All posts in category Misc.

In Praise of Fun Projects

Over the past year, I’ve watched a few people I know in digital life sink a fair amount of time into statistical modeling projects that other people might see as “just for fun,” if not downright frivolous. Last April, for example, public-health grad student Brett Keller delivered an epic blog post that used event history models to explore why some competitors survive longer than others in the fictional Hunger Games. More recently, sociology Ph.D. student Alex Hanna has been using the same event history techniques to predict who’ll get booted each week from the reality TV show RuPaul’s Drag Race (see here and here so far). And then there’s Against the Spread, a nascent pro-football forecasting project from sociology Ph.D. candidate Trey Causey, whose dissertation uses natural language processing and agent-based modeling to examine information ecology in authoritarian regimes.

I happen to think these kinds of projects are a great idea, if you can find the time to do them–and if you’re reading this blog post, you probably can. Based on personal experience, I’m a big believer in learning by doing. Concepts don’t stick in my brain when I only read about them; I’ve got to see the concepts in action and attach them to familiar contexts and examples to really see what’s going on. Blog posts like Brett’s and Alex’s are a terrific way to teach yourself new methods by applying them to toy problems where the data sets are small, the domain is familiar and interesting, and the costs of being wrong are negligible.

Lego-Raspberry-Pi-case

A bigger project like Trey’s requires you to solve a lot of complex procedural and methodological problems, but all the skills you develop along the way transfer to other domains. If you can build and run a decent forecasting system from scratch for something as complex as pro football, you can do the same for “seriouser” problems, too. I think that demonstrated skill on fun tasks says as much about someone’s ability to execute complex research in the real world as any job talk or publication in a peer-reviewed journal. Done well, these hobby projects can even evolve into rewarding enterprises of their own. Just ask Nate Silver, who kickstarted his now-prodigious career as a statistical forecaster with PECOTA, a baseball forecasting system that he ginned up for fun while working for pay as a consultant.

I suspect that a lot of people in the private sector already get this. Academia, not so much, but then they’re the ones who wind up poorer for it.

8 Comments
by dartthrowingchimp on March 20, 2013  •  Permalink
Posted in Forecasting, Misc., Statistics
Tagged , , ,

Posted by dartthrowingchimp on March 20, 2013

http://dartthrowingchimp.wordpress.com/2013/03/20/in-praise-of-fun-projects/

Big Data Won’t Kill the Theory Star

A few years ago, Wired editor Chris Anderson trolled the scientific world with an essay called “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” After talking about the fantastic growth in the scale and specificity of data that was occurring at the time—and that growth has only gotten a lot faster since—Anderson argued that

Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

In other words, with data this rich, theory becomes superfluous.

Like many of my colleagues, I think Anderson is wrong about the increasing irrelevance of theory. Mark Graham explains why in a year-old post on the Guardian‘s Datablog:

We may one day get to the point where sufficient quantities of big data can be harvested to answer all of the social questions that most concern us. I doubt it though. There will always be digital divides; always be uneven data shadows; and always be biases in how information and technology are used and produced.

And so we shouldn’t forget the important role of specialists to contextualise and offer insights into what our data do, and maybe more importantly, don’t tell us.

At the same time, I also worry that we’re overreacting to Anderson and his ilk by dismissing Big Data as nothing but marketing hype.  From my low perch in one small corner of the social-science world, I get the sense that anyone who sounds excited about Big Data is widely seen as either a fool or a huckster. As Christopher Zorn wrote on Twitter this morning, “‘Big data is dead” is the geek-hipster equivalent of ‘I stopped liking that band before you even heard of them.’”

Of course, I say that as one of those people who’s really excited about the social-scientific potential these data represent. I think a lot of people who dismiss Big Data as marketing hype misunderstand the status quo in social science. If you don’t regularly try to use data to test and develop hypotheses about things like stasis and change in political institutions or the ebb and flow of political violence around the world, you might not realize how scarce and noisy the data we have now really are. On many things our mental models tell us to care about, we simply don’t have reliable measures.

Take, for example, the widely held belief that urban poverty and unemployment drive political unrest in poor countries. Is this true? Well, who knows? For most poor countries, the data we have on income are sparse and often unreliable, and we don’t have any data on unemployment, ever. And that’s at the national level. The micro-level data we’d need to link individuals’ income and employment status to their participation in political mobilization and violence? Apart from a few projects on specific cases (e.g., here and here), fuggeddaboudit.

