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Category Archives: Problem Solving

The SWPL Paradox: Why Rule By High IQ Fails Miserably

All around the world, the development of nations correlates pretty well with a population’s average IQ.   Therefore, we could designate a ruling caste based on IQ, right, and be better off?  Easy enough.

In real life, though, the high IQ upper middle class and above already has great power just by being high status trend-setters congregating in the center of big cities where all the machinery of influence lies.

Yet rather than bringing in an age of enlightened rule, the SWPLs have consistently collaborated against their own people and tried to destroy their nation, from misguided feel-good idealism and putting their short-term gains first.

We need only imagine for a moment what the USA would look like if the yuppie intelligentsia seized total control and enacted their agenda in full.

The USA would become a temperate zone version of Brazil with no borders, no concept of unity, and crushingly low wages.  Poverty, strife, and tribal warfare would run rampant.  Just like the rest of the third world the SWPL elite would live in walled-off compounds where they would shop at Whole Foods and sip frappuccinos in peace, forgetting the rest of society exists while donating money to Somalia with crocodile tears of signalled virtue streaming down their faces—just like they do already.

Actually, I’m sure they’d ruthlessly “gentrify” any areas they moved into, relying on their armed enforcers to force out anyone who couldn’t be removed by skyrocketing rents so they could have another cute shopping district where they could buy organic dog biscuits for their toy pets.

Total rule by educated urban professionals would have been more violent, unjust, impoverished, and oppressive than Central American dictatorships.  Already, they have long since lost the mandate of heaven through their gross negligence and incompetence, a fact even they are becoming dimly aware of as national politics steadily slips out of their control.

Why were they as a group worse at ruling than any Joe Sixpack taken off the street would have been?  At least average Joe might not have been actively malicious and contemptuous towards the people he’s ruling.

The first, most obvious flaw, is lack of skin in the game.  Rule from provincial capitals encourages disconnection.  However smart someone may be, you have to have experience.  You can’t become a doctor or a pilot by reading books. Having a culture that spreads equalist propaganda to people who don’t have the experience to know otherwise just makes it even worse and assures terrible leadership and government.  Yet, they chose to accept what they were taught without critical thinking and swallowed the scam hook, line, and sinker.  This fact is perhaps more significant.

While IQ predicts the performance of population as a whole, it’s clear we need further criteria to determine who should rule.  Clearly, personality traits, neurotype, thinking style matter just as much.

A good example is the massive under-performance of East Asian people relative to their IQ and huge populations for many centuries even though they are objectively smarter than Europeans.

Elite US universities have to include special subjective rules to ensure model minority students don’t become a majority.  Chinese and upper-caste Indians outcompete whites without too much difficulty.  Yet in spite of this, their homelands have been stagnant fiefdoms of foreign powers until recently.

From what I’ve observed, they have the same problem as the SWPL lesser aristocracy.  Their high levels of organization and extreme conscientiousness and work ethic makes them the perfect cubicle workers.  But they are incapable of independent thought and action.

Every friend I’ve had who’s been in academia says the same things about students from Asia: They cram and memorize stuff for when they need it, but don’t really understand the material. What’s more, many of them had to be in good favor with their government’s brainless indoctrination to get selected for foreign study in the first place.

As far as I can tell, upper middle class whites are those most compatible with the Asian mindset and probably originate from similar selective pressures.  Their neurotype and phenotype is just not as widespread.
I recall watching League of Legends championships and noticed the few whites were as weedy, gracile, and diminutive as their Asian counterparts.  Both the white and Asian players exhibited these traits to a greater extreme than an average Asian would.  What we call “nerds” in the West simply became more normal in densely populated rice civilizations.

Another test is necessary, then, besides IQ to determine a ruling caste.  While autistic bean-counters are put to good use within the low to middle ranks of bureaucracy, they are incapable of rule.  It is not even in their temperament to rule themselves, let alone others.

There has to be a way to test for genuine curiosity and understanding as opposed to just manipulating symbols.  Even more so, a barbaric penchant for internal locus of control rather than civilized unquestioning conformity.

From my own experience and reading history, it seems humans come up with new ideas and conquer when they have good IQ and teamwork combined with just the right amount of barbarian wildness and individualism.

It seems otherwise hard to explain how the rocky tip of the Balkan peninsula or a small and rainy island might have created the most enduring ideas and inventions while having the greatest military might and empires of their times.

When I was in Korea sometime back, I thought their society very much superior in many ways.  High IQ made itself obvious in everything from monumentally more effective city planning to more subtle signs like having a whole TV channel devoted to the game of go.
But watching crowds stand patiently at crosswalks, almost in rank and file, even when no cars were nearby told me in one instant why they’ve never conquered the world and why their cousins in the North consent to be ruled by one of the world’s worst governments.
A nation of Africans with 100 average IQ would have potential to become far more dangerous than they are.  Neurotype and temperament matters in people just like it does in breeds of dogs—it’s just considered very impolite to notice.

Social Engineering Should Be Tested First

The best intentioned reformers often make things even worse.  But so would anyone trying to solve massive, complicated problems on the first try. It’s actually more surprising anything ever goes right.
It amazes me looking back over history to see how reformers and revolutionaries try to apply their ideologies without ever having tested them. Imagine a tech company releasing a new device without extensively testing it first or a computer programmer writing code for an entire program without ever trying to compile it. Ridiculous, yet that’s what people try to do all the time. Too often the result is disaster.
The higher castes have greater agency through which they deal with greater responsibilities. They can’t just say “oops” when there’s a big logistical screwup and a couple million people starve to death.
Any responsible sentient being in power naturally has a system to test new ways of organization before implementing them on a large scale.
Observing differences between local governments and the study of history provides lots of fertile material for hypotheses, but the devil is in the details.

There would have to be some sort of R and D department for trying out new social technologies. Perhaps there could even be reality shows of a sort where in the first round groups of maybe 150 or so live under a hypothetical social model then those groups that make it past the elimination get expanded up to 1000 and so on. There would be rules to keep it ethical. People who “die” in the experiment would just be “voted off the island” and sent home. Not being “real” would of course distort the data, but perhaps money or other incentives could make the results worthwhile. Someone who “dies” might lose all their prize money, representing a total loss.

Or to make this simpler maybe a reform simply gets tried first in a small town or a single city first and upvoted or nexted based on results. Perhaps there might be an actual experimental province set aside with discrete zones. Those who chose to live there would simply vote with their feet. In the absence of any Berlin walls, it would quickly become evident which zones people like and which they avoid and what type of people or demographics prefer different systems. Of course, the experimental province might give unrepresentative data if they attracted outliers of the population, but it could be a start. Not to mention, there would probably have to be incentives to get people to choose to live in experimental land.  Perhaps they’d sign contracts to stick around in experimentland for a year or two or else they lose all their bonuses.
As enough information was amassed from real life experiments maybe computer simulations would become more effective at projecting results and maybe programs could be written to project hypotheses for ideal social organizations taking every aspect of human nature into account that maximize both raw competitiveness and creativity/adaptability to new stressors.

Throughout history, groups have settled on something that works for the time period and then try to perpetuate it ad nauseum across milennia.  Talmudic Judaism was a brilliant way to coordinate a particular Semite tribe over 2000 years ago.  Islam turned out to be the right solution for quarreling Arab city states about 1300 years ago.  But one of the things we immediately notice is that all these systems buy a professional suite of anti-virus software to prevent change to that successful formula, even if it’s a thousand years later.
Sadly, social technologies tend to stagnate because they only ascend to apotheosis in the first place because they have serious protection against change.
The challenge before us then is how to design a society to be both resilient and highly adaptable to new stressors, so that when the next big asteroid hits, we aren’t among the dinosaurs.

Abolishing Compulsory Schooling

The problem with compulsory public schooling is most kids don’t want to be there.  It’s really just taxpayer daycare while parents are busy at work.  My whole youth I remember two dominant emotions most people had for school: boredom and contempt.
I remember well the textbooks we were issued, that must have cost 200 dollars apiece and each of them was trashed and filled with the lewd graffiti scribbles of a captive audience.  Nobody trashes resources they care about and respect.
In a properly run state, the people have a sense of awe and respect at all levels and the way public daycare works now gives its inmates 18 long years of instruction in official incompetence, undermining the credibility of the ruling order for anyone inclined to think for themselves.

The first step would be to stop making school compulsory.  One of the best and most reliable ways to earn contempt in this world is to keep giving people nice things they haven’t earned, even after they spurn your offerings.  They learn you’re an easy mark—that they can take a steaming dump on your face and won’t get called out.  The parents learn they can just forget about their kids for 18 years using taxpayer nannies and the kids learn that no matter what they do, they’re stuck there getting thousands of dollars spent on them every year.

