A List of Rules

When I first heard that you could program a computer as a child I was amazed. A strange wonder that I could only spend a few shared minutes with at school, something that could draw, add, and write far faster than I could ever dream of – and I could tell it what to do? I wasn’t quite sure how to inform it to bend to my every wish, so I started with Turtle (actually called LOGO I later found) upon my teacher’s recommendation. I fed Turtle long lists of instructions – move forward, draw a line, turn left, repeated in all and any ways I could think of. He would draw glowing green shapes across my screen, and never tired.

The Need for Modularity

The only problem was that while the Turtle seemed infallible, I certainly could not say the same. I was making the classic beginner’s mistake – I would write one giant chunk of code that was supposed to cause my turtle to dance in precisely the way I wanted. Any little mistake would send it widely off course and I would end up with a mess that barely looked like the original design at the best of times. I later learned that using a programming concept called “modules” could help me isolate these errors and make code more efficient and reusable. Just like a company could have a manufacturing and engineering division which could communicate with standardized blueprints, a program could have different modules that would exchange data in a standardized manner. A modular program is more stable since mistakes are typically limited in influence to the module they’re contained in, and each module can be modified by separate influences with only the understanding that they are supposed to behave and communicate in a certain manner.

Damage as Evidence

So is our mind modular? Well, if it wasn’t, we could assume that a brain injury affecting a certain part of the brain would have a consistent and general impact across all of our consciousness. The only problem is that we generally only see a nonspecific mental decline like this from a nonspecific trauma, say impact blows to the head over a long period of time. Injuries in specific areas seem to be correlated with deficits in certain mental abilities – while leaving others totally intact.

A stroke can basically be thought of as an incident where blood flow is drastically affected in a certain specific area of the brain. This subregion of the brain is unable to function due to lack of blood flow, and very strange things can occur.

Howard Engel is a Canadian novelist who had a stroke. Upon waking one morning, he found that the morning paper seemed to be written in some strange script, an alphabet he could not understand. Everything else appeared normal, except his visual cortex had been damaged in a specific area which prevented him from visually parsing letters and words. As a writer, he despaired – it seems that his livelihood had been lost. Soon he realized a critical distinction which gave him hope – he may be unable to read visually, but could he write? Howard sat down and traced these strange looking symbols, his pen gliding over the bizarre shapes over and over. And eventually, the concepts came back to him. In a strange sense, he could now read again. Years and years of writing had associated certain movements of his hand with letters and concepts. Instead of words in his head put to paper by hand movements and a pen, he had to move concepts in the opposite direction – moving his hand over shapes written previously by others, the concepts echoed back up his motor cortex.

And it worked. There was irreparable damage to his visual cortex, a critical module malfunctioning. So he hacked his brain, redistributing resources from his motor cortex which had been trained to recognized these same symbols and concepts necessary for reading by his constant writing. Howard now traces the shapes he sees on the inside of his front teeth with his tongue. His speed has steadily increased, and he says he can now read about half of the subtitles in a foreign film before they flash off the screen.

It doesn’t seem too strange to suggest that there are different localized modules in the brain for motor control, visual interpretation, and other concepts easily identifiable with different aspects of the physical world. But are there modules with finer distinctions, working on different parts of our mental experience rather than different parts of the physical world?

The Wason Selection Task

The Wason selection task is a very interesting experiment in the field of psychological reasoning. Before I spoil it for you by talking too much, let’s just do it right now. Look at the following cards.

Assume the cards have a number on one side, and a color on the other. What cards need to be flipped over to make sure that all even numbers have red backs? Make sure you’ve picked a card or cards.

Got it? Now a bit of unsettling but ego-salvaging news. When this experiment is done with undergraduates, only 10 to 20 percent get it right. The correct answer is to flip over two cards: the number 4 to make sure it has a red back, and the blue card to make sure that it doesn’t have an even number on the other side. Most people suggest flipping over the 4 and the red card – this is wrong, as it doesn’t matter if the red card has an odd number on the other side.

Now let’s mix it up a bit. Instead of numbers and colors, let’s try people and social activities. Assume the cards have a drink on one side, and a person of a certain age on the other. What cards need to be flipped over to make sure everyone drinking beer is old enough to do so?

Now the answer flows quickly and easily, and almost everyone gets it correct. We need to flip over the beer card to make sure that the person on the other side is old enough, and we need to flip over the card showing the underage drinker to make sure they’re playing by the rules.

The weird thing is that both examples are logically equivalent. Instead of numbers and colors, we’ve just used people and drinks. But something very important has occurred, and it happens time and time again as these tests are administered. It seems that people are fast and accurate at solving this task only if it is described as a test of social obligations. They both can be described identically with logic – but that doesn’t appear to matter to our mind. We appear to have a module dedicated to social reasoning and conflict, and can only solve these problems quickly if it involves determining if someone is cheating or breaking social conventions. This ancient module would hold significant survival value – a general logic verification module not quite so much.

Modules Upon Modules

There appears to be significant evidence for a modular mind, not just in terms of divisions between senses such as sight or hearing and other actions like movement but also more abstract modules that deal with concepts such as social rules. Stroke victims can literally rewire their brains, passing concepts upward into their consciousness through paths never intended to be used in such a strange manner, duplicating the work of other modules lost to injury. These modules live in a strange world of physical interaction and abstract mental space, a huge interconnected mass with no clear outline behind it. The big question now becomes: is a sufficiently complex system able to understand itself, and are we that system?

Clearly different situations might have different approaches. I was very interested in how different weightings of these rules might behave, so I decided to create a program that would allow you to change the relative importance of each rule at will. You can see a video of it in action below (the three sliders on the bottom left control the influence of the various rules).

