The Emergent Fool

A summary of the 18 points  from an insightul and concise (only 4 page long) paper on Complex System failure (via Infectious Greed).  Number 7 and 8 explain why history rhymes:

“7. Post-accident attribution accident to a ‘root cause’ is fundamentally wrong. Because overt failure requires multiple faults, there is no isolated ‘cause’ of an accident. There are multiple contributors to accidents. Each of these is necessary insufficient in itself to create an accident. Only jointly are these causes sufficient to create an accident.

8. Hindsight biases post-accident assessments of human performance. Knowledge of the outcome makes it seem that events leading to the outcome should have appeared more salient to practitioners at the time than was actually the case. Hindsight bias remains the primary obstacle to accident investigation, especially when expert human performance is involved.”

Do yourself a favor and invest in reading it.  Better yet, pass it on to those blaming any specific party for the current economic crisis whether it’s the bankers, the regulators, the borrowers, or the rating agencies.

(Back from Alex’s European adventures)

Michael Martin of Broken Symmetry with two incredibly insightful posts on Soros’ theory of reflexivity, distinction between social and physical sciences, and the ability of markets to regulate us as well as themselves.

1. “Are markets flawed? Or is it competition?“.  Martin’s response to Soros’ criticism of markets’ ability to self-regulate:
“Individuals spontaneously order into firms. What benefit is there to such integration if markets put less constraints on the same individuals? The function of markets is to synchronize buyers and sellers who cannot otherwise integrate their needs within a firm. Soros has it exactly backwards. It’s not that markets are suitable only for individual choices; it’s that individual choices are suitable only for markets.

In this context, Soros would do well to consider some of the New Institutional Economics and Organizational Theory literature, which provides theory on how and when institutional culture develops. I don’t disagree with his point that institutional rules are needed. Just his point that government institutions are necessary or sufficient to meet those needs.”

 The optimist in me believes that “artifical inteligence” akin to google type algorithms could be created to simulate our political choices/decisions to help squeeze out the middleman of politician and create a market/forum of political ideas.  Change.org is a start.  Does anyone know of any other examples?

2. “Reflexivity Goes Deeper than Soros Himself Seems to Realize“:
“The cycle is manifest in the activities of people. The mathematical world is revealed, step by step, through consensus among living and dead mathematicians. The mental world represents the model everybody has, including mathematicians, of what exists. Both of these are embedded in a physical world along with a Noah’s ark of other animals and a Garden of Eden of other living things.

We exercise control over our existence by formulating theories about what exists. There are plenty of things that exist that no person imagined to exist until a theory was developed that permitted for experiments, which in turn were consistent with other experiments, and so on. Nobody doubts anymore that we are made of atoms, quarks, and leptons. Yet none of us has seen any of these things. And if we were to stop looking for them, there is no doubt in my mind that we would eventually forget about them — leaving their existence as ghostly as it was a hundred years ago.

There is no dichotomy between social and natural science. Rather, social science should embrace these constraints that have been on all science for as long as we’ve been doing experiments.”

Imagine you are in your early twenties, out of college several years and your best friend, who recently came into an inheritance of $300K cash told you they could think of no better way to invest the money than to invest it in you.  Not the company you started, not as a loan, but invest it in YOU, as if you were a startup.  In return your friend said all they wanted was 3% of your gross income for the rest of your life.  Do you think you would take it?

Now what if your friend said that they didn’t care what you did with the money or how much you made each year.  If you wanted to sit on a beach in Nicaragua learning to surf, go work in the Peace Corps, stay at home and do your art projects, whatever you want it would be fine, just as long as you made sure to always pay the 3% of whatever you make (as little as that may be).

And finally, what if your friend said you could buy out of your obligation at any point for $6 million in cash.  Then would you take the deal?

. . .

Personal Investment Contracts

. . .

Personal Investment Contract

Phil Gordon and I recently made such an investment in a person we both know very well, call her Marge.  The thing about Marge is that she’s one of these people you know — you can feel it in your bones — that she’s a superstar.  She may have a string of projects and startups that don’t end up producing much in terms of tangible results — in fact she already has.  But you know that all of this “failure” is simply building Marge’s brand equity.  She’s learning how to navigate in the world, how to build value (whether it be monetary value, social good, or however you define it).  She’s also making connections with people who are taking notice of her talent, love her undefinable qualities as a person, and who just want to somehow help her succeed in her life’s mission and be a part of her success.  Everyone who meets Marge knows it’s simply a matter of time before her success is tangible.  Maybe she’ll end up as a founder of a billion dollar startup, maybe her book will top the NY Times Bestseller list, maybe she wins a MacArthur Genius award.  Or maybe over the 40+ years of her career doing what she absolutely loves and was made to do, she will touch the lives of millions of people.

From our perspective as investors, it doesn’t really matter what path Marge chooses or what twists and turns that path reveals.  We’ve already determined that she’s a winner and she will adapt accordingly.  The cash investment was intended to smooth out the earnings curve so that Marge won’t have to take jobs that don’t further her life goals just so she can eat and pay rent.  And even if she blows through the cash, she’s still gotta eat and pay rent, which means she will find a way to make money (while pursuing her dreams).  Maybe one year she makes $10K.  Down the road she herself inherits some money and coincidentally that same year is paid handsomely on a consulting gig and ends up making $400K.  Or perhaps she finds that she loves climbing the corporate ladder and steadily increases her salary from $50K to $500K over the course of 20 years.  Assuming Marge makes an income of some sort for 40 years, she only has to average $250K (in today’s dollars) for us investors to get our money back.

Now here’s were it gets interesting for the investors.  It’s very unlikely that we will be negative on our investment over the course of Marge’s lifetime, unless she dies or becomes incapacitated (which happens of course; there’s no such thing as a risk-less investment).  And in poker parlance, we are “freerolling” to make a substantial return if she hits it big and/or she decides she wants to buy out.  But even if we don’t make a ton of money off of Marge, we know that our investment will have made a significant positive impact on the world.  Why?  Because we hand-picked her as “the one” out of the thousands of people we’ve met over the years to invest in.  Amongst those other there are surely many winners, they’re just not… Marge.

. . .

Simple, Flexible

You are welcome to download and use the document above as you like, it’s hereby placed in the public domain.  Obviously Phil and I have to disclaim any responsibility for what you do with it, and we cannot give you any legal advice.  We are very comfortable that we are not breaking any laws or regulations and we’ve had a team of lawyers and personal agents vet and refine the basic template from both the investor’s standpoint and the investee’s.