Society has forgotten that school is for those who want to learn and the needs of those who learn best come first.
All through my youth in public schools even the most competent teachers struggled against the dead weight of students who were forced to be there. These students weren’t interested to begin with, but being forced encouraged them to passively aggressively disrupt classes for everyone else.
Teachers could have found ways to mitigate this if the system had backed them up, but instead the bureaucracy forced them to teach to the lowest common denominator, a decent strategy for an ant colony perhaps, but not the way to success for a civilization.

The purpose of schools is to teach willing, sufficiently talented students. People who don’t want to study have no business being students. That’s all.
I look back on my first 18 years of school and ask myself “In all that massive investment of time and taxpayer money, what did they teach that I’ve actually used in the real world?”
I could think of two things everyone needs to know for basic participation in society that school teaches, if we don’t learn at home.
-Basic literacy
-Basic arithmetic
That’s all most people will need or ever want to know.
And a decent proportion at the bottom of aptitude will never learn even these very well.

So I would posit that we could still have a compulsory workshop on the public dime, a year worth of classes or so spread out over a few years of life perhaps where everyone still gets taught to read, write, and perform basic mathematical operations. Before public schools, a few months of school here and there when not needed on the farm seemed to get the job done for most people. Those kids that like it and can handle the basics can then go to school.
For the rest, maybe we still have state daycare just to prevent the emergence of child gangs roving the streets, but there would be no more confusion. It would be called what it is. The kids there wouldn’t go to classes. They’d get movies, lunches, maybe some activities. No one will consider that 14 year old that still goes to daycare a student. They’d just be children, no higher ranked than 1st graders. It would still be a bullshit waste of millions of people’s time but still better than what we do now: almost 2 decades of make-believe.
This distinction would be important, because all taxpayers would pay for real schools, just like we all pay for roads and the military. However, those who want to use public daycare would pay all the taxes for it, so they can’t just waste everyone else’s time and money.

A better way I think, would be to keep children busy even if they don’t go to school. They might learn and practice work-related skills until they reach minimum working age and can go out and get a job. Most 10 year olds would be better off learning how to type fast, mop a floor, cook the perfect burger, use microsoft office, or how to use basic tools rather than learning earth science or “social studies.” They’d be better off by age 15 than millions of 20-somethings coming out of college with 0 experience and unemployable degrees.

I thought of a lowering in working age so kids could join the job market earlier but it quickly occurred to me that jobs are already scarce in a post-industrial economy and one of the functions of public schools is to delay the entry of young people into the job market. Even colleges serve to relieve pressure on older workers and give warm bodies a way to stay on the shelf until the economy actually needs them. One of the ironies of our entire modern lifestyle is how we destroy huge amounts of youthful productivity and wealth on a big ceremonial pyre for the sake of wealth production and call it the best system on earth, the best of all possible systems.

So, really, our underlying problem is the hollowness of The Economy as God. With no higher purpose or mission, we struggle along aimlessly applying flimsy bandaids or even eating our young to keep the status quo superficially intact. The truth is modern labor has become so productive that we don’t need to work that much but The Economy requires that every adult seems busy in a way that shows up on the balance sheets. It would be much harder to maintain the illusion were we to abolish the public daycare system and return education to its rightful place in society.
Millions of kids would go home and maybe some millions of adults would realize it’s more profitable just to stay home with the kids than pay for daycare, relieving more pressure on the job market than locking up teenagers ever did.
Millions more kids might spend their formative years learning how to be successful workers rather than learning boring facts about the Earth’s core or the Founding Fathers that they will soon forget.
Millions more kids with even a bit of brains and curiosity would be sent by their parents to school where they would learn a broad range of knowledge without constant disruption.
Because non-students would be filtered out, public schools would have a reasonable baseline of quality anywhere you go. Middle caste and above would no longer be forced into just a few crowded neighborhoods with “good school districts” where all the money that would have nurtured children goes into the mortgage instead. Those starting out their lives among the lower castes would get a chance to rise.

Sorting out the Castes: Testing for Delayed Gratification

Delayed gratification is one of the most important principles in dividing lower castes from higher.
The need for instant gratification is the eternal and unmistakable mark of low class, poverty, and despair.
If we want to know what kind of people live in a neighborhood, all we have to do is take one quick glance down and see if people are throwing their trash on the ground or in trash cans.  If we walk into a store is the liquor and baby formula out in the open or is it kept behind glass?  It shouldn’t be that difficult to evaluate individuals as we evaluate a neighborhood.

In general, as people get more intelligent the more they can understand the abstract concept that if everyone chooses to cooperate by passing up littering or shoplifting now everyone gets the greater reward of a pleasant community to live in and stores full of easily accessible goods later.
Even smart people with low character tend to pay for their groceries because they prefer to expend their energy on much more valuable prizes with lower risk over a much longer time. They don’t let small prizes distract them when they could be gutting people’s 401k accounts instead.
Stupid people with low character on the other hand distinguish themselves by taking huge risks for small, uncertain, temporary gains. Their inability to understand probability and calculate risk/reward always gives them away.
Some ability for delayed gratification and long term planning is an absolute prerequisite to move up into the middling to upper middling castes.

To move into the highest castes though, the ability to inhibit desires has to be extended to another degree of abstraction.
The highest people need to have the ability to care about a good beyond themselves, to consider the good of an entire people or even the species. They must care about events beyond their own lifetimes.
The ultimate act of delayed gratification and the mark of a high human is planting trees that will never give shade in our lifetimes.
Clever people of the middle castes, on the other hand, busily hoard away for college, a house, or retirement but have little thought beyond the narrow scope of their own parochial circumstances. Their inability to understand a bigger picture always gives them away.
I have met many clever upper middle class SWPLs who wallow in aimless hedonism but don’t have the moral intelligence to care what happens after they die nor the wisdom to understand why their poverty of purpose has left them cynical and jaded.  Perhaps even more upper middles indulge in saccharine feel-good idealism that helps break the ice and gives cheap social proof at cocktail parties.  They know well in the back of their minds they’ll never have to test their beliefs against the real world.  In fact, having to care about the real world is an indicator of boorishness in their insulated universe.  Being insulated in itself, of course, is the defining mark of petty nobility.
Our present system is heavily influenced by these characteristic upper middle attitudes. These are the people who thrive in the meritocracy of credentials, “networking”, “extracurriculars”, “fellowships”, and standardized tests. Ironically, those with greater vision and imagination are pretty effectively weeded out by their criteria.  This is why we need a formalized caste system, to cut through the bullshit of those people far enough above to dazzle the lower ranks, but not so high as to be completely unrelatable.
The rightful rulers, of course, should be unrelatable to the average person.

Sorting Out the Castes: Easy Disqualifiers

Within 30 seconds of looking at someone’s facebook, their room, a list of their favorite hobbies, what they’ve bought lately we get a rough sense of what type of person they are.  It’s possible we could be mistaken but generally quick judgments work.  In modern society we’re told we can’t accurately judge and categorize people but in reality it’s not only doable, it’s pretty easy.
In real life, it’s generally safe to assume that a passing frat bro is more into jack and coke and fireball whiskey than single malt scotch or that a black dude with baggy pants and expensive shoes isn’t a Babylon 5 fan.  That hipster sitting nearby at the coffee shop probably isn’t into nascar(unless he’s being “ironic”), the rugged looking man with the big pickup truck probably doesn’t listen to NPR.  There’s exceptions of course, but even very crude anecdotal stereotypes work most of the time in real life.  So it’s not that extraordinary to expect that we could sort people correctly at least 90% of the time with a very low amount of effort.  If it was broken down to more of a science, I figure people could be put in the right place almost all the time.
If all a system needs to do is sort people out better than the present system, that’s a pretty low bar.

Perhaps we start with easy disqualifiers:

-Regularly buys lottery tickets, gambles against the house.
-Regularly uses payday loans and maxes out credit cards without compelling emergency reasons.
-Buys products from infomercials, web ads, spam emails.
-Doesn’t understand basics of how government works.
-Doesn’t have a basic idea of or curiosity about nation or world outside of their area.
-Buys all junk food at the grocery store and over-indulges in it.
-Doesn’t read, watch, or listen to anything that isn’t light entertainment.
-Buys flashy cars and clothes they can’t afford.
-Hopelessly, non-functionally addicted to any drug they come into contact with.

People that meet these criteria demonstrate they lack critical thinking and judgment. They lack the brain power to understand how probability or compound interest works. They don’t have the impulse control to manage complex choices or delayed gratification. In our present system they are mercilessly parasitized and exploited and they’re fair game because we’re all “equal.” Sorted into their proper caste, kept away from all positions of responsibility, it would be understood they are inherently vulnerable to the clever and must be protected as an adult would protect children or animals.
A pretty simple computer algorithm could probably instantly remove at least the bottom 10-20% or so without having to give evaluations or examinations to millions of people. Just data mining people’s real life behavior could probably make the initial rough cuts.