	Alignment: This slider adjusts how much each fish wants to head in the same direction as other fish around it.
	Cohesion: This slider adjusts how close each fish wants to be to its neighbors.
	Separation: This slider adjust how much each fish wants to space itself out from the others.
	Click anywhere in the water to add a new fish.
	Press this button to reset the simulation.

I hope you enjoy it – please leave me a comment if you have any questions, comments, or find any bugs.

These seething clouds of birds have no leader, no planning, and yet produce a dance that seems choreographed in its precision and beauty. So how do they do it? Some strange ability given to them, a unique skill among the animal kingdom? Well, perhaps not quite. Consider that humans seem to be able to move in crowds with the same ease, although some might say with a bit less beauty.

People seem to move in clumps, in the same direction, and manage to not trample each other or collide. There seem to be parallels in crowds of organisms, that this order emerges consistently and that somehow each animal “knows” what to do on a small scale, resulting in cohesive and sensible group movement. The question then becomes – what exactly are we doing unconsciously to organize like this?

The Boid Model

In 1986 Craig Reynolds produced a computer model describing how large groups of animals such as schools, herds, and flocks could be able to move in unison with no central coordination. He called his creations “boids”, and imagined that each would follow some simple, sensible rules to navigate around. He also knew a single animal would never be able to keep track of every other animal in the group, and assumed that they would only pay attention to their immediate neighbors in the flock.

So if you were a boid, what rules would you follow?

Alignment: Look around you. Where is everyone else going? Probably a good idea to go there too. Alignment is a rule that finds the average direction your neighbours are going, and tells you to go there too.
Cohesion:Predators look to pick off stragglers on the edge of the pack. Cohesion is a rule that finds the average position of your neighbors and tells you to go there, pulling you into the relative safety of the center of the pack.
Separation: When crowds get big, they can get dangerous. Animals can trample each other, and birds can collide. Separation is a rule that tells you to make sure to give your neighbors some space.

How do we know how to do this? Well, the tautological answer is that it simply works. The beauty of these rules is that each of them is amazingly simple, and seem to make sense to us on an intuitive level. We tend to go with the crowd, stick together, and still try to give everyone a bit of personal space. But are these rules enough to produce the complex dance of the starlings, or are we missing some detail?

A Boid Dance

It turns out these simple rules produce patterns of movement that are anything but.

We can see that it looks flowing and wonderfully organic – but it’s not quite the same as the starling flocks. We can play with the boid model, tuning its various parameters to see how the resulting crowds might react.

A school of fish trying to avoid predators might be modeled best by weighting the cohesiveness rule very heavily, attempting to keep together at all costs while not worrying too much about personal space or where precisely they’re heading as long as it’s away from what’s trying to eat you.
A flock of geese flying south for the winter will focus on heading in a certain direction (alignment), then space themselves out to ensure they can see in front of themselves (seperation) while staying close enough and on the same plane to experience the aerodynamic benefits of flying in a group (cohesion) producing the “flying V” we see so often in fall.
Migrating animals are primarily concerned with making sure they’re going in the same direction as everyone else (alignment) while ensuring that no one is trampled (seperation) and that young and weak animals are protected in the center of the herd (cohesion). For instance, the wildebeest stampede that killed Simba’s father in the Lion King was generated using the same theory as the boid model.

This simple model has a wide range of possible applications, and a huge amount of flexibility allowing it to produce a wide range of behavioural simulations. This gorgeous installation of light graffiti uses the rules we’ve just discussed to create hypnotizing patterns of artificial creatures.

The boid model has been used to animate realistic looking behavior over a huge range of media. If you’ve seen a computer generated crowd moving in a vaguely sensible manner anywhere, it likely uses the same basic theory. The power of the model comes from its construction, assuming simple behaviors competing for influence that result in a complex outcome not unlike our own consciousness or political systems. Not too shabby for a research project from over 20 years ago.

In the previous post, we discussed the Prisoner’s Dilemma and saw how a simple strategy called Tit-for-Tat enforced the Golden Rule and won a very interesting contest. But does Tit-for-Tat always come out on top? The most confounding thing about the strategy is that it can never win – at best, it can only tie other strategies. Its success came from avoiding the bloody battles that other more deceptive strategies suffered.

The major criticism of Axelrod’s contest is its artificiality. In real life, some may say, you don’t get to encounter everyone, interact with them, and then have a tally run at the end to determine just how you did. Perhaps more deceptive strategies would do better in a more “natural” environment where losing doesn’t mean you get another chance at another opponent, but that your failures cause you to simply die off.

Artificial Nature

So now let’s look at the same game, with the same scoring system, only this time there’s a twist. Assume that this contest takes place in some sort of ecosystem that can only support a certain number of organisms, and they must fight among each other for the right to reproduce. There will be many different organisms, and they will all be members of a certain species, or specific strategy. We can then construct an artificial world where these strategies can battle it out in a manner that seems to reflect the real world a bit better.

In order to determine supremacy, we’ll play a certain number of rounds of the game, called a generation. At the end of the generation, the scores are tallied for each strategy, and a new generation of strategies is produced – with a twist. Higher scoring strategies will produce more organisms representing them in the next generation, while lower scoring strategies will produce less. Repeat this for many generations, observe the trends, and we can see how these strategies do as part of a population that can grow and shrink, rather than a single strategy that lives forever.

So let’s look at an example. Suppose we have a population that consists of the following simple strategies:

So what will happen? Was Tit-for-Tat’s dominance a result of the structure of the contest, or is it hardier than some might think? A graph of the changing populations over 50 generations may be seen below.