Lacking the data we need to properly test our models, we fill the space with stories. As Daniel Kahneman describes on p. 201 of Thinking, Fast and Slow,

You cannot help dealing with the limited information you have as if it were all there is to know. You build the best possible story from the information available to you, and if it is a good story, you believe it. Paradoxically, it is easier to construct a coherent story when you know little, when there are fewer pieces to fit into the puzzle. Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.

When that’s the state of the art, more data can only make things better. Sure, some researchers will poke around in these data sets until they find “statistically significant” associations and then pretend that’s what they expected to find the whole time. But, as Phil Schrodt points out, plenty of people are already doing that now.

Meanwhile, other researchers with important but unproven ideas about social phenomena will finally get a chance to test and refine those ideas in ways they’d never been able to do before. Barefoot empiricism will play a role, too, but science has always been an iterative process that way, bouncing around between induction and deduction until it hits on something that works. If the switch from data-poor to data-rich social science brings more of that, I feel lucky to be present for its arrival.

9 Comments
by dartthrowingchimp on February 23, 2013  •  Permalink
Posted in Misc.
Tagged , , , , ,

Posted by dartthrowingchimp on February 23, 2013

http://dartthrowingchimp.wordpress.com/2013/02/23/big-data-wont-kill-the-theory-star/

Why Is Academic Writing So Bad? A Brief Response to Stephen Walt

On his Foreign Policy blog, Stephen Walt picks up on a Daily Dish thread and asks, “Why is academic writing so bad?” He suggests a few reasons but concludes that, for the most part, scholars write poorly on purpose. In his view, bad writing is “a form of academic camouflage designed to shield the author from criticism.”

Is this really such a mystery, though? Writing well is hard to do, and it depends in no small part on talent. Like all talents, the ability to write well is probably distributed normally across the population. Most people are mediocre at it, some are really bad, and some are really good. Scholars just happen to work in a profession where writing is the preferred form of communication. Map that normal distribution onto a profession that churns out a ton of writing, and you’ll get the result we see.

Walt’s argument implies that most scholars could write well but choose not to. I find that hard to believe. I think the kind of dense, jargony writing Walt sees as camouflage is actually easier for most people to produce than the concise writing he rightly prefers. Skill and good editing are what get you from the former to the latter. Skill varies widely, and anyone who’s ever written for an academic journal or press knows that peer reviewers and editors usually give you zero help with your prose.

What’s more interesting, I think, is why academia doesn’t select for writing skill, given how much writing scholars are expected to do. You don’t see a lot of terrible writing in top newspapers and magazines because editors don’t want to hire and retain journalists who make their jobs that much harder. Orchestras don’t hire musicians who have great ideas about melody and harmony but can’t play.

Of course, it’s possible that academia would reward excellent writing if it got the chance, but the best writers are simply choosing to take their skills elsewhere. I suspect this self-sorting process does play a role. Writing for a living doesn’t make very many people rich, but neither does scholarship, and writers have a lot more room to be playful in their work outside academia.

Still, as a social scientist, I have to think that incentives within the profession have some effect, too. When reading each others’ work, scholars (ahem) tend to skim. Readers of quantitative papers often jump to the charts and tables summarizing the results and only selectively scan the other bits. The intended audiences for most academic writing are colleagues who speak the same jargon. Peer reviewers care a lot more about the novelty of one’s findings than the quality of the language used to convey them. In this environment, scholars can’t expect to be rewarded for time spent making marginal improvements to their prose, and they behave accordingly. As Trey Causey put it on Twitter this morning, “Everyone admires work that’s important and well-written. No one cares about unimportant but well-written work.”

16 Comments
by dartthrowingchimp on February 16, 2013  •  Permalink
Posted in Misc.
Tagged , , ,

Posted by dartthrowingchimp on February 16, 2013

http://dartthrowingchimp.wordpress.com/2013/02/16/why-is-academic-writing-so-bad-a-brief-response-to-stephen-walt/

On the Limits of Our Causal Imagination

This morning, while I was driving my boys to school, my 13-year-old son said:

When I was a kid, I thought you controlled the car with the steering wheel. I would see you go like this <pushes arms out> and like this <pulls arms in> and thought that was how you made it go.

