On Simplicity

You can build complex arguments on a very simple foundation
Ted Underwood

Celestial Navigation

The world is full of very complex algorithms honed to solve even more complex problems. When you use your phone as a GPS, it’s not a simple matter of triangulating signals from towers or satellites. Because your GPS receiver has to know the precise time (in nanoseconds) at the satellites it’s receiving signals from, and because the satellites are moving at various speeds and orbiting at an altitude where the force of gravity is significantly different, calculating times gets quite complicated due to the effects of relativity. The algorithms that allow the GPS to work have to take relativity into account, often on the fly, and without those complex algorithms the GPS would not be nearly so precise.

Precision and complexity go hand-in-hand, and often the relationship between the two is non-linear. That is, a little more precision often requires lot more complexity. Ever-higher levels of precision get exponentially more difficult to achieve. The traditional humanities is a great example of this; a Wikipedia article can sum up most of what one needs to know regarding, say, World War I, but it takes many scholars many lifetimes to learn  everything. And the more that’s already been figured out, the more work we need to do to find the smallest next piece to understand.

This level of precision is often important, insightful, and necessary to make strides in a field. Whereas before an earth-centered view of the universe was good enough to aid in navigation and predict the zodiac and the seasons, a heliocentric model was required for more precise predictions of the movements of the planets and stars. However, these complex models are not always the best ones for a given situation, and sometimes simplicity and a bit of cleverness can go much further than whatever convoluted equation yields the most precise possible results.

Sticking with the example of astronomy, many marine navigation schools still teach the geocentric model; not because they don’t realize the earth moves, but because navigation is simply easier when you pretend the earth is fixed and everything moves around it. They don’t need to tell you exactly when the next eclipse will be, they just need to figure out where they are. Similarly, your phone can usually pinpoint you within a few blocks by triangulating itself between cellphone towers, without ever worrying about satellites or Einstein.

Geocentric celestial navigation chart from a class assignment.

Whether you need to spend the extra time figuring out relativistic physics or heliocentric astronomical models depends largely on your purpose. If you’re just trying to find your way from Miami to New York City, and for some absurd reason you can only rely on technology you’ve created yourself for navigation, the simpler solution is probably the best way to go.

Simplicity and Macroanalysis

If I’ve written over-long on navigation, it’s because I believe it to be a particularly useful metaphor for large-scale computational humanities. Franco Moretti calls it distant reading, Matthew Jockers calls it macroanalysis, and historians call it… well, I don’t think we’ve come up with a name for it yet. I’d like to think we large-scale computational historians share a lot in spirit with big history, though I rarely see that connection touched on. My advisor likes shifting the focus from what we’re looking at to what we’re looking through, calling tools that help us see the whole of something macroscopes, as opposed to the microscopes which help us reach ever-greater precision.

Whatever you choose to call it, the important point is the shifting focus from precision to contextualization. Rather than exploring a particular subject with ever-increasing detail and care, it’s important to sometimes take a step back and look at how everything fits together. It’s a tough job because ‘everything’ is really quite a lot of things, and it’s easy to get mired in the details. It’s easy to say “well, we shouldn’t look at the data this way because it’s an oversimplification, and doesn’t capture the nuance of the text,” but capturing the nuance of the text isn’t the point. I have to admit, I sent an email to that effect to Matthew Jockers regarding his recent DH2012 presentation,  suggesting that time and similarity were a poor proxy for influence. But that’s not the point, and I was wrong in doing so, because the data still support his argument of authors clustering stylistically and thematically by gender, regardless of whether he calls the edges ‘influence’ or ‘similarity.’

I wrote a post a few months back on halting conditions, figuring out that point when adding more and more detailed data stops actually adding to the insight and instead just contributes to the complexity of the problem. I wrote

Herein lies the problem of humanities big data. We’re trying to measure the length of a coastline by sitting on the beach with a ruler, rather flying over with a helicopter and a camera. And humanists know that, like the sandy coastline shifting with the tides, our data are constantly changing with each new context or interpretation. Cartographers are aware of this problem, too, but they’re still able to make fairly accurate maps.

