Prediction Markets

[This is a draft from a few months ago, but I'm posting it in hopes that it'll prompt me to finish it. Maybe it will stimulate someone to want to hear more (or, do YOU want to finish it? If so, get a copy of "Infotopia" by Harvard Law prof Cass Sunstein, if you haven't already. I started writing this up after reading this book.).]

Prediction Markets are a fancy name for the age-old idea of betting on future events or outcomes, or "putting your money where your mouth is", in other words. In a prediction market, participants can place bets on whether, say, health care will pass, or whether Obama will get re-elected (if he runs), or (more controversially) when the next major terrorist incident will occur on American soil, and so on. Researchers have found in recent years that Prediction Markets are remarkably accurate. In many cases, they outperform the predictions of subject matter experts, and other types of group-aggregation predictors like polls, or deliberative groups. For example, the Iowa Electronic Markets, run by the University of Iowa since 1998, is a popular prediction market that has psroven effective in guessing election outcomes. The IEM results had Bush with 50.45% of the vote, and Kerry with 49.55%, compared to the actual 51.56% for Bush and 48.44% for Kerry. The result, based simply on people placing bets on the final outcome of the election, outperformed professional polling results.

The question is, why? Why is betting such a powerful tool for predicting the future? Participants in Prediction Markets--bettors--don't have to have a Ph.D. in the electoral process; they don't have to have access to special information; they don't really need any credentials at all, short of agreeing to partipate by putting up some initial money and hoping to get more by guessing correctly. And yet, collectively, the Prediction Markets often outperform the experts.

The story of why Prediction Markets (hereafter PMs) work so well is best told by telling another story, one of how deliberative groups often don't work well. In a deliberative group, members, well, deliberate: they discuss and analyze some topic or set of possible outcomes and decide on a course of action, or a most likely outcome, or a best policy, depending on what is getting deliberated. Deliberative groups often do produce good outcomes (perhaps the most famous example of the success of deliberation is the ratification of the American Constitution, where sustained discussion about the details of the Constitution resulted in the document we have today).

Systems Accidents

Laurence Gonzales, in his best-selling Deep Survival, discusses a class of accidents that happen in complex, real-world systems, called systems accidents. The term was first introduced by John Perrow in his now famous work on accidents, Normal Accidents, published in 1984. Perrow argued that in certain kinds of systems, catastrophic accidents, while rare, are inevitable. What's worse, and what made Perrow's work so controversial, attempts to avoid such accidents only make them more inevitable, because the safety measures put in place to expunge them also make the systems where they occur more complex. It is precisely the complexity of such systems which make them prone, one way or the other, to rare but catastrophic failure.

Gonzalez (citing Perrow) points to two features of complex systems that make them prone to systems accidents. One, the systems must support "unintended complex interactions among components and forces". Two, they must be "tightly coupled", or constructed in such a way that forces, even if initially small, can magnify as they propagate from component to component. In systems with such features, the result of forces acting on or in the system cannot be exactly known: seemingly insignificant events can chain together in unexpected ways, leading to unintended results (feature one), and some of these interactions can have global effects: the system itself can be "blown apart" by ostensibly innocuous forces that, by cause and effect, release the energy in the whole system destructively (feature two). Given these features, systems accidents will occur; they are a natural result of the complexity of such systems.

Gonzalez mentions a few examples of Perrow's complex systems, like the modern airline, a "large mass containing explosive fuel, flying at high speeds, and operating along a fine boundary between stability and instability". Something seemingly insignificant happens (say, the onboard toilet malfunctions), and it leads to a catastrophic failure, one that is nearly impossible to predict in advance and, as Perrow argued, cannot be eliminated in kind, because the safety components one might introduce to solve some problems will themselves create complexities that lead to other problems that won't be understood until an accident occurs, too. (The toilet leading to a catastrophic crash is a real example.)

Gonzalez connects Perrow's work on systems accidents with branches of mathematics and science concerned with so-called nonlinear systems, like chaos theory. Chaos theory has entered into the popular or "pop" science vocabulary (it was mentioned in the movie Jurassic Park, for instance) as a catch-all phrase for systems with states that are difficult or impossible to predict because they have a "sensitive dependence on initial conditions", as science writer James Gleick put it in his 1987 book Chaos. Turbulence is a classic example of a chaotic system: applying physics equations to predict where a cubic inch of water in a turbulent stream will be in, say, 10 seconds is effectively impossible, because the initial conditions of the cube of water--including all of the factors that can act on it--have to be specified so exactly, and again and again as the cube propagates in the turbulent stream, that no amount of computation can possibly determine its later position. True, the prediction is possible in principle, since the laws governing dynamic systems like flowing water are known, but not in practice, because the calculations we need for prediction require such an exactitude and are so complex that not even fantastically powerful computers (say, composed of all of the matter in the universe) could compute predictions of the position of the cube after some (relatively small) window of time. It's effectively impossible to predict the downstream position of the cube of water, in other words. This realization, that chaotic systems give rise to unpredictable behavior, led to notions about unpredictability like the "butterfly effect", where it's quipped that the air displaced by a butterfly's wings in Tokyo might, a month later, create a hurricane in the Gulf of Mexico.