What a perfect illustration of how our minds imperfectly construct causality. Sitting in the back seat when he was younger, my son couldn’t see my feet as I drove; he could only see my hands. When he wondered what caused the car to speed up and slow down, he built a complete mental model from observed materials. It didn’t occur to him that I might be doing things he didn’t see—that the real causes of the car’s acceleration and deceleration might lie hidden from his view. Only in retrospect did that idea seem silly. At the time, that mental model made complete sense to him, and he implicitly entrusted his life to it every time he climbed in that car.

Think about that next time you’re trying to explain something as complex as the flow and ebb of a social movement or the collapse of a state.

2 Comments
by dartthrowingchimp on December 17, 2012  •  Permalink
Posted in Misc.
Tagged , ,

Posted by dartthrowingchimp on December 17, 2012

http://dartthrowingchimp.wordpress.com/2012/12/17/on-the-limits-of-our-causal-imagination/

Mid-Career Reflections on Non-Academic Work for Social Scientists

Over at Al-Jazeera English, Sarah Kendzior has just tossed a large and very cold bucket of water on the professional asiprations of many aspiring scholars with a bleak but frank assessment of the declining state of the academic job market. Writing about a friend she saw at this year’s annual meeting of the American Anthropological Association, Kendzior tells us that

My friend is an adjunct. She has a PhD in anthropology and teaches at a university, where she is paid $2100 per course. While she is a professor, she is not a Professor. She is, like 67 per cent of American university faculty, a part-time employee on a contract that may or may not be renewed each semester. She receives no benefits or health care. According to the Adjunct Project, a crowdsourced website revealing adjunct wages—data which universities have long kept under wraps—her salary is about average. If she taught five classes a year, a typical full-time faculty course load, she would make $10,500, well below the poverty line. Some adjuncts make more. I have one friend who was offered $5000 per course, but he turned it down and requested less so that his children would still qualify for food stamps.

If things are that bad in academia, I suspect more and more young Ph.D.s are going to be looking for work elsewhere. I’ve never held an academic job, but I have spent the 15 years since I received my doctorate working on related issues in the private sector, so I thought I’d take Kendzior’s lament as a cue to offer some mid-career reflections of my own on the non-academic alternatives. I can only speak from personal experience, but I’ve been asked about those experiences enough times to think there’s an audience out there somewhere that might want to hear about them, so here goes:

Let’s get one thing straight right off the bat. That dream job you’ve always pined for, the one where you get paid well to engage in thoughtful conversations with smart people on topics that interest you, and maybe sometimes even bend the ear of the powerful? It doesn’t exist, or at least it might as well not. Sure, there are a handful of think-tank perches that probably come close, but there are so few of them that your chances of landing one are statistically indistinguishable from zero. If you thought getting into a top-level Ph.D. program was competitive, imagine how much harder it would have been if there’d been just one or two programs in the whole country for the same applicant pool, and personal connections and professional experience were also part of the equation. You might have written a great dissertation, even published a buzzworthy paper in top journal, but you’ve never been an ambassador or a deputy assistant secretary or an NSC staffer? Fuggeddaboudit.

I say that as someone who’s both had those dreams and occasionally bemoaned the unfairness of it all. But you know what? It’s not unfair, it’s life, and the sooner you figure that out, the sooner you can move on to a more practical response to the very hard problem of trying to earn a living and being happy at the same time.

In some ways, this whole conundrum reminds me of the elite post-collegiate runners I had a chance to run with (or behind, really) in grad school. They were a pretty humble crowd on the whole, but every once in a while, someone would bemoan the dinky prize money, the dearth of quality sponsorships, and the nearly nonexistent TV coverage on which the whole problem seemed to be founded. If we could just get distance running covered properly on TV, the thinking went, people would start paying attention, and the career opportunities would finally grow. Add some pizzazz, make it more like NASCAR, get better commentators…

Sadly, though, I think they had it backwards. The market had spoken, and viewers and shoe-buyers and teen athletes just didn’t care enough about track and road running to make it a viable career for all but the very best and the luckiest. These guys were excellent at something they loved to do, but that something held little interest for the rest of the world. It turns out that excellence and passion were only two-thirds of the package. To make a decent living, you also need a market—other people who value that excellence and passion enough to want to pay you for it. But, hey, don’t take my word for it; go ask a poet or a painter or a musician.