It’s a hard world to start. ALL-C immediately starts being decimated by the deception of ALL-D and RAND who start surging ahead, while Tit-for-Tat barely hangs on. ALL-D’s relentless deception allows it to quickly take the lead, and it starts knocking off its former partner in crime, RAND. Tit-for-Tat remains on the ropes, barely keeping its population around 10% as ALL-C and RAND are quickly eliminated around it.

And then something very interesting happens. ALL-D runs out of easy targets, and turns to the only opponents left – Tit-for-Tat and itself. Tit-for-Tat begins a slow climb as ALL-D begins to eat itself fighting over scraps. Slowly, steadily, Tit-for-Tat maintains its numbers by simply getting along with itself while allowing ALL-D to destroy each other. By 25 generations it’s all over – the easy resources exhausted, ALL-D was unable to adapt to the new environment and Tit-for-Tat takes over.

This illustrates a very important concept – that of an evolutionarily stable strategy. ALL-D was well on its way to winning, but left itself open to invasion by constant infighting. ALL-C initially had the highest population but was quickly eaten away by more deceptive strategies. Tit-for-Tat on the other hand was able to get along with itself, and defended itself against outside invaders that did not cooperate in turn. An evolutionarily stable strategy is something that can persist in this manner – once a critical mass of players start following it, it cannot be easily invaded or exploited by other strategies, including itself.

I Can’t Hear You

But there’s one critical weakness to Tit-for-Tat. We’re all aware of feuds that have gone on for ages, both sides viciously attacking the other in retaliation for the last affront, neither one precisely able to tell outsiders when it all started. And if we look at the strategies each use in a simplistic sense, it seems that they’re using Tit-for-Tat precisely. So how did it go so horribly wrong?

It went wrong because Tit-for-Tat has a horrible weakness – its memory is only one move long. If two Tit-for-Tat strategies somehow get stuck in a death spiral of defecting against each other, there’s no allowance in the strategy to realize this foolishness, and be the first to forgive. But how could this happen? Tit-for-Tat is never the first to defect after all, so why are both Tit-for-Tat strategies continually defecting?

The answer is that great force of nature, noise. A message read the wrong way, a shout misheard over the wind, an error in interpretation – all can be the impetus for this first initial defection. No matter that it was pointless and incorrect, the strategy has changed. While Tit-for-Tat’s greatest strength is that it never defects first, its greatest weakness is that it never forgives first either.

All of these simulations we’ve seen so far do not include noise, and it can have a catastrophic effect on the effectiveness of Tit-for-Tat. Its success was built on the strategy of never fighting among itself and allowing other deceptive strategies to destroy themselves by doing the same – but with noise, this advantage becomes a fatal weakness as Tit-for-Tat’s inability to be taken advantage of is turned against itself.

So what does a simulation including noise look like? You can see one below, and it contains an additional mystery strategy, Pavlov. Pavlov is very similar to Tit-for-Tat but slightly different – it forgives far more easily.

We see a similar pattern to our previous simulation. ALL-D has an initial population spike as it knocks off the easy targets, but Tit-for-Tat and Pavlov slowly climb to supremacy with ALL-D eventually eating scraps. But the influence of noise causes Tit-for-Tat to fight among itself, and Pavlov begins what previously seemed impossible – to begin to win against Tit-for-Tat.

Puppy Love

So what is Pavlov and why does it work better in a noisy environment like the real world? Well, Ivan Pavlov was the man who discovered classical conditioning. You probably remember him as the guy who fed dogs while ringing a bell, and who then just rang the bell – and discovered that the dogs salivated expecting food.

The strategy is simple – if you win, keep doing it. If you lose, change your approach. Pavlov will always cooperate with ALL-C and Tit-for-Tat. If it plays ALL-D however, it will hopefully cooperate, lose, get angry about it and defect, lose again, switch back to cooperation, and so on. Like a tiny puppy or the suitor of a crazy girlfriend, it can’t really decide what it wants to do, but it’s going to do it’s damndest to try to succeed anyways. It manages to prevent the death spiral of two Tit-for-Tat strategies continually misunderstanding each other by obeying a very simple rule – if it hurts, stop doing it. While it may be slightly more vulnerable to deceptive strategies, it never gets stuck in these self-destructive loops of behavior.

So there’s a lesson here – life is noisy, and people will never get everything correct all the time. Tit-for-Tat works very well for a wide variety of situations, but has a critical weakness where neither player in a conflict is willing or able to forgive. So the next time you’re in a situation like that, step back, use your head, and switch strategies – it’s what this little puppy would want you to do, anyways.

We live in a world with other people. Almost every decision we make involves someone else in one way or another, and we face a constant choice regarding just how much we’re going to trust the person on the other side of this decision. Should we take advantage of them, go for the quick score and hope we never see them again – or should we settle for a more reasonable reward, co-operating in the hope that this peaceful relationship will continue long into the future?

We see decisions of this type everywhere, but what is less obvious is the best strategy for us to use to determine how we should act. The Golden Rule states that one should “do unto others as you would have them do unto you”. While it seems rather naive at first glance, if we run the numbers, we find something quite amazing.

A Dilemma

In order to study these types of decisions, we have to define what exactly we’re talking about. Let’s define just what a “dilemma” is. Let’s say it has two people – and they can individually decide to work together for a shared reward, or screw the other one over and take it all for themselves. If you both decide to work together, you both get a medium-sized reward. If you decide to take advantage of someone but they trust you, you’ll get a big reward (and the other person gets nothing). If you’re both jerks and decide to try to take advantage of each other, you both get a tiny fraction of what you could have. Let’s call these two people Alice and Bob – here’s a table to make things a bit more clear.

This specific order of rewards is referred to as the Prisoner’s Dilemma, and was formalized and studied by Melvin Dresher and Merrill Flood in 1950 while working for the RAND Corporation.