Imagine just taking away the vote from the dumbest and most impulsive 10% or so of the US population. There would probably be massive systemic improvements and an upgrade in political discourse overnight as if by magic. Just ponder a moment the magnitude of this lowest-hanging fruit alone.
Just weeding out those obviously unfit for civic life and placing them in an undercaste alone opens up huge possibilities before we even get started.

Life Lessons from Starcraft 2

Computer games can be time wasters when we’re just playing against a computer. MMOs tend to be a waste in the absence of an end goal in an environment that’s deliberately designed to be aimless and addictive.

Games of strategy, however, tend to exercise the mind and spirit, especially when your opponents are other humans. This is a role Starcraft II fills admirably with its server packed with thousands of people, a game against fresh opponents always ready to play in a few seconds. Each match has clear objectives and an ending, unlike MMOs. In an hour one can play 3-4 different matches finding out what works and what does not.
One quickly finds even at the lowest level of play human opponents are far more dangerous and unpredictable than any AI.
About every 15-25 minutes you can go through life’s conflicts in miniature. It doesn’t take long to see certain patterns emerge, that certain philosophies work optimally while others are mediocre or fail outright.
I will try to list some of the lessons I’ve learned from starcraft that have proved valuable in real life:

1. Experience trumps wits.   Some idiot who’s simply spent more time playing the game will beat you when you’re new, no matter how fast and clever you think you are. You might think you’re smart, but it’s not as much an advantage as you think if you haven’t put in the time and effort.
Coasting on raw ability alone fails miserably in a contest that relies on learned skills. An ordinary guy who knows an optimal build order to execute a sound strategy will destroy a genius who’s trying to figure everything out for the first time.
This is why there’s plenty of average joes doing well in life while everyone knows that “smart” guy who’s losing at life.
Starcraft 2 teaches there’s no such thing as “potential” only results.

2. Success is a numbers game. You have to lose (a lot) to ever be a winner. As you get better the matchup will try to move you up the ladder to people who are your equal or better in ability.
You lose your ego fast when you constantly go up against opponents who you’ll lose to half the time. You’re never allowed to just stay comfortable crushing people who are below you. Every time a new game begins, you’re up against someone you can’t take lightly. By the time you learn enough to play even half decently, you’ve suffered dozens of humiliating defeats and know what it feels like when the winner decides to be an asshole.
Even if you get good, you know there’s no shortage of people who can slap you around effortlessly. You realize gloating in victory is for children who know little of life. A real life Big Man is above such silliness.

3. Time is the most important resource. A dumbass who’s simply faster than you will destroy you every time. If you aren’t ready when he comes for you, if you can’t react fast enough, that’s it, you’re dead. All your boasting and bragging how you’re a master strategist is for naught. Knowing kung fu makes no difference if you’re dropped right away by a swift punch to the jaw.
Imagine getting extra moves in chess! You’d be able to destroy players monumentally smarter and more skilled than you. The day is often decided simply by getting there first with the most.
The importance of time in deciding conflict can’t be doubted if we glance at the American Civil War. A bit more speed would have prevented the rebel armies from uniting at Manassas. A bit more speed could have threatened Washington after First Manassas. A bit more speed could have destroyed Lee’s army at Gettysburg. Longstreet saved Lee from defeat at The Wilderness by arriving at exactly the right time. A little more speed and there would have been no months-long siege of Petersburg…
Starcraft drills this lesson into your head mercilessly. If you’re playing terran and that bunker is completed just a few seconds too late before that zerg rush hits, it’s game over.

4. To everything there’s a golden mean. Goldilocks and Aristotle had the right idea. Too aggressive, you die. Not aggressive enough, you die. Starcraft teaches you the hard way to have a feel for exactly what kind of approach a situation calls for. When we’re first learning to drive a car, we sway back and forth in the lane, compensating then overcompensating. Soon, we drive straight.
In real life, though, we tend to make a major mistake that causes us to overcompensate to an equally faulty extreme. Then we waste years of our lives compounding our error until continuation becomes so painful we’re forced to re-evaluate our strategy. A few decades later, the lucky among us are finally able to drive that car somewhat competently, the rest never learn.
With starcraft, it becomes possible to see a model of that grand learning process in miniature.

5. Your brilliant ideas mean nothing until you try to execute them. Even a simple plan falls apart when you’re under pressure. Being adaptable in the moment is more valuable than making grandiose complicated plans. This is why armchair generals fail. A game like starcraft becomes a laboratory to test your hypotheses about what will work and what will not.
In real life, we can’t formulate a philosophy and then have a series of 20 minute tests to see if it really works as a guide to our actions. But starcraft allows us to come somewhat close to that. Through trial and error we learn that some approaches are objectively better than others. After trying something 20 times and getting your ass kicked every time, you’re forced to stop rationalizing. That approach doesn’t work. Now, no demagogue, ideologue, or politician will ever convince you otherwise; you’re immune to their poisonous talk of relativism because you’ve experienced objective truth for yourself, often painfully.
In real life, winning conflict requires the same principles as engineering. You want the simplest, lowest investment solution that effectively solves the problem. The more complexity, the more points of failure. Evolution shows us this philosophy is one of the underlying laws of reality. A “fit” living thing accomplishes its goals as efficiently as possible with as few points of failure as possible.
The pages of history are littered with egotistical generals who broke this universal law, thinking themselves military geniuses to the bitter end.

6. Always go for decisive objectives that put your opponent under mortal threat, which forces him to try to stop you with all his resources. As with chess, you want to risk your army for proportionate gains. A new player might wreck his opponent’s new expansion base only to find his own main base is now being gutted. Dealing a painful but not mortal blow allows the opponent to retaliate—and they might well kill rather than wound you. If the opponent is constantly forced to prevent unacceptable losses, you control the game. It’s hard to be aggressive in chess when the King keeps getting put into check! If you can seize the initiative, you’ll usually win.

7. The line between defeat and victory is a narrow one. If you forget detectors, that could cost you the game when cloaked units show up. One small oversight and you instantly lose the game, even if you were otherwise in a position to finish your opponent. In real life, battles both literal and figurative are often decided by the smallest mistakes. This is another great lesson that crushes the ego. It’s hard to be an arrogant victor when you’re keenly aware one small mistake would have reversed the outcome.

8. Decisiveness wins. Even the poorest strategy will sometimes succeed if someone commits to it completely and without hesitation. With indecisiveness, we divide and conquer ourselves. In real life, a weak faction like the North Vietnamese can defeat even an overwhelmingly strong faction that is indecisive, uncommitted, with no clear objectives. Without a clear mission to fulfill or a clear course of action to achieve it, there is no such thing as victory.

9. Starcraft teaches us to be less critical of those who have great responsibility. Even a mere game that shows how easy it is to screw up teaches perspective. Bad luck and small mistakes can easily bring disaster even to the competent. Even those who prove incompetent at the highest levels often stand far above the average guy on the street. You begin to realize that herdbeasts who mock and complain endlessly about their betters are just misbehaving children. They have never known leadership or great responsibility themselves yet deign to criticize as if they were equals.

Starcraft 2 is certainly not a perfect microcosm of real life. For one thing, the playing field is far too orderly and predictable. We never have that much information when making real decisions. If two opponents played 100 different strategy games against each other for the very first time barely knowing the rules, that would be much more like real life. In fact, I think sloppy bronze league play may simulate real life best. But the controlled environment of starcraft allows us to test ideas more extensively. It invites us to reflect on our own lives and contemplate how the mindset we learn playing battle after battle applies to real conflicts we face.
Looking over the battlefield, what are the most effective actions we can take to defeat the obstacles before us? What objectives are vital and which are distractions?
Many now seem to view life as some kind of sentimental TV drama, but to me it is perhaps just another game, the Great Game.

terran, wall, 6 pool, cheese, stop, block

Terran wall blocks early game allins such as Zerg’s “6 pool”. The SCV stands by to repair any attempts to make a breach. Reacting quickly and keeping cool under pressure is critical to survival.

Mental Calculators

Interesting piece on how mental calculators are competing against each other to quickly solve math problems:

The high point of the abacus calendar is the All Japan Soroban Championship, which took place earlier this year in Kyoto.

And the high point of the championship is the category called “Flash Anzan” – which does not require an abacus at all.

Or rather, it requires contestants to use the mental image of an abacus. Since when you get very good at the abacus it is possible to calculate simply by imagining one.