And this is the crux of the matter. If we’re trying to contextualize our data, if we’re trying to open our arms to collect everything that’s available, we need to keep it simple. We need a map, a simple way to navigate the deluge that is human history and creativity. This map will not be hand-drawn with rulers and yard-sticks, it will be taken via satellite, where only the broadest of strokes are clear. Academia, and especially the humanities, fetishizes the particular at the expense of the general. General knowledge is overview knowledge, is elementary knowledge. Generalist is a dirty word lobbed at popular authors who wouldn’t know a primary source if it fell on their head from the top shelf, covering them in dust and the smell of old paper.

Generality is not a vice. Simplicity can, at times, be a virtue. Sometimes you just want to know where the hell you are.

For these maps, a reasonable approximation is often good enough for most situations. Simple triangulation is good enough to get you from Florida to New York, and simply counting the number of dissertations published at ProQuest in a given year for a particular discipline is good enough to approximate the size of one discipline compared to another. Both lack nuance and are sure to run you into some trouble at the small scale, but often that scale is not necessary for the task at hand.

Two situations clearly shout for reasonable approximations; general views and contextualization. In the image below Matthew Jockers showed that formal properties of novels tend to split around the genders of their authors; that is, men wrote differently and about different things than women.

Network graph of 19th century novels, with nodes (novels) colored according to the gender of their authors.

Of course this macroanalysis lacks a lot of nuance, and one can argue for years which set of measurements might yield the best proxy for novel similarity, but as a base approximation the split is so striking that there is little doubt the apparent split is indicative of something interesting actually going on. Jockers has successfully separated signal from noise. This is a great example of how a simple approximation is good enoughto provide a general overview, a map offering one useful view of the literary landscape.

Beyond general overviews and contextualizations, simple models and quantifications can lead to surprisingly concrete and particular results. Take Strato, a clever observer who died around 2,300 years ago. There’s a lot going on after a rainstorm. The sun glistens off the moist grass, little insects crawl their way out of the ground, water pours from the corners of the roof. Each one of these events are themselves incredibly complex and can be described in a multitude of ways; with water pouring from a roof, for example, you can describe the thickness of the stream, or the impression the splash makes on the ground below, or the various murky colors it might take depending on where it’s dripping from. By isolating one property of the pouring rainwater, the fact that it tends to separate into droplets as it gets closer to the ground, Strato figured out that the water moved faster the further it had fallen. That is, falling bodies accelerate. Exactly measuring that acceleration, and quite how it worked, would elude humankind for over a thousand years, but a very simple observation that tended to hold true in many situations was good enough to discover a profound property of physics.

A great example of using simple observations to look at specific historical developments is Ben Schmidt’s Poor man’s sentiment analysis. By looking at words that occur frequently after the word ‘free’ in millions of books,  Ben is able to show the decreasing centrality of ‘freedom of movement’ after its initial importance in the 1840s, or the drop in the use of ‘free men’ after the fall of slavery. Interesting changes are also apparent in the language of markets and labor which both fit well with our understanding of the history of the concepts, and offer new pathways to study, especially around inflection points.

Ben Schmidt looks at the words that appear most frequently following the word ‘free’ in the google ngrams corpus.

 Toward Simplicity

Complex algorithms and representations are alluring. Ben Fry says of network graphs

Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is “complex” fits just fine with the creator’s own interpretation of it.

And the same is often true of algorithms; the more complex the algorithm, the more we feel it somehow ‘fits’ the data, because we know our data are so complex to begin with. Oftentimes, however, the simplest solution is good enough (and often really good) for the broadest number of applications.

If there is any take-home message of this post, as a follow-up to my previous one on Halting Conditions, it’s that diminishing returns doesn’t just apply to the amount of data you’ve collected, it also applies to the analysis you plan on running them through. More data aren’t always better, and newer, more precise, more complex algorithms aren’t always better.