Chaos theory applies to Perrow's complex systems because, just like with the butterfly effect, such systems have a sensitive dependence on the initial conditions that might lead, ultimately, to systems accidents. No one thought about the toilet malfunction beginning a chain that led to the crash; similarly, the sensitivity of O-rings (remember them?) to cold weather was known before the Challenger explosion, but no one put together that the particular O-rings installed, along with the exact temperature in Cape Canaveral the morning of the fateful 1986 launch, would by cause and effect result in the explosion. But it happened, and if Perrow is right, such systems accidents will always happen, one way or the other. Some types of systems accidents will be analyzed and fixed, but others will emerge, and while they will remain rare, they will remain inevitable, too.

What's interesting about Gonzalez's treatment of systems accidents via Perrow is his application of them to human hobbies and activities. He tells stories of experienced mountain climbers who are killed precisely because they applied their experience to situations, and put in place safety measures based on what worked in past attempts. By and large, the safety measures climbers employ do work: teams of climbers roped together help reduce the likelihood that a particular climber will fall, because when someone slips, the other climbers can "self-arrest", which means they plant their ice axes in the snow and help break the fall of the climber who slipped. But sometimes, they don't.

They key to understanding Gonzalez's examples is to get his distinction between general trends and specific situations. Safety systems address general risk: mountain climbing is in general more safe today than, say, a hundred years ago. Like a seatbelt worn in a car, the safety procedures climbers employ on mountains function to lessen the number of accidents and the damaging consequences of them when they do occur. But, as Gonzalez points out, sometimes the safety system itself causes or exacerbates an accident in a specific (unpredictable) situation, just as Perrow argued it will. The safety system introduces massive amounts of energy into systems of climbers, making them tightly coupled, and it introduces the possibility that some situations will lead, by cause and effect, to the "blowing apart" of the constructed system, as all of that energy put into the system (for safety), suddenly becomes the energy that magnifies the accident. As Gonzalez describes so well in Deep Survival, this is exactly what happened to a group of experienced climbers who self-arrested on Mount Hood to save a member of their team from falling. Several people died in the accident; had the safety measures not been used, one person would have fallen, and though this is of course tragic, it's not worse than what did happen, which became one of the worst mountain climbing disasters ever.

The point of Deep Survival is not that we should throw out safety measures, or give up on making them better. The point is that systems create complexities--whether in nature, or by our own design--that limit prediction and make deterministic solutions impossible. Chaos and complexity gaurantee that life will remain messy, incomplete, and in large part unpredictable. They gaurantee that, try as we might, we'll never, as Dostoevsky once remarked, erect a "crystal palace of reason", where everything is in place, and understood, and predictable. Systems accidents are here to stay, and though our best efforts help reduce the inherent risk of living and acting in the world, they can't eliminate it, and in many cases they succeed in making the risk that remains even graver, and the accidents we occasionally do have even more catastrophic.

Sam Harris Talk

An interesting talk by Sam Harris, of "The End of Faith" fame, at TED. This is worth listening to, about 18 minutes. His idea is that there are scientific facts that tell us a lot about what we should value, which begins to bridge the gap between science and morality, ultimately making it possible to have a "science of morality".

The Tone in Washington

Two recent columns, from the New York Times and Washington Post, respectively, pretty much book-end the "Bush was evil, Bush was good" debate. On one end, Paul Krugman wants to charge Bush with war crimes. On the other, Charles Krauthammer asks us to acknowledge our debt to Bush.

Whether you're liberal or conservative minded, have a look. It's worth reading them back to back.

For my part, I always get a little uncomfortable when intelligent people reach such radically different conclusions from (one would assume) the same set of facts and observations. Feels a little nihilistic to me, like there's no real fact of the matter, only different and perhaps too firmly held opinions, determined by fundamentally different, and incompatible, ideologies. Yet, perhaps our problem is not so much our failure to find objective truths but rather the way that we find (or think we find) them, by constructing political ideologies -- broad, sweeping systems that purport to explain the political world around us and to determine how we ought to think and act. Perhaps.