And sorry, we are not accepting applications nor will we consider investing in you.  But if you have people who believe in you and trust you as much as Phil and I do in Marge, then show them this blog post and convince them to invest.  The Personal Investment Contract (PIC) can be calibrated for just about any situation where the investor believes the person they are investing in is (a) a true superstar, and (b) completely trustworthy.  Here are the key numbers to keep in mind:

• Investment Amount – This should be determined by the entrepreneur such that they feel like they have enough breathing room to pursue their passion for at least a couple of years, or longer if they feel like supplementing their income themselves.
• Annual Return Payment – The idea is keep this low enough so as not to be a burden on the entrepreneur, but high enough to be attractive for the investor in combination with the Termination Amount.
• Termination Amount – If the ARP is low, this should be high; if the ARP is high, this should be low.  It’s the slider that trades risk for reward.

. . .

Examples PICs

• Example 1: Technologist or Business Person
  • Investment: $250K
  • ARP: 2%
  • Buyout: 10x ($2.5M)

• Example 2: Social Entrepreneur
  • Investment: $150K
  • ARP: 5%
  • Buyout: 5x ($750K)

• Example 3: Do Gooder or Research Scientist
  • Investment: $100K
  • ARP: 10%
  • Buyout: 1x ($100K)

. . .

Important Details

Despite the fact that the contract is ridiculously simple (three pages!), there are some key details in the contract that we believe make this work.  The first is the clause that says the entrepreneur has to give the investor a year’s notice that they intend to buy out.  This is so that the investor can’t be cut out of a big, pending deal that closes soon after the entrepreneur buys out.  It’s possible that the entrepreneur gives notice but for whatever reason (turn of fortune?) can’t come up with the cash required a year later.  That’s fine, the contract stays in effect and the entrepreneur can give notice again in the future.

The second important detail is that the Termination Amount isn’t really just the buyout multiple on the original investment but it also crucially includes the ARP times the net fair market value of all unrealized gains made during the course of the contract.  The reason for this is as follows.  What happens if the entrepreneur buys a house or invests in a business which becomes the dominant (or even just a significant) portion of their net worth by the time they want to buy out.  The investor rightly feels like they contributed to that gain and should get their fair share.  The entrepreneur may not want to (or even be able to) realize the gain at the time of the buyout, e.g. they still want to own and live in the house, or the business they invested in isn’t public yet.  But the investor shouldn’t have to take the worst of the deal.  Hence the fair market value assessment is made (by third party arbiter if necessary) and the investor gets paid.  For instance, consider a PIC using the numbers from Example 1 above.  Entrepreneur buys some property that appreciates by $20M, so the actual Termination Amount becomes ($20M x 2%) + $2.5M = $2.9M.

There are sure to be loopholes that we didn’t close, and it would be great if you could bring those up in the comments section below so the template can be adjusted or variants of the PIC can be made.  Ultimately we decided that because we are investing in people we can trust, and we want to foster that sense of trust and fiduciary obligation, it was better to have the contract be short and to the point, rather than cryptic and air-tight.  Yes, there could be problems down the road, but then again if one party really wants out of a contract or wants to bend the rules in their favor they will be able to.  We’d rather enter into a handshake agreement where we are partners in the success of a budding superstar — as motivated to help them achieve their goals as they are to leverage our resources, experience and connections — than to take advantage of someone because of their temporary circumstances.

. . .

Replicate, Don’t Grow

The first thing that angel investors or venture capitalists think about (once they decide they like the model) is how can they create a fund to achieve scale.  Caution! This way there be dragons.  A PIC is fundamentally a personal investment reliant on mutual trust and respect, not a mechanical device suited to turn into a factory.  PICs can achieve scale, but it will happen from the bottom up, rather than top down.  That is, they are meant to replicate, not grow.

. . .

Feedback

If you have any feedback or experience with this sort of investment, we’d love to hear it!  Share your stories in the comments below.

. . .

There are way too many “happenings” in complex systems research, theory and application to keep track of everything, but here are a few of note that came across my desk recently…

• AAAI Fall Symposium – CAS & The Threshold Effect – Nov. 5 – 7, 2009 – Arlington, VA
  • Schedule ; Facebook Group ; LinkedIn Group
• Call for Contributions: Special Issue of the ACM Transactions on Autonomous and Adaptive Systems on Self-Adaptive Systems: Models & Algorithms
  • Submission deadline: December 15, 2009
• Teleconferences on Complex Systems: free and open to anyone

If you know of other “happenings” feel free to post them in the comments and may highlight them in a future post.

The debate over vaccination is raging (c.f. Wired article) and it smacks of one of those conundrums that is unlikely to get resolved by scientific inquiry.  I offer the following hypothesis and a way out of the dilemma.

Hypothesis: Vaccination is something that is good at the societal level but bad at the individual level.  That is, it is a tragedy of the commons.  You want all your neighbors to get vaccinated so they don’t pass on the germs to you, but there is enough risk from the vaccination process (at least for certain ones) that you’d rather not do it yourself.

The mathematics of the commons tragedies suggests that there are two ways out.   One is to change the payout/incentive structure, in other words, make the vaccine’s less risky to the individual, or at least change the perception of the individual risk (as the Wired article suggests).  The problem with manipulating perception is, what if you’re wrong?  The marketplace of ideas can be efficient, crowd wisdom can be greater than individual understanding.  And even in the cases it’s not, the market can remain irrational longer than you can remain alive.

The good news is there’s another way out.  Just as with the Prisoner’s Dilemma, you can iterate.

What would this mean in the case of vaccines?  It would mean that as a society we must recognize that if we “play the game” enough times we will find that not vaccinating as a whole leads to poorer outcomes to the the individual.  That means YOU.  And thus it becomes recognized that taking the “I’m not going to vaccinate” stance is immoral, or at least unacceptable.  Sure there will be “defectors”, just as there are people who don’t vote.  But those who don’t vaccinate — just like those who don’t vote — do so quietly.  They don’t shout it from the rooftops or let their neighbors know.  And sometimes they even lie and say that they did vote when they really didn’t….

The level of defection is inversely proportional to the level of transparency — the less your neighbor can find out about your behavior, the more likely you are to defect.  Thus, we solve the dilemma by making public the record of everyone who vaccinates, along with their address.  Those not on the record are assumed to be defectors.

Ever since he taught my Econ 1 class, I’ve liked John Taylor.  He always struck me as a practical guy, especially for a macroeconomist. So I was not surprised at what I found when I followed Arnold Kling link to Taylor’s analysis of the stimulus.  Using Department of Commerce data, he calculates that a whopping 0.3% points of the 5.7% point recovery of GDP growth from the first to second quarter is attributable to the stimulus. Of all the positive impacts, private investment accounted for 75% of the total recovery.