So where does that leave a social scientist trying to make a living outside the academy? As far as I can tell, there are really two major options: either a) you seek work as a social scientist, probably either for the government or for a government contractor; or b) you look to apply your strongest talents—research design, analytical writing, statistical analysis, whatever—in some other field where your Ph.D. is irrelevant but your skills are not.

I’ve spent most of my 15 years since grad school doing the former, so I can speak more confidently about that path. The good news is that there seems to be a sizable number of public-sector and contractor jobs for social scientists nowadays; the pay’s often pretty good; and the demand appears to be growing. Lots of government agencies hire social-science Ph.D.s to work on interesting problems, and many of them also pay outside firms lots of money to do still-more research they can’t cover in-house.

The bad news is that these jobs often won’t involve doing research on the topics that most interest you. When someone else is footing the bill, you answer the questions they want answered, not the ones you find most intriguing. What’s more, the more senior you are, the more time you’re likely to spend managing and writing and reviewing proposals instead of researching and writing. Some firms have a “senior scientist” career path, but the reality for most Ph.D.s is that more experience means more management, not more freedom. This is not academia without the course load and faculty meetings; it’s bureaucracy and hierarchy of another kind.

If your work requires a high-level security clearance—and nowadays many of these jobs do—then you’ll also want to consider how much you value the freedom to speak and write spontaneously in public on the subject of your work before you sign up. When you receive that clearance, you make a promise to guard what you know, and that means giving the government a chance to review your utterances on work-related topics before you spout off. In an age of real-time global publishing and constant social-media chatter, that can be a hard promise to keep. But…you signed a contract, and your career, among other things, depends on your ability to uphold it. I don’t mean to discourage anyone from going down that path, but I do mean to encourage anyone considering it to do so fully mindful of the commitments it entails.

I have less to say about the other path I mentioned because I have only dabbled in it, mostly in the past year and a half, when I’ve been trying to make a living as an independent consultant. As the academic job market shrivels, though, I suspect more and more social scientists will go this route.

Here, I have one thought to offer: If this is the path you expect to follow, I doubt it’s worth going through a Ph.D. to get there. If you plan to trade on a specific skill, you’ll probably get a better return on your time and money from learning and demonstrating that skill than you would from training for a career you don’t plan to pursue. To be a great carpenter, you don’t need to know the history of carpentry, how to design a house, how to cut and cure lumber, and how to teach those things to other people, and the time you spend learning those things is time you’re not spending honing your craft. I’m increasingly convinced that the same logic applies to would-be Ph.D.s who are really budding data geeks or writers or teachers.

The longer this post goes, the more self-indulgent it gets, so I’ll stop there and invite readers to weigh in. As someone still trying to figure out how to make this work, I’d love to hear from other Ph.D.s who’ve made a living outside the academy about their experiences and any advice they can offer, and I suspect that other readers would, too.

37 Comments
by dartthrowingchimp on August 20, 2012  •  Permalink
Posted in Misc.
Tagged

Posted by dartthrowingchimp on August 20, 2012

http://dartthrowingchimp.wordpress.com/2012/08/20/mid-career-reflections-on-non-academic-work-for-social-scientists/

The Legitimacy Fallacy

I’ve never thought much of the concept of political legitimacy, and a recent rereading of Seymour Martin Lipset’s Political Man has reminded me why. In theoretical discourse on political stability and change, legitimacy is the Ouroboros, the mythical serpent locked in a circle as it eats its own tail. We appeal to legitimacy when we need to explain the persistence of political arrangements that defy our materialist predictions, and when those arrangements do finally collapse, we say that their failure has revealed a preceding loss of legitimacy. In statistical terms, legitimacy is the label we attach to the residual, the portion of the variance our mental models cannot explain. It is a tautology masquerading as a causal force.

In the chapter of his 1960 classic that got me thinking about this topic again, Lipset writes (emphasis mine) that

The stability of any given democracy depends not only on economic development but also upon the effectiveness and the legitimacy of its political system. Effectiveness means actual performance, the extent to which the system satisfies the basic functions of government as most of the population and such powerful groups within it as big business or the armed forces see them. Legitimacy involves the capacity of the system to engender and maintain the belief that the existing political institutions are the most appropriate ones for the society.