Spend your time coming up with clever, simpler solutions so you have more time to interpret your results soundly.

Science Systems Engineering

Warning: This post is potentially evil, and definitely normative. While I am unsure whether what I describe below should be doneI’m becoming increasingly certain that it could be. Read with caution.

Complex Adaptive Systems

Science is a complex adaptive system. It is a constantly evolving network of people and ideas and artifacts which interact with and feed back on each other to produce this amorphous socio-intellectual entity we call science. Science is also a bunch of nested complex adaptive systems, some overlapping, and is itself part of many other systems besides.

The study of complex interactions is enjoying a boom period due to the facilitating power of the “information age.” Because any complex system, whether it be a social group or a pool of chemicals, can exist in almost innumerable states while comprising the same constituent parts, it requires massive computational power to comprehend all the many states a system might find itself in. From the other side, it takes a massive amount of data observation and collection to figure out what states systems eventually do find themselves in, and that knowledge of how complex systems play out in the real world relies on collective and automated data gathering. From seeing how complex systems work in reality, we can infer properties of their underlying mechanisms; by modeling those mechanisms and computing the many possibilities they might allow, we can learn more about ourselves and our place in the larger multisystem. 1

One of the surprising results of complexity theory is that seemingly isolated changes can produce rippling, massive effects throughout a system.  Only a decade after the removal of big herbivores like giraffes and elephants from an African savanna, a generally positive relationship between bugs and plants turned into an antagonistic one. Because the herbivores no longer grazed on certain trees, those trees began producing less nectar and fewer thorns, which in turn caused cascading repercussions throughout the ecosystem. Ultimately, the trees’ mortality rate doubled, and a variety of species were worse-off than they had been. 2 Similarly, the introduction of an invasive species can cause untold damage to an ecosystem, as has become abundantly clear in Florida 3 and around the world (the extinction of flightless birds in New Zealand springs to mind).


Both evolutionary and complexity theories show that self-organizing systems evolve in such a way that they are self-sustaining and self-perpetuating. Often, within a given context or environment, the systems which are most resistant to attack, or the most adaptable to change, are the most likely to persist and grow. Because the entire environment evolves concurrently, small changes in one subsystem tend to propagate as small changes in many others. However, when the constraints of the environment change rapidly (like with the introduction of an asteroid and a cloud of sun-cloaking dust), when a new and sufficiently foreign system is introduced (land predators to New Zealand), or when an important subsystem is changed or removed (the loss of megafauna in Africa), devastating changes ripple outward.

An environmental ecosystem is one in which many smaller overlapping systems exist, and changes in the parts may change the whole; society can be described similarly. Students of history know that the effects of one event (a sinking ship, an assassination, a terrorist attack) can propagate through society for years or centuries to come. However, a system not merely a slave to these single occurrences which cause Big Changes. The structure and history of a system implies certain stable, low energy states. We often anthropomorphize the tendency of systems to come to a stable mean, for example “nature abhors a vacuum.” This is just the manifestation of the second law of thermodynamics: entropy always increases, systems naturally tend toward low energy states.

For the systems of society, they are historically structured constrained in such a way that certain changes would require very little energy (an assassination leading to war in a world already on the brink), whereas others would require quite a great deal (say, an attempt to cause war between Canada and the U.S.). It is a combination of the current structural state of a system and the interactions of the constituent parts that lead that system in one direction or another. Put simply, a society, its people, and its environment are responsible for its future. Not terribly surprising, I know, but the formal framework of complexity theory is a useful one for what is described below.


