Space & Times

by Francis Tseng

Chaos and Reductionism

December 10 2013

I’ve been thinking more about complexity and came across a great Stanford lecture by Professor Robert Sapolsky titled Chaos and Reductionism which introduces the concept of complexity by contrasting it with the reductionist approach to analyzing systems; this is a big note dump for that lecture:

adventure time zoom

Reductionism – the foundational (Western) scientific idea that, if you want to understand a complex system, break it down (reduce it) to its component parts. When you understand its individual parts, you will be able to understand the system. There are implicit notions of additive and linear natures to the combination of these parts – that you add the parts together and they will increase in their complexity in a linear manner which leads to the complex system.

With this approach, you could see the starting state of a system and predict its final, mature state/consequence (or work backwards from its final state to its starting state), without having to go step-by-step. That is, extrapolation – using rules to predict similar cases beyond just the present case (where the rules were first observed).

There’s also the concept of a “blueprint”, that is, there’s some expectation of what you expect to see when you extrapolate.

Another important aspect of reductionism is that there is variability. For instance, amongst people, there’s an average body temperature but some variability in the specific values across individuals. What do you make of variability? Reductionism takes variability as noise – something to be gotten rid of or avoided. “Instrument” error, where “instrument” ranges from a person’s observation to machinery. To mitigate this, reductionism believes that further reduction will reduce this noise. The closer you are, the more you’ll be able to see what’s actually going on. So, the better you get at getting down to the details – through new techniques or equipment – there will be less variability.

“At the bottom of all these reductive processes, there is an iconic and absolute and idealized norm as to what the answer is. If you see anybody not having [a body temperature of] 98.6, it’s because there’s noise in your measurement systems – variability is noise, variability is something to get rid of, and the way to get rid of variability is to become more reductive. Variability is discrepancy from seeing what the actual true measure is.”

Reductionism has been the driving force behind science – coming up with new techniques or equipment to measure things more closely so we can see how things “truly” work.

For instance – if you want to understand how the body works, you need to understand how organs work, and to understand how organs work, you need to understand how cells work, etc, until you get all the way to the bottom. And then once you understand the bottom, you add everything back up and now you understand the body. But this approach is problematic – biology, and certainly plenty of other systems, don’t work that way.

neuronal layers of retina
Neuronal layers of retina. The paths are traced directly back.

In neurobiology, two major researchers (Hubel and Wiesel) came up with a reductionist explanation of how the cortex works; they found that there were individual cells in the retina which connected directly to neurons in the cortex, that you could trace the starting state (which retina cell has been stimulated) to the ending state (which neuron fires) directly (a point-for-point relationship). In this most basic layer of the cortex, neurons could recognize a dot and one dot only, and it was the only neuron that could recognize it. And in the next layer, those neurons can recognize a particular line at a certain angle, and other neurons would recognize those lines at different angles, etc. So using the same reductionism behind the retina=>neuron=>dot, you can extrapolate to retina=>neuron=>dot=>line. And then the next layer, you have neurons that can recognize a particular curve. If you know what’s going on at any of these other levels, you can trace that state to the other states (i.e. what’s going on at the other levels). And it was believed that you could continue to extrapolate in this way – that there’s a particular neuron responsible for a particular arrangement of sensory information. Above that curve layer would be neurons that recognize particular sets of curves, and so on. Eventually towards the top you’d have a neuron that would recognize your grandmother’s face from a particular perspective/at a particular angle.