Sale, One Day Only!

Now of course the question is – if you’re in this situation, what is the best thing to do? First suppose that we’re never, ever going to see this other person again. This is a one time deal. Absent any moral consideration, your best option for the most profit is to attempt to take advantage of the other person and hope that they are clueless enough to let you, capitalism at its finest. You could attempt to cooperate, but that leaves you open to the other party screwing you. If each person acts in their own interest and is rational, they will attempt to one-up the other.

But there’s just one problem – if both people act in this way, they both get much less than they would if they simply cooperated. This seems very strange, as the economic models banks and other institutions use to model human behavior assume this type of logic – the model of the rational consumer. But this leads to nearly the worst possible option if both parties take this approach.

It seems that there is no clear ideal strategy for a one time deal. Each choice leaves you open to possible losses in different ways. At this point it’s easy to toss up your hands, leave logic behind, and take a moral stance. You’ll cooperate because you’re a good person – or you’ll take advantage of the suckers because life just isn’t fair.

And this appears to leave us where we are today – some good people, some bad people, and the mythical invisible hand of the market to sort them all out. But there’s just one little issue. We live in a world with reputations, with friends, and with foes – there are no true “one time” deals. The world is small, and people remember.

In it for the Long Run

So instead of thinking of a single dilemma, let’s think about what we should do if we get to play this game more than once. If someone screws you in the first round, you’ll remember – and probably won’t cooperate the next time. If you find someone who always cooperates, you can join them and work together for your mutual benefit – or decide that they’re an easy mark and take them for everything they’ve got.

But what is the best strategy? In an attempt to figure this out, in 1980 Robert Axelrod decided to have a contest. He sent the word out, and game theorists, scientists, and mathematicians all submitted entries for a battle royale to determine which strategy was the best.

Each entry was a computer program designed with a specific strategy for playing this dilemma multiple times against other clever entries. The programs would play this simple dilemma, deciding whether to cooperate or defect against each other, for 200 rounds. Five points for a successful deception (you defect, they cooperate), three points each for mutual cooperation, one point each if you both tried to screw each other (mutual defection), and no points if you were taken advantage of (you cooperate, they defect). Each program would play every other program as well as a copy of itself, and the program with the largest total score over all the rounds would win.

So what would some very simple programs be?

ALL-C (always cooperate) is just like it sounds. Cooperation is the only way, and this program never gets tired of being an upstanding guy.

ALL-D (always defect) is the counterpoint to this, and has one singular goal. No matter what happens, always, always, always try to screw the other person over.

RAND is the lucky dunce – don’t worry too much, just decide to cooperate or defect at random.

You can predict how these strategies might do if they played against each other. Two ALL-C strategies would endlessly cooperate in a wonderful dance of mutual benefit. Two ALL-D strategies would continually fight, endlessly grinding against each other and gaining little. ALL-C pitted against ALL-D would fare about as well as a fluffy bunny in a den of wolves – eternally cooperating and hoping for reciprocation, but always getting the shaft with ALL-D profiting.

So an environment of ALL-C would be a cooperative utopia – unless a single ALL-D strategy came in, and started bleeding them dry. But an environment entirely made of ALL-D would be a wasteland – no one would have any success due to constant fighting. And the RAND strategy is literally no better than a coin flip.

Time to Think

So what should we do? Those simple strategies don’t seem to be very good at all. If we think about it however, there’s a reason they do so poorly – they don’t remember. No matter what the other side does, they’ve already made up their minds. Intelligent strategies remember previous actions of their opponents, and act accordingly. The majority of programs submitted to Axelrod’s competition incorporated some sort of memory. For instance, if you can figure out you’re playing against ALL-C, it’s time to defect. Just like in the real world, these programs tried to figure out some concept of “reputation” that would allow them to act in the most productive manner.

And so Axelrod’s competition was on. Programs from all over the world competed against each other, each trying to maximize their personal benefit. A wide variety of strategies were implemented from some of the top minds in this new field. Disk drives chattered, monitors flickered, and eventually a champion was crowned.

And the Winner Is…

When the dust settled, the winner was clear – and the victory was both surprising and inspiring. The eventual champion seemed to be a 90 lb weakling at first glance, a mere four lines of code submitted by Anatol Rapoport, a mathematical psychologist from the University of Toronto. It was called “Tit-for-Tat”, and it did exactly that. It started every game by cooperating – and then doing exactly what the other player did in their last turn. It cooperated with the “nice” strategies, butted heads with the “mean” strategies, and managed to come out on top ahead of far more complex approaches.

The simplest and shortest strategy won, a program that precisely enforced the Golden Rule. But what precisely made Tit-for-Tat so successful? Axelrod analyzed the results of the tournament and came up with a few principles of success.

• Don’t get greedy. Tit-for-Tat can never beat another strategy. But it never allows itself to take a beating, ensuring it skips the brutal losses of two “evil” strategies fighting against each other. It actively seeks out win-win situations instead of gambling for the higher payoff.
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• Be nice. The single best predictor of whether a strategy would do well was if they were never the first to defect. Some tried to emulate Tit-for-Tat but with a twist – throwing in the occasional defection to up the score. It didn’t work.
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• Reciprocate, and forgive. Other programs tended to cooperate with Tit-for-Tat since it consistently rewarded cooperation and punished defection. And Tit-for-Tat easily forgives – no matter how many defections it has seen, if a program decides to cooperate, it will join them and reap the rewards.
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• Don’t get too clever. Tit-for-Tat is perfectly transparent, and it becomes obvious that it is very, very difficult to beat. There are no secrets, and no hypocrisy – Tit-for-Tat gets along very well with itself, unlike strategies biased toward deception.