In Flash Anzan, 15 numbers are flashed consecutively on a giant screen. Each number is between 100 and 999. The challenge is to add them up.

Simple, right? Except the numbers are flashed so fast you can barely read them.

I was at this year’s championship to see Takeo Sasano, a school clerk in his 30s, break his own world record: he got the correct answer when the numbers were flashed in 1.70 seconds. In the clip below, taken shortly before, the 15 numbers flash in 1.85 seconds. The speed is so fast I doubt you can even read one of the numbers.

I’ve often wondered at how 3d visual displays, like Google Glass, are going to change the way we work with and augment data. It may be possible that we can speed up our own performance dramatically alongside computers that we work with.


Calculating prodigies are individuals who are exceptional at quickly and accurately solving complex mental calculations. With positron emission tomography (PET), we investigated the neural bases of  the cognitive abilities of an expert calculator and a group of non-experts, contrasting complex mental calculation to memory retrieval of arithmetic facts. We demonstrated that calculation expertise was not due to increased activity of processes that exist in non-experts; rather, the expert and the non-experts used different brain areas for calculation. We found that the expert could switch between short-term effort-requiring storage strategies and highly efficient episodic memory encoding and retrieval, a process that was sustained by right prefrontal and medial temporal areas.

Inspired by Ribot’s psychological work (1881), they believed in the existence of not one type of memory but several partial, special, and local memories, each devoted to a particular domain. In all arithmetical prodigies, memory for digits is abnormally developed compared with other memories. Inaudi was considered to be an auditory memory-based mental calculator; when memorizing digits, he did not rely onthe appearance of the items or create visual imagery of any kind. Rather, he remembered digits principally by their sounds. Inaudi’s methods of calculation and memorization were original and different from those used by Diamandi, who was a typical visual memory-based mental calculator. The experiments presented in the 1893 article were among the first scientific demonstrations of the importance to psychology of studying different types of memory. The present work gives a translation of this pioneering experimental article on expert calculators by Charcot and Binet, instructive for the comprehension of normal memory. that people with mild intellectual disabilities (ID) have difficulty in ‘weighing up’ information, defined as integrating disparate items of information in order to reach a decision. However, this problem could be overcome by the use of a visual aid to decision making. In an earlier study, participants were taught to translate information about the pros and cons of different choices into a single evaluative dimension, by manipulating green (good) and red (bad) bars of varying lengths (corresponding to the value ascribed). Use of the visualcalculator increased the consistency of performance (and decreased impulsive responding) in a temporal discounting task, and increased the amount of information that participants provided to justify their decisions in scenario-based financial decision-making tasks.

Previous research has demonstrated that people with mild intellectual disabilities (ID) have difficulty in ‘weighing up’ information, defined as integrating disparate items of information in order to reach a decision. However, this problem could be overcome by the use of a visual aid to decision making. In an earlier study, participants were taught to translate information about the pros and cons of different choices into a single evaluative dimension, by manipulating green (good) and red (bad) bars of varying lengths (corresponding to the value ascribed). Use of the visualcalculator increased the consistency of performance (and decreased impulsive responding) in a temporal discounting task, and increased the amount of information that participants provided to justify their decisions in scenario-based financial decision-making tasks.

The results suggest that the visual calculator has practical applicability to support decision making by people with mild ID in community settings.

Among the many examples of the congenital form are the calendar calculators, who can quickly provide the day of the week for any date in the past; the musical savants, who have perfect pitch; and the hyperlexics, who (in one case) can read a page in 8s and recall the text later at a 99% level. Other types of talents and artistic skills involving three-dimensional drawing, map memory, poetry, painting, and sculpturing are also observed. One savant could recite without error the value of Pi to 22,514 places. Persons with the acquired form develop outstanding skills after brain injury or disease, usually involving the left frontotemporal area. This type of injury seems to inhibit the “tyranny of the left hemisphere,” allowing the right hemisphere to develop the savant skills. Another way to inhibit the left frontotemporal area is to use transcranial magnetic stimulation in normal subjects; nearly one-half of these subjects can then perform new skills during the stimulation that they could not perform before. This type of finding indicates the potential in all of us for the development of savant skills in special circumstances.

In the present study, we examined cortical activation as a function of two different calculation strategies for mentally solving multidigit multiplication problems. The school strategy, equivalent to long multiplication, involves working from right to left. The expert strategy, used by “lightning” mental calculators (Staszewski, 1988), proceeds from left to right. The two strategies require essentially the same calculations, but have different working memory demands (the school strategy incurs greater demands). The school strategy produced significantly greater early activity in areas involved in attentional aspects of number processing (posterior superior parietal lobule, PSPL) and mental representation (posterior parietal cortex, PPC), but not in a numerical magnitude area (horizontal intraparietal sulcus, HIPS) or a semantic memory retrieval area (lateral inferior prefrontal cortex, LIPFC). An ACT-R model of the task successfully predicted BOLD responses in PPC and LIPFC, as well as in PSPL and HIPS.

No gross anatomical differences were observed. By morphological assessment, cerebral volume (1362 mL) was larger than normative literature values for adult males. The corpus callosum was intact and did not exhibit abnormal structural features. The right cerebral hemisphere was 1.9% larger than the left hemisphere; the right amygdala and right caudate nuclei were 24% and 9.9% larger, respectively, compared with the left side. In contrast, the putamen was 8.3% larger on the left side. Fractional anisotropy was increased on the right side as compared with the left for 4 of the 5 bilateral regions studied (the amygdala, caudate, frontal lobe, and hippocampus). Fiber tract bundle volumes were larger on the right side for the amygdala, hippocampus, frontal lobe, and occipital lobe. Both the left and the right hippocampi had substantially increased axial and mean diffusivities as compared with those of a comparison sample of nonsavant adult males. The corpus callosum and left amygdala also exhibited high axial, radial, and mean diffusivities. MR spectroscopy revealed markedly decreased γ-aminobutyric acid and glutamate in the parietal lobe.

See also:

How To Drive Cockroaches And Mosquitoes Extinct

No amount of attempts at squashing them all, poisoning them, diseasing ever works.

But there’s a simple strategy that’s obvious if we think about it.

The Earth can support only a finite amount of biomass.

Every gram of that total filled by people, crops, and livestock reduces the amount left over for everything else.

So in theory, if you just kept making more people and domesticated organisms, there would eventually be nothing left over to support pests…Or anything else for that matter.


Dealing With Complexity – Solving Wicked Problems

Allen Downey, in his book Think Complexity, identifies a shift in the axes of scientific models:

Equation-based –> Simulation-based

Analysis –> computation

These new models allow us to not only predict behavior, but to also introduce randomness and give agents more detail than we see in classical approaches like Game Theory.

DARPA and various other government agencies and corporations lead the way in the early years for simulations. And slowly it filtered down through the intellectual strata until some K-12 programs started using NetLogo to teach kids about cell structures, the behavior of gas molecules and emergent complexity. The options at our fingertips still aren’t anywhere near as good as they will be in 5 or so years, but it’s what we have to work with.

Allen goes through several pages of changes in scientific modeling caused by the equation to simulation and analysis to computation shift, you can read it on page 16-22 if you’re curious (the book is free in PDF form).

This brings me around to the other point of my post: humans are horrible at working with complexity for a lot of reasons. One of the biggest I’ve seen so far is working memory. There is too much information, and people can’t sort through it quickly enough. And even when they can, they can’t hold enough of it inside of their heads to make the connections they need to understand their situation and plan out possible contingencies.

The average person can hold 5-9 objects in their working memory at a time, which seriously hinders their ability to figure out large complex scenarios with hundreds of thousands of probabilities. Simulations can work around this by giving you real time feedback on changes in variables while incorporating randomness, but for regular analysis finding ways to “visualize” information seems to work well, more on this in a minute.