But no one has ever been able to demonstrate the widespread existence of the “grandmother neuron”. There are very few “sparse coding” neurons (“sparse coding” meaning you only need a few neurons to recognize some complex thing) which are similar, i.e. a single neuron that responds to a face, and only a specific type of face – there was a rather bizarre study where these upper levels were studied in Rhesus monkeys, and they found a single neuron which would only respond to pictures of Jennifer Aniston, and if that wasn’t strange enough, the only other thing they would respond to was an image of the Sydney Opera House.

orientation selectivity
Orientation selectivity

If you think about it, the number of necessary neurons at each level increases quite a bit for each level. At the lowest level, you have 1:1 neurons for retina cells (to recognize dots). But then, if you have a neuron for each line at each angle formed by these dots – that’s a lot more neurons (“orientation selectivity”; there are many ways for lines to be oriented). And this happens at every level – so there just simply aren’t enough neurons in visual cortex (and the brain) to represent everything. So this reductionism falls apart here.

In this case, the new approach involves the idea of neural networks. Complex information isn’t encoded in single neurons, but rather in patterns of activation across multiple neurons – i.e. networks of neurons.

Bifurcating Systems


A bifurcating system is a branching system – each branch keeps splitting off into more branches – which is “scale free”. Viewing the system at one level looks just like viewing the system at another level (i.e. it is “scale free”). It has the nature of a fractal.

scale free
An example of “scale free” – at any level, it looks the same

The circulation system is an example of this, so is the pulmonary system, and so are neurons. How does the body code for the creation of a bifurcating system? The reductionist approach might come to theorize that there’s some gene that codes, for example, when an aorta bifurcates (splits into two), and then a different gene that specifies the next bifurcation, etc. But again, there aren’t enough genes to code this way.


In the development of biological systems, there is a degree of chance involved. For instance, Brownian (random) motion at the molecular level causes cells to split with uneven distributions of mitochondria, which interferes with the reductionist characteristic of being able to trace a system from its start to its final state.

brownian motion
Brownian motion

There was a study trying to predict the dominance hierarchy that would emerge from a particular starting configuration of fish. First, the dominance hierarchy of a group of fish was established by using a round-robin technique; that is, pairing each fish off with one another, seeing which one dominates, and then generating the hierarchy by inferring that the first that dominated every round is the top fish, the one that dominated the second most is the next in the hierarchy, etc. Then the fish were released together to see how predictive this hierarchy was of natural conditions. The hierarchy had zero predictability for what actually happened. The fish can calculate to some degree logical outcomes of social interactions, and uses this ability to strategize their own behavior. However, this necessitates that they observe these social interactions to begin with, and that is largely left to chance: they could be looking in the wrong direction, and miss out on the information to strategize on.

A Taxonomy of Systems

So there are systems which, after some level, are non-reductive. They are non-additive and non-linear. Chance and sheer numbers of possible outcomes are characteristic of these systems. These systems are “chaotic” systems, and they are beyond this reductionist framework.

There are deterministic systems – those in which predictions can be made.
There are periodic deterministic systems, which have rules consistent in such a way that you can directly calculate some later value, without having to calculate step-wise to that value; they are linear. That consistency provides an ease of predictability; you can rest assured that the rule holds in an identical manner for the entire sequence. These systems are reductive; the reductionist approach works here. There is a pattern which repeats (i.e. is periodic).

For example:
1,2,3,4,5 has a rule of “+1”; it is very easy to answer: what’s the 15th value?

In contrast, aperiodic deterministic systems are much more difficult to project in this way. There are rules to go from one step to another, like in periodic deterministic systems, but you need to calculate predictions stepwise, figuring out one value, then applying the rule again to figure out the next, ad nauseum. There aren’t repeating patterns – although there’s a consistent rule that governs each step, the relationship between each step is inconsistent. Arbitrary values in a sequence, for example, cannot be derived from the rules without manually calculating each preceding value in the sequence.

There are nondeterministic systems, where the steps are random; there is no consistent rule. The nature of one value does not determine the subsequent value. Chaotic systems are often taken to be nondeterministic systems, but they are not.

Chaos (via)

Chaotic systems, have rules for each step, but the relationship for each step is non-linear, they’re not identical. That is, they are aperiodic deterministic systems. There is no pattern which repeats. So the only way to figure out any value in such a sequence is to calculate step-wise to that position. So, you can figure out the state of the system in the future, but it’s not “predictable” in the same way that periodic deterministic systems are.