So perhaps we could take back the $291B in stimulus money that hasn’t been spent yet.  Probably the single easiest way to reduce the deficit.

I just saw the most important talk I have seen in 300+ TED, Pop!Tech, etc talks that I’ve watched.  And at the risk of hyperbole, I will say that the worst case scenario is that Daniel Nocera simply wins a Nobel Prize (and yes, I’m willing to bet at even odds that it happens in under 10 years from today).  But if the system is able to scale through replication, it will be at least as important as penicillin in terms of ending human suffering and will have a bigger impact on the world as a whole.  Here’s why:

• Input: Water (clean, saltwater or dirty water)
• Outputs: Electricity + Pure drinkable water
• By products: nothing (other than what was in the water)
• Resources required to assemble: all abundant and most have substitutes
• Knowledge required to assemble: simple
• Cost to assemble: relatively cheap

Essentially what Nocera has done is reverse engineered and re-created a super-simplified photosynthesis process.  It’s a closed loop (i.e. autocatalytic) so it’s actually more efficient to run his reactor on a fixed amount of pure water.  But if you want you can use a flow of new water (say, parasite infested water) and as a side effect you get clean water out; all you have to do is have a way to dispose of the impurities that get separated.  You could do that manually if necessary, but once you have energy, that becomes easier and may be automated.

Here’s why I’m really excited.  The system is so simple that it can be built and maintained locally by the bottom billion, for the bottom billion, without the need for an electricity grid.  Sounds like a micro-franchise model that could be taught at places like Barefoot College and could simultaneously create economic development and solve the world’s biggest humanitarian problem, both as a side effect.

And it can be purchased for home use by the rest of us taking our homes off the grid, paying for itself and becoming cash flow positive at some point.  Same for businesses.  What about portable energy, like for cars?  Well, if you have a surplus of energy and water, you can charge hydrogen fuel cells.  Or you can spin up flywheels, store electricity in lithium ion batteries, etc.

The biggest risks I can see are twofold:

1. There crops up some collateral effects of running the system indefinitely that emerge over time and at great scale (e.g. some trace byproducts which were too subtle to notice get concentrated to the point of becoming toxic).
2. The patent on the invention creates a roadblock to replicating the system across the globe.

The reason I’m willing to wager on the Nobel Prize is that I don’t think these risks would sink that ship.  I think it’s worthy of a Nobel in one of the sciences already.  While those can take decades to be awarded, I am comfortable about the 10 year mark because as we all know, the Nobel Peace Prize is winnable in 10 months.

Are others as excited about this as I am yet?  Here’s a clue: when the president of MIT learned about Nocera’s invention she called just one person to bring it to the world, someone she thought could understand just how big it is and someone who could properly shepherd it and nurture it.  He’s a venture capitalist who at one point had been a world-changing inventor himself.  His name is Bob Metcalf, and he invented ethernet, the communication transport mechanism of the Internet.

Ever wish you could reinvent the entire systems of government you live under without starting a costly war, revolution or having to win an election?  No?  Well, Patri Friedman has (wondered, that is).  And so has a growing number of seasteaders, ordinary folks (and the occasional PayPal billionaire).  Or to be more precise, as Patri explained at this year’s Idea Project confab [sign up now for next year, it may sell out quick!] they believe we should at least get to choose from some reasonable options.  Currently your choices are some form of democracy, autocracy, or theocracy.  And switching costs are high.

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.

•

Valuestan

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

•

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.

. . .

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

. . .

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) ?

. . .

The following question was given as a homework problem in a course I’m TAing:

CNBC had an interesting program on the current financial crisis. They located one investor who noticed that since the late 1990’s housing prices have been growing 10 percent every year (that is, each year, the average home price is 1.1 times the average price in the previous year) while income was only increasing by 5 percent each year (that is, each year, the average income was only 1.05 times the average of the previous year).

Explain why it is “absolutely clear that this situation could not go on forever”, in the words of the investor (who made over a billion dollars because of this observation).

This simple question goes right to the heart of the financial collapse. I would only add that, not only did this particular investor make billions off this observation, but our whole economy lost trillions, because the vast majority of financial decision makers were either unable or unwilling to make this same observation.

(Anyone who needs help with the mathematics of this problem can meet me in the comments.)

Having addressed the uninsured and doctor’s visits, the next health care problem on my list is hospital spending. It represents the largest share health care costs, $696.5B in 2007 or roughly 32%.

Now, it’s worth repeating that I don’t object to increased spending per se. It might be perfectly normal given personal preferences and growing wealth. I do object to distortions caused by the current system. I have identified three areas where we could save money through eliminating distorting policies.

Barriers to Competition

Hospitals are a highly protected industry.  I found this Forbes article an excellent overview of the problem. Generally, competition from so-called “specialty hospitals” improves care and reduces costs for both specialty hospital patients and community hospital patients (see this overview of the relevant research). However, like most businesses, community hospitals don’t like competition so they engage in anti-competitive practices and regulatory capture games.

Eliminating such abuses could save 2.4%, the cost reduction that the entrance of specialty hospitals into a market produces  according to this study.  That would be close to $20B/year.  But I would go farther.  I would require all hospitals to publish costs and outcomes for different treatments.  Moreover, they would have to further disclose the price discounts they offered to insurance networks. A more transparent market would drive costs down even farther.

The standard objections to these measures are typical anti-competitive propaganda. Opponents say specialty hospitals duplicate infrastructure, which drives up costs.  Tell that to PC manufacturers.  They had to duplicate all their infrastructure but look at how costs have plummeted due to competition.  Why are hospitals any different?  Opponents say that doctors who own hospitals have a conflict of interest.  Tell that to Apple who has to make phones that people really want if they are going to make any money.  Why are hospitals any different?  And the list goes on…

Drug Development Restraints

Excepting cosmetic procedures, almost everyone would prefer to avoid consuming hospital care if they could at reasonable cost. The biggest substitute for hospital care is taking drugs that prevent hospitalization. Unfortunately, the market for drugs (and medical devices) is tightly controlled, increasing costs and stifling innovation in the drug market. I contend that these factors increase hospitalization beyond the efficient level.

This review article claims that $100B/year worth of hospitalization stems from people not adhering to their medicine regimens. Obviously, formulations and devices that improved adherence would reduce this number.  Moreover, I believe it also implies that there are a substantial number of other hospital admissions that could be avoided if more drugs were available.

As I’ve advocated before, eliminating Phase III trials (in favor of some sort of probationary approval) and reducing the term of patent protection would accelerate drug discovery and reduce costs.  I also think that streamlining the approval of drug delivery devices in particular would help address the issue of adherence.