Nassim Nicholas Taleb’s Black swan theory represents one of the many articulations of the “Unknown Unknown” categories we have to deal with. They are the side effect of an environment too complex for most people to understand. He used two examples, the 9/11 attack and the mortgage meltdown:
An example Taleb uses to explain his theory is the events of 11 September 2001. 9/11 was a shock to all common observers. Its ramifications continue to be felt in many ways: increased levels of security; “preventive” strikes or wars by Western governments. The coordinated, successful attack on the World Trade Center and The Pentagon using commercial airliners was virtually unthinkable at the time. However, with the benefit of hindsight, it has come to be seen as a predictable incident in the context of the changes in terrorist tactics

Common observers didn’t think it was possible, however many experts had already considered such a scenario:

After the 1988 bombing of Pan Am Flight 103 over Lockerbie, Scotland, Rescorla worried about a terrorist attack on the World Trade Center. In 1990, he and a former military colleague wrote a report to the Port Authority of New York and New Jersey, which owns the site, insisting on the need for more security in the parking garage. Their recommendations, which would have been expensive, were ignored, according to James B. Stewart‘s biography of Rescorla, Heart of a Soldier.[7]

After Rescorla’s fears were borne out by the 1993 World Trade Center bombing, he gained greater credibility and authority, which resulted in a change to the culture of Morgan Stanley,[7] whom he believed should have moved out of the building, as he continued to feel, as did his old American friend from Rhodesia, Dan Hill, that the World Trade Center was still a target for terrorists, and that the next attack could involve a plane crashing into one of the towers.[8] He recommended to his superiors at Morgan Stanley that the company leave Manhattan. Office space and labor costs were lower in New Jersey, and the firm’s employees and equipment would be safer in a proposed four-story building. However, this recommendation was not followed as the company’s lease at the World Trade Center did not terminate until 2006. At Rescorla’s insistence, all employees, including senior executives, then practiced emergency evacuations every three months.[9]

Feeling that the authorities lost legitimacy after they failed to respond to his 1990 warnings, he concluded that employees of Morgan Stanley, which was the largest tenant in the World Trade Center (occupying 22 floors), could not rely on first responders in an emergency, and needed to empower themselves through surprise fire drills, in which he trained employees to meet in the hallway between stairwells and go down the stairs, two by two, to the 44th floor.[7]

  • March 2001 – Italian intelligence warns of an al Qaeda plot in the United States involving a massive strike involving aircraft, based on their wiretap of al Qaeda cell in Milan.
  • July 2001 – Jordanian intelligence told US officials that al-Qaeda was planning an attack on American soil, and Egyptian intelligence warned the CIA that 20 al Qaeda Jihadists were in the United States, and that four of them were receiving flight training.
  • August 2001 – The Israeli Mossad gives the CIA a list of 19 terrorists living in the US and say that they appear to be planning to carry out an attack in the near future.
  • August 2001 – The United Kingdom is warned three times of an imminent al Qaeda attack in the United States, the third specifying multiple airplane hijackings. According to the Sunday Herald, the report is passed on to President Bush a short time later.
  • September 2001 – Egyptian intelligence warns American officials that al Qaeda is in the advanced stages of executing a significant operation against an American target, probably within the US.

Likewise, the mortgage meltdown was technically a black swan, but was easily predictable if you saw the pattern of ownership which clearly indicated fraud.

Taleb’s answer to this problem is not to try and predict possible future scenarios, but to simply make yourself more resilient. I don’t disagree with resilience, but I think an expanded approach can be taken here. The flaw that lead to the Black swans was not being able to make connections with information. If we don’t know what scenarios are most likely, we could just as easily end up putting too much effort on defense instead of looking for exponential returns on our resources.

How do we know when we’re looking at a very complex problem? Complex systems tend to be made up of diverse agents with interdependent relationships that change over time. So the question and the answers are changing. The behavior, emotions and motivations of the people in the problem are shifting. The connections between them also change. What does that mean?

For that we turn to Rittel and Webber:
Ten Criteria for Wicked Problems

Rittel and Webber characterise wicked problems by the following 10 criteria. (It has been pointed out that some of these criteria are closely related or have a high degree overlap, and that they should therefore be condensed into four or five more general criteria. I think that this is a mistake, and that we should treat these criteria as 10 heuristic perspectives which will help us better understand the nature of such complex social planning issues.)

1. There is no definite formulation of a wicked problem.

“The information needed to understand the problem depends upon one’s idea for solving it. This is to say: in order to describe a wicked problem in sufficient detail, one has to develop an exhaustive inventory for all the conceivable solutions ahead of time.” [This seemingly incredible criterion is in fact treatable. See below.]
2. Wicked problems have no stopping rules.

In solving a tame problem, “… the problem-solver knows when he has done his job. There are criteria that tell when the solution or a solution has been found”. With wicked problems you never come to a “final”, “complete” or “fully correct” solution – since you have no objective criteria for such. The problem is continually evolving and mutating. You stop when you run out of resources, when a result is subjectively deemed “good enough” or when we feel “we’ve done what we can…”
3. Solutions to wicked problems are not true-or-false, but better or worse.

The criteria for judging the validity of a “solution” to a wicked problem are strongly stakeholder dependent. However, the judgments of different stakeholders …”are likely to differ widely to accord with their group or personal interests, their special value-sets, and their ideological predilections.” Different stakeholders see different “solutions” as simply better or worse.
4. There is no immediate and no ultimate test of a solution to a wicked problem.

“… any solution, after being implemented, will generate waves of consequences over an extended – virtually an unbounded – period of time. Moreover, the next day’s consequences of the solution may yield utterly undesirable repercussions which outweigh the intended advantages or the advantages accomplished hitherto.”
5. Every solution to a wicked problem is a “one-shot operation”; because there is no opportunity to learn by trial-and-error, every attempt counts significantly.

“… every implemented solution is consequential. It leaves “traces” that cannot be undone … And every attempt to reverse a decision or correct for the undesired consequences poses yet another set of wicked problems … .”
6. Wicked problems do not have an enumerable (or an exhaustively describable) set of potential solutions, nor is there a well-described set of permissible operations that may be incorporated into the plan.

“There are no criteria which enable one to prove that all the solutions to a wicked problem have been identified and considered. It may happen that no solution is found, owing to logical inconsistencies in the ‘picture’ of the problem.”
7. Every wicked problem is essentially unique.

“There are no classes of wicked problems in the sense that the principles of solution can be developed to fit all members of that class.” …Also, …”Part of the art of dealing with wicked problems is the art of not knowing too early which type of solution to apply.” [Note: this is very important point. See below.]
8. Every wicked problem can be considered to be a symptom of another [wicked] problem.

Also, many internal aspects of a wicked problem can be considered to be symptoms of other internal aspects of the same problem. A good deal of mutual and circular causality is involved, and the problem has many causal levels to consider. Complex judgements are required in order to determine an appropriate level of abstraction needed to define the problem.
9. The causes of a wicked problem can be explained in numerous ways. The choice of explanation determines the nature of the problem’s resolution.

“There is no rule or procedure to determine the ‘correct’ explanation or combination of [explanations for a wicked problem]. The reason is that in dealing with wicked problems there are several more ways of refuting a hypothesis than there are permissible in the [e.g. physical] sciences.”
10. [With wicked problems,] the planner has no right to be wrong.

In “hard” science, the researcher is allowed to make hypotheses that are later refuted. Indeed, it is just such hypothesis generation that is a primary motive force behind scientific development (Ritchey, 1991). Thus one is not penalised for making hypothesis that turn out to be wrong. “In the world of … wicked problems no such immunity is tolerated. Here the aim is not to find the truth, but to improve some characteristic of the world where people live. Planners are liable for the consequences of the actions they generate …”

How, then, does one tackle wicked problems? Some 20 years after Rittel & Webber wrote their article, Jonathan Rosenhead (1996), of the London School of Economics, presented the following criteria for dealing with complex social planning problems – criteria that were clearly influenced by the ideas presented by Rittle, Webber and Ackoff.
Accommodate multiple alternative perspectives rather than prescribe single solutions
Function through group interaction and iteration rather than back office calculations
Generate ownership of the problem formulation through stakeholder participation and transparency

Facilitate a graphical (visual) representation of the problem space for the systematic, group exploration of a solution space
Focus on relationships between discrete alternatives rather than continuous variables
Concentrate on possibility rather than probability

The morphology grid is somewhat popular for mapping out these types of problems, figure out the “finite states”, the root variables that cause change, and then map them out in a grid format. Mark Proffitt’s Predictive Innovation does a good job of this:

Likewise, Lt. General Paul Van Riper mentions that the best leaders tend to be good at managing complex problems:

The Big Science and Technology Problems of the 21st Century

The big problems are mostly the same as in the 20th century and most of them stretch back much farther than that.

In fact, X Prize last year it declared a top eight list of key challenges that could end up being public competitions in the coming months or years.  The eight concepts or challenges included:

1. Water (“Super ‘Brita’ Water Prize”) – Develop a technology to solve the world’s number one cause of death: Lack of safe drinking water:

2. Personal Health Monitoring System (“OnStar for the Body Prize”) – Develop and demonstrate a system which continuously monitors an individual’s personal health-related data leading to early detection of disease or illness.

3. Energy & Water from Waste – Create and demonstrate a technology that generates off-grid water and energy for a small village derived from human and organic waste.

4. Around the World Ocean Survey – Create an autonomous underwater vehicle that can circumnavigate the world’s oceans, gathering data each step of the way.