Reductive, periodic deterministic systems can reach a tipping point, when enough force is applied to them, where they break down and become chaotic, aperiodic deterministic systems.

Periodic systems are in an equilibrium, such that, if you disturb the system temporarily, it will eventually reset back into that periodic equilibrium. This is called an “attractor”. This point of equilibrium would be considered the “true” answer of such a system.


In this image, you can see the circle in the center is the attractor, and the system spirals back to it.

Chaotic systems, on the other hand, have no equilibrium to reset back to. They will oscillate infinitely. So they are unpredictable in this sense. This is called a “strange attractor”. There is no “true” answer here because there is no stable settling point that could be considered one. Rather, the entire fluctuation, the entire variability, could be considered the “true” answer – the system itself.


Here you can see the strange attractor, which is never reached, and the system just fluctuates constantly.

But if you’re measuring the system, it can be very deceiving. Say you take a measurement and it’s on one of these points. Say it’s (6, 3). And you want to try and predict what the next point is be. And you calculate the next point, and so on, and then you return to your starting point, and you think, oh – it’s not chaotic at all, it’s periodic! But if you look closer, you see that this isn’t the case. You started on (6, 3.7), and now you’ve ended up on (6, 3.8). It’s not actually the same point.

Or, to give a better example, you actually started on (6.38791587129873918751982739172398179872147), and the second time around, you’ve actually landed on (6.38791587129873918751982739172398179872148). The numbers are very close, but they are not the same. They are variable. And thus the next point after each is different, and the path after the new point is completely different from the path after the starting point. The tiny little difference gets amplified step by step by step – the “butterfly effect”. So these systems become practically unpredictable as a result.

No matter how good your reductive tools are, no matter how accurate your measurements and techniques are, that variability is always there. In this sense, such chaotic systems are scale-free: no matter how closely you look, no matter what level you’re looking at, that variability is there, and its effects will be felt. This undermines the reductionist approach which dictates that the closer you look at a system, the less noise you will have, and the better and truer understanding you will have of it. But it’s not noise resulting from technique or measurement inadequacy – it is part of the phenomenon, it is a characteristic of the system itself.


Here’s where fractals come in.

koch fractal
Zooming in on a Koch fractal

A fractal is information that codes for a pattern, a line in particular, which is one-dimensional, but this line has an infinite amount of complexity, such that, if you look closer and closer at the line, you still see that complexity. So it is infinitely long, but in a finite space, and it starts to seem more like a two-dimensional object. But it isn’t! A fractal is an object or property that is a fraction of a dimension. It’s not quite two-dimensional, but it’s also more than one-dimensional – it’s somewhere in between.
Fractals can also be described more simply as something that is scale-free: at any level you look at it, the variability is the same.

The bifurcating systems mentioned earlier are fractals. And, rather than having their branching coded explicitly by genes, for example, they are governed by a scale-free rule.

Does this matter in practice?

Sapolsky (the lecturer) and a student performed a study where they took some problem in biology – the effect of testosterone in behavior – and gathered every study approach this problem from different levels: at the level of society, the individual, the cell, testosterone, etc. For each study they gathered the results and calculated a coefficient of variation, which is the percentage of your result that variation represents.

For example, if I have a measurement of 100 with +/- 50, then the coefficient of variation is 50%.

Then you can take the average coefficient of variation across each level.

If the reductionist argument holds, then you should see a decreasing coefficient of variation – that is, a decreasing of noise – as you move from broad to narrow. But there is no such trend.

But what if there was noise in their own measurements? That a lot of the papers they surveyed weren’t very good, perhaps sloppily measured or something. So they looked at the number of times cited as an indicator of the paper’s quality, and re-did the analysis (the 10% papers by this metric). But the results were the same.