To be sure, a scepticism of ideology is not new to America. It undergirds an intellectual movement -- pragmatism-- that emerged here in the 19th century with Constitutional scholars like Oliver Wendel Holmes Jr., as well as philosophers such as Charles Pierce, William James, Dewey and others. The emergence of American pragmatism in the 19th century is, like many intellectual movements, multifarious, but at least in part and perhaps centrally it can be seen as a response to the idealism that helped ignite a powder keg, the American Civil War. As Holmes, who fought bitterly in the Civil War as a young man and who nearly died there in battle, wondered: if all of our ideals led us to the carnage of the Civil War -- if our politicians and intellectuals couldn't save us from nearly tearing the Union apart -- of what moral and practical benefit were those ideals? His thought of course found its expression in monumental works like The Common Law. His contemporaries -- James, Pierce, and Dewey -- wrote the more theoretical underpinnings of political pragmatism and are still part of the philosophical canon (though perhaps, after World War II, less so).

President-Elect Obama may or may not be a pragmatist, in the grand tradition of Holmes and the great 19th century philosophers. He is fond of studying Lincoln, I hear (a good sign). At any rate, we will indeed need change in Washington. First on the list, I hope, will be a change in the often too partisan tone. I'm hopeful that Mr. Obama's eloquent words and to some early extent his first deeds as President-Elect signal just that.

Stuck on a Hill

In artificial neural network (ANN) research, there's a well known problem of local minima (or maxima). I've worked a bit with ANNs but much more with a (superior) learning algorithm, Support Vector Machines (SVMs). Unlike the latter, ANNs require heuristics to "converge" on an optimal solution given some very large decision surface. The heuristics, to simplify a bit, are intended to get the algorithm to converge on a global, not local, solution. Local solutions are unwanted because they can appear to be global (good) solutions, given some snapshot of the decision surface, but in fact are very bad solutions when one "zooms out" to see the larger picture. This phenomenon is perhaps best illustrated with geographical imagery. If I am walking up and down hills, en route to a very large mountain, a local maxima might be the top of a foothill. But it would hardly be a global maxima, like the top of the mountain. The point is that ANNs can converge on the foothills, telling us that it's the mountain.

ANNs notoriously suffer from this limitation, but the "blindness problem" is endemic to all statistical learning algorithms, including SVMs (though, at least in theory, you can get an optimal solution with an SVM). Using such algorithms to learn from past experience (i.e., training data), you generate an approximation function for the underlying distribution you're trying to model. You see the function as worth the time it took to gather training data, select the features, and train, if it approximates pretty well the underlying target distribution you're interested in. You can tell if it pretty well approximates the underlying distribution if it keeps getting things right, and you don't have to keep making excuses for it (say, by saying that "it's complicated").

Anyway, we can view ANNs, SVMs, and other statistical learners as essentially inductive systems, in the sense that, given a set of prior examples, they learn a rule (classifier) that allows us to predict something about new, unseen examples. They generalize, in the sense that unseen examples that match a profile (learned from the training data), even if not an exact fit, may still be classified correctly. It's not a simple one-to-one match. Hence, generalize.

The problem with the generalization performance of all such systems is two-fold. One, they're limited by the set of features that were chosen (a person selects features that are relevant, like "is followed with 'inc.'" for classifying organization mentions in free text). Two, even given optimal feature selection, the generalization performance of inductive systems is always hostage to the information in the training data. We collect examples to use to train the system, and, in the end, we hope that the training data was adequate to model the true distribution (the thing we really want to predict). Problem is, whatever hasn't occurred yet in this true distribution, can't possibly be collected from past examples, and so the entire approach is hostage to the fact that things change in real-life environments, and the larger "pattern" of the true distribution as it unfolds in time may not be captured in the training data. Whenever this happens, the approximation function does not model the true distribution. Bummer.

Now, a feature in particular of such inductive systems (we can substitute "supervised statistical learning sytems" if it sounds more fancy) is this local minima or maxima worry, which I introduced with regard to ANNs, but which is really just a handy way of introducing the general problem of generalizing from past cases to future ones writ large. And it is a problem. Consider time sequence prediction (as opposed to, say, sequence classification such as the well-known document classification task in IR research). In time sequence prediction, the goal is take a sequence of elements at time t, and predict the next element at time t+1. Applying this multiple times you can predict a large sequence of elements through some time n.

And this is where the inductive problem comes in, because if the data you're using to predict the next elements came from some set of prior elements, it's possible that the prior elements (your training data), gave you a model that will get you stuck on a foothill, or, will see the top of a small hill as a bottom valley, and so on. You can't be sure, because the future behavior of the true distribution you don't have. And this is why, in the end, induction--however fancy it gets dressed up in mathematical clothing--not only can be wrong, in theory, but often is, in practice.

We can't see into the future, unfortunately. If we could, we could fix the inductive problem by simply adding in the additional information about the true distribution that we're missing in our training data. But in that case, of course, we'd hardly need the approximation.