Lipset goes on to argue that this concept has predictive power; if we know how legitimate a system is, we can anticipate whether or not it will survive challenging times, or what social scientists sometimes refer to as “exogenous shocks.” To illustrate this point, he contrasts the fate of various democracies in Europe in the face of the Great Depression. “When the effectiveness of various governments broke down in the 1930s, those societies which were high on the scale of legitimacy remained democratic, while such countries as Germany, Austria, and Spain lost their freedom, and France narrowly escaped a similar fate.”

Voilà, right? I mean, going into the 1930s, I’m sure every astute social observer could have told you that those four countries were the ones where “belief that the existing political institutions are the most appropriate ones for the society” was weakest; that citizens in neighboring countries did not harbor similar doubts; and that those variations in beliefs would largely determine the trajectories European countries would follow through the coming storm. Otherwise, this remarkably accurate after-the-fact prediction would be nothing more than common hindsight bias.

More damning, though, are Lipset’s brief comments on Thailand. After discussing mid-century Europe, he writes that

From a short-range point of view, a highly effective but illegitimate system, such as a well-governed colony, is more unstable than regimes which are relatively low in effectiveness and high in legitimacy. The social stability of a nation like Thailand, despite its periodic coups d’etat, stands out in sharp contrast to the situation in neighboring former colonial nations.

Looking back from 2012, that view of Thai politics seems almost laughably wrong-headed. In the past few decades, the regime that Lipset identified as an exemplar of social stability founded on high legitimacy has been wracked by periodic waves of mass unrest and separatist insurgency. The latest and still-ongoing wave of mass upheaval has now lasted nearly a decade and concerns precisely the question at the core of Lipset’s definition of legitimacy: are the existing institutions the most appropriate ones for Thai society? Based on their voting patterns and participation in mass protests, it now seems clear that many Thais think not, but an oligarchical alliance of monarchists, militarists, and well-to-do urbanites still has enough power to resist attempts to fully dislodge the old regime.

To be fair to Lipset, I suspect few observers of Thai politics in the early 1960s would have foreseen the ruptures that seem inevitable with hindsight (and if this article from a May 1996 issue of Time is exemplary of the information available at the time, it’s easy to see why). But then, that’s really the problem, isn’t it? If we can’t reliably observe legitimacy or know that it’s crumbling until people behave in ways that show it has, what value is it adding to our theories of political change?

In a terrific working paper that disassembles the problem far more thoroughly than this blog post, political scientist Xavier Marquez accepts that legitimacy may have some value as a summary concept in casual discussions of politics, but he shows that it just doesn’t work as an element of explanatory or even normative political theory. As Xavier puts it, explanations of political stability that appeal to legitimacy “are strictly speaking tautological: they do not so much explain stability as restate the problem of stability in different terms.” If you’re interested in comparative politics, you really should read the whole thing, but the following passage (emphasis mine) nicely summarizes his reasoning:

To the extent that the concept of legitimacy appears to have some explanatory value, this is only because explanations of social and political order that appeal to legitimacy in fact conceal widely different (and often inconsistent) accounts of the mechanisms involved in the production of obedience to authority and submission to norms. Very often legitimacy works as a residual concept, a sort of virtus dormitiva that is used to explain the persistence of social and political order wherever obvious coercion or material incentives appear unable to account for its stability. But like most such residual concepts, it tends to hide the wide variety of mechanisms that actually sustain social order, including epistemic deficits, collective action problems, signalling conventions, emotional attachments, and cognitive biases. I thus suggest that explanatory social science would be better off abandoning the concept of legitimacy for more precise accounts of the operation of these mechanisms in particular contexts.

Maybe I’m being too harsh on the concept. Analogizing to physics, maybe legitimacy is more like the dark matter of political development, a substance we cannot observe but whose existence we can infer from the otherwise strange behavior of human particles in visible political systems.

The problem with that analogy is that the theoretical models we have of social systems are nowhere near as well-developed and specific as the ones physicists have used to infer the existence of dark matter. No one has seen dark matter, but physicists can and have used careful observation of many related phenomena to develop a fairly sharp idea of what it is (and isn’t). For now—and maybe forever—social scientists have nothing that even comes close. Until we can find a way to reliably observe preferences and beliefs across a wide variety of cultural contexts, appeals to legitimacy are going to keep us stuck in a pre-scientific world, where things can be true because they just make sense.