It isn’t that reductionism is not useful. It is still very useful. It is a lot simpler, and while it isn’t completely “accurate”, that is, there is room for error, it can still paint broader strokes that is still actionable and reflective of the world.

For instance, if I want to test the efficacy of a vaccine, I don’t want to get down to the level of each individual and see how it works, I’d want to be a bit broader and say, ok, this group got the vaccine, this one didn’t, how do they compare? And that’s very useful information.

Thoughts on Complexity

November 24 2013
Black Box, aka Garabed Bashur, in Cable & Deadpool, issue 22.

(These are some scattered thoughts partly inspired by a conversation with a friend several months ago.)

It’s a given that the modern world in which we live is characterized by an inexorable and ubiquitous complexity. With all the laws that govern us, both explicit legislation and implicit social etiquette, the vast organizations and bureaucracies with which we interface or become deeply entrenched in, the wondrous yet esoteric technical brilliance of the machines that sustain and entertain us, the vast but obfuscated historical legacies that have shaped the world today, the intricate and mystic chaos of economies whose ebb and flow touch our daily lives, and so on – it’s undeniable that there is far more than any one could ever know or understand. And with all the new systems we are constantly creating, each of them with their own rules and logic and intimate details to learn, the domain of what there is to know expands infinitely faster than our understanding can.

Naturally, we tend to specialize – we come to familiarize ourselves with a particular facet of a particular domain, and that becomes our whole world. Perhaps this alienates us from others, making it difficult to relate and even empathize with those who live in different knowledge-worlds. Popular culture staves off this alienation by providing a common ground, one which is meant to be simple – not complex – so that it is accessible to all (or most). But both popular culture and this domain isolationism do not acknowledge that we have no choice but to interface with many of these complex systems and concepts throughout our lives.

Black Box and Cable face off in Cable & Deadpool, issue 22.

Such overwhelming complexity manifests itself in anxiety and a feeling of no control, that is, the modern condition, eloquently put by John Updike:

“a sensation of anxiety and shame whose center cannot be located and therefore cannot be placated; a sense of an infinite difficulty within things, impeding every step; a sensitivity acute beyond usefulness, as if the nervous system, flayed of its old hide of social usage and religious belief, must record every touch as pain.” (ix) Kafka, Franz. The Complete Stories of Franz Kafka. Ed. Nahum Norbert Glatzer. New York: Schocken Books, 1971.

It must be mitigated in some way. Complexity is made manageable through abstraction – the simplification and generalization of complex system and ideas, describing them in vocabularies closer to shared understandings as opposed to esoteric domain-specific knowledge. This has a few applications in areas I’m interested in:

Democracy & News

Democratic society, broadly speaking, expects that its citizens be adequately informed about policies and legislation and keep up with the relevant news, so that their participation is effective (this is admittedly idealized, but it has some truth). However, a democratic process must acknowledge this complexity insofar as it is impossible for any individual to develop a sufficient, nuanced understanding in all relevant topics that would merit a confident participation in the deciding or debating of policy regarding all topics. The problem of modern complexity (here, in the form of information overload) is intractable, the only approach that feels plausible to me is an abstraction for each domain, but abstraction inevitably means simplification, and thus crucial nuance is lost, nuance which may become vastly magnified when a policy is implemented at scale.

Transmission of Ideas

Any idea which is to become popular and widely disseminated–in the absence of ubiquitous and comprehensive education–must necessarily undergo a process of abstraction, that is, a rounding of the edges, a reduction in complexity, so that it is more transmissible and easily digested. But ideas are often carry their greatest weight in their nuance. Nuance is that which distinguishes them from doppelgängers, manipulative or misguided imitators, or just other similar ideas. It seems inevitable that such a reducing process lose that detail.