The contest attracted so much attention that a second one was organized, and this time every single entry was aware of the strategy and success of Tit-for-Tat. Sixty-three new entries arrived, all gunning for the top spot. And once again, Tit-for-Tat rose to the top. Axelrod used the results of these tournaments to develop ideas about how cooperative behaviour could evolve naturally, and eventually wrote a bestselling book called The Evolution of Cooperation. But his biggest accomplishment may be showing us that being nice does pay off – and giving us the numbers to prove it.

It consisted of 30 questions, all current issues with well established alternative positions in philosophy under intense discussion. They surveyed 1803 philosophy faculty members and/or PhDs and 829 philosophy graduate students, and then tabulated the results. I also went through and gave each question a “controversy” score – the lower the score, the lower the consensus in the answers (for the curious, via mean square error).

Is this the real life – or is it just fantasy?

The least controversial issue was Question 6, “External world: idealism, skepticism, or non-skeptical realism?“. This dealt with the structure of the external world, where and how it exists, and what we can know about it.

4.2% of respondents believed that the external world was best described by idealism, that reality is totally dependent on the mind. Plugged into the Matrix? Your “reality” could be described by an idealist perspective.

4.8% chose the viewpoint of skepticism, that the external world can never really be known in its true form.

9.2% reinforced some stereotypes of philosophers and gave an answer best described as “other”.

81.6% of respondents agreed in the clearest consensus of the survey that the external world was best described by a perspective of non-skeptical realism. This means that “reality” exists independent of the mind (the realist part, we aren’t making it all up in our heads) and that we can draw reasonable and consistent conclusions from it (the non-skeptical part).

Life may not be a waking dream after all. Now, onto controversy!

Kill one to save a thousand?

You are a superhero. The love of your life dangles from a fraying rope above a pit of spikes, while nearby a speeding train full of orphans rushes toward the edge of a cliff. You only have enough time to save one of the two – what do you do?

Normative ethics is the branch of philosophy that deals with questions like these – how “should” you act in a certain situation? Is there certain approach one should use? This was the subject of one of the most controversial issues, Question 20 – “Normative ethics: deontology, consequentialism, or virtue ethics?“.

18.1% chose the perspective of virtue ethics, first advocated for in a significant sense by Aristotle. Virtue ethics emphasizes the character of the person who is faced with a difficult decision. Do you save the children in the train or your dangling love? It doesn’t matter – what matters is your character and your intent in making the decision.

23.6% argued for consequentialism, the philosophy that spawned the approach of “the ends justify the means”. Whether an action is right or wrong depends on the outcome of the situation, not the specific actions you chose. There are a number of different variants of this which would “score” the final situations according to whether it maximized happiness, economic benefit, liberty, love, or any number of possible ideals to yourself or to others. If you were an egoist you would send the orphans over the cliff and save your love to maximize your own happiness. If you were a utilitarianist, you would save the orphans because it would benefit the largest number of people.

25.8% of respondents chose deontology, where it is the actions you take that are judged rather than the results of those actions. Deontologists are concerned with rules and duties, and an action following these duties can be considered morally correct even if it produces dire consequences. If you were a married superhero who had sworn a vow to protect his love – orphans be damned, there is a duty to perform.

32.3% of philosophers, presumably not wanting to commit to paper their rationale as to whether they’d kill orphans or their love, chose “Other”.

What about the rest?

Here’s a table ranking the “consensus” on each issue, from least to most controversial. The lower the mean square error, the less disparity there is in the magnitude of responses – and so less consensus is reached. I think this is a pretty decent metric, if you have any better suggestions, feel free to leave a comment.

Now you can go to fancy cocktail parties and hold your own in philisophical debates – or at least have the confidence that you’re stating a position that cannot be proven wrong.

My eight year old self would be bitterly disappointed technology today has not progressed far enough to make that wish a reality. At the time, we had to settle the debate by another manner – asking an adult, a source of concrete and immutable knowledge. The answer I was given was that everyone sees the same colors of course (although why this was so obvious was never really clear) and if they didn’t it wouldn’t matter much since we couldn’t tell. Color was “real” – bits of light had a color (later I found out we could call it the wavelength of a photon), it hit our eyes, and our brains converted it to a beautiful image.

The only problem is that this is wrong.

Color as Wavelength

Well, alright. Before you get upset, it isn’t completely wrong. We were all taught about Sir Isaac Newton who discovered that a glass prism can split white light apart into its constituent colors.

While we consider this rather trivial today, at the time you’d be laughed out of the room if you suggested this somehow illustrated a fundamental property of light and color. The popular theory of the day was that color was a mixture of light and dark, and that prisms simply colored light. Color went from bright red (white light with the smallest amount of “dark” added) to dark blue (white light with the most amount of “dark” added before it turned black).

Newton showed this to be incorrect. We now know that light is made up of tiny particles called photons, and these photons have something called “wavelength” that seems to correspond to color. Visible light is made up of a spectrum, a huge number of photons each with a different wavelength our eyes can see. When combined, we see it as white light.

So this appears to resolve my childhood debate. Light of a single wavelength (like that produced by a laser) corresponds to a single “real” color. The brain just translates wavelengths into colors somehow, and that is that. There’s just one problem.

We’re missing a color!

Color as Experience

To find out just what we’re missing, we have to consider how we can combine colors. For instance, you learned some basic color mixing rules as a kid. In this case, let’s use additive color mixing since we’re mixing light.

Let’s find two colors on the spectrum line, and then we can estimate the final color they’ll produce when you mix them by finding the midpoint.

Red and green make yellow.

Green and blue make turquoise.

Red and blue make…

Green? What? That doesn’t seem to make any sense! Red and violet make pink! But where is pink in our spectrum? It’s not violet, it’s not red – it seems like it should be simultaneously above and below our spectrum. But it’s not on the spectrum at all!