Rule Following

"This was our paradox: no course of action could be determined by a rule, because any course of action can be made out to accord with the rule."
Ludwig Wittgenstein, Philosophical Investigations

"If the rule you followed brought you to this, of what use was the rule?"
Anton Chigurh, No Country for Old Men


One the great myths of modern society is that we're following rules to obtain outcomes. I mean rules, roughly, in the sense described here, although I'll also feel free, for these purposes, to equivocate a bit between plans and rules. No harm should be done for now.

So the myth of rule following. We see it in software development (no one seems to notice, or if they do, they dare not mention, that the "rule" was changed a thousand times between conception and completion of project), we see it in the economy, the social sciences, and indeed everywhere that the veneer of science and technology and the almost pathological need for certainty manages to obscure deeper truths about the fragility of our capacities.

Retrodiction, not prediction, is what we're best at, though it is unfortunately and for obvious reasons of little interest. And for various psychological reasons that I'm neither qualified nor interested in researching directly, we're strikingly good at painting failure after failure to predict what comes next with ex post facto explanations that make things just so. Political science is perhaps paradigmatic. It was common in the 1950s to prognosticate about how the (now defunct) USSR would be the preeminent superpower by the 1970s. France (yes, France) was widely thought to be emerging in the 1970s. Japan in the 1980s. China of course today. Our ability to keep proclaiming, generation after generation, our cock-sure predictions about the future state of human societies in the next year, five years, decade (or God forbid, century), is simply amazing, and defies logic. Yet we keep doing it. And we will keep doing it, in spite of all evidence of consistent failure to the contrary.

The psychology of rule following tells us that there's a rule (or a set of rules) that we followed to get to a result (or that will allow us to predict a future result). And when we achieve the result, we tend to confirm the application of the rule, when in fact (chances are) we've made innumerable on-the-fly judgements to get to our result, and then we've tidied things up after the result was achieved by giving credit to the rule. So everything fits. Feels like progress.

On the other side of the coin, when a result is not achieved, instead of recognizing the general problem of using rules, we tend to assume that the particular rule (we claimed to) use, was in fact not adequate. And we set about looking for a new rule, which will of course not be adequate in many contexts too. Such is the nature of our (unexamined) selves. In a deeper and more honest sense we might someday admit that progress (at least in messy, complex situations that we're immersed in), is mostly a function of insights, adaptive thinking as the environment changes, and, well, luck. But we don't see it this way. It doesn't sound like something an expert would say.

So I think that in complex systems (like the weather, or any system where human choice can enter in), our capacity to formulate generalizations that tell us how things will be at time t+n, when we refer to them at time t, is effectively a chimera (whenever n is large enough, which depends on features of the system). Things are constantly new, and different. We formulate plans, and rules, and they guide us, but very loosely, because the environment is constantly in flux. Rules we've grabbed onto "work", only because we keep adjusting things to make them seem to work. The real driver is rather our own wits and insight. And with these much more powerful tools, software does get developed. The Surge in Iraq works. The Space Shuttle (mostly) arrives at the Space Station. And when I get correct directions and follow them, I typically get where I'm going (even if unexpected snags happen). And on and on.

Anyway, in some other post I promise to explain in more depth exactly how rule-following is a mirage, which I haven't yet done (I've asserted mostly only that it is). To be continued. Until then, rest assured that our rules are grains of salt. They just masquerade as so much more.

Forgotten Thymos

We have, mainly, two great axes today upon which modern explanations of social phenomena turn. One, the economic/Marxist reduction; two, the religious/secular dichotomy, the latter a distinction which has carried such a load of late that it is in danger of spilling out and spoiling everyone's complacency.

A third analysis of why we do what we do comes from German philosopher Hegel, who plucked from Ancient Greece the tripartite conception of the person (reason, desire, and thymos). It is the thymos, he said, that drives history. Thymos is roughly the desire to be recognized. It's what makes us feel shame. Or pride. It is rooted in a conception of human freedom that is, at bottom, the notion that we are not understood completely by desires or by reason (roughly, by Marx or by Enlightenment secularism). The thymotic urge is a warriors urge, in us all, and finds its basic expression in the willingness to risks one's life to prove that one is free. Animals, when thirsty, just drink. People have the ability to delay desire, and they have the ultimate capacity to demonstrate that they are not animals by risking their lives (in modern times, we can cash this out as "dying for symbols of freedom", like medals, or honors).

Unfortunately I'm travelling tomorrow and have (true to form) hardly packed or even gotten my hotel room yet, so I can't burrow into this to the depth that it deserves.

Suffice it for now to say that the modern mind, trying to fit bad things around us into either an economically disadvantaged model (Marx) or a whacky-religious model (Enlightenment), is I think inadequate. What they want, and what makes them feel so alive, and so much better than the desiring "last men" who no longer care about honor but just material comforts before they too perish from the Earth, is recognition. How to change the debate such that they can have it, is surely worth talking about.