Systems of great complexity provide the opportunity for value generation/profit. Complexity is capable of generating scarcity: a scarcity of understanding. I think a possible trend is: a system of complexity emerges, whether or natural or artificial, and then the first wave is people who are knowledgeable or understand this complexity, and they come in and their service is basically advising you on how to navigate that complexity (a consultant, basically), but then the next wave, which if executed successfully makes the first wave obsolete, which is understanding the complexity to the point where patterns and be culled from the chaos, and using these patterns, simplifying abstractions are built on top, thus reducing the complexity for everyone. But if this complexity can be manufactured, then it seems these properties would lead to exploitation.


An extension of the previous point is that complex systems also provide the opportunity for inequality: there are those who have mastered a system and those who remain bewildered by it, and this gap of understanding is a foundational inequality that can yield other inequalities (such as wealth).


Scapegoating is a form of abstraction, albeit a very messy one. It collapses complex issues into something less intimidating, more manageable, into a tangible entity with well-defined boundaries and serves to absolve a party from the responsibility of participating in these issues (that is to say, relieving them of any guilt), and attempts to outsource their resolution (that is to say, placing the responsibility of solutions onto others)? As the issues we face increase in complexity, multitude, enormity and abstractness, will we increasingly rely on the practice of scapegoating?

Until we can download understanding into the brain a la The Matrix, there will be no silver bullet for complexity. It’s navigation will require some degree of abstraction, but we can affect the quality of that abstraction, and develop an understanding that abstraction necessarily loses nuance which may be crucial.

The latter, I believe, can be solved through education. In particular, curriculums which promote critical thinking. Recognizing that ideas shouldn’t be taken as-is, that they may require a bit of scrubbing or digging to unearth their true form, and that a healthy skepticism is necessary in all things.

The former, from my perspective, is a design and engineering challenge. Natural language processing has a lot of promise here. Of course, it is not a panacea, but it is an invaluable tool in outsourcing some human processing. And I’ve seen more and more potent design applied to things like news readers and other info-deluge experiences. It’s something I’m really excited to work on!

Black Box in Cable & Deadpool, issue 21.

Moral Mazes: The Pathology of Bureaucracy

November 17 2013

I recently finished Robert Jackall’s Moral Mazes: The World of Corporate Managers, a book which focuses on the workings of the corporate environment, but has learnings that extend to the nature of bureaucracies in general. I was interested in it not only because it was one of Aaron Swartz’s favorite books but also because bureaucracy is undoubtedly an integral part of our social order, and whatever behaviors are cultivated there almost certainly find their way into our daily lives. With the “revolving door” that seems characteristic of modern politics, there’s a great deal of shoulder-rubbing between the political and corporate domains, and as such the latter culture influences the former. To put it another way, for many Americans the world of corporate politics comes to define the social reality of their entire life. Understanding it, then, seems crucial to understanding our modern society.

At its broadest, Moral Mazes dismantles notions that meritocracy actually exists and dispels the mythos of hard work having any meaning in a corporation. Success in a company, according to Jackall, is accomplished almost exclusively through politics. The formation of relationships such as alliances and patronage arrangements (having a higher-up look out for your career) are critical to success. It isn’t that numbers don’t matter, but rather: whatever hard work an individual accomplishes, and whatever fruits (or harm) might come as a result, matter little for that individual since blame and credit are shifted and appropriated by those with more clout.

Because of this structure, managing social relationships amongst co-workers, superiors, and even subordinates (since you never know who might be your boss tomorrow) becomes of the utmost importance. The maintenance of these relationships is accomplished through a deeply nuanced etiquette – protecting those in your alliances, knowing how to be a “team player” and understanding the subtle expectations in such a role, and so on. A strange sort of information asymmetry can come into play here, where being “kept in the dark” can be an unnerving and hostile tactic, but at the same time, there is the expectation that superiors be spared from details in order to absolve them of responsibility should things go wrong (130). The result of these complex social relationships is that the strategic presentation of self is crucial to success.