So we’re forced to realize a very interesting conclusion. The wavelength of a photon certainly reflects a color – but we cannot produce every color the human eye sees by a single photon of a specific wavelength. There is no such thing as a pink laser – two lasers must be mixed to produce that color. There are “real” colors (we call them pure spectral or monochromatic colors) and “unreal” colors that only exist in the brain.

A Color Map

So what are the rules for creating these “unreal” colors from the very real photons that hit your eye? Well, in the 1920s W. David Wright and John Guild both conducted experiments designed to map how the brain mixed monochomatic light into the millions of colors we experience everyday. They set up a split screen – on one side, they projected a “test” color. On the other side, the subject could mix together three primary colors produced by projectors to match the test color. After a lot of test subjects and a lot of test colors, eventually the CIE 1931 color space was produced.

I consider this to be a map of the abstractions of the human brain. On the curved border we can see numbers, which correspond to the wavelengths in the spectrum we saw earlier. We can imagine the spectrum bent around the outside of this map – representing “real” colors. The inside represents all the colors our brain produces by mixing – the “unreal” colors.

So let’s try this again – with a map of the brain instead of a map of photon wavelengths. Red and green make yellow.

Green and blue make turquoise.

Blue and red make…

Pink! Finally! Note that pink is not on the curved line representing monochromatic colors. It is purely a construction of your brain – not reflective of the wavelength of any one photon.

Is Color Real?

So is color real? Well, photons with specific wavelengths seem to correspond to specific colors. But the interior of the CIE 1931 color space is a representation of the a most ridiculously abstract concept, labels that aren’t even labels, something our brain experiences and calculates from averaged photon wavelengths. It is an example of what philosophers call qualia – a subjective quality of consciousness.

I later learned that my childhood argument was a version of the inverted spectrum argument first proposed by John Locke, and that the “adult” perspective of everyone seeing the same colors (and it not really mattering if they didn’t) was argued by the philosopher Daniel Dennett.

I have come no closer to resolving my question from long ago of “individual spectrums” – but for the future, I vow to pay more attention to the idle questions of children.

When explanations are lacking, the mystical is often brought up to fill the void. This has degenerated today into complete pop-psychology crap such as The Secret or What the Bleep Do We Know, but the role that human consciousness plays as an “observer”, if any, was considered very early by the founders of these theories. These arguments brought forth by some of the finest thinkers of our time echo to this day.

Niels Bohr

Winner of the Nobel Prize in Physics in 1922, employed by the Manhattan Project, and father of the Bohr model familiar to every high school student, Niels Bohr was first accused by Einstein of introducing “mystic” elements in his explanation of quantum physics – mystic elements which in Einstein’s view had no place in science.

This was part of the famous Bohr-Einstein debates, and was perhaps not a fair criticism. Bohr appeared to not worry excessively about the “reality” underpinning the equations of quantum theory, and was simply more concerned about the equations of quantum theory rather than their implications. He rejected the hypothesis that the wave function collapse requires a conscious observer, insisting that “It still makes no difference whether the observer is a man, an animal, or a piece of apparatus”.

His view is perhaps best summarized in the following quote recalled by Heisenberg:

This argument looks highly convincing at first sight. We can admittedly find nothing in physics or chemistry that has even a remote bearing on consciousness. Yet all of us know that there is such a thing as consciousness, simply because we have it ourselves. Hence consciousness must be part of nature, or, more generally, of reality, which means that, quite apart from the laws of physics and chemistry, as laid down in quantum theory, we must also consider laws of quite a different kind. But even here I do not really know whether we need greater freedom than we already enjoy thanks to the concept of complementarity.

In short, if the numbers work out, don’t worry too much.

Wolfgang Pauli

But some did worry. Pauli was a skeptic’s skeptic – a man so dedicated to rationality it led him down a strange path. In 1927 the Solvay Conference was busy reaching consensus that Bohr’s approach was the best way to regard quantum physics (the Cophenhagen Interpretation), but Pauli was equally confident in a different interpretation. He tried to trace out just what part of consciousness it is that seems to prevent an in-depth, rational understanding. Deeply influenced by Schopenhauer’s The World as Will and Representation, Pauli appropriated his concept of a will “which breaks through space and time”.

He viewed that the acquisition of knowledge in mathematics or quantum physics “gives rise, however, to a situation transcending natural science” that can even acquire a “religious function” in human experience. This is not a belief in the religions of old, but as Pauli states “I do not believe in the possible future of mysticism in the old form. However, I do believe that the natural sciences will out of themselves bring forth a counter pole in their adherents, which connects to the old mystic elements.”

Perhaps the most interesting viewpoint on Pauli was that of Heisenberg, who viewed Pauli’s paradigm as even more rational than Bohr’s equation-focused approach because only he acknowledged that the scientific evidence pointed to something pre-rational or ‘mystical’. Pauli claimed that consciousness was presented philosophically by mystics and studied scientifically by psychologists. With the advent of quantum mechanics, physicists should then be able to unify both approaches. Unfortunately, we continue to wait.

Albert Einstein

Einstein was a scientific superstar, with fame not equalled to this day. One day, a quote was making the round in British newspapers that Einstein subscribed to the theory that “the outer world is a derivative of consciousness”. His response was swift and critical.

No physicist believes that. Otherwise he wouldn’t be a physicist. Neither do [Eddington and Jeans]. . . . These men are genuine scientists and their literary formulations must not be taken as expressive of their scientific convictions. Why should anybody go to the trouble of gazing at the stars if he did not believe that the stars were really there?