The above picture, from the Wikipedia article on metastability, provides an example of what’s described above. The ball is resting in a valley, a low energy state, and a small change may temporarily excite the system, but the ball eventually finds its way into the same, or another, low energy state. When the environment is stable, its subsystems tend to find comfortably stable niches as well. Of course, I’m not sure anyone would call society wholly stable…

Science as a System

Science (by which I mean wissenschaft, any systematic research) is part of society, and itself includes many constituent and overlapping parts. I recently argued, not without precedent, that the correspondence network between early modern Europeans facilitated the rapid growth of knowledge we like to call the Scientific Revolution. Further, that network was an inevitable outcome of socio/political/technological factors, including shrinking transportation costs, increasing political unrest leading to scholarly displacement, and, very simply, an increased interest in communicating once communication proved so fruitful. The state of the system affected the parts, the parts in turn affected the system, and a growing feedback loop led to the co-causal development of a massive communication network and a period of massively fruitful scholarly work.

Scientific Correspondence Network

Today and in the past, science is embedded in, and occasionally embodied by, the various organizational and communicative hierarchies its practitioners find themselves in. The people, ideas, and products of science feed back on one another. Scientists are perhaps more affected by their labs, by the process of publication, by the realities of funding, than they might admit. In return, the knowledge and ideas produced by science, the message, shape and constrain the medium in which they are propagated. I’ve often heard and read two opposing views: that knowledge is True and Right  and unaffected the various social goings on of those who produce it, and that knowledge is Constructed and Meaningless outside of the social and linguistic system it resides in. The truth, I’m sure, is a complex tangle somewhere between the two, and affected by both.

In either case, science does not take place in a vacuum. We do our work through various media and with various funds, in departments and networks and (sometimes) lab-coats, using a slew of carefully designed tools and a language that was not, in general, made for this purpose. In short, we and our work exist in a  complex system.

Engineering the Academy

That system is changing. Michael Nielsen’s recent book 4 talks about the rise of citizen science, augmented intelligence, and collaborative systems as not merely as ways to do what we’ve already done faster, but as new methods of discovery. The ability to coordinate on such a scale, and in such new ways, changes the game of science. It changes the system.

While much of these changes are happening automatically, in a self-organized sort of way, Nielsen suggests that we can learn from our past and learn from other successful collective ventures in order to make a “design science of collaboration.” That is, using what we know of how people work together best, of what spurs on the most inspired research and the most interesting results, we can design systems to facilitate collaboration and scientific research. In Nielsen’s case, he’s talking mostly about computer systems; how can we design a website or an algorithm or a technological artifact that will aid in scientific discovery, using the massive distributed power of the information age? One way Nielson points out is “designed serendipity,” creating an environment where scientists are more likely experience serendipitous occurrences, and thus more likely to come up with innovated and unexpected ideas.

Can we engineer science? http://www.flickr.com/photos/seattlemunicipalarchives/4818952324

In complexity terms, this idea is restructuring the system in such a way that the constituent parts or subsystems will be or do “better,” however we feel like defining better in this situation. It’s definitely not the first time an idea like this has been used. For example, science policy makers, government agencies, and funding bodies have long known that science will often go where the money is. If there is a lot of money available to research some particular problem, then that problem will tend to get researched. If the main funding requires research funded to become open access, by and large that will happen (NIH’s PubMed requirements).

There are innumerable ways to affect the system in a top-down way in order to shape its future. Terrence Deacon writes about how it is the constraints on a system which tend it toward some equilibrium state 5; by shaping the structure of the scientific system, we can predictably shape its direction. That is, we can artificially create a low energy state (say, open access due to policy and funding changes), and let the constituent parts find their way into that low energy state eventually, reaching equilibrium. I talked a bit more about this idea of constraints leading a system in a recent post.

As may be recalled from the discussion above, however, this is not the only way to affect a complex system. External structural changes are only part of the story of how a system grows shifts, but only a small part of the story. Because of the series of interconnected feedback loops that embody a system’s complexity, small changes can (and often do) propagate up and change the system as a whole. Lie, Slotine, and Barabási recently began writing about the “controllability of complex networks 6,”  suggesting ways in which changing or controlling constituent parts of a complex system can reliably and predictably change the entire system, perhaps leading it toward a new preferred low energy state. In this case, they were talking about the importance of well-connected hubs in a network; adding or removing them in certain areas can deeply affect the evolution of that network, no matter the constraints. Watts recounts a great example of how a small power outage rippled into a national disaster because just the right connections were overloaded and removed 7. The strategic introduction or removal of certain specific links in the scientific system may go far toward changing the system itself.

