Just successively more accurate models.

Sumner uses this bit of philosophy to justify abolishing inflation, not, “…the phenomenon of inflation, but rather the concept of inflation.”  More specifically, price inflation. He explains why this concept is ill-defined and not only unnecessary, but confusing, for understanding the macroeconomy.  He asserts that we should expunge it from our models.  It doesn’t really exist anyway, so if models do better without it, we won’t miss it in the least.

Related posts:

1. Kevin Gets Acknowledged by a Real Economist
2. This Sentence is False
3. Is Money an Emergent Phenomenon?