The Puzzling Economy

What is the relation between economic theory and prosperity? It damn perplexes me. But I'm no economist. Still, consider: many of us are convinced that FDR's Keynesian approach helped pull us out of the Great Depression (but NYT luminary Paul Krugman has argued, recently that FDR botched it by not doing enough). And many of us are convinced that Reagan's supply side changes helped usher in the prosperity of the late 80s and 90s. Two radically different policies. So I ask: what's the real connection between economic theory and prosperity? I suspect it is tied very closely to circumstances. Very complicated circumstances.

On the extremes, things get easier to figure out: really bad economic policy is highly correlated with really low levels of prosperity. But somewhere in the middle (say, with regulated markets, like in the U.S.), it's hard to figure out what worked before, and pray tell, what will work again. As Nassim Nicholas Taleb suggests in his eminently readable "The Black Swan, The Impact of the Highly Improbable", maybe our best strategy is just to have smart, open-minded people keep tinkering in hopes that the right recipe will emerge. Trial and error. And when something finally works, we'll take credit for it, ex post facto, as if it was just obvious that X was the way (someone-very-close-to-me tells me that these Latin phrases are distracting and pedantic, like this long parenthetical, Godelian remark).

Anyway, such is the nature of things. We need to keep up the illusion that we're on it. And when the next big innovation comes, the markets will free up, and prosperity will (again) be ours. And a politician will take all of the credit.

Generalizations

A little theory. About generalizations. On Monday.

Think of a "generalization" as a rule or conditional of the form "If X then Y", where X and Y can stand for everyday things like "If you need eggs then go to the store" or for scientific things like "If a fixed amount of gas is kept at a fixed temperature, then pressure and volume are inversely proportional". In the latter case we might also use the word "law", as in scientific laws.

A whole lot more can be said about generalizations, laws, and the like, but this admittedly cursory intro will do for present purposes.

Now, something we've discovered in the history of science is that the power of generalizations reduces drastically when applied to complex systems. In contrast, generalizations--say, the inverse square law in physics--have enormous predictive power when applied to very large systems where details can be ignored (to "the very large" as Hawking puts it). It's interesting that generalizations work wonderfully also for the really small; say, with quantum mechanical explanations of subatomic phenomena (where the generalizations are statistical in nature, but still general, powerful, and well-defined). But what's common to successful generalizations in either case is the lack of complexity in the systems in which they apply. Celestial mechanics ignores quite a lot. We want to know how long it will take for one body to orbit another, but we don't inject millions of other possible interactions (say, possible meteorites) to perform the calculations. Likewise, we isolate photons or other quantum phenomena in order to use quantum mechanics to predict outcomes.

Sure, classical mechanics -- Newtonian, and we can include Einstein's theories of relativity for these purposes-- are composed of really beautiful, powerful generalizations. So strange, then, that they are so irrelevant to prediction in everyday experience. The location at some time t + n given the location at time t of an entire planet is knowable given our classic theory. But something seemingly simple--the movements of a particular cubic inch of water in a mountain stream--is not. Why is the world like this?

We use other generalizations for predicting outcomes in complex systems. Mostly, however, we don't use laws but past experience. This is true of course with people (we rely on our knowledge of past events to make plausible inferences about future ones), and with computers, where models of complex systems invoke observed prior cases and relevant features (where "relevance" is added by the human) to generalize to likely future outcomes given unseen data. Laws don't do the predictive work in messy systems (we may assume, of course, that laws governing the relation between pressure, temperature, and so on all continue to apply in such systems nonetheless).

Humans, of course, make use of generalizations in everyday experience. They constitute "heuristics" or "rules of thumb". These are generalizations that no one expects will always apply. We know they admit of exceptions, but still they capture correlations between events of certain types that make them useful. Don't get into a car with a stranger, I tell my children, knowing full-well that there are scenarios where that is exactly what they should do (say, to save them from a maniac on the street).


But now things start to get messy. In everday experience, humans are what I call tightly coupled to changing circumstances--to facts--in a way that classical generalizations are expressely designed to avoid. We don't care about details of the celestial bodies when computing their trajectory through space. We care about some fixable features (their mass and velocity, mostly). We do care about these details when navigating through life. A person is a large object, and the slight raising of an eyebrow is a relatively small change in this object. But it might matter to someone (matter a lot), depending on some or other context.


So, this connection we have to changing circumstances implies that our cognitive or inferential abilities must be constantly, highly sensitive to details. This is, of course, exactly how it is with us. And an interesting consequence of this feature of everyday thinking is that our use of generalizations is circumscribed; they're not doing the inferential "heavy lifting" (what is?). Indeed, our stock of generalizations about the everyday world constantly become relevant, then irrelevant, and relevant again depending on context. (Some, like our belief that we won't float away into space, remain robust, though equally useless.) It's interesting that the world is like this. I think a whole lot follows from it, and I'll try to find some time to spell this out more later.