10 Comments
by dartthrowingchimp on August 17, 2012  •  Permalink
Posted in Misc.
Tagged , , ,

Posted by dartthrowingchimp on August 17, 2012

http://dartthrowingchimp.wordpress.com/2012/08/17/the-legitimacy-fallacy/

Ignorance Is Not Always Bliss

Contrary to the views of some skeptics, I think that political science deserves the second half of its name, and I therefore consider myself to be a working scientist. The longer I’ve worked at it, though, the more I wonder if that status isn’t as much a curse as a blessing. After more than 20 years of wrestling with a few big questions, I’m starting to believe that the answers to those questions are fundamentally unknowable, and permanent ignorance is a frustrating basis for a career.

To see what I’m getting at, it’s important to understand what I take science to be. In a book called Ignorance, neurobiologist Stuart Firestein rightly challenges the popular belief that science is a body of accumulated knowledge. Instead, Firestein portrays scientists as explorers—”feeling around in dark rooms, bumping into unidentifiable things, looking for barely perceptible phantoms”—who prize questions over answers.

Working scientists don’t get bogged down in the factual swamp because they don’t care all that much for facts. It’s not that they discount or ignore them, but rather that they don’t see them as an end in themselves. They don’t stop at the facts; they begin there, right beyond the facts, where the facts run out.

What differentiates science from philosophy is that scientists then try to answer those questions empirically, through careful observation and experimentation. We know in advance that the answers we get will be unreliable and impermanent—”The known is never safe,” Firestein writes; “it is never quite sufficient”—but the science is in the trying.

The problem with social science is that it is nearly always impossible to do the kinds of experimentation that would provide us with even the tentative knowledge we need to develop a fresh set of interesting questions. It’s not that experimentation is impossible; it isn’t, and some social scientists are working hard to do them better. Instead, as Jim Manzi has cogently argued, the problem is that it’s exceptionally difficult to generalize from social-scientific experiments, because the number and complexity of potential causes is so rich, and the underlying system, if there even is such a thing, is continually evolving.

This problem is on vivid display in a recent Big Think blog post in which eight researchers identified as some of the world’s “top young economists” identify what they see as their discipline’s biggest unanswered questions. The first entry begins with the sentence, “Why are developing countries poor?” The flip side of that question is, of course, “Why are rich countries rich?”, and if you put those two questions together, you get “What makes some economies grow faster than others?” That is surely the most fundamental riddle of macroeconomics,  and yet the sense I get from empirical economists is that, after centuries of inquiry, we still really just don’t know.

My own primary field of political development and democratization suffers from the same problem. After several decades of pondering why some countries have democratic governments while others don’t, the only thing we really know is that we still don’t know. When we pore over large data sets, we see a few strong correlations, but those correlations can’t directly explain the occurrence of relevant changes in specific cases. What’s more, so many factors are so deeply intertwined with each other that it’s really impossible to say which causes which. When we narrow our focus to clusters of more comparable cases—say, the countries of Eastern Europe after the collapse of Communism—we catch glimpses of things that look more like causal mechanisms, but the historical specificity of the conditions that made those cases comparable ensures that we can never really generalize even those ephemeral inferences.

It’s tempting to think that smarter experimentation will overcome or at least ameliorate this problem, but on broad questions of political and economic development, I’m not buying it. Take the question of whether or not U.S.-funded programs aimed at promoting democracy in other countries actually produce the desired effects. This sounds like a problem amenable to experimental design–what effect does intervention X have on observable phenomenon Y?–but it really isn’t. Yes, we can design and sometimes even implement randomized controlled trials (RCTs) to try to evaluate the impacts of individual interventions under specific conditions. As Jennifer Gauck has convincingly argued, however, it’s virtually impossible to get clear answers to the original macro-level questions from the micro-level analyses RCTs on this topic must entail when the micro- to macro- linkages are themselves unknown. Add thick layers of politicization, power struggles, and real-time learning, and it’s hard to see how even well-designed RCTs can push us off of old questions onto new ones.