Inability to Commit to a Lower Standard of Care

Lastly, we the problem of end-of-life care. There has been a lot of angst over the so-called “Death Panels” discussed as part of health care reform. I admit that it gives me the willies.  But I think the problem is that the government is pursuing an interest in getting you to die quietly.

But consider a private alternative.  You’re somewhere between 40 and 60.  You’re pretty healthy.  You have a choice of two major medical insurance plans.  One covers heroic end-of-life measures for terminal conditions.  One doesn’t.  The second one is 30% cheaper.  Personally, an extra few months hooked up to machines in intensive care plus a vanishingly small chance of a miracle recovery isn’t worth it to me.  I prefer to spend my money on safety and prevention thank you very much.  But that’s just my personal choice.  You could chose differently.

This decision doesn’t give me the willies.  People make all sorts of decisions that statistically shorten their lives by this amount: where they live, what activities they pursue, and what jobs they do. This is a completely voluntary decision well in advance of the event.  The only problem is that providers must be convinced that such agreements are enforceable. Otherwise the providers can’t count on the savings and premiums remain high. This is a problem the government can do something about: assign rights and enforce contracts.

All in all, I think these three measures might save a couple of hundred billion per year.  They would certainly lead to a more efficient outcome.

This is a followup to Ben’s post on Human Cultural Transformation Triggered by Dense Populations.  Too many links for this to be accepted into the comments directly…

In thinking about these questions, it helps me to remind myself of the difference between evolution and emergence. Evolution happens whenever you have a population of agents with heritable variation and differential reproduction rates. There are at least two types of emergence, both of which can create new types of agents. Various self-reinforcing mechanisms lead to stronger and more stable agency. We may not even recognize the emergence of nascent agents for what they are until said agency (or coherence) becomes strong enough. For instance, many people have a hard time wrapping their head around cultural agency of any form.

Obviously none of us on here have a problem with the concept of non-human agency, but as Alex and Ben collectively point out, cultural agents depend on human agents for their very existence.  Yet as they become more coherent they inevitably come into conflict with human agency (i.e. what’s good for the organization diverges from what’s good for its constituents). This is the fundamental yin-yang dynamic of the creation of new levels of organization and complexity.

It is worthwhile asking what the future holds for humanity. This is what Kevin and I were on about in this whole superorganism and singularity thread:

• Superorganism and Singularity
• Superorganism Considered Harmful
• Response to Superorganism Considered Harmful
• Superorganism as Terminology
• Superfoo
• Focusing on Autonomy
• Going Meta on Autonomy

Summary is:

1. we disagree on whether there will be a single overarching Gaia-esque Super-agent on earth or whether there will just be a rich ecology of many interacting “small s” super-agents with no strong “big S” Super-agent
2. we disagree on how to measure “autonomy” so we can’t come to a consensus on what life will be like for humans
3. we didn’t really dive too deeply into the extent and nature of interaction between human agents and super-agents

This last point is interesting to me since it appears from the evidence that as each new level emerges, several things happen:

• communicative interactions between higher level and lower level agents increases
• level boundaries become less strict so that levels “overlap”
• the amount of co-evolution between the lower-level population and higher-level population — i.e. multilevel evolution — also increases

To make this claim more concrete, compare for instance the difference (in the above regards) between these three dyadic systems:

A) atom –> molecule

B) cellular organism –> multicellular organism

C) human –> corporation

All thoughts, disagreements, questions welcome…

Biologically,modern humans first appeared 160,000 to 200,000 years ago. But the transition to complex human societies, with art, music, advanced tools, occurred a good deal more recently, and moreover, occured at different times in different parts of the world. An article in June’s Science magazine (see a less technical write-up here) argues, based on historical evidence and computer simulations, that in each case the transition was triggered once the population density had reached a critical threshold. At this threshold, there is sufficient interaction to allow for complex ideas to be passed down through generations, enabling rapid cultural evolution.

This highlights an interesting evolutionary tension: as I’ve written before, evolutionary theory tells us that cooperative behaviors are more likely to evolve (biologically speaking) in populations that are dispersed over space rather than densely packed. But I’m beginning to think that cultural evolution may be different enough from biological evolution to require its own body of theory.

Is it any wonder most people go through more than one relationship before getting married?  Do you really find the right person or figure out that it’s a system?  Hollywood, what are you doing to us?!:

“(OPRAH.com) — Tricia was depressed. That was her only problem. Although her life had all the right ingredients — successful husband, decent job, close-knit family — Tricia felt so low that she sometimes threatened suicide.

Her parents, who’d been drinking, bickered about their mutual infidelity. Her sister wept like a fire hydrant. Tricia’s husband shouted that they were ruining his life.

In short, Tricia’s depression was not her only problem. Instead, it was what I call her designated issue.

Tricia herself would be recognized by systems therapists as a “designated patient,” the one person in a family or group who’s singled out as sick or abnormal, allowing everyone else to feel healthy by comparison.

A designated patient “carries” the group’s dysfunction. A designated issue performs the same service for an individual, dominating our psyches so that other troubles can go unnoticed.” Source

“Systemic therapy neither attempts a ‘treatment of causes’ nor of symptoms, rather it gives living systems nudges that help them to develop new patterns together, taking on a new organizational structure that allows growth.”^[1]

One of the hidden themes of The Feast this past week has been how to scale successful social ventures.  This has been on my mind a lot recently as I have been working informally with both Self Enhancement, Inc. (SEI) and Decision Education Foundation (DEF) on this puzzle.  SEI is extremely successful in the Portland locale where they began 25+ years ago, achieving 98% high school graduation rate (working against hard socioeconomic realities).  Like with many models that are very successful “in the small”, the biggest challenge is to translate that same success to larger scales (e.g. all across America, or all around the world).  DEF is attempting to build scalability into its model from the start, and has found that this is extremely challenging.

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.

Don’t throw the data mining baby out with the black swan.  It’s what you DO with the data that creates problems, not the misunderstanding according to Taleb himself.  If policy makers aren’t looking at this, they’re not changing anything:

“So the central lesson from decision-making (as opposed to working with data on a computer or bickering about logical constructions) is the following: it is the exposure (or payoff) that creates the complexity —and the opportunities and dangers— not so much the knowledge ( i.e., statistical distribution, model representation, etc.). In some situations, you can be extremely wrong and be fine, in others you can be slightly wrong and explode. If you are leveraged, errors blow you up; if you are not, you can enjoy life.

So knowledge (i.e., if some statement is “true” or “false”) matters little, very little in many situations. In the real world, there are very few situations where what you do and your belief if some statement is true or false naively map into each other. Some decisions require vastly more caution than others—or highly more drastic confidence intervals. For instance you do not “need evidence” that the water is poisonous to not drink from it. You do not need “evidence” that a gun is loaded to avoid playing Russian roulette, or evidence that a thief a on the lookout to lock your door. You need evidence of safety—not evidence of lack of safety— a central asymmetry that affects us with rare events. This asymmetry in skepticism makes it easy to draw a map of danger spots.”