There is an unceasing evaluation of peers, assessing both their capacity for the moral compromise which is often necessary in such environments (“moral fitness”) and their position within the social hierarchy of the organization (13). At the same time, individuals are always looking to each other for social cues, to keep a grasp on the current form of the social order, which may arbitrarily shift according to the arrangement of superiors at the top of the company. This unstable landscape creates an environment of inexorable anxiety and uncertainty, which is further intensified by the contradictory facade of workplace harmony: “[these] ongoing conflicts…are usually hidden behind the comfortable and benign social ambiance that most American corporations fashion for their white-collar personnel.” (39)


Many of these aspects of corporate bureaucracy contribute to expediency being the primary mode of problem-solving. That is, quick solutions that disregard externalities or other potentially harmful long-term effects. We see this approach manifest in attitudes that have become characteristic of the large corporation, such as a flippancy towards environmental concerns. The nature of promotions exacerbate this emphasis on short-term solutions. There is a practice of “outrunning mistakes”, where one is promoted to a different position before any damaging long-term effects of one’s decisions come to a head (95). The onus falls on someone else, and it’s hard to cultivate accountability when no one’s around long enough to be held accountable.

In order to rationalize such expedient actions against criticism (or to preempt it), both within the corporation and to the public, “vocabularies of justification” are used (14). Public relations may be used to make controversial actions more palpable, which may be especially effective through the particularly devious practice of establishing “fronts” – official-sounding institutions which present themselves as legitimate, unbiased scientific authorities or representatives of small business owners, when in fact they are more often than not organized and funded by a few large corporations.

These vocabularies are extensively used within the corporation to discuss decisions, strategies, and so on. Their opaqueness and ambiguity further aggravate the social anxiety and uncertainty of the workplace. But one must present oneself in such a way that communicates a mastery of this language.


Because success in the corporate world is accomplished primarily through means of self-presentation, those who seek such success are constantly re-evaluating themselves and mutating their values and morals as necessary, so that they appear flexible enough for the expediency that bureaucracy requires. This requires the “object[ification] of the self with the same kind of calculating functional rationality that one brings to the packaging of any commodity”, which Jackall refers to as “psychic asceticism” (218). And here the book concludes rather tragically, ending with a description on how the frustration of self-compromise such objectification requires bleeds into a manager’s home life:

On the other hand, over a period of time, psychic asceticism creates a curious sense of guilt, heightened as it happens by narcissistic self-preoccupation. Such guilt, a regret at sustained self-abnegation and deprivation, finds expression principally in one’s private emotional life. One drinks too much; one is subject to pencil-snapping fits of alternating anxiety, depression, rage, and self-digust for willingly submitting oneself to the knowing and not knowing, to the constant containment of anger, to the keeping quiet, to the knuckling under that are all inevitable in bureaucratic life. One experiences great tensions at home because one’s spouse is unable to grasp or unable to tolerate the endless review of the social world of the workplace, the rehearsals of upcoming conversations, or the agonizing over real or imagined social slights or perceptions of shifts in power alignments. One wishes that one had spent more time with one’s children when they were small so that one could grasp the meanings of their adolescent traumas. Or one withdraws emotionally from one’s family and, with alternating fascination and regret, plunges ever deeper into the dense and intimate relationships of organizational circles where emotional aridity signals a kind of fraternity of expediency. Many try at times to escape the guilt with Walter Mitty-like fantasies of insouciant rebellion and vengeful retaliation; but one knows that only if and when one rises to high position in a bureaucratic hierarchy does one have the opportunity to turn the pain of self-repression against one’s fellows. (218)

I am fortunate enough that I work in a “flat” organization that manages to avoid the more rigid hierarchies. But that isn’t to say there are none. It seems that bureaucracy inevitably emerges, in some form, in any large organization of people – but is it equally inevitable that it be such a destructive force? Are there ways to design its workings to avoid these more harmful consequences?

Jackall, Robert. Moral Mazes: The World of Corporate Managers (Twentieth Anniversary Edition). New York: Oxford University Press, 2010.