The Bogey Man

Liberals fulminate endlessly about the moral corruption in Big Business (Ralph Nader is far and away the most energetic and clear spokesman here); Conservatives seem endlessly wary of Big Government. There's something about the human psyche, apparently, that requires a bogey man. Concentrates the thoughts. We're best when we're in full fighting mode.

So then, reign in Big Business, says the Left, and reduce the size of Government, says the Right (the real Right). And curiously, those that would shrink the power of one, would grow the power of the other. What gives?

Lord Acton's famous refrain that "power tends to corrupt and absolute power corrupts absolutely" is the philosphical flame that illuminates the positions of Left and Right. But I think there's an important difference. Government is a more absolute power; people can quit jobs, and switch employers, or just opt out of working completely. But Government occupies a more intimate and comprehensive space. Acton's wisdom, while applying to all institutions that have power, ought particularly to be heeded with regard to Government. This thought I'll explain momentarily, but first let's discuss business.

The virtue (and vice) of business is to maximize profit. It's the stated objective, and well-known to all. It's out in the open and transparent. We all know this, and pretending otherwise is rediculous (recall the scene in the movie "Aviator" when Senator Owen Brewster assures everyone that President of Pan-American Airways Juan Trippe "is not interested in making a profit... he's a great American". To which Leonardo DiCaprios' Howard Hughes replies "I'm sure his stock holders will be happy to here that".)

Thus businesses rise or fall on their ability to fulfill the profit objective. When they succeed, they create jobs, employing more and more people as they grow, and they raise the standard of living in the direct, healthy sense of providing liquid cash to people in exchange for work. They're (ideally and mostly) meritocratic as well, in the sense that people with demonstrable skills and knowledge tend toward promotion, and salaries are based on responsibility (businesses give more according to responsibility, while Government takes more according to income).

Government, on the other hand, spreads an umbrella over the entirety of its citizens. You can't "quit" government, unless you expatriate. It also--and unlike business--exersizes a specifically moral activity in that it is with government that fundamental notions of fairness are given action with policy. Government, in this sense, plays more the role of a parent or family to its citizens than business ever can. Which is why Acton's caution ought to be heeded all the more: power does indeed corrupt, and absolute power (read: Government) corrupts absolutely.

To review the point: Government's relation to people tends to be comprehensive in a way that business cannot be. It tells us whether we should wear helmets when we ride bicycles, how many drinks are acceptable before driving, how much income we ought to give back for the public good (a term which is, of course, interpreted by Government itself), what we ought to eat, what the penalties for various infractions are, and on and on and on. Its natural tendency as an institution is to act in a parental manner, instructing us in various ways, shaming and prodding us into various behaviours, and slapping our wrists when we do otherwise. Much of this control is of course necessary (I'm well aware), but the present point remains. Pejorative phrases like "Nanny State" for an over reaching government are apt.

So the full point is: because of the tendency for Government to reach into every aspect of human life, this particular institution tends much more toward absolute corruption then businesses existing under the umbrella of governments can ever manage. We ought, therefore, to heed Acton's warning accordingly. We shouldn't grow Government gleefully, we should find ways to limit it.


Now, for some reason completely lost on me, many of us never get this. Liberals--and I have many Liberal friends, and they are otherwise quite intelligent--recoil instinctively at the suggestion that we ought to treat Government with caution (although I've noticed that they're much more receptive when Conservatives are in charge). They regard (Liberal) Government in almost autonomic, unquestioning fashion, as the sole, singular force for good. This is of course certainly the case in a properly circumscribed sense--I'd rather not act as my own police force during a murderous riot--but without qualification its absurd. Government is an institution that, as it grows larger, tends naturally to reduce the autonomy of its citizens. How is this good?

To conclude, Government's inertia, like all institutions with power, is always toward more power, not naturally toward the good. And because the relation between citizens and Government tends to be one of dependency (think Welfare State), its thrust as a function of its size will tend to be away from personal liberty and autonomy. It is, in this sense, a Bogey Man, although I'll admit that it's one we certainly can't do without.

In some other post, I'll weigh in on the recent 700 billion bail out, and why I think conservatives should have opposed it, with the hopes of both preserving principle and benefiting in economic practice.

Told Ya

The Political Animal does a fine job of conflating progressive taxation and income redistribution. Thanks for making my point so well...

Diatribe, Addendum

Clarification: "spreading the wealth" does not equal "progressive income taxation"...

Just in case anyone has this straw man in mind. Here's why: spreading the wealth means, point blank, from the rich, to the poor. It's direct income redistribution. Wealth spreading is different than simply taking a greater share from those who have more, which is progressive taxation.