If you want a quick refresher of The Black Swan.  Know what quadrant your distribution can belong to and what “moments” of the distribution are important to the decision you’re trying to make.  There are more rules/tips if you follow the link:

“Bottom Line: The Map

Things are made simple by the following. There are two distinct types of decisions, and two distinct classes of randomness.

Decisions: The first type of decisions is simple, “binary”, i.e. you just care if something is true or false. Very true or very false does not matter. Someone is either pregnant or not pregnant. A statement is “true” or “false” with some confidence interval. (I call these M0 as, more technically, they depend on the zero^th moment, namely just on probability of events, and not their magnitude —you just care about “raw” probability). A biological experiment in the laboratory or a bet with a friend about the outcome of a soccer game belong to this category.

The second type of decisions is more complex. You do not just care of the frequency—but of the impact as well, or, even more complex, some function of the impact. So there is another layer of uncertainty of impact. (I call these M1+, as they depend on higher moments of the distribution). When you invest you do not care how many times you make or lose, you care about the expectation: how many times you make or lose times the amount made or lost.

Probability structures: There are two classes of probability domains—very distinct qualitatively and quantitatively. The first, thin-tailed: Mediocristan”, the second, thick tailed Extremistan. Before I get into the details, take the literary distinction as follows:

In Mediocristan, exceptions occur but don’t carry large consequences. Add the heaviest person on the planet to a sample of 1000. The total weight would barely change. In Extremistan, exceptions can be everything (they will eventually, in time, represent everything). Add Bill Gates to your sample: the wealth will  jump by a factor of >100,000. So, in Mediocristan, large deviations occur but they are not consequential—unlike Extremistan.

Mediocristan corresponds to “random walk” style randomness that you tend to find in regular textbooks (and in popular books on randomness). Extremistan corresponds to a “random jump” one. The first kind I can call “Gaussian-Poisson”, the second “fractal” or Mandelbrotian (after the works of the great Benoit Mandelbrot linking it to the geometry of nature). But note here an epistemological question: there is a category of “I don’t know” that I also bundle in Extremistan for the sake of decision making—simply because I don’t know much about the probabilistic structure or the role of large events.

The Map

Now it lets see where the traps are:

First Quadrant: Simple binary decisions, in Mediocristan: Statistics does wonders. These situations are, unfortunately, more common in academia, laboratories, and games than real life—what I call the “ludic fallacy”. In other words, these are the situations in casinos, games, dice, and we tend to study them because we are successful in modeling them.

Second Quadrant: Simple decisions, in Extremistan: some well known problem studied in the literature. Except of course that there are not many simple decisions in Extremistan.

Third Quadrant: Complex decisions in Mediocristan: Statistical methods work surprisingly well.

Fourth Quadrant: Complex decisions in Extremistan: Welcome to the Black Swan domain. Here is where your limits are. Do not base your decisions on statistically based claims. Or, alternatively, try to move your exposure type to make it third-quadrant style (”clipping tails”).”

A superb discussion of the need and risks of financial innovation.  Evolution, complexity, simplicity, and why an equivalent of an FDA approval process may be just what the doctor ordered.  Unfortunately critical thinking is probably still required:

“We are told, particularly in the U. S., that innovation is good. So it is, as a rule, but that does not mean that innovations are good. Most of them are not. To use an evolutionary analogy, innovations are the analog of mutations. Most mutations are detrimental, but some are beneficial, improving the fitness of the organism. These mutations survive and spread.

Likewise, economic innovations compete in the marketplace for survival. Some succeed, many fail. The fact that human beings consciously produce innovations means that they have a better chance of survival than random mutations, but they still face the struggle to survive.

What does it mean for an innovation to be fit? Well, its environment is human beings. It competes for the money of humans. In general, then, it must fulfill some human need or desire.

But a successful innovation need not benefit its environment. The predator does not benefit its prey, not the parasite its host. To succeed, the predator or parasite cannot do too much damage, however.

Financial dealings with consumers often have a predatory aspect, taking advantage of their ignorance, lack of sophistication, and economic weakness. The term, “loan shark”, reveals the potential for predation. You can argue that loan sharks would not exist if there were no demand for them. They certainly provide a service. However, in our society we deem that they do more harm than good. Survival in the marketplace does not mean benefiting society, even if that is normally the case.

If consumer financial instruments have a predatory aspect, what about those aimed at sophisticated investors? As recent history has shown, many of them got taken, too. And the complexity of the instruments played no small part in that. People bought what they did not understand.

I do not understand my telephone. However, if it fails, I can get another for a reasonable amount of money. I know the risks involved. But if you do not understand a financial instrument, you do not understand its risks. That fact argues against complexity in those instruments.

Min

September 28, 2009 at 11:39 am

• I particularly like Min’s allusion to mutations in this context. Most of what has been termed “financial innovation” over the last several years has actually been highly complex financial “mutations” which permitted select small groups of market mavens to extract enormous monetary profits for short periods of time until others were able to determine how they were managing what was usually a financial arbitrage and copied their practice – bringing them all down to minimal returns. This was repeated over and over again with different small groups reaping enormous, but temporary, financial benefits. The problem became critical when the overload of mathematical constructs required more and greater securitization of assets to continue functioning and maintaining the illusion of systemic solvency. The masters of finance were simply front-loading all of the long-term profits in the system into their pockets and back-loading all of the risk into the future. The future is now and our masters are once again working to re-bubbleize the economy since this is the only game they know.

John Hemington

September 28, 2009 at 4:45 pm

Reply

Humbly, both Shiller’s article and the blog entry miss the point. I would agree with Shiller that economic change -specialization, the division of labour and diversity of preferences- moves to greater complexity. This is true of all human artifacts – for instance the emergence of a new language -from pidgin to creole- is characterized by a movement from clarity to complexity – human interaction would fall apart if it relied on blunt instructions rather than on a panoply of qualifications and conditions.

Finance is no different – it has to respond to new shocks, diversify new types of risk and match new classes of savers and borrowers. Hardly surprising that finance will become complex as the activities to which it responds and reflects also become more complex.

The irony is that rampant quantification and modeling which financial innovation has embraced with gusto in the last few decades is uniquely ill-equipped to deal with these realities. At its most unrestrained, financial innovation denies complexity. Its real failure is that it is not complex enough. If our analytical tools cannot keep up, we should trim our sails. Here Shiller’s faith in more information, more complete databases and more muscular computing power sometimes feels like the toils of an intrepid explorer who refuses to give up the ghost.