I’m not sure where this puts me. To be honest, I increasingly wonder if my attraction to forecasting has less to do with the lofty scientific objective of using predictions to hone theories and more to do with the comfort of working on a more tractable problem. I know I can never really answer the big questions, and my attempts to do so sometimes leave me feeling like I’m trying to bail out the ocean, pouring one bucket at a time onto the sand in hopes of one day catching a glimpse of the contours of the floor below. By contrast, forecasting at least provides a yardstick against which I can assess the incremental value of specific projects. On a day-to-day basis, the resulting sense (illusion?) of progress provides a visceral feeling of accomplishment and satisfaction that is missing when I offer impossibly uncertain answers to deeper questions of cause and effect. And, of course, the day-to-day world is the one I actually have to inhabit.

I’d really like to end this post on a hopeful note, but today I’m feeling defeated. So, done.

5 Comments
by dartthrowingchimp on July 31, 2012  •  Permalink
Posted in Complexity Politics, Misc.
Tagged , , ,

Posted by dartthrowingchimp on July 31, 2012

http://dartthrowingchimp.wordpress.com/2012/07/31/ignorance-is-not-always-bliss/

Prometheus Bungled

[NB: It's Saturday morning, and I'm at the tail end of a week's vacation, so I'm digressing from the usual fare to write about a movie. Apologies for the distraction.]

The “science” part of science fiction is important. Good science fiction employs a reality we recognize, but with specific things tweaked or altered. These alterations serve as a way to explore how those things shape our present reality, and how the future—or some counterfactual past or present—might differ. As Philip K. Dick suggests, good science fiction isn’t fantasy, it’s futurism.

For this to work, the alterations have to be scientifically plausible. Fusion reactors are interesting, because they connect to rules and theories we know and are therefore imaginable. Magic energy cubes, on the other hand—things like the Tesseract in the ho-hum Avengers franchise—are frustrating, because they aren’t connected to our lived existence. (Equally important from a narrative perspective, magic energy cubes don’t impose any constraints on the action that we can recognize ahead of time and therefore can’t serve as sources of tension and suspense.)

The elegance of science lies in its simplicity. The basic components and rules are simple; the complexity we inhabit emerges from their interaction and combination. All the things we see around us are composed of few dozen elements, which are, in turn, composed of a handful of subatomic particles. Combinations of simple geometric elements produce dazzling fractal forms that appear over and over again in nature, often in very different contexts. Sentience emerges from clusterings of relatively simple cells—or, perhaps, circuits.

Starting from these premises, I was frustrated and disappointed by Prometheus, the recently released prequel to Ridley Scott’s 1979 science-fiction masterpiece, Alien. I was only nine years old when Alien first came to theaters, so I didn’t much appreciate it at the time. As a Star Wars fan, I was happy to go to any movie set on a spaceship, but I had to head for the lobby after the “indigestion” scene in the galley and only saw bits of the rest of the film through the doors at the back of the theater.

Going back to Alien as an adult, though, I’ve become a big admirer. I don’t much like horror films. What makes Alien so appealing to me now is its smart application of one of one of science’s most elegant theories—evolution—to answer a profound philosophical question. On Earth, humans seem (to ourselves, anyway) to stand at the top of the pyramid, so powerful that we can build ships to expand our reach into space to scavenge and prey elsewhere. As soon as we do that, though, we also expand the circle of our existence into a wider universe in which other creatures might occupy similar niches on other planets. In this wider circle, predator can become prey, parasite can become host, and natural selection can take new turns. Humans become more evidently animal and are clearly maladapted to many of the new environments in which they might land. The stories we tell about our special place in the universe crumble in our first encounters with species from its other corners. The common denominator for life in this wider perspective is the urge to survive and reproduce, and on this larger scale, we may not be so great at it after all. If the question is “What makes us humans special?”, Scott’s answer is a resounding “Not much.”

And then we get Prometheus. Where Alien relied on the elegance of a simple scientific idea, Prometheus injects narrative complexity and scientific ambiguity in a plot that grabs from biology and evolution but ultimately rejects them. The long evolutionary line connecting humans to primordial goo on Earth is cut, replaced with the quasi-mystical idea of a process begun much more recently by Engineers whose own origins are left unexplained. The simple biological imperative of survival through predation is supplanted by a worn and ambiguous political story about warring societies and weapons of mass destruction.

Good science fiction is really hard to write. Most attempts I’ve seen or read have foundered or failed because they have interwoven so many inventions and ideas that we can’t follow the threads back to our lived existence. The core of science is the experimental method, and one of the guiding principles of that method is control. To answer questions about cause and effect, you focus your gaze by varying the hypothesized cause while holding confounding factors constant.