5. Transforming Parentless Youth – Dramatically and positively change the outcome for significantly at risk foster children, reducing the number of incarcerations and unemployment rate by fifty-percent or more.

6. Brain-Computer Interface (“Mind over Matter”) – Enable high function, minimally invasive brain to computer interfaces that can turn thought into action.

7. Wireless Power Transmission – Wireless transmission of electricity over distances greater than 200 miles while losing less than two percent of the electricity during the transmission.

8. Ultra-Fast Point-To-Point Travel – Design and fly the world’s fastest point-to-point passenger travel system

#1 is probably done. Though it’s possible to create solutions at different scales of production.

#2 is going to be interesting as hackers will add functions to their sensors, and malicious ones will disrupt other peoples sensors for fun and profit.

I’ve heard of many implentations of #3, so it’s going to come down to what is most economical.

#4 is probably done, though a more robust version that can go deeper will be required to really satisfy the spirit of the goal.

#5 is quite difficult considering everything in our economy is forcing more people to be unemployed in the traditional sense. This is a judo problem, you can’t fix it within the normal means.

On #6, I’ve seen some simple EEG style sensors that can be integrated into games, but for the most part Brain-Machine interfaces are Sci-Fi. It’s easier to run prosthetics off of nerve impulses coming through limbs rather by sensing brainwaves without implants. So it’s going to take awhile to crack that problem. 3d interfaces are hitting the market now, both in VR headsets and 3d intractable  xbox kinect sensors:

The skeleton drawing system the kinect sensors use is software-based and can be modified, but other companies have already launched “improved” sensors that can be used on their own for 3d interaction.

#7 is interesting and we’ll have to see what is the most economical way of tackling it.

#8 needs to factor in safety, otherwise it won’t be widely used.

Some of the NRC’s problems are less thrilling, the benefits aren’t as clear to the man on the street, and it sort of reads like a list of “stuff we were going to do anyway, but we made a report for it”:

From the National Research Council report, the five challenges are:

1. How can the U.S. optics and photonics community invent technologies for the next factor of-100 cost-effective capacity increases in optical networks?

2. How can the U.S. optics and photonics community develop a seamless integration of photonics and electronics components as a mainstream platform for low-cost fabrication and packaging of systems on a chip for communications, sensing, medical, energy, and defense applications?

3. How can the U.S. military develop the required optical technologies to support platforms capable of wide-area surveillance, object identification and improved image resolution, high-bandwidth free-space communication, laser strike, and defense against missiles?

4. How can U.S. energy stakeholders achieve cost parity across the nation’s electric grid for solar power versus new fossil-fuel-powered electric plants by the year 2020?

5. How can the U.S. optics and photonics community develop optical sources and imaging tools to support an order of magnitude or more of increased resolution in manufacturing?

More interestingly, there is no way these questions can cover the whole of desires and needs that technology must fill for the 21st century. What are they missing?

Google’s Director Of New Projects – On Innovation

Dr. Astro Teller goes over his system for creating innovation, pointing out that:

  • Innovation is counter-intuitive, otherwise we would already be doing it. This means that experts in a field usually will not be able to predict (1) how useful an innovation will be because they are so deeply entrenched in the system. (2)
  • Ideally you should find 3-4% of experts in a field you a innovating in to agree with you, look for the “weird” ones. They should be willing to join you in your project. If everyone agrees with you, you are too late. If no one agrees with you, you are wrong about it.
  • You can’t judge your feelings on it, you either get a metric to judge progress of an innovation or follow the inventor as he makes progress on his work.
  • It’s important that you have a story about how the innovation would effect people, so that you can create something that actually changes people’s lives.
  • A story has to mention the problem, a product or service that solves the problem, you have to show that there is something hard, be it in creating it or in the legal barriers, and show why it wasn’t already fixed by the market a few years ago.
  • He recommends making people also give him 10 bad ideas along with the one good one, to show that they have been exploring lots of little bets that could pay off, instead of just hinging their success on one “big idea”.
  • It’s also super important to figure out what tools you used to implement that idea, and then spend time on improving those tools so you can innovate and implement the idea even better the next time around.
  • He even goes as far as throwing out the code his team writes for the first 6 months, to give them a lot of room to explore a huge goal and then start fresh with what they learned without having to hook it on to an old system.
  • Rebuilding from scratch allows you to iron out the errors and correct dysfunction. If you can’t rebuild it, you didn’t understand it deeply enough in the first place.
  • Separate the people who have an emotional or practical stake in preventing innovation. You don’t want to start a popularity contest among people who don’t want you to win.
  • You can’t break too many assumptions at the same time, otherwise it will be too far outside of the range of most people to market.
  • He gets his engineer’s to say no more than yes. Engineer’s are emotionally attached to their projects, you need to get them to separate themselves from the project. He even provides an incentive for them to kill their own projects, offering them a bonus if the project works, no bonus if it doesn’t or a bonus +10% or more if they are willing to kill their project and start on something new. The cost of running dumb projects is huge compared to bonuses.
  • You have to even kill the good ideas so that the great ones can prosper.
  • If you want to give people a lot of room to succeed, you have to give them a lot of room to fail and make a mess.
  • Innovation tends to happen in small groups with lots of structure.
  • It can be easier to create a new version with exponential gains, instead of relying on incremental gains. It forces you to stretch your brain to find new ways of solving the problem. You already know you can’t get huge gains using traditional methods. (3)
  • The easiest way to fix problems is to transplant ideas from one field to another.
  • “Me too” ideas can make money, like making new iterations of facebook for doctors, dentists, ect… but they are not innovations. Innovations are a new class. (4)
  • The best fields for innovation are where people are torturing themselves. What is the most painful and awkward thing people are doing in spite of the challenges, how can we make that easier and then sell it?
  • Engineers are taught to solve problems, entrepreneurs and designers are taught to change problems. An engineer will create a great vase, a designer will create a “mechanism to display flowers”, to loosen up the constraints and find the best solution possible.(5)
  • Profit motives tend to drive success, even in social problems. Profit motives make the enterprise more sustainable. Profit motives and making the world a better place are not mutually exclusive.
  • Find areas where big problems exist and there has to be a better solution, even if you don’t know what it is.
  • Some things are on paths that are predictable, in other cases the future must be invented and cannot be predicted.

Most of the books on innovation were made by academics, and usually either written for housewifes or as a dry academic text. Teller does a better job in describing how to create a good incentive system for innovation:

1. Even when elites are aware of technologies they tend to downplay their importance, even the experts in the field didn’t estimate the changes wrought by gunpowder, automobiles or the pc. Tons of engineers had to change their skills almost overnight when the transistor overthrew the vacuum tube. The cybernetic steam engine governor was made by a kid who just wanted to play marbles. The elite’s curiosity wasn’t stimulated enough and they had too much emotional investment in the status quo.

2. Reid Hoffman: “A side note on invention and innovation: when you have an idea for a startup„ consult your network. Ask people what they think. Don’t look for flattery. If most people get it right away and call you a genius, you’re probably screwed; it likely means your idea is obvious and won’t work. What you’re looking for is a genuinely thoughtful response. Fully two thirds of people in my network thought LinkedIn was stupid idea. These are very smart people. They understood that there is zero value in a social network until you have a million users on it. But they didn’t know the secret plans that led us to believe we could pull it off. And getting to the first million users took us about 460 days. Now we grow at over 2 users per second.” Link

3. This is also called the Jack Welch Strech, See The Art Of Asking The Right Questions Link

4. It’s easier and more reliable to make money by copying other people. See First Mover Advantage v. Ecosystem/Fast Follower Advantage Link

5. Increasingly the field of marketing, engineering and design are merging into one as benefits are being custom tailored to products and distribution networks, see Airbnb Link

The Mathematician’s Trap & How Intelligent People Deceive Themselves

This little story about the great mathematician John Von Neumann has always been one of my favorites. I will tell it the way I first heard it. I have since heard a few variations on the story, leading me to think that there may be a component of Urban Legend to it. But I really don’t care, because I think it’s such a great story that it’s worth retelling.

John Von Neumann was considered by many to be one of the most brilliant minds of the twentieth century. He reportedly had an IQ of 180. He was a pioneer of Game Theory, which was very important during the nuclear arms race. (Because GT assumes that all players act in their own best enlightened self-interest, GT turned out to be a much better model for evolutionary biology than for human behavior.) He was also one of the two people (Alan Turing being the other) who is credited with being the father of the modern computer.

The story goes that someone once posed to Von Neumann the following problem:

Two trains are 20 miles apart on the same track heading towards each other at 10 miles per hour, on a collision course. At the same time, a bee takes off from the nose of one train at 20 miles per hour, towards the other train. As soon as the bee reaches the other train, it bangs huwey and heads off at 20 miles per hour back towards the first train. It continues to do this until the trains collide, killing the bee.