All innovation -technological or financial- is liable to failure – why would things be any different when people are stepping into dark. No activity is exempt. The difference is that the consequences of failure are extraordinarily high in some areas -nuclear power, aircraft design, financial engineering to name a few (check out the literature on high-reliability organisations). They cannot be tamed by trial and error learning when the potential for catastrophic losses means that the first error will also be the last trial.

Denying the similarities between technological and financial innovation analytically (no doubt unintentionally) cuts off the one of the great routes that has contributed to progress and confidence in these areas – regulation. Why shouldn’t new financial instruments be subject to an ex ante approval process like the FDA does for new drugs? Nobody would tolerate the banking equivalent of holding more capital in those sectors.

nathan s

September 28, 2009 at 7:08 pm

Reply

• nathan s: “I would agree with Shiller that economic change -specialisation, the division of labour and diversity of preferences- moves to greater complexity. This is true of all human artefacts – for instance the emergence of a new language -from pidgin to creole- is characterised by a movement from clarity to complexity – human interaction would fall apart if it relied on blunt instructions rather than on a panoply of qualifications and conditions.”

To invoke evolution again, the reason that evolution appears to move towards greater complexity is that it started with simplicity. In reality evolution also has moves towards simplicity. (BTW, this is the answer to the “intelligent design” argument from “irreducible complexity”. First there was an increase in complexity, and then a reduction in complexity to a point where further reduction would destroy the new functionality.) We see the same in language evolution. English has lost the inflections that its cousin, German, maintains. And we see it in finance. Puts and calls are simplifications of stocks. Their simplicity allows them to be used to build complex combinations that would be impossible with stocks.

“Denying the similarities between technological and financial innovation analytically (no doubt unintentionally) cuts off the one of the great routes that has contributed to progress and confidence in these areas – regulation. Why shouldn’t new financial instruments be subject to an ex ante approval process like the FDA does for new drugs? Nobody would tolerate the banking equivalent of holding more capital in those sectors.”

As I have said, new financial instruments, like new designer drugs, should be regulated upon creation, for similar reasons. :)

Min

September 28, 2009 at 8:30 pm

Reply “

Source

Mendeley.com is doubling every 10 weeks and is on track to surpass the biggest academic databases in the world next year.  What I find fascinating is that it is based on the Last.fm music algorithm/idea, which is now crossing disciplines into science of all things:

 “How does it work? At the basic level, students can “drag and drop” research papers into the site at mendeley.com, which automatically extracts data, keywords, cited references, etc, thereby creating a searchable database and saving countless hours of work. That in itself is great, but now the Last.fm bit kicks in, enabling users to collaborate with researchers around the world, whose existence they might not know about until Mendeley’s algorithms find, say, that they are the most-read person in Japan in their niche specialism. You can recommend other people’s papers and see how many people are reading yours, which you can’t do in Nature and Science. Mendeley says that instead of waiting for papers to be published after a lengthy procedure of acquiring citations, they could move to a regime of “real-time” citations, thereby greatly reducing the time taken for research to be applied in the real world and actually boost economic growth. There are lots of research archives. For the physical (but not biological) sciences there is ArXiv, with more than half a million e-papers free online – but nothing on the potential scale of Mendeley. Around 60,000 people have already signed up and a staggering 4m scientific papers have been uploaded, doubling every 10 weeks. At this rate it will soon overtake the biggest academic databases, which have around 20m papers.” via Guardian.

There are probably better collaborative platforms out there, but this will get Mendeley a following in the community, which is far more important.  How long before these scientists make the data open source?

Or how about Wikibooks?  It is a free library of education textbooks that can be edited by anyone.  We are only one iteration away from having summaries, reviews, and discourse of ALL books available for free.  If you think there’s a copyright issue, this my help.  Amazon.com has plenty of summaries in the reviews section.  The music model is changing from distribution to performance and so will other intellectual property.  Just because you have a good idea, doesn’t mean you get to sell a stack of pages.  The lag in my opinion is due to our obsession with entertainment (music and movies) over education.   What do you guys think?

Just not for “remembering” things.  We are not memory machines, we are learning machines:

“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)

Via the indispensable Tyler Cowen, a new paper from Johnson and Fowler explores whether overconfidence is, in fact, adaptive. They show that it it is under some very reasonable assumptions.  They model competition for resources as a two-player game and then analyze the evolutionary dynamics of populations playing this game.

The basic result is that overconfidence is beneficial in proportion to two factors: (1) the size of the payoff relative to the cost to play and (2) uncertainty about competitor capabilities.  There are two optimal strategies for a population, overconfidence (which minimizes unclaimed resources) and underconfidence (which minimizes conflict costs).  Unbiased self-perception is always dominated by these strategies. However, an overconfident person can successfully invade an underconfident population while the reverse is not true.  So overconfidence is the stable solution.

The direct implication is that resources get destroyed.  It is optimal for an individual to be overconfident, but then he ends up fighting with other overconfident individuals, which imposes costs.  If you think about it for a minute, this is a pretty important fundamental problem.  All of the big societal decisions we face have potentially big payoffs (or avoidance of costs), but it’s really unclear who has the best expertise to make a recommendation.  So we get a bunch of “experts” telling us they are absolutely right.

Note that if it is public knowledge how “good” someone is, the “overconfidence premium” goes to zero.  This is why forcing experts to make public predictions is so important.  Then you can figure out how good they really are.

Are you bluffing enough or too much?  At work, at home, with friends?  Peter principle?  Good guys finish last?  As in poker, bluffing the bluff wins games.  Now we have The Game and various pickup artists passing on the knowledge (big goofy hat required):

 ” …we present an evolutionary model that shows overconfidence actually maximizes individual fitness and populations will tend to become overconfident, as long as the resources at stake during conflicts exceed twice the cost of competition. This is because overconfident individuals make more challenges when there is uncertainty about the strength of opponents (and thus the outcome of conflicts), while less confident individuals shy away from many conflicts they would win. Where the value of a prize is at least twice the cost of trying, overconfidence is the best strategy. The model suggests that the conditions under which humans would have evolved to have a “rational” unbiased view of their own capabilities are exceedingly rare, and it helps to explain why resource-rich environments can paradoxically create more conflict. Moreover, the fact that overconfident populations are evolutionarily stable may be one reason why overconfidence persists today in politics, business, and finance, even if it causes occasional disasters.”

http://arxiv.org/ftp/arxiv/papers/0909/0909.4043.pdf

A few more findings on how we discover and learn (in case you don’t have a dog as I assumed in the post about Discovery and Being Self Aware ).  Computational Approaches section discusses the use of artificial inteligence to help scientists make discoveries:

Scientific thinking as problem solving
“In a similar vein, Klahr and Dunbar (1988) characterized scientific thinking as a search in two problem spaces, an hypothesis space and an experiment space.” (hypothesis space is highly related to emergence)

Scientific thinking as hypothesis testing
“Using this approach, researchers have found that subjects usually try to confirm their hypotheses rather than disconfirm their hypotheses. That is, subjects will conduct an experiment that will generate a result that is predicted by their hypothesis. This is known as confirmation bias. Many researchers have shown that it is very difficult to overcome this type of bias. Mynatt, Doherty, and Tweney (1977) devised a task in which subjects had to conduct experiments in an artificial universe and found that subjects attempt to confirm their hypotheses. Dunbar (1993) has found that while subjects do try to confirm hypotheses, their hypotheses will change in the face of inconsistent findings. Klayman has argued that people possess a positive test bias – people attempt to conduct experiments that will yield a result that is predicted by their current hypothesis, and that under certain circumstances, this is a good strategy to use (Klayman & Ha, 1988).”

Experimental approaches to the development of scientific thinking
“Many researchers have noted that children are like scientists; they have theories, conduct experiments and revise their theories. Thus, while most researchers agree that scientists and adults have much more complex knowledge structures than children, the developmental question has been whether there are differences between children and adults abilities to formulate theories and test hypotheses.”
“Overall, recent research on the development of scientific reasoning indicates that, once amount of knowledge is held constant, few radical differences between children and adults abilities to test hypotheses and design experiments.”

Computational Approaches
“Early computational work consisted of taking a scientific discovery and building computational models of the reasoning processes involved in the discovery. Langley, Simon, Bradshaw, and Zytkow (1985) built a series of programs that simulated discoveries such as those of Copernicus and Stahl. These programs have various inductive reasoning algorithms built into them and when given the data that the scientists used, were able to propose the same rules. Computational models since the mid 1980’s have had more knowledge of scientific domains built in to the programs. For example, Kulkarni and Simon (1988) built a program, with much knowledge of biology and experimental techniques, and simulated Krebs’ discovery of the urea cycle. The incorporation of scientific knowledge into the computer programs has resulted in a shift in emphasis from using programs to simulate discoveries to building programs that are used to help scientists make discoveries. A number of these computer programs have made novel discoveries. For example, Valdes- Perez’s (1994) has built systems for discoveries in chemistry, and Fajtlowicz has done this in mathematics (Erdos, Fajtlowicz & Staton, 1991). See Darden (1997) for a summary of work on computational models of scientific discovery.”

Real-World Investigations of Science
“He has found that much of the scientists’ reasoning is concerned with interpreting unexpected findings. In fact over 50% of the findings that the scientists obtained were unexpected. As a consequence, scientists have developed specific strategies for dealing with unexpected findings that are very different from the strategies seen in the hypothesis testing literature. Dunbar has also found that scientists use analogies from similar – rather than dissimilar- domains in proposing new hypotheses . Furthermore the scientists distribute reasoning among members of a laboratory. For example, one scientsist may add one fact to an induction, another scientist add another fact, and yet a third scientist might make a generalization over the two facts. This type of research on real-world science is now making it possible to see what aspects of scientific thinking are important. By fusing together findings from real-world science with the results of the more standard experimental methods, it should be possible to build detailed models of scientific thinking that, when implemented, can be used by scientists to help make discoveries.”

from http://www.utsc.utoronto.ca/~dunbarlab/pubpdfs/DunbarMITECS.pdf

Now that we’ve solved the problem of the uninsured, it’s time to move on to the problem of doctor’s visits. Spending on physician and clinical services was $479B in 2007, 22% of total health care spending. Only hospital spending accounts for a larger share at 32% (I’ll be addressing this category in a subsequent post).

First, let me say that I have no problem with increased spending per se.  We’ve increased spending on entertainment as well as health care and almost nobody has a problem with that. They’re both signs of increased prosperity.  However, our current system encourages an economically inefficient level of spending. That’s the problem we need to fix.

If we want to get close to the efficient level of spending on doctor’s visits, here’s what we need to do:

1. Eliminate insurance payment of primary care. The risk pooling benefits of insurance only work for rare events or unknown losses. When you use insurance to pay for common events of known magnitude, you are playing the Diner’s Dilemma and most people overconsume. Moreover, you get additional social losses from administrative overhead and reduced incentive to compete on quality. So we should tax any insurance plan that covers primary care (excepting organizations like Kaiser that are paying for essentially all of your care).
2. Establish personal Health Savings Accounts (HSAs). To reduce the sticker shock of (1), we should give people the ability to pay into personal HSAs roughly the same way they pay into personal IRAs. They will still respond to pricing incentives in the outpatient services market, but the use of pre tax dollars will soften the blow and encourage saving.
3. Require pricing disclosure. Partly due to strategic behavior in negotiating reimbursement from insurance companies and partly due to wanting to extract the maximum surplus from patients, doctors and labs are reluctant to disclose their prices.  Unfortunately, this behavior makes it difficult for patients to respond to pricing signals and decreases service innovation by obscuring differentiation. Therefore, we should require doctors and labs to publicly disclose their general price lists and give patients specific estimates before rendering services.
4. Eliminate barriers to “Wal-Mart Medicine”. Doctor’s probably like (1). The are probably mixed on (3).  They probably won’t like this. One of the reasons that trips to the doctor’s office are so expensive is that they just aren’t very efficient operations. Normally, competition would squeeze out inefficiency but doctors are effectively insulated from competition through a variety of subtle and not-so-subtle regulations.  Among the biggest are local restrictions on “retailer clinics” through companies like Wal-Mart and state restrictions on the use of Nurse Practitioners (NPs) and Physician Assistants (PAs). Retailer clinics cost substantially less and provide equivalent care (at least for some basic needs) according to a recent study. Then if you look at salary data from PayScale, NPs and PAs cost about 40% less than family practice doctors. Here, I depart from my usual libertarian bent and advocate using the withholding of federal funds to blackmail encourage local and state authorities to comply.
5. Fund startups in health advisory and tracking. The first four measures will create a much more open and transparent market for outpatient services. As in other such markets, there are probably a lot of advisory and tracking services that could improve decision making and efficiency. Imagine a self-help applications that advises when a trip to a Wal-Mart clinic is sufficient versus when it’s worth the money to go to a more boutique operation. Or a sophisticated rating and cost comparison services by zip code.

With these measures in place, we would most likely get a richer market that spans Wal-Mart clinics staffed primarily by NPs and PAs that cost $35-50 per visit to high end boutiques where a 30 minute consultation with a star doctor costs $300-$500. Each person would spend much closer to the economically efficient level given their personal circumstances and preferences.