Progressive taxation seeks greater tax revenues by taking more money from those who have plenty. Flat taxers will argue the fairness of this as well (see my prior post), but as a strategy for tax revenue generation, it's been pretty roundly adopted by advanced economies, and it has shown itself to be fairly resilient to charges of obvious unfairness.

On the other hand, this business of wealth redistribution is much more contentious, particularly in America. It moves directly from tax revenues from the wealthy to more income to the poor: from those who have more, directly to those who have less. So, the "Marxist" or "Communist" charges we've heard blurted around the fringes of the McCain campaign, while clearly incendiary and political (in the pejorative sense), are not without a certain tout court plausibility.

All this said, politicians talking to scary looking dudes like "Joe the Plumber" might occasionally choose less-than-optimal phrases to convey their meaning; I'm happy to assume that Obama is not--in any real sense--some Trojan Horse Marxist. More likely, he'd like to help some of the middle class by using tax revenue from members of the upper class. We're already doing this, with varying degrees of success. We'll see, if Obama is elected, if he's got the right strategy to make it succeed with American voters. They'll be baaaack, in what may seem like a long or a very short four years. With a Democratic House and Senate alongside an Obama administration, if it's a long four we'll all know exactly who to blame. Right or wrong, the pendulum swingeth, and it surely will swingeth again.

Diatribe

"I think that when you spread the wealth around, it's good for everybody." (Barack Obama, to enough-already "Joe the Plumber").

What, exactly, does this friggin' mean? I mean this in an entirely non-partisan sense. What the hell does he mean? Who is spreading it? The people who have it? Geez, if I'm languishing in luxury, eating grapes and drinking fine wine, and my neighbor is sitting in abject misery for lack of some crackers, I'll sure as hell "spread the wealth"... but who is the "you" that Obama is referring to?

This seems partisan, but really, there's a deeper point. He's saying that "I think that when government has the ability to take from some to give to others, it's good for everybody." In some limited sense it's true. But in some expansive sense it's sure as hell not. Spreading the friggin' wealth? It's not margarine for God sakes. What the comment reveals is failure to understand that it's not a zero sum game, as if "We all have some X, but some more than others, and we can't count on getting any more, so we'd better gosh darn share it...". What's next, rapping our knuckles with a ruler? This is manifestly... ahh crap forget it...

The American People

Has anyone noticed that politicians, pundits, and just about anyone on the T.V. or air waves now refers to "Americans" as "the American people"? It's everywhere now. Bush isn't immune (though also not exclusive). Nor McCain. Or Obama. Why? What's wrong with the succinct, and perfectly adequate--in the sense of referring to what's intended--"Americans" moniker?

Maybe it's a silly question. Maybe the talking heads just need some lexical variety. But I'll take the bait. I'll suspect otherwise. Here's how I propose to develop the suspicion:

Used by itself (that is, as a noun, not an adjective), "American" seems to have become a loaded word--it now carries with it some cultural baggage that might at any time backfire, with terrifying social consequences (he's not a real American, or that's an attitude typical of an American, or I'm proud to be an American... this last a statement which, when uttered with a country music twang, is equivalent to pronouncing yourself an ignoramus...). On the other hand, "the American people", well, that's just the smart, hard working humans who happen to live and work in the United States of America. They're not, for gosh sakes, Americans! How vulgar! They're just those citizens out there. Members of the set of American citizens. Ahh yes, the slow, steady, creepy creep of political correctness. Could it be? Could it be!

Well, until we get it sorted out, do feel free to proclaim: "I'm proud to be a member of the American people!" (although you'll sound like a geek for doing it); this is immune to any backlash that simply proclaiming yourself an American might invite. (I have, however, heard rumors that this may itself get further modified to allow only a reference to being a member of a set of humans living in North America, between Canada and Mexico. Will confirm if true.) But, to return. At all costs, go easy on the singular American label, please. Don't be vulgar.

Postlogue

Later it was reported that many heard, in the hallways, on the streets, the resounding battle cry "Members of the set of American people, unite!" Indeed. We have nothing to lose but...

Flat Debate (what debate?) over the Flat Tax

Ah, yes, the flat tax. A substantive idea with a panoply of economic data supporting its usefulness. It gets chucked into a presidential campaign by a dude known to us, affectionately, as "Joe the Plumber": the ultimate softball for the always eager to swing Huffington Post. Huffington delivers the easy ad hominem dismissal. I have a different take. Contra Huffington, and digging deeper than Joe the Plumber's endorsement (Joe, no fault to you), it's clear that the flat tax proposal has earned a place in American politics and around the world generally. It has, and continues to be, richly debated. Why is this so? Where Huffington offers only arrogant, dismissive rhetoric, I'll take the time to explain.

Let's cash out the theory first. We go right to the meat of it with the notion of fairness, or "justice".