Not only is science is a complex adaptive system, it is a system which is becoming increasingly well-understood. A century of various science studies combined with the recent appearance of giant swaths of data about science and scientists themselves is beginning to allow us to learn the structure and mechanisms of the scientific system. We do not, and will never, know the most intricate details of that system, however in many cases and for many changes, we only need to know general properties of a system in order to change it in predictable ways. If society feels a certain state of science is better than others, either for the purpose of improved productivity or simply more control, we are beginning to see which levers we need to pull in order to enact those changes.

This is dangerous. We may be able to predict first order changes, but as they feed back onto second order, third order, and further-down-the-line changes, the system becomes more unpredictable. Changing one thing positively may affect other aspects in massively negative (and massively unpredictable) ways.

However, generally if humans can do something, we will. I predict the coming years will bring a more formal Science Systems Engineering, a specialty apart from science policy which will attempt to engineer the direction of scientific research from whatever angle possible. My first post on this blog concerned a concept I dubbed scientonomy, which was just yet another attempt at unifying everybody who studies science in a meta sort of way. In that vocabulary, then, this science systems engineering would be an applied scientonomy. We have countless experts in all aspects of how science works on a day-to-day basis from every angle; that expertise may soon become much more prominent in application.

It is my hope and belief that a more formalized way of discussing and engineering scientific endeavors, either on the large scale or the small, can lead to benefits to humankind in the long run. I share the optimism of Michael Nielsen in thinking that we can design ways to help the academy run more smoothly and to lead it toward a more thorough, nuanced, and interesting understanding of whatever it is being studied. However, I’m also aware of the dangers of this sort of approach, first and foremost being disagreement on what is “better” for science or society.

At this point, I’m just putting this idea out there to hear the thoughts of my readers. In my meatspace day-to-day interactions, I tend to be around experimental scientists and quantitative social scientists who in general love the above ideas,  but at my heart and on my blog I feel like a humanist, and these ideas worry me for all the obvious reasons (and even some of the more obscure ones). I’d love to get some input, especially from those who are terrified that somebody could even think this is possible.


  1. I’m coining the term “multisystem” because ecosystem is insufficient, and I don’t know something better. By multisystem, I mean any system of systems; specifically here, the universe and how it evolves. If you’ve got a better term that invokes that concept, I’m all for using it. Cosmos comes to mind, but it no longer represents “order,” a series of interlocking systems, in the way it once did.
  2. Palmer, Todd M, Maureen L Stanton, Truman P Young, Jacob R Goheen, Robert M Pringle, and Richard Karban. 2008. “Breakdown of an Ant-Plant Mutualism Follows the Loss of Large Herbivores from an African Savanna.” Science319 (5860) (January 11): 192–195. doi:10.1126/science.1151579.
  3. Gordon, Doria R. 1998. “Effects of Invasive, Non-Indigenous Plant Species on Ecosystem Processes: Lessons From Florida.” Ecological Applications 8 (4): 975–989. doi:10.1890/1051-0761(1998)008[0975:EOINIP]2.0.CO;2.
  4. Nielsen, Michael. Reinventing Discovery: The New Era of Networked Science. Princeton University Press, 2011.
  5. Deacon, Terrence W. “Emergence: The Hole at the Wheel’s Hub.” In The Re-Emergence of Emergence: The Emergentist Hypothesis from Science to Religion, edited by Philip Clayton and Paul Davies. Oxford University Press, USA, 2006.
  6. Liu, Yang-Yu, Jean-Jacques Slotine, and Albert-László Barabási. “Controllability of Complex Networks.” Nature473, no. 7346 (May 12, 2011): 167–173.
  7. Watts, Duncan J. Six Degrees: The Science of a Connected Age. 1st ed. W. W. Norton & Company, 2003.