Einstein’s opposition to Bohr’s view or more “mystical” approaches is often cast as the great divide between the philisophies of idealism and those philosophies based on realism. Pauli often referred to Einstein’s “philosophical prejudice” assuming that reality is independent of any mind. In fact, his approach and objections were far more subtle. His major concern was the incredibly subjective aspect of consciousness introducing an unmeasurable “geist”, and this clash with the precise and well defined philosophical principles of physics such as locality or determinism.

This led to Einstein’s famous attempt at “breaking” quantum physics, the EPR paradox. At first a thought experiment which appeared to demonstrate quantum physics violating the seemingly well established principle of locality, later experiments showed that quantum physics instead proved locality false.

Violations of locality and determinism seemed to bother Einstein greatly, and this can be seen in his famous quote objecting to the randomness involved in wave function collapse under Bohr’s interpretation, that “God does not play dice”.

Bohr, summing up the debate perfectly, replied “Einstein, stop telling God what to do with his dice.”

John von Neumann

The “last of the great mathematicians”, von Neumann solved one of the great problems of quantum theory. While the theory itself was established and experimentally verified, it lacked a “deep” mathematical understanding based on an axiomatic approach. He treated the world as a Hilbert space, an infinite dimensional structure.

While classical mechanics approached the world as a collection of points with six different characteristics (position and momentum along the x, y, and z axis), von Neumann’s approach considers a quantum system as a point in infinite dimensional space, corresponding to the infinite amount of possible outcomes. This led to very interesting implications in terms of “measurement”. While the “measurement” of a classical system simply involved finding one or more of those six values, the “measurement” of a quantum system involved mathematical operators acting on an infinite amount of values to produce a finite measurement.

The interesting conclusion arises when we consider the “real” interpretation of these mathematical operators. While we may say that an scientific instrument has caused wave function collapse, we run into the problem that no physical system (and a scientific instrument is a physical system completely described by quantum mechanics) can cause wave function collapse. We can describe the entire ensemble perfectly as a Hilbert space. But we do not experience this Hilbert space – we measure and experience only finite values.

The conclusion von Neumann reached is that consciousness, whatever it is, appears to be the only thing in physics that can ultimately cause this collapse or observation. This does not mean that consciousness is “required” for the universe to work, but that wave function collapse appears to be caused by consciousness and we observe only a tiny slice. We are therefore an “abstract ego” acting as a measurement device on the infinite values of true reality.

Today

Today, the argument has largely died down, a combination of practicality and lack of any suitably shocking experimental results. The majority of physicists today take the approach of “it works”, namely that quantum physics produces accurate predictions of the real world and that the mathematical formalism is just that – a mathematical formalism that produces accurate results.

It may not reflect the true reality of the world (whatever it is), but it is suitably accurate to any level of precision that we are physically able to obtain. One may stay awake at night wondering “why”, but one will not get much work done with this approach. Perhaps more clarity lies in the future, but in the meantime – we will have to tolerate crap that tells us we can “will” our way to riches with quantum mechanics (and coincidentally make the authors rather rich, will indeed) instead of a rational approach dedicated to the pursuit of truth.

Myosin Heavy Chain 16

The human and chimpanzee genomes have both been mapped, so we are able to make comparisons between them. This is extremely useful, as chimpanzees and humans shared a common ancestor, but genetic lines split apart approximately 7 million years ago. So examining the differences may tell us something about how humans evolved.

There is a protein called myosin heavy chain 16 (aka MYH16) which in chimpanzees and other non-human primates is expressed almost exclusively in their powerful jaw muscles. These strong jaws are an adult trait – a logically complex one that would be more sensitive to random mutations.

And that’s exactly what seems to have happened. Non-human primates have DNA that codes for the complete MYH16 protein. The corresponding part of human DNA is missing a random chunk – which causes a frameshift mutation.

Frameshift Mutations

What is a frameshift mutation? Well, first let’s find out how we build proteins. We have a strand of messenger RNA (imagine a long tape with letters on it) which a ribosome (hell, imagine a tiny elf) uses to produce proteins. The critical thing to consider is that a ribosome builds a protein by reading three nucleotides at a time, and these three nucleotides code for a certain amino acid. These amino acids are chained together to produce proteins. Some combinations of three nucleotides can also act as “punctuation marks”.

So our wee elf looks closely at the long tape of letters, and starts off with the first three. His “frame”, the little chunk he works on, is three letters long. This frame is an instruction to build a certain amino acid, which he makes. He then goes along the tape, three letters at a time, making an amino acid each time that he sticks onto the last. This will eventually create a long chain of amino acids that we call a protein. But each frame doesn’t need to code for just an amino acid – it can also code for other instructions (those “punctuation marks”) starting or stopping this chaining process.

Now you may have guessed what a frameshift mutation is by now – it’s where a single letter in our tape disappears, or a new random one gets thrown in, causing our frame to get shifted slightly. This means that the resulting triplets after this error will be horribly wrong. It’s like the difference between

HEY MAN HOW ARE YOU BRO and
HEY MAN HWA REY OUB RO_ or HEY MAN HOQ WAR EYO UBR O__

if one were to speak in sentences containing only three letter words. The first sentence makes sense if we parse three letters at a time. The two others have a random letter removed, and a random letter added in. If we parse them three letters at a time, the sentence turns into garbage halfway through! The resulting nonsense (or malformed protein) is a result of a random insertion or deletion of information (nucleotides) and our “frame”, the manner in which we interpret it.

Consequences

So a frameshift mutation occured in early humans that affected the production of the protein MYH16. This protein is involved in the strong powerful jaws that primates have, but not humans. We often think of mutations as a simple little “blip” in the genetic code, but the way our bodies parse this code can cause cascading effects. Instead of MYH16 having a slightly different amino acid in a random spot from a random mutation, the specified amino acids after the mutation will change completely!