“I am not discouraged, because every wrong attempt discarded is another step forward” – Thomas Edison

“Give a man a fish; you have fed him for today. Teach a man to fish; and you have fed him for a lifetime”.  But what if we teach a man to learn?

Was Thomas Edison a genius or “merely” a hard working tinkerer?  Did Newton really discover gravity when an apple fell on his head or was this “discovery” incremental and inevitable?  It is becoming apparent that existence of genius is mostly a product of our imagination, since most “geniuses” are a result of effort and practice.  Thomas Edison not only had an incredible number of inventions, but also an incredible number of failed inventions.  10,000 hours to become the best?  Guess what, that # is dynamic and based on how well the body of knowledge is arbed of inefficiencies.  Tyler Cowen regularly reminds us of “Markets in Everything” to show that markets necessary but not sufficient to allocate resources optimally.  Some even think the technological progress is inevitable!

If a breakthrough is made we never look at “risk adjusted” genius of the discovery by factoring in all the failed hypotheses.  We never bother to ask HOW the hypothesis came about.  Studying emergence completes the scientific method, by simulating the implications of our understanding/model of reality, which we can then either test against data from the real world (physical systems) or at the very least eliminate and improve current theories about social sciences.

Anyone who’s been remotely interested by emergent phenomena should by now start realizing that it pops up in nature, physical sciences, social sciences, the brain, and pretty much anywhere we look.  However it is not simply an attempt to create a more complex model – it is a phase shift in the way of thinking.  It is an improvement of the scientific method.  It is an attempt to create a systematic way of forming hypotheses.

Yes, artificial intelligence of this sort is highly sensitive to the initial inputs and some say will always be less complex than the agent creating it, but who ever said that the scientific method will replace us.  Talk about throwing the baby out with the bath water.  Evolution and survival of the fittest are only half of the story.  All life simply strives for better ways of predicting the future.  Haven’t you ever seen a dog learn? J  It is time for this old dog to learn how to learn new tricks.  A human aided with “artificial intelligence” is emergently superior to one without such a tool.  Yes, we are in many ways better at predicting than a machine, but the point is to catch ourselves in the error of our ways through the use of data mining.  Once sensitivities are detected, data should be collected and mined.  Yes, there are plenty of questions and fields which are too complex or have to be looked at the whole, but if there is something to be found and ARBITRAGED away through rationality, it will be found.  Some take 10,000 hours, some take 20,000.  It is the same as the relationship between the bid/ask spread and the liquidity of the market.  There are also major structural historic path dependence issues.  There is a potential of going back and forth between different states and seemingly being trapped in a sort of gambling paradox, but even if I am a drunk looking for my car keys where the light pole is, simply because it’s easier to see, if I DO see a $20 bill on the ground, I will gladly forgo the opportunity cost, bend over and pick it up.  I assure you that the risk adjusted probability weighted net present value of this endeavor is positive.  Because guess what, we ARE human (we’re not quite yet dancer).  People make mistakes, have sins, and are sometimes a product of the inefficiencies of the current systems.  That $20 could have ended up there for a million reasons as a result of the irrationality of humanity.

Learning, Capitalism, and Politics

That is why investing is an acceptable form of gambling.  More advanced and adaptive beings are “profiting” for the inefficiencies of others.  The process where such trade isn’t necessarily beneficial/fair for both parties is if the more “advanced”/”adaptable” being is CREATING and deceiving the less complex being into matrix slavery (where a steak is better than a burger, but worse than lobster).  That is why education is good “investment” and shall also be seen as such. It is one of the best ways out of the matrix.

It is also incredibly dangerous to allow high leverage, because miscalculations cannot be allowed to pose systemic risk.  So let’s fire up our data miners (it’s not a dirty word;  Blog) and simulators and turn that alpha into a larger beta for the individual and the society.  But we must not forget about the simpler “beings”.  It sounds like such a dangerous implication, but the incredible waves of unintended consequences tell us to curb our confidence (not enthusiasm! (I’m not making a political argument, just acknowledging the trade off)).  Growth is important, but so is fairness and stability.  The most finely tuned system will result in the fastest growth, which will result in more optimal allocation of resources and in turn faster growth.  Capitalism is about to go 2.0.  The phase shift will hopefully eclipse our current debts through an improvement of quality of life thus wealth, whether earned through wages or investments.  This also explains why one cannot simply impose one or two aspects of a complex system on a simpler one and expect to always succed.  Yes free markets are necessary, but they are not sufficient.

Religion

Aided intelligence is why Neo went inside the Matrix.  We either simply choose to get off the data mining rollercoaster at one point or another or are eliminated by evolution.  The choice to get on was made eons ago and by a much simpler entity in a less complex world.  Whether you think God kicked off the race is irrelevant to the participant.  If you wanna believe, believe.  Religion itself is evolution/emergence, but please don’t burn me on a cross for this, cause I couldn’t care less.  However we have to be incredibly careful with certain aspects of our policies, because even if God may have created this mess, we surely can end all life as we know it with or without his assistance.  If you teach a man to learn, he shall become self aware.

It is also an inevitable thought, that we are less complex than other humans.  Although the dimensions and importance of such implications are not negative.  The net present value of future pleasure is positive or at least we perceive it to be so, by not committing suicide.  Thus the meaning of life remains intact.  However in a country of blind, one eyed man is either king or dead.  Choose your pond wisely.

Still think that the tree ends on Homo Sapien?  We definitely created this matrix.  How do you get in?

“Spoon boy: Do not try and bend the spoon. That’s impossible. Instead… only try to realize the truth.
Neo: What truth?
Spoon boy: There is no spoon.
Neo: There is no spoon?
Spoon boy: Then you’ll see, that it is not the spoon that bends, it is only yourself.”

Declan McCullagh of CBSNews reports that a Department of Treasury analysis released under the Freedom of Information Act estimates that a cap and trade program would raise $100B to $200B a year in taxes. Those taxes come from us one way or another. Recall that my estimate of the cost to cover the uninsured is about 2/3rds of that amount ($63B to $126B).

So we have a fortuitous illustration of the tradeoffs we have to make.  There are two issues, priorities and effectiveness. It’s not that I don’t think there is some merit to reducing CO2 emissions.  Rather, I think there are other problems that are higher priority with solutions that are more likely to be effective.  Health care for the poor is one of those.  I’m willing to pay an extra $1000/year to solve health care for the poor.  I’m not willing to pay an extra $1500/year on top of that to address global warming.

Some say a picture is worth a thousand words.  One can probably get a bulk deal on 292 pictures (via Marginal Revolution)