The Networked Structure of Scientific Growth

Well, it looks like Digital Humanities Now scooped me on posting my own article. As some of you may have read, I recently did not submit a paper on the Republic of Letters, opting instead to hold off until I could submit it to a journal which allowed authorial preprint distribution. Preprints are a vital part of rapid knowledge exchange in our ever-quickening world, and while some disciplines have embraced the preprint culture, many others have yet to. I’d love the humanities to embrace that practice, and in the spirit of being the change you want to see in the world, I’ve decided to post a preprint of my Republic of Letters paper, which I will be submitting to another journal in the near future. You can read the full first draft here.

The paper, briefly, is an attempt to contextualize the Republic of Letters and the Scientific Revolution using modern computational methodologies. It draws from secondary sources on the Republic of Letters itself, especially from my old mentor R.A. Hatch, some network analysis from sociology and statistical physics, modeling, human dynamics, and complexity theory. All of this is combined through datasets graciously donated by the Dutch Circulation of Knowledge group and Oxford’s Cultures of Knowledge project, totaling about 100,000 letters worth of metadata. Because it favors large scale quantitative analysis over an equally important close and qualitative analysis, the paper is a contribution to historiopgraphic methodology rather than historical narrative; that is, it doesn’t say anything particularly novel about history, but it does offer a (fairly) new way of looking at and contextualizing it.

A visualization of the Dutch Republic of Letters using Sci2 & Gephi

At its core, the paper suggests that by looking at how scholarly networks naturally grow and connect, we as historians can have new ways to tease out what was contingent upon the period and situation. It turns out that social networks of a certain topology are basins of attraction similar to those I discussed in Flow and Empty Space. With enough time and any of a variety of facilitating social conditions and technologies, a network similar in shape and influence to the Republic of Letters will almost inevitably form. Armed with this knowledge, we as historians can move back to the microhistories and individuated primary materials to find exactly what those facilitating factors were, who played the key roles in the network, how the network may differ from what was expected, and so forth. Essentially, this method is one base map we can use to navigate and situate historical narrative.

Of course, I make no claims of this being the right way to look at history, or the only quantitative base map we can use. The important point is that it raises new kinds of questions and is one mechanism to facilitate the re-integration of the individual and the longue durée, the close and the distant reading.

The project casts a necessarily wide net. I do not yet, and probably could not ever, have mastery over each and every disciplinary pool I draw from. With that in mind, I welcome comments, suggestions, and criticisms from historians, network analysts, modelers, sociologists, and whomever else cares to weigh in. Whomever helps will get a gracious acknowledgement in the final version, good scholarly karma, and a cookie if we ever meet in person. The draft will be edited and submitted in the coming months, and if you have ideas, please post them in the comment section below. Also, if you use ideas from the paper, please cite it as an unpublished manuscript or, if it gets published, cite that version instead.

Flow and Empty Space

Thirty spokes unite in one nave and on that which is non-existent [on the hole in the nave] depends the wheel’s utility. Clay is moulded into a vessel and on that which is non-existent [on its hollowness] depends the vessel’s utility. By cutting out doors and windows we build a house and on that which is non-existent [on the empty space within] depends the house’s utility. Therefore, existence renders actual but non-existence renders useful.

-Laozi, Tao Te Ching, Susuki Translation

(NOTE 1: Although it may not seem it from the introduction, this post is actually about humanities research, eventually. Stick with it and it may pay off!)

(NOTE 2: I’ve warned in the past about invoking concepts you know little about; let me be the first to say I know next to nothing about Eastern philosophy or t’ai chi ch’uan, though I do know a bit about emergence and a bit about juggling. This post uses the above concepts as helpful metaphors, fully apologizing to those who know a bit more about the concepts for the butchering of them that will likely ensue.)