The World’s End

November 7 2013


Note: This post contains SPOILERS!

I just finished watching The World’s End, the new Edgar Wright/Simon Pegg/Nick Frost film. For the most part, the film is silly and fun as I had expected and hoped, but the film takes an interesting turn at the end.

At the climax of the story, it is revealed that an extraterrestrial sentience is essentially colonizing Earth for assimilation into the utopian galactic planetary alliance called “the Network”. The protagonists learn that at their town and a couple thousand others across the planet, residents are being prepared for this assimilation so that these towns can steer the rest of Earth’s population towards the societal maturity and level of cooperation necessary for being accepted into the Network.

What comes out is a tale of totalitarian utopianism, which becomes a bit clearer when it’s revealed that all but three of the town’s population had to be replaced by replicas to achieve the Network’s prerequisite degree of harmony. The only way such an system of rule could be accepted by the populace was by forcing them to accept it, or by outright replacing them with those who were designed to accept it (the replicas).

The Network’s ambassador is literally a projection of authority, patronizing the populace through standard totalitarian rhetoric of sacrifice, cast as necessary for a broader betterment of society.

The value of such sacrifice is predicated on the fact that this sacrifice allows us to exist as (or become part of, in the film’s case) an enlightened, liberated society. We’re free from enacting the “same cycles of self-destruction again and again”, which the sentience chastises the characters about. But when that sacrifice is that autonomy, the protection of which is the stated purpose of that sacrifice – well, then what’s the point? All you’re left with is a husk of a society, built on order without spirit, and where the cause negates the effect.

But perhaps the more nuanced argument here is that the preservation of some autonomy requires the sacrifice of some other autonomy. That is, freedom itself may be too free, in that the allowance of all possibilities necessarily includes destructive or harmful possibilities as well. So the question, then, is: who decides that? Who decides what freedoms are off-limits?

In the movie, the alien sentience and its totalitarian-utopian offer are essentially overthrown by the various human communities it has targeted, mainly through the objection that freedom should be all or none. “We are the human race, and we don’t like being told what to do,” proclaims the protagonist.

I guess what this is then is a fundamentally anarchist declaration, insofar as it’s saying that restrictions on freedom should not come on high from any illegitimate authority (here, a foreign sentience that has no history on Earth). If there must be restrictions on freedom, they should instead be derived from our freedoms themselves.

And while I wish I could leave it here, a seedling of doubt remains. Are we capable of making such decisions for ourselves? I have been struggling to understand my own stance on this.

Nevertheless, in the end, when prompted by the entity: “Just what is it that you want to do?”, the characters reprise the Peter Fonda sample from Primal Scream’s “Loaded” which opened the film:
“We wanna be free…we wanna be free to do what we wanna do…and we wanna get loaded…and we wanna have a good time. And that’s what we’re gonna do.”

Villainy and Utopia

July 23 2013

“Every villain is the hero of his own story.” – attributed to many

Recently I was re-reading Alan Moore’s Watchmen and it got me thinking…

How do you fix the world? This is a loaded question and beyond the scope of any reasonable discussion, but it’s something that a lot of literature and popular media take on. In particular, the fantastic license that comics and graphic novels take is a great tool for exploring the potential answers to this possibly (probably) impossible question.

We are perpetually in a time where there’s distrust against the prevailing systems of rule and order that try to or purportedly try to guide us – us being members of both sovereign nations and the world – towards prosperity, peace, and security. No one ever does it “right”, and any contender always thinks they can do better.

I’ve always found it interesting that in most comics, the heroes aren’t typically the ones stepping up to this challenge. Rather than attempt to fix the world, they try to save it, maintaining the world more or less in its current state – not radically utopianize it. Their heroics seem to be directed only at those who try to tip the scale in favor of evil.

The characters in comics that do try to “fix” the world, more often than not, end up being the villains.