Libertarian Philosopher King Robert Nozick offered a memorable picture of the sort of fairness that undergirds the flat tax proposal, when he proposed the following thought experiment. Imagine that there is some distribution of wealth in the U.S. that we all accept as fair (call this, following the usual academic love of unnecessary variables, D1). So, ex hypothesi, we all start with the cash in our pockets that we all agree is fair; we start with distribution D1. Now suppose a celebrity (Nozick's example was the dated but still venerated Wilt Chamberlain) decides that he or she wants more than D1, but proposes only to allow people to choose how they spend their fair share. Now here's how it happens. Loving our celebrity and his or her enrichening performances, many of us end up freely contributing some portion of our fair share to enjoy the performance, with the result that he or she ends up with more money than the rest of us.
This is to say, D1 no longer holds. The celebrity ended up with much more cash.

And this is exactly Nozick's point: D1--we all agreed--was just. But we only freely gave our share to others, with the result that our celebrity ended up with more. So where did the injustice sneak in? Where did it enter in? We all freely parted with some of our wealth. But, ex hypothesi, we all started with a fair distribution. So by the free exersize of human interest, some of us ended up with more. Which is contrary to D1. And, Nozick says, so much the worse for D1. Fair is fair.

What underlies this conception of justice is the notion that fairness is simply equality of opportunity, rather than outcome. Nozickian fairness is fairness that gaurantees that everyone can spend or create wealth according to their interests and abilities. If this scheme--a scheme where people have freedom to spend or create--ends up with inequalities of outcome (we ended up giving our celebrity more gold), then so be it. The government ensured that we could fairly exersize our interests using our resources; the outcomes are however this works out.

Great, but what, you may well be thinking at this point, does this have to do with flat tax proposals? Here's how it works. Government needs some of your money, in order to perform the functions of government (e.g., provide for defense), but it doesn't take more from you because you have more. Fair is: a certain percentage of your money goes to the government, according to the rules we've agreed (voted) on. If you happen to be the celebrity, it's not the business of government to take this into account when it comes time to pay taxes. Bully for you, celeb. You ended up with your gold however you did. (Forget, for our purposes here, questions such as what to do with inherited wealth, or we'll never get through this.) Government doesn't care if others gave you more gold. It only sets a rate that it taxes, and demands that you pay taxes at that rate. It's blind, by design, to your own particular wealth-accumulating accomplishments. On the Nozick line, that's what "fair" means.


In contrast, equality of outcome theorists such as John Rawls (and many others) consider, well, outcomes. Do we all end up with a fair distribution of resources, regardless of how we started? To Rawlsians, inequalities of wealth are ipso facto examples of unfairness, because "fair" means aiming for equality of outcome. (This is, of course, at loggerheads with the "opportunity" camp, and one might insightfully conclude at this point that the two views of fairness are incommensurable. The fact that such discussions in America tend to ground out in incommensurability is a troubling fissure in our intellectual landscape. The problem has been discussed at length by others, notably philosopher Alasdair MacIntyre in his seminal 1980s work "After Virtue", but as weighty philosophical discussions typically go, it hasn't percolated up into discussions of policy, as I think it should.)

But back to the flat tax. The pressing point is that Huffington Post types, either ignorant or uncaring of this rich philosophical discussion, simply act as if Conservatives, like poor Joe the Plumber, spout out flat tax proposals because they're either too dim witted or poorly educated to realize that it's not fair. But as I hope I've shown, it depends on what you think fairness is in the first place. How dare they presume that there's only one take on it.

Now, for some "praxis" to all this theory, it turns out that there are many countries that have actually implemented a flat tax. I'm not a comparative politics guy, and I'm well aware that the success or failure of particular economies are the subject of much debate, but my point is simply that there is a debate. It turns out that Joe the Plumber's not alone. To take one example of a debatable flat tax success, consider Lithuania. Wikipedia has this to say:

"Lithuania, which levies a flat tax rate of 24% (previously 27%) on its citizens, has experienced amongst the fastest growth in Europe. Advocates of flat tax speak of this country's declining unemployment and rising standard of living. They also state that tax revenues have increased following the adoption of the flat tax, due to a subsequent decline in tax evasion and the Laffer curve effect."

Sounds pretty dim witted to me. Again, I'm not claiming to be an expert on Lithuania, or on flat tax policies in general for that matter. But my point is simply this: knee jerk condemnations of particular tax proposals, like those cheerily published by the Huffington Post, are not, shall we say, the paragon of educated, liberal democratic discussion. It would be preferable, in my view, for us to dig a little deeper before pulling the trigger on ridicule. Whatever its ultimate merits, the flat tax is an idea that has played a key role in intelligent debate from first-class thinkers far back into our history. Arguably, it also has been put to the empirical test by countries with, it would seem, not so dim witted results. Maybe Joe the Plumber is on to something.