Back to our friend the bee. We now have an expression for how far the bee flies after n legs
7. d’n = 2D * 1/2*(3^n – 1)/3^n = D*(3^n – 1)/3^n
and we need to solve it for how far the bee flies before dying. In actuality, the bee will stop flying (okay, “in actuality” this would never happen) when the distance between the trains is less than the body length of the bee. However, since the summation quickly converges on the solution, we can assume that the bee is a point, ignore the famous paradox, and do the summation up to n=infinity.
7. d’n = D*(3^n – 1)/3^n = D*(3^n/3^n – 1/3^n) = D*(1 – 1/3^n)
Quick refresher on infinite limits: as we let n get infinitely large, 3^n approaches infinity, and 1/3^n approaches zero. Therefore the limit as n approaches infinity is
d = limit(n–>infinity)[D*(1 – 1/3^n)] = D*(1-0) = D = 20 miles!

We have just solved this problem by the infinite series method. Infinite series are very important in Mathematical Analysis and Pre-Calculus as they form the basis for derivatives and integration and everything which is Calculus and Differential Equations. That’s why all students of Math, Physics and Engineering get to do a ton of infinite series problems before they graduate. The reason I call this problem the “Mathematician’s Trap,” is because virtually all mathematicians who see this problem will try to solve it the way we just did.

However, if you were to give the problem to someone who’s only had basic Algebra, they might solve it differently. The trains crash at the midway point which is at 10 miles. Since each train is going 10 mph, this takes one hour. During that same hour, the bee is flying at 20 mph, therefore the bee flies20 miles! Wow, that was a lot easier!

The moral of the story is that being smarter or better educated can often times put you at a disadvantage. When someone is trained at doing something a certain way, that action is virtually automatic. It takes great insight to be able to “step outside of the box” and ask if there’s an easier way to do it. (I’m currently working on another post on just this subject which should hopefully be up soon.) Even brilliant mathematicians will fall into the trap, which brings us back to John Von Neumann.

When posed with the above problem (or some variation of it), JVN took all of five to ten seconds to come up with the correct solution. This floored the questioner who said “I’m impressed that you didn’t fall for the Mathematician’s Trap.” After getting a perplexed look from our genius, he asked “How did you solve the problem?”

“By infinite series, of course!”


 Another good one:

Highly intelligent people may turn out to be rather poor thinkers.

They may need as much, or more, training in thinking skills than, other people.  This is an almost complete reversal of the notion that highly intelligent people will automatically be good thinkers.

1)    A highly intelligent person can construct a rational and well-argued case for virtually any point of view.  The more coherent this support for a particular point of view, the less the thinker sees any need actually to explore the situation.  Such a person may then become trapped into a particular view simply because he can support it (see Hypothesis Traps).

2)    Verbal fluency is often mistaken for thinking.  An intelligent person learns this and is tempted to substitute one for the other.

3)    The ego, self-image and peer status of a highly intelligent person are too often based on that intelligence.  From this arises the need to be always right and clever.

4)    The critical use of intelligence is always more immediately satisfying than the constructive use.  To prove someone else wrong gives you instant achievement and superiority.  To agree makes you seem superfluous and a sycophant.  To put forward an idea puts you at the mercy of those on whom you depend for evaluation of the idea.  Therefore, too many brilliant minds are trapped into this negative mode (because it is so alluring).

5)    Highly intelligent minds often seem to prefer the certainty of reactive thinking (solving puzzles, sorting data) where a mass of material is placed before them and they are asked to react to it.  This is called the “Everest effect” since the existence of a tough mountain is sufficient reason for the best climbers to react to it.  In projective thinking, the thinker has to create the context, the concepts, and the objectives.  The thinking has to be expansive and speculative.  Through natural inclination or perhaps early training, the highly intelligent mind seems to prefer the reactive type of thinking.  Real life more usually demands the projective type.

6)    The sheer physical quickness of the highly intelligent mind leads it to jump to conclusions from only a few signals.  The slower mind has to wait longer and take in more signals and may reach a more appropriate conclusion.

7)    The highly intelligent mind seems to prefer – or is encouraged – to place a higher value on cleverness than on wisdom.  This may be because cleverness is more demonstrable.  It is also less dependent on experience (which is why physicists and mathematicians often make their “genius” contributions at an early age).

See also:

How To Hack Your Sleep

In the late 1930’s, a wealthy amateur scientist named Alfred Lee Loomis and his colleagues watched an EEG monitor for brain electrical activity during sleep, and they made a pretty remarkable discovery: there are actually five main parts to each of several phases of sleep that occur during a normal night. One of these stages is called REM (rapid eye movement), and it is where most of the benefit of sleep comes from. Ironically, it is in REM sleep that the brain looks the least asleep. In fact, it looks awake. This is the phase where dreams occur.

It seems that all you really need to survive and feel rested is the REM phase, which is only a tiny portion of your actual sleep phases at night. You only spend 1-2 hours in REM sleep during any given night, and the rest is wasted on the other seemingly useless phases. This is where the opportunity to hack the brain presents itself. What if you could find a way to cut out the other phases and gain 4-5 more hours of productive wakeful time?


The Too Many Aptitudes Problem

Most jobs and tasks are best performed by folks with certain high and low aptitude combinations (plus other things like training, of course). High aptitudes beyond job needs cause problems. The optimum combination for any given job or task resembles a recipe–a lot of some things, some of this, a bit of this, and none of that.

Just one wrong high aptitude can make a job intolerable for a person–like onions in a chocolate cake. A person with a strong knack for working with others might hate solitary work and quit, but be tremendously productive and satisfied as part of a team. Whether a high or low aptitude is good or bad depends on the context. Anything can be an advantage or disadvantage depending on the situation. Talent is no exception.

Most people have about four or five strong talents out of the roughly two dozen independent aptitudes known to exist. Most jobs require about four or five. As many as 10% of the population has double that number of aptitudes–and that is a problem for them and their employers. The Johnson O’Connor Research Foundation, the oldest aptitude-testing organization in the country, has statistical evidence that people with too many aptitudes (TMAs) are less likely to obtain advanced education and/or succeed in a career than those with an average number of talents.

Being a TMA is a very mixed blessing. Strong talents are extremely powerful internal forces. One of the most important implications of my aptitude research is the strong possibility that emotional intensity is directly correlated with the intensity of a talent. Someone operating at a high-intensity level of talent (including reasoning) will also be operating at a high-intensity level of emotion. Every thought, memory or perception is directly connected to emotion–a wholistic phenomenon.

It is quite possible that TMAs are continually operating in a hypersensitive manner. People hypersensitive to external and internal data in many forms and operating at a high emotional intensity level might very well become overstimulated. Ongoing overstimulation could explain the paralysis felt by some TMAs. They are so overwhelmed by perceptions, memories, thoughts and feelings that they can’t commit themselves to anything. Many of them need a lot of time alone to regenerate. Yet, this same turbulence can also lead to great insight and creativity.

The existence of a powerful force implies difficulty in learning to harness that force. Having a lot of strong talents is a bit like dealing with high voltage. You can do a lot of things with high voltage. However, it can also fry you. It takes a lot more knowledge and more safety precautions to work with high voltage rather than low. A lot of that voltage for TMAs is emotional. Few people know how to handle normal emotion, let alone powerful, ongoing emotion. Link

The Dangerous Art of the Right Question

Real questions, useful questions, questions with promising attacks, are always motivated by the specific situation at hand.  They are often about situational anomalies and unusual patterns in data that you cannot explain based on your current mental model of the situation, like Poirot’s letter.  Real questions frame things in a way that creates a restless tension, by highlighting  the potentially important stuff that you don’t know. You cannot frame a painting without knowing its dimensions. You cannot frame a problem without knowing something about it. Frames must contain situational information.

The same dynamic occurs at personal and global levels. Here are terrible personal questions:

  1. How can I be happy?
  2. What career do I want?
  3. How can I lose weight?

Here are examples of corresponding questions that are useful:

  1. Are people with strong friendships happier than loners? (Answer: yes)
  2. What is the top reason people leave jobs? (Answer: they dislike their immediate manager)
  3. What causes food addiction? (Answer: carefully-engineered concoctions of salt, sugar and fat)

Here are terrible global questions:

  1. How can we create peace in the Middle East?
  2. What can we do about global warming?
  3. How can we reform Wall Street?