This seems like a contradiction, because to want to fix the world – that is, to turn it into a place free of the fears and concerns that plague our world today – is clearly a “good” aspiration and must be motivated by at least some goodwill and morality (which may be misguided). So why is it the objective of these villains?

Tales of the Black Freighter

Watchmen, and it’s Tales of the Black Freighter subplot, is a great exploration of this. Amongst the wreckage of his ship, a sole-surviving sailor encounters the demonic mass of the Black Freighter, a ship of the damned that raids towns to murder the inhabitants and pillage their souls. In his sun-soaked and parched delirium, he becomes convinced that the ship is heading towards the port where his family lives, and does whatever he can to intercept the invaders.

As he presses on to his village, his actions become increasingly compromising and his mind becomes increasingly warped, until finally he arrives. Blinded by his singular, searing goal, he is convinced the village has already been lost. Refusing to let his home be overrun, he murders innocents who he believes to be invaders, until finally he murders his wife, who he mistook for a pirate patrolling his house.

Realizing his unforgivable transgression, he gives himself to the Black Freighter, lamenting:

“Noble intentions had led me to atrocity. The righteous anger fueling my ingenious, awful scheme was but delusion.” – Alan Moore’s Watchmen, Chapter XI.

Good, moral intentions are far more malleable than we realize. Without much effort, through imperceptible deviances we can find that our actions have totally betrayed their original motivation. This seems a common theme throughout all of humanity – in history, in politics, in media, in friends, in family, in ourselves.

Is this quality the genesis of all “evil” people? Are we generally well-intentioned, but fail to regularly step back and properly evaluate our actions against those intentions? Is every villain just a misguided idealist, with the best interests of the world at heart?

If the villain is right…

The Black Freighter subplot serves as a a microcosm of the main world of Watchmen, which I won’t spoil here (read it, it’s great!), but I came across an episode of another world-domination-driven supervillain, Dr. Doom, which closely paralleled these same themes.

In this particular episode, Dr. Doom wants to access a vault of a very precious material, vibranium, which he requires for many of his world-threatening constructions. The vault is guarded by the Panther God, Bast, who administers a test for all those who would try to enter the vault. The test assesses the purity of the soul; if there is any impurity in your being or intent, you are disintegrated.

Here is how Dr. Doom fares:

Dr. Doom

Dr. Doom

Dr. Doom

Dr. Doom

scans from scans-daily

That fantastic comic license comes into play here. Dr. Doom is a sorcerer, so he has the literal foresight to see that his tyrannical actions are the only way humanity’s peace and prosperity. Is he still a villain then?

The City as a Classroom

July 13 2013

Brooklyn Public Library

Passing by the Brooklyn Public Library’s ornate and imposing doors, I was reminded of this bit from P.D. Smith’s City: A Guidebook for the Urban Age:

“In the seventeenth century, the Atlantis legend was one of the inspirations for ideal cities, such as Tommaso Campanella’s The City of the Sun (1602). A free-thinking Dominican monk imprisoned and tortured for heresy by the Inquisition, Campanella’s urban utopia is built on a hill with seven concentric walled circles, the middle ones rising up above the outer rings. The design was influenced by Pieter Bruegel the Elder’s famous 1563 painting The Tower of Babel with its seven ascending concentric levels. Just as in Bruegel’s painting and in the original ziggurats on which it was based, the City of the Sun has at its centre, on the summit of the hill, ‘a great temple of marvellous workmanship’. The temple is round and its dome is decorated with sparkling star maps, as well as astrological verses. Indeed, the city functions as an encyclopaedia of natural and esoteric knowledge, each circle being decorated with illustrations from the sciences – trees, herbs, metals, as well as real and fantastic animals. This is the city as classroom, where the inhabitants absorb enlightenment by osmosis, as they go about their daily lives.” (Smith, P.D., 2012, City: A guidebook for the urban age, Chapter 2, emphasis mine)