The astute reader may have noticed that, besides being a sometimes-historian and a sometimes-data-scientist, the third role I often take on is that of a circus artist. Juggling and prop manipulation have been part of my life for over a decade now, and though I don’t perform as much as I used to, the feeling I get from practicing is still fairly essential in keeping me sane. What juggling provides me that I cannot get elsewhere is what prop manipulators generally call a state of “flow.”

Look! It's me in a candy store!

The concept draws from a positive psychology term developed by Mihály Csíkszentmihályi, and is roughly equivalent to being in “the zone.” Although I haven’t quite experienced it, this feeling apparently comes to programmers working late at night trying to solve a problem. It’s also been described by dancers, puzzle solvers, and pretty much anyone else who gets so into something they feel, if only for a short time, they have totally lost themselves in their activity. A fellow contact juggler, Richard Hartnell, recently filmed a fantastic video describing what flow means to him as a performer. I make no claims here to any meaning behind the flow state. The human brain is complex beyond my understanding, and though I do not ascribe any mystical properties to the experience, having felt “flow” so deeply, I can certainly see why some do treat it as a religious experience.

The most important contribution to my ability to experience this state while juggling was, oddly enough, a t’ai chi ch’uan course. Really, it was one concept from the course, called song kua, “relax the hips,” that truly opened up flow for me. It’s a complex concept, but the part I’d like to highlight here is the relationship between exertion and relaxation, between a push and a pull. When you move your body, that movement generally starts with an intention. I want my hand to move to the right, so I move it to the right. There is, however, another way to move parts of the body, and this is via relaxation. If I’m standing in a certain way, and I relax my hip in one directoin, my body will naturally shift in the opposite direction. My body naturally gets pulled one way, rather than me pushing it to go there. In the circus arts, I can now quickly reach a flow state by creating a system between myself and whatever prop I’m using, and allowing the state of that system to pull me to the next state, rather than intentionally pushing myself and my prop in some intentional way. It was, for me, a mind-blowing shift in perspective, and one that had absolutely nothing to do with my academic pursuits until last night, on a short plane ride back from Chicago APA.

In the past two weeks, I’ve been finishing up the first draft of a humanities paper that uses concepts from complex systems and network analysis. In it, I argue (among other things) that there are statistical regularities in human behavior, and that we as historians can use that backdrop as a context against which we can study history, finding actions and events which deviate from the norm. Much recent research has gone into showing that people, on average, behave in certain ways, generally due to constraints placed on us by physics, biology, and society. This is not to say humans are inherently predictable – merely that there are boundaries beyond which certain actions are unlikely or even impossible given the constraints of our system. In the paper, I further go on to suggest that the way we develop our social networks also exhibits regularities across history, and the differences against those regularities, and the mechanisms by which they occur, are historically interesting.

Fast-forward to last night: I’m reading a fantastic essay by anthropologist Terrence W. Deacon about the emergence of self-organizing biological systems on the plane-ride home. 1 In the essay, Deacon attempts to explain why entropy seems to decrease enough to allow, well, Life, The Universe, and Everything, given the second law of thermodynamics. His answer is that there are basins of attraction in the dynamics of most processes which inherently and inevitably produce order. That is, as a chaotic system interacts with itself, there are dynamical states which the system can inhabit which are inherently self-sustaining. After a chaotic system shuffles around for long enough, it will eventually and randomly reach a state that “attracts” toward a self-sustaining dynamical state, and once it falls into that basin of attraction, the system will feed back on itself, remaining in its state, creating apparent order from chaos for a sustained period of time.

Deason invokes a similar Tao Te Ching section as was quoted above, suggesting that empty or negative space, if constrained properly and possessing the correct qualities, act as a kind of potential energy. The existence of the walls of a clay pot are what allows it to be a clay pot, but the function of it rests in the constrained negative space bounded by those walls. In the universe, Deason suggests, constraints are implicit and temporally sensitive; if only a few state structures are self-sustaining, those states, if reached, will naturally persist. Similar to that basic tenant of natural selection, that which can persist tends to.