Here are potentially useful corresponding questions:

  1. Do Israelis and Arabs communicate in different ways (Answer: yes)
  2. Why are summers getting warmer and wetter, while winters are getting colder and snowier? (Answer: I don’t know; climatologists might)
  3. Is the principle of limited liability a necessary condition for a free market economy? (Answer: I don’t know)

  1. The Poirot Method: This is the basic trail-of-clues method of focusing on an anomaly that your current mental model cannot account for. Since my colleague Dave and I often argue about Poirot vs. Holmes, let me throw the Holmes camp a bone (heh!): the classic Holmes’ question of the “dog that didn’t bark in the night” is an excellent insight question.
  2. The Jack Welch Method: Also known as the “stretch.” You ask ridiculously extreme versions of ordinary formulaic questions. Instead of asking “How do we grow market share 3% in the next year?” You ask, “How do we grow our market 10x in the next 3 months?” The question so clearly strains and breaks the existing mental model that you are forced to think in weirder ways (the question is situation-driven because numbers like 3%, 1 year, 10x and 3 months will need to come from actual knowledge).
  3. The 42 Method: Sometimes the right answer is more easy to find than the right question. Entrepreneurs are often in this boat. They don’t know who will use their product or why, but they just know that their product is the answer to some important question somewhere.  They are often wrong, but at least they are productively wrong. If you don’t get the “42″ reference, don’t worry about it.


SPACED REPETITION – Learning & Memorization

Resourcefulness & Control

Like real world resourcefulness, conversational resourcefulness often means doing things you don’t want to. Chasing down all the implications of what’s said to you can sometimes lead to uncomfortable conclusions. The best word to describe the failure to do so is probably “denial,” though that seems a bit too narrow. A better way to describe the situation would be to say that the unsuccessful founders had the sort of conservatism that comes from weakness. They traversed idea space as gingerly as a very old person traverses the physical world. [1]

The unsuccessful founders weren’t stupid. Intellectually they were as capable as the successful founders of following all the implications of what one said to them. They just weren’t eager to. Link

There are great startup ideas lying around unexploited right under our noses. One reason we don’t see them is a phenomenon I call schlep blindness. Schlep was originally a Yiddish word but has passed into general use in the US. It means a tedious, unpleasant task.

No one likes schleps, but hackers especially dislike them. Most hackers who start startups wish they could do it by just writing some clever software, putting it on a server somewhere, and watching the money roll in—without ever having to talk to users, or negotiate with other companies, or deal with other people’s broken code. Maybe that’s possible, but I haven’t seen it.

One of the many things we do at Y Combinator is teach hackers about the inevitability of schleps. No, you can’t start a startup by just writing code. I remember going through this realization myself. There was a point in 1995 when I was still trying to convince myself I could start a company by just writing code. But I soon learned from experience that schleps are not merely inevitable, but pretty much what business consists of. A company is defined by the schleps it will undertake. And schleps should be dealt with the same way you’d deal with a cold swimming pool: just jump in. Which is not to say you should seek out unpleasant work per se, but that you should never shrink from it if it’s on the path to something great.

The most dangerous thing about our dislike of schleps is that much of it is unconscious. Your unconscious won’t even let you see ideas that involve painful schleps. That’s schlep blindness.

How do you overcome schlep blindness? Frankly, the most valuable antidote to schlep blindness is probably ignorance. Most successful founders would probably say that if they’d known when they were starting their company about the obstacles they’d have to overcome, they might never have started it. Maybe that’s one reason the most successful startups of all so often have young founders.

In practice the founders grow with the problems. But no one seems able to foresee that, not even older, more experienced founders. So the reason younger founders have an advantage is that they make two mistakes that cancel each other out. They don’t know how much they can grow, but they also don’t know how much they’ll need to. Older founders only make the first mistake.

Ignorance can’t solve everything though. Some ideas so obviously entail alarming schleps that anyone can see them. How do you see ideas like that? The trick I recommend is to take yourself out of the picture. Instead of asking “what problem should I solve?” ask “what problem do I wish someone else would solve for me?” If someone who had to process payments before Stripe had tried asking that, Stripe would have been one of the first things they wished for.


What would someone who was the opposite of hapless be like? They’d be relentlessly resourceful. Not merely relentless. That’s not enough to make things go your way except in a few mostly uninteresting domains. In any interesting domain, the difficulties will be novel. Which means you can’t simply plow through them, because you don’t know initially how hard they are; you don’t know whether you’re about to plow through a block of foam or granite. So you have to be resourceful. You have to keep trying new things.

Be relentlessly resourceful.

That sounds right, but is it simply a description of how to be successful in general? I don’t think so. This isn’t the recipe for success in writing or painting, for example. In that kind of work the recipe is more to be actively curious. Resourceful implies the obstacles are external, which they generally are in startups. But in writing and painting they’re mostly internal; the obstacle is your own obtuseness. [2] Link

What  Control Is 

As a former spec ops guy, pilot, author, CEO, etc.  (control heavy professions), I’ve learned that being in control is NOT about:

  • Controlling the behavior of anybody else.
  • Control over EVERY detail and every situation (the micro environment).
  • Control of everything that’s going on in the world (the macro environment).

If you attempt any of the above, you are a control freak.  You won’t be happy with yourself, people will find you miserable to be around, and you will be unlikely to achieve the results you seek.

Real control, the kind of control that keeps you alive on dangerous missions and gets you out-sized results regardless of how difficult things become, is simple.  It’s control over:

  • Preparation.  Planning.  Skills.  Resources.  Enter every situation ahead of the power curve.
  • Direction.  No plan survives first contact.  Know where you are going.
  • Process.   How you get things done, matters.

As you can see, real control is about knowing how to think correctly. Link

Some Suggestions For Building Self Control:

  • Simulate poverty. Sleep on the floor for 2 weeks. Use very light padding if your back is too sensitive.
  • Wear raggedy, old clothing for these 2 weeks.
  • Fast for a day, cut down on your overall calorie content for the 2 week period
  • Stop whatever you are doing, and on a set time, find a simple object like a doorknob and focus only on that object for 5 minutes. If any thoughts come, let them wash over you and restore your focus only on the sight of the object. Increase it up to 30 minutes gradually, so that you can pull yourself out of whatever train of thought you are in and achieve a hard focus.
  • Spend 30 days straight without whining about anything. Reset the clock each time you fuck it up. You should focus only on analyzing and fixing problems, not getting negative emotions involved into them.
  • Don’t ejaculate at all for 14 days and make sure you lift weights. Other people have gone into more detail so I won’t rehash it all again.
  • Do a CARVER matrix on any goals you have and figure out what is most important and what should slide. Knowing that you’re working on hard problems makes it easier to focus

Rules of Productivity, Solving Problems

How do we get more work done? It is a question that every manager and every passionate worker faces. Yet, for the most part, teams operate on gut instinct and habit. The results are less than optimal.

Topics covered include:

Having a methodology of solving problems, a way of prioritizing problems, a way of figuring out how much cognitive load you are carrying, how much time you can work without losing concentration and knowing how to focus your mind all effect your ability to solve problems. With a bit of experience and feedback, you should be able to feel when your brain’s performance is going down, and stop and then recover.
As time goes on, ideas are very likely to become more valuable and will be judged by their clarity, usefulness and market demand. Make sure you are a good idea creator.

2 Ways Of Working Through Hard Problems

1. Write the problem out and then read it aloud. Sometime’s it’s easier to make sense of something if you hear it. 2. Create pictures of what you are thinking about, if you need to be creative link them with other images you already have (IE Roman Room Method). A lot of people have a hard time picturing things vividly, so you have to work up to being able to really use the technique properly.

Richard Feynman – Explaining Magnets

Here he gets into why explaining “Why” is so difficult.

US Army Open Source Intelligence Link Directory

The Power Curve

De-escalation – Theory & Practice

De-escalation is the step when force is imminent (how’s that for ‘soft’ language- more real: if you don’t do something now the fight will be on in a few seconds). It is “talking ’em down”. It ranges from sympathy to weird non-sequitors to treating a threat like a thoughtful question to pure intimidation. It is a skill, and a more varied and more versatile skill than anything physical. But it is a skill, not an answer.
Some memorable successes:

“Turn your head to the side.”
“Why, mother fucker?” He glared hard.
“Cause you look like you’re thinking about fighting and you seem like a nice guy, so if you do start to fight and I smash you into the wall right there, if you turn your face to the side you won’t break any teeth.”
His glare changed to something more puzzled.
“It’s just a courtesy. You seem like a nice guy and you don’t need any dental bills. Just turn your head to the side.”
“I won’t be any trouble.”
“I appreciate that.”

“What’s your goal today, partner?” This is one of my universals. Most of the people who want to fight are unhappy, without really thinking about why, and want to do something, without really thinking about what. Once they put into words what they want , e.g.”I wanna go home” they often clearly see how fighting is not a step in that direction.


Local Maximum And Local Minimum

How Your Memory Works

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