So you might think that we’ll have some odd protein that’s mostly normal, and the parts after the mutation affected by the frameshift will be wonky. But – and this is an important but – the triplets code for “punctuation marks” too, remember? In this MYH16 mutation, it turns out that this frameshift caused a punctuation mark (aka a stop codon) to just pop up – so the protein is cut off far sooner than it should be! Not too good for any traits relying on that protein.

Look at the differences between these gorilla and human skulls below. The large bony ridges on the gorilla skull on the left are where the larger jaw muscles attach – otherwise they would literally tear off of the skull. You can also see how the gorilla skull seems “empty” on the sides – that’s because it is filled with large jaw muscles, reducing space available for the brain. The red tinted parts are where the jaw muscles attach – you can see how much more “anchoring” a gorilla’s jaw muscle requires.

And this is where it gets interesting. This mutation in our human ancestors happened approximately 2.4 million years ago. Right before our ancestors stopped looking like primates and started looking like us. If you lacked the protein that operated a powerful jaw muscle, you could not carry a large jawbone around and use it effectively. If you can’t carry a large jawbone around, there is strong selection pressure for those with smaller jaws to survive. If your jaw gets smaller, then the loading of the jaw on the skull decreases – bony ridges disappear, and the skull can get larger and lighter since it doesn’t need to be as strong. A larger and lighter skull can accommodate a bigger brain.

It appears that a random mutation, flipping a single bit of genetic information, has beautifully complex cascading results. Viewing the world as a hostile agent of noise and fury, winding down to an eventual death by entropy is wrong. You can fold a piece of paper, give it to a child, and have them cut crude holes in it with cheap scissors – and when you unfold it, the snowflake is beautiful.

So too can randomness be folded and twisted by logical structures in biology and physics – and the result is our amazing world.

One proposed mechanism for the evolution of humans from primates is neoteny, where juvenile traits are retained and adult adaptations lost. This has been observed in foxes subject to behavioural selection. For instance, look at this young chimpanzee.

This picture is from a 1926 study by the German anthropologist Adolf Naef. He describes it as “the the most human-like picture of an animal, of any that is known to me.” The little guy does seem to have a rather regal and refined air about him, but we can’t just wave our hands and call it case closed at this point. Can we look at the development of a chimpanzee and see if there are any quantifiable parallels?

Bone structure is a great place to start. Chimpanzees, like humans, have a skeleton that changes shape and size as the organism matures.

The two skulls on the far left are those of an infant chimpanzee (top) and an infant human (bottom). Bone structure and shape are very similar, with the classic huge head and tiny cute face we seem programmed to love. The two skulls in the middle are of a adolescent chimpanzee (top) and an adult human (bottom). We can see the jaw start to lengthen in both, and their overall similarity. The final picture on the top right is of an adult chimpanzee, who has a significantly larger and more powerful bite than any adult human.

So what does this show us? Well, humans and chimpanzees appear to have very similar development in terms of bone structure as they grow up, except that humans just seem to… stop at a certain point. There are a multitude of theories as to why this happens, but they all seem to follow the pattern of certain behaviours being selected for which affect the balance of hormones in the body that control the development of adult features. This is called neoteny.

Now neoteny doesn’t mean that every single part of the entire animal becomes more juvenile, or that the animal becomes less complex overall. It’s a selective reduction in complexity – traits that appear later in the animals development (ie adolescence) become less likely to appear.

So how did humans get their unique features? It’s very difficult to select for traits like a bigger brain or hairlessness when those traits don’t appear in the wild in any real frequency to begin with. Viewing human evolution through this lens seems to indicate that change would be very slow, and very hard to do.

But what if instead of selecting for a simple trait, we (or the species as a whole) selects for a behaviour? The neat thing about selecting for this is that hormones have a strong influence on behaviour. So we are partly selecting for certain hormone levels or actions. These hormones also share logical relationships with other hormones, and act in many different parts of the body, not just the parts of the brain influencing behaviour.

If we put significant selection pressure on a species, we are effectively increasing the mutation rate (ie “mutant” creatures tend to be selected more). Increases in mutation rates would be more likely to affect more logically complex proteins arising later in life involved in the development of adolescent features (due to more references to more parts of the mutating DNA) rather than less logically complex proteins that would be involved in juvenile features.

As a result, we now have a mechanism for how these bizarre traits that we simply don’t see in the wild can become so common, so quickly, and also a predicted side effect – neoteny.

But how could this end up as an advantage? It seems that mutations are destroying those adult adaptations that made the organism successful in the first place. But what if the world changes simply because you and others like you live in it? We like to think of physical strength as the be all and end all of “dominance”, but I think this is only true if you’re “one chimp against the world”. A chimp who can more accurately figure out social structure and how to manipulate his place in it could be far more successful in breeding than a chimp who is simply stronger than average.

A chimpanzee’s ability to learn is drastically reduced upon reaching maturity. But baby chimps…

Baby chimps will eagerly mimic a human caretaker – sticking out their tongues, opening their mouth wide, or making their best effort at a kissy face. Not only is the basic mechanism of learning there (imitation), it appears to be very focused on social relationship. And this ability decreases with age! It seems that the retention of juvenile traits is not the burden it appears at first.

So the origin of humanity? Well, it’s still up in the air. But I think it’s incredibly likely that we literally changed ourselves – that living together created environmental pressures (namely social ones) that selected for behaviour in an incredibly complex manner, where the ability to learn and social skills were valued and led to reproductive success. All too often we look for outside pressures in evolution, when some of the most magnificent examples (like the plumage and mating rituals of birds of paradise) are simply a result of everyone agreeing to play an elaborate game.