The example Deason first uses is that of a whirlpool forming in the empty space behind a rock in a flowing river.

Consider a whirlpool, stably spinning behind a boulder in a stream. As moving water enters this location it is compensated for by a corresponding outflow. The presence of an obstruction imparts a lateral momentum to the molecules in the flow. The previous momentum is replaced by introducing a reverse momentum imparted to the water as it flows past the obstruction and rushes to fill the comparatively vacated region behind the rock. So not only must excess water move out of the local vicinity at a constant rate; these vectors of perturbed momentum must also be dissipated locally so that energy and water doesn’t build up. The spontaneous instabilities that result when an obstruction is introduced will effectively induce irregular patterns of build-up and dissipation of flow that ‘explore’ new possibilities, and the resulting dynamics tends toward the minimization of the constantly building instabilities. This ‘exploration’ is essentially the result of chaotic dynamics that are constantly self-undermining. To the extent that characteristics of component interactions or boundary conditions allow any degree of regularity to develop (e.g. circulation within a trailing eddy), these will come to dominate, because there are only a few causal architectures that are not self-undermining. This is also the case for semi-regular patterns (e.g. patterns of eddies that repeatedly form and disappear over time), which are just less self-undermining than other configurations.

The flow is not forced to form a whirlpool. This dynamical geometry is not ‘pushed’ into existence, so to speak, by specially designed barriers and guides to the flow. Rather, the system as a whole will tend to spend more time in this semi-regular behaviour because the dynamical geometry of the whirlpool affords one of the few ways that the constant instabilities can most consistently compensate for one another. [Deason, 2009, emphasis added]

Self-Organizing System (http://www.flickr.com/photos/lapstrake/3164577339/)

Essentially, when lots of things interact at random, there are some self-organized constraints to their interactions which allow order to arise from chaos. This order may be fleeting or persistent. Rather than using the designed constraint of a clay pot, walls of a room, or spokes around a hub, the constraints to the system arise from the potential in the context of the interactions, and in the properties of the interacting objects themselves.

So what in the world does this have to do with the humanities?

My argument in the above paper was that people naturally interact in certain ways; there are certain basins of attraction, properties of societies that tend to self-organize and persist. These are stochastic regularities; people do not always interact in the same way, and societies do not come to the same end, nor meet their ends in the same fashion. However, there are properties which make social organization more likely, and knowing how societies tend to form, historians can use that knowledge to frame questions and focus studies.

Explicit, data-driven models of the various mechanisms of human development and interaction will allow a more nuanced backdrop against which the actualities of the historical narrative can be studied. Elijah Meeks recently posted, about models,

[T]he beauty of a model is that all of these [historical] assumptions are formalized and embedded in the larger argument…  That formalization can be challenged, extended, enhanced and amended [by more historical research]… Rather than a linear text narrative, the model itself is an argument.

It is striking how seemingly unrelated strands of my life came together last night. The pull and flow of juggling, the bounded ordering of emergent behaviors, and the regularities in human activities. Perhaps this is indicative of the consilience of human endeavors; perhaps it is simply the overactive pattern-recognition circuits in my brain doing what they do best. In any case, even if the relationships are merely loose metaphors, it seems clear that a richer understanding of complexity theory, modeling, and data-driven humanities leading to a more nuanced, humanistic understanding of human dynamics would benefit all. This understanding can help ground the study of history in the Age of Abundance. A balance can be drawn between the uniquely human and individual, on one side, and the statistically regular ordering of systems, on the other; both sides need to be framed in terms of the other. Unfortunately, the dialogue on this topic in the public eye has thus-far been dominated by applied mathematicians and statistical physicists who tend not to take into account the insights gained from centuries of qualitative humanistic inquiry. That probably means it’s our job to learn from them, because it seems unlikely that they will try to learn from us.


  1. in The Re-Emergence of Emergence, 2009, edited by Philip Clayton & Paul Davies.