Good science fiction often does the same. Alien puts humans in deep space, hypothesizes about what might have evolved in those strange (to us) environments, and runs one trial of an experiment to see which species would survive by dominating those encounters. (In repeated iterations of this experiment, my money’s on the aliens, the lazy digressions of the sequels notwithstanding.) Prometheus replaces this elegant design with mysticism and hand-waving more characteristic of religion than science, and it’s a much poorer film for it.

5 Comments
by dartthrowingchimp on July 14, 2012  •  Permalink
Posted in Misc.
Tagged ,

Posted by dartthrowingchimp on July 14, 2012

http://dartthrowingchimp.wordpress.com/2012/07/14/prometheus-bungled/

Top 5 Albums to Play When Writing Stats Code

I like to listen to music when I’m working, but not all music works equally well for all tasks. When I’m writing prose, for example, I constantly get distracted if I play music with a narrative or clever wordplay in the lyric; the words in the song keep pushing out the words in my head. When I’m writing (or, more often, debugging) stats code, I find that certain pieces of music actually help me get and stay in a nice flow. So, in the brilliant tradition of High Fidelity, here are my Top 5 records to play when writing stats code:

5. Cocteau Twins, Heaven or Las Vegas. This one has words, but god bless you if you can understand what the heck she’s singing. All I hear is a lush run of sound with a fantastic bass line.

4. Beethoven’s piano sonatas. I usually listen to Volume II of the three-volume, many-disc Glenn Gould edition, which gives me a few hours of uninterrupted music. My favorite piece is the allegro section of the opening “Pastorale,” which always makes me feel like I’m meandering down a country lane on horseback on a sunny day.

3. Kronos Quartet Performs Philip Glass. I wonder if Glass’s “repetitive structures” help turn on parts of the brain that deal in math and logic. In any case, this recording of a few of his string quartets feels romantic to me in spite of its modern structures, and it never gets old to my ears.

2. Daft Punk, Tron:Legacy Soundtrack. I know, I know, it’s pretty much a cliché, but if you can tolerate the snippet of Jeff Bridges’ character geeking out about The Grid, this album kills for code-writing.

1. Sviatoslav Richter, The Authorised Recordings of J.S. Bach compositions for piano. Put on good over-ear headphones, and listening to this is like slipping into another world where there’s just one long, unbroken thread of gorgeous sound. It’s like coding in a vacuum.

6 Comments
by dartthrowingchimp on June 2, 2012  •  Permalink
Posted in Misc.
Tagged , , ,

Posted by dartthrowingchimp on June 2, 2012

http://dartthrowingchimp.wordpress.com/2012/06/02/top-5-albums-to-play-when-writing-stats-code/

House Votes to Defund Political Science Program: The Irony, It Burns

From the Monkey Cage this morning:

The Flake amendment Henry wrote about appears to have passed the House last night with a 218-208 vote. The amendment prohibits funding for NSF’s political science program, which among others funds many valuable data collection efforts including the National Election Studies. No other program was singled out like this…This is obviously not the last word on this. The provision may be scrapped in the conference committee (Sara?). But it is clear that political science research is in real danger of a very serious setback.

There’s real irony here in a Republican-controlled House of Representatives voting to defund a political-science program at a time when the Department of Defense and “intelligence community” are apparently increasing spending on similar work. With things like the Minerva Initiative, the Political Instability Task Force (on which I worked for 10 years), ICEWS, and IARPA’s Open Source Indicators programs, the parts of the government concerned with protecting national security seem to find growing value in social-science research and are spending accordingly. Meanwhile, the party that claims to be the stalwart defender of national security pulls in the opposite direction, like the opposing head on Dr. Doolittle’s Pushmi-pullyu. Nice work, fellas.

3 Comments
by dartthrowingchimp on May 10, 2012  •  Permalink
Posted in Misc.
Tagged , , ,

Posted by dartthrowingchimp on May 10, 2012

http://dartthrowingchimp.wordpress.com/2012/05/10/house-votes-to-defund-political-science-program-the-irony-it-burns/

« Older Posts
Follow

Get every new post delivered to your Inbox.

Join 3,357 other followers

%d bloggers like this: