Information, Uncertainty, Incentives, and Trust.

Sandeep Baliga at the Cheap Talk blog has an outstanding summary of the contributions by Bengt Holstrom and Oliver Hart, the latest winners of the Nobel Prize in Economics.

The Holstrom-Hart Nobel is a bit personal to me, albeit through an indirect route, via one of my former teachers Paul Milgrom.  Paul liked to talk about how he came to graduate school not for PhD, but for MBA because he wanted to be an actuary, and how he found ways to apply actuarial thinking to economic theory.  Given the contributions by Holstrom and Milgrom that I found most enlightening brought together statistics and epistemology to a theory of incentives, this is an apt starting point for my reflection on their work.

The example by Baliga is an excellent illustration of the basic problem:  a worker at a burger joint does two things, one easily observable (the number of burgers), the other not so observable (the work in the kitchen).  By tying the incentives only to the number of burgers sold, the principal winds up discouraging kitchen work, and in so doing, subverting his own interests.  The solution is to create a low-powered set of incentives that depend comparatively little on burger sales.

But this opens up a whole slew of other questions.  Two questions pop into my mind immediately because these concern my eventual work in political science, especially with regards the relationship between voters and elected officials.  First, does the principal really know where the unseen parts of the business is?  Second, how does the principal know if the kitchen is being genuinely looked after?

In the legislative arena, the functional equivalent of burger sales come from the public record of legislative accomplishments and actions:  the number of bills, the voting record, etc.   Yet, these constitute comparatively little (and often, easily “faked”) aspects of the legislative work. Fenno and Mayhew, back in 1960s and 1970s, had written about how valued the “gnomes” (to borrow Mayhew’s terminology) who slave away at the unseen aspects of legislative and policymaking work without public accolades are by the legislative insiders, who reward them with currency that are particularly valuable intralegislatively.  Yet, this understanding is not shared by the members of the voting public, nor, apparently, by political scientists lately.  Very few regular voters appreciate how complicated the inner workings of the legislative process is, the kind of hidden negotiations and compromises that are needed to put workable bills and coalitions together–especially bipartisan coalitions.  Still, there is an implicit understanding that, without legislative outcomes, something isn’t being done right, that their agents are shirking somewhat and somehow that prevents their production–perhaps they are right in their suspicion.

The more problematic might be the obsession of the political science in putting data in place of theory (notwithstading the immortal Charlie Chan quote, “Theory, like fog on eyeglass, obscures facts.”–because “data” is not same as “facts.”)  The visible part of the legislative accomplishments, often padded by “faked” votes designed only to put votes on records (for example, the increasingly innumerable but meaningless “procedural” votes in the Senate designed only to publicly show who’s on which side, more  or less), are used to generate various statistics that purport to measure things like “ideology,” which, in turn, are assumed to be homologous to Euclidean space, and are fitted into models.  Since the measures are derived from the observed facts, they describe what goes on fairly accurately–but with significant exceptions that change over time, which are usually dismissed with the claim that they are mere “errors” and “nuisance.”

Fenno and Mayhew thought things differently.  Granted, they didn’t have the kind of legislative data or the tools for analyzing them that their more modern counterparts do (this is literally true:  the changes in Congressional rules around 1975 immediately tripled the number of recorded votes in the House, for example–coinciding neatly with the changes in House organization that followed the ouster of Speaker McCormick, engineered by the liberal Democrats.)  They saw the paucity of data that prevented data intensive analysis on their part as a normal part of the political process, where the seen and the unseen coexist and the importance of the unseen aspects of politics is deemed as important, even by those who did not know the specifics–e.g. the voters.  That brings the question back to what prompted to Holstrom to wonder, why so few contracts are written based on the “sufficient statistic” criterion, and as such, echoes the argument by Weber 100 years into the past (to be fair, there’s a paper by Oliver Williamson on this very point–if I could find it.)  Weber’s argument was twofold.  First, the compensation for the “professional” (“bureaucrat” in his terminology) should be low-powered, set without much regard for the visible indicators of performance because how exactly the professional “performs” is too noisy and complicated to measure with precision.  In turn, the professional should develop a code of ethics and honor–“professional conduct,” literally–whereby their work is carried out dutifully and faithfully without regard for the incentives in the contracts.  If you will, the mail will be delivered with utmost effort, as a point of honor, through rain, snow, or sleet, because that’s what mailmen do, so to speak. Most important, both must be part of the common knowledge:  the professionals “know” that they will be paid no matter what, while the principals “know” that the professionals are doing their utmost, even though the results are not necessarily obvious.  In other words, I don’t know what exactly they are doing, but whatever it is, I know it’s important, dang it.

This is a difficult equilibrium to sustain, with a LOT depending on the players’ beliefs, and potentially open to a lot of abuse and suspicion.  Mike Chwe might say that these beliefs, in turn, would require a lot of cultural trapping to sustain, various rituals carried out to show that the “professionals” indeed are being “professional.”  The “home style” by the legislators whereby they return home and engage in various ritualistic interactions with their voters to show their tribal solidarity might be seen in the same regard.  One might say that a lot of seemingly irrational socio-cultural activities, such as belief in creationism, are exactly that as well.  Of course, this is the kind of equilibrium that IS being subverted by the tilt towards visible data:  as we can see below, the correlation between Democratic shares of House votes and the DW-Nominate scores of the incumbents (with signs adjusted):

correlation

What the graph is showing is that, if you know the voting records of a House member in the preceding session of Congress, you can predict his vote share with increasing accuracy as 20th century progressed.  It does mean that the voters were becoming more “policy-minded,” in the sense of measuring their evaluation of the politicians more on the basis of visible record, but does it mean that the voters were becoming more “rational”?  To claim that would presuppose that the performance of the burger joint depends only on the burger sales and that kitchen is irrelevant to its success. Holstrom (and Max Weber before him) would say in no uncertain terms that that’s stupid.  But what does this mean for the trends in politics today?  I’ve been making a series of argument (and was halfway through a book manuscript) on this very point, but shockingly few people seemed to care, even if, I strongly suspect, the mess of the 2016 elections is a sharp reminder of this problem.

This is an illustration of the potential danger that the data-intensive environment of today is posing us:  because we have so much data, we become contemptuous of the unquantifiable and unaware of the potential limitations of the data that we are using.  If the data is always right, so to speak, i.e. has zero error, there can be no statistics that can be done with it, so to speak.  Then we’d know THE answer.  We do statistics to be less wrong, not necessarily to be “right” (I’m paraphrasing my old stats prof.)  If we insist on mistaking statistics (or indeed “science”) for the “right answer,” woe be upon us.

PS.  One great irony is that, while, intellectually, Paul was one of major influences on my way of thinking, I had precious little interaction with him when I was actually at Stanford. By the time he was teaching his “module,” (Stanford econ reorganized its graduate courses  when I was there so that we had 4 “modules” instead of 3 quarters.  Go figure) I was fairly deep in my occasional depressive spirals and was unable to do practically anything, let alone prepare for prelims.  In a sense, studying for econ prelims is easy–you literally have to study the textbooks and know the formulas, so to speak–just the answers you are supposed to know, even though, admittedly, the questions will be hard.  But depressed people have the hardest time doing routine chores when locked up, figuratively speaking, without anyone talking to them.  It is easy, in a sense, for people who have no stakes to think that depressed people ought to be locked up by themselves until they are better.  In practice, what almost always happens is that, after being locked up for a few months, they will be far more damaged than when they began.  But talking to depressed people requires way too much commitment for people without stakes of their own, too much to be asked of “strangers.”

 

Baseball, Information, and the Brain

This fangraphs article is possibly the most fascinating thing that I had read about neuroscience behind (quick) decision-making, ever.

The problem with seeing the ball out of a pitcher’s hand, obtaining some information, and translating it into reaction is that the information is usually too complex, too wrapped in uncertainty, and the amount of time available is too small.  The article is probably being fair saying that how most batters cannot really describe or explain what it is that they see or how they process the information–it is not really a deliberate “analytical” process, but it is still a reaction that is both learned and “analytical” if in a slightly different sense–of having a fairly small set number of probable reactions, learned through both experience, analysis, and “intinct,” into which a batter can switch into rapidly–a set of mental shortcuts if you will.  A useful analogue might be parties in politics:  there are just two bins, or 4, depending on how one conceptualizes the universe:  there are liberals and conservatives, Democrats and Republicans.  Most politics fit into these categories (or the combination thereof).  If it’s not any of these, the brain will be confused in the short term, and without an obsessive interest in figuring things out–and this kind of interest is rare in politics, especially this requires leaving opinions behind–it is not worth delving into such things too deeply.  So most people operate through two step process:  does it fit the usual boxes of politics, and if it doesn’t, do I care, with the answer to the latter question usually being a big “no.”

The same is true with hitting a baseball, and presumably, with most other activities requiring a quick reaction:  nobody who is any good is probably so simple minded to have just one box, so to speak.  But most people will have just a few boxes, which, thankfully for them, would account for most of the universe.  (The same applies to sabermetrics:  most of these usual boxes will yield predictable results–i.e. high frequency of fly balls probably means the pitcher is not as good as his ERA indicates, for example–the idea behind FIPS)  But if the expectations can somehow be subverted, you can fool the hitters.  While a strange politicians who is not exactly liberal or conservative nor a Democrat or a Republican will confuse the voters and lose elections–becaused confused voters don’t vote–getting batters confused is a useful skill, if you are a pitcher, and all the better if you can confuse the sabermetricians along the way, because, that way, your methods might be so complex that the batters won’t be able to adjust to you easily either.

Politics and Curiosity.

Dan Kahan, whose work I like a lot, has a fascinating new paper out.

The great advance that Kahan and his coauthors make is to attempt systematically defining and quantifying “curiosity.”  I am not sure if what they are doing is quite right:  enjoying science documentaries, for example, does not mean one is or is not “curious.”  (I’d found some science documentaries to be so pedantic that and assertive of the filmmakers’ own views that they were nearly unwatchable, but good science documentaries point to the facts, then raise questions that follow from them without overtly giving answers, for example).  But a more useful perspective on curiosity comes from how one reacts to an unexpected observation:  a curious person reacts by wondering where the oddity came from and investigating the background thereof; an incurious person starts dismissing the oddity as irrelevant.  The third component of their instrument, the so-called “Information Search Experiment,” however, gets at this angle more directly.

Observe that curiosity is, potentially, at odds with simple scientific knowledge.  On surface of the Earth, the gravitational acceleration is approximately 9.8m/s^2.  There was a physicist  wtih web page dedicated to scientific literacy (that I cannot find!) who had a story about how his lab assistant “discovered” that, under some conditions, the measured gravitational acceleration is much smaller.  While this finding was undoubtedly wrong, there are different approaches with which this could have been dealt with:  the incurious approach is to dismiss it by saying that this simply cannot be, because the right answer is 9.8m/s^2.  The curious approach is to conjecture the consequences that would emerge were the different value of the gravitational acceleration true and investigate whether any one of them also materializes.  The usual approach taken, even by scientifically literate persons, is the former, especially since they know, with very little variance, that the gravitational acceleration has to be 9.8m/s^2.  It is rare to find people who react by taking the latter path, and to the degree that “scientific literacy” means “knowing” that the variance of 9.8m/s^2 being the correct answer is small, it is unsurprising that “scientific literacy” is often actually correlated with closed-mindedness and politically motivated reasoning.  (which Kahan had found in earlier studies)

This does make for an interesting question:  I had mused about why creationism can be a focal point, but the proposition that 1+1 = 3 cannot.  Quite simply, 1+1 = 3 is too settled a question (or rather, ruled out by too-settled consensus) to serve as a focal point, while, for many, evolution is not yet sufficiently settled a question.  To the degree that, on average, social consensus tends to converge to the truth (even if not always the case), overtly false “truisms” cannot serve as focal points indefinitely–even if they might persist far longer than one might expect, precisely because they are so useful as focal points.  But the more accepted truisms are, the more likely that contrary findings–even true ones–are to be dismissed without further question as simply being “abnormal.”  In the example above, the probability that a lab assistant simply made a mistake that led to abnormal finding is simply too high compared to there being an actual discovery.  As such, this is not worth wasting time investigating further, beyond berating the hapless  lab assistant for not knowing what he is supposed to be doing.  However, to the extent that “knowledge” is simply an awareness of the conventions, it systematically underestimates the variance in the reality and discourages curiosity as a waste of time.  This, furthermore, is not without justification as the conventions reflect “established truths” that are very nearly certainly true (i.e. with very little variance.)  When people become too sure of the received wisdom where the true variance is actually quite high, a lot more legitimate discoveries are bound to be tossed out with dismissiveness.(Underestimating variance in the name of the received wisdom is exactly how the great financial meltdowns happen:  to borrow the line from the movie The Big Short, those who defy the conventional wisdom will be ridiculed by being badgered with “are you saying you know more than Alan Greenspan?  Hank Paulson?”  Well, physics progressed because, on some things, some insignificant young man named Albert Einstein knew more than Isaac Newton–before he became the Albert Einstein.  Physicists took the chance that Einstein might actually know more than Newton, rather than dismissing him for his pretensions.  The rest is history.  (NB:  one might say that the structure of physics as a way of thinking probably made this easier:  Einstein was able to show that he might be smarter than Newton because he showed what he did without any obvious mistake using all the proper methodology of physics.  But then, physics established that it is about the right methodology and logic, not about the “results.”  This is, in turn, what bedeviled Galileo:  he might have gotten the answer more right than the contemporary conventional wisdom, in retrospect, in terms of approximating the reality–although he was still more wrong than right overall–but he could not precisely trace the steps that he took to get to his answers because the methodology to do so, quite frankly, did not yet exist–they would be invented by Newton centuries later.)

The real scientific literacy, one might say, should consist of a blend between scientific literacy and curiosity:  knowing where the lack of variance is real and where the lack of variance only reflects the reflected consensus, so to speak.  Is 1+1 =2 really true, or does it seem true because everyone says it is?  I have to confess that I do now know what the best answer to this question is.  On simple questions like 1+1, demonstrating the moving parts may be easy enough.  On more complex questions, it is far easier to simply tell people, “trust us:  X is true because that is true, and we should be trusted because of our fancy credentials that say that we know the truth.”  Perhaps, beyond some level, truth becomes so complex that a clear demonstration of the moving parts may no longer be possible.  If so, this is the only path for even partial “scientific literacy,” especially since simple broad awareness of the social conventions that are approximately right (i.e. right mean, wrong variance) might be more desirable socially than everyone wandering about looking for real answers without finding them.

Unfortunately, this turns “science” back to a question of religion and faith.  Rather than product of scientific investigation doused with suitable amount of skeptical curiosity, “science facts” simply become truisms that are true because “high priests” say so, with the real moving parts consigned to “mysteries of the faith,” with the potential for a great deal of abuse, including the persecution of the heretics, literal or figurative, most of whom may be cranks, but may also include some real insights that happen to deviate from the received wisdom more than it is expected to.  This is, of course, politically motivated reasoning revisited, with the sober implication that we may not be able to separate “politics” and “religion” from “science” easily.

 

Variance Centric Thinking and Making Sense of Trumpism

Carl Beijer’s argument about sources of support for Trump is an excellent example of how variance-based thinking beats means-centric thinking.

For a means-based thinker, Trump’s message is about trade, racism, and other ideas that don’t make a great deal of sense.  However, since his message can be placed on a map, so to speak, it follows this line of thinking that his followers are motivated by the mean of his message that can be calculated.  Since the mean of his message lies in the realm of insanity, it follows that those who follow his message must be as crazy as well.

The variance-centric thinking does not presume to measure the mean of Trump’s message:  it is all over the place so that calculating the mean does not make much sense.  (If the distribution is very fat, you can be very far away from the mean and still catch a meaty part of the distribution.)  They key is simply that Trump is different, drawing an usual, but very identifiable following who may or may not take his proposals seriously.  The important thing is to identify those who are–and why they don’t fit the existing paradigm, rather than why they fit Trump’s paradigm.  To paraphrase Tolstoy, if all unhappy families are different, asking why they are unhappy will yield too many answers none of which will be universally true.  It makes more sense to ask why aren’t they happy–that is, what their deviations are from the formula of the happy families and start the analysis from there.  (In the like vein, the taste preference of Bud Light drinkers cannot be cleanly identified–they are a heterogeneous group who choose Bud Light either because it is cheap, they like the ads, they have no taste, they had odd taste that is not being met by anyone else, or because they genuinely like Bud Light.  If you think you can profit by identifying the “Bud Light taste” and profit by jacking up the price, you will be in for a huge disappointment.)

So the Trump voters are…different and heterogeneous, but they are similar in that they are unhappy with the status quo and are drawn to Trump for many different but understandable reasons–most of which are directly or indirectly tied to the economic and social changes.  Trying to put them all in the box, by identifying their mean and using it as a stand in for them all is bound to disappoint because their variance is so huge.

PS.  In a sense, it is really a matter of knowing the limits of your data:  can you profit by knowing the true mean of Trumpists’ (or Bud Light drinkers’) preferences?  What is the variance on their tastes, preferences, or motivations?  If they come with sufficiently small variance, then yes, you do have a lot to gain by knowing their mean preference with precision.  If they don’t, then all the extra data will only give you an erroneous belief that you know what the truth is, not the truth itself, which is inherently too variable to be pinned down.

Coalitions and Inclusiveness

The very insightful Carl Beijer, who seems to understand the underpinnings of rational choice political science far better than many self-claimed rational choicers, has three brief but excellent posts that are worth a bit of discussion.

The first concerns seemingly an old debate, whether Clinton is any more electable than Sanders.  To those accustomed to thinking in spatial model terms, it is fairly “obvious” that Clinton is more “moderate” than Sanders, as is the average Clinton voter, compared to the average Sanders voter.  Thus, it is intuitive, by the “median voter” logic, that Clinton should have an edge.  The fallacy of this argument is that the spatial underpinnings of alleged “ideology” is itself built on shaky foundations.  Given the Hotelling logic, spatial “distance” is simply a stand-in for preference.  The median is represented, in the spatial logic because she is not committed to one side or the other.  As Beijer points out, in order for Clinton to be more electable versus Trump, that must mean that the Sanders voter would not vote for a Trump but a Clinton voter might.  In practice, this is exactly the opposite:  Sanders represents a diverse coalition that included a large minority of discontented working class whites.  Even if they may not be the “average” Sanders voter, these are the voters who might under some conditions, be lost to Trump.  The core Clinton voters, on the other hand, are committed Democrats who would not vote for any Republican, Trump or anyone else.  Sans this “swingability,” they are not exactly “moderate.”  What does wind up depicting them is the mistaken use of DW Nominate type scores:  Clinton’s allies in Congress are moderate because they are voting with the Republicans on some issues:  most notably, free trade and foreign affairs in recent years.  Their willingness to defect to the Republicans on these issues does not imply that they are “moderate” on other issues, especially those concerning socio-cultural matters.  That they are willing to side with the Republicans in support of interventions abroad and promoting free trade, rather than making them “moderate” vis-a-vis Trump, actually makes them far more stridently anti-Trump.

Of course, this reveals an important sense that Clinton and her backers are “moderate”:  they can draw the support of the regular Republicans (or, at least they hope they do) which, realistically, Sanders would not be able to.  But, taken together with the greater “swingability” of the Sanders voters, or, at least, a large minority thereof, this sets the stage for another point that Beijer raises:  is there such a thing as a Democratic coalition?  Clinton and her allies do not care to retain the support of the Sanders supporters.  They are too busy courting the regular Republicans whose support they are (too) eager to capture.  While “rigged” may not be the most accurate description, DNC emails do illustrate what every “institutions” person in political science should be (too) familiar with:  that most institutions are rigged because their keepers put their hand firmly on the scale, tilting them to their advantage.  The problem that Beijer raises, though, is the corollary to this problem that is rarely raised:  yes, on average, the institutions are rigged, but, if so, and if every outcome is tilted in favor of the institutional insiders, why should the outside faction cooperate by playing through them?  I would not go so far as to say that the institutions and the process need to be “fair,” as Beijer does, but that there needs to be high enough probability that the outsiders would win, even if, on average they might lose.  This uncertainty, deliberately inserted into the game and maintained assiduously, is essential for keeping the institutions stable (if you talked to me about correlated equilibria in game theory, you would have come across a variation on this theme before).   Take away this uncertainty and make the game both rigged AND low variance, you are asking for trouble.

This sets up Beijer’s third point:  does it make sense for the voters to vote strategically, for the lesser of two evils?  It does not, but perhaps not necessarily for the reason Beijer brings up.  Clinton represents a low variance candidate whose mean is not very satisfictory.  Trump represents a high variance candidate whose mean is farther than Clinton’s.  Yet, both are sufficiently far from the voters away from the “middle” that the difference in their means may not mean much, and with sufficiently high variance, the conditional probably of getting a “better” outcome at a given point from a “worse” mean, but bigger variance distribution is greater than one from a “better” mean, but a lower variance.  Now, a spatial modeller would say that this would be washed out by the higher probability of even worse outcomes, but not necessarily if the utility for far away outcomes are discounted–while I can’t work out the math at the moment, for the suitably large variance, as long as the differences are small enough, the voter is actually better off choosing a high variance candidate whose mean is far away.  So, somewhat ironically, it is possible that voting for the seemingly greater evil (but with high variance) might be fully rational!

When Parties Can’t Decide…

Michael Barone has an excellent if inconclusive summary of the big picture concerning the present political situation that goes beyond who’s winning and who’s losing.  Ultimately, the point is simple:  in the present day politics, parties are not position to “decide” much of anything by force of insider consensus imposing its will on the outsiders.  This begs the question, though:  why not, especially if it has been able to do so for decades?

There are two dimensions that undergird the power of the party insiders:  one is subtle, but much more important, the other is rather more obvious, but relatively inconsequential.  The obvious but relatively inconsequential is their control over the institutions and their ability to set the agenda through them.  The trouble with the institutional control is that, while it allows the insiders to exclude from the “menu,” if you will, the potentially popular alternatives that they do not want, its continuation depends on the willingness of the masses to continue playing within the framework of the institutions that they dominate.  If you will, the attitude of the insiders would be:  “they may not like our food, but if they want to eat out, they have no choice.”  But the reason that the voters, most of whom are political outsiders, are willing to play along is a combination of ignorance, disinterest, and trust, which together comprise the more important even if subtle reason why parties get to decide.  The masses don’t know what other possibilities exist off the menu, they are not sufficiently dissatisfied with what is on the menu so as to actively seek outside options, and they are sufficiently trusting of the restaurateurs to believe that they are being given reasonable, even if less than ideal options.  If they can be sure that what the restaurateurs offer is indeed bad for them, and that a better alternative exists outside somehow, they will jump the ship not only with certainty but with alacrity.

The second aspect of how parties can maintain power explains the paradox of parties that Keith Krehbiel has kept raising for almost two decades now:   under a majority rule and a unidimensional policy space, there is nothing that stops the floor median from becoming the winning alternative.  Why doesn’t the floor median always win?  The answer is that the spatial model is a poor depiction of the real life politics.  (yes, if you know me in real life, you’d also know that I’d been saying a version of this for a decade also)  Within the spatial framework, all voters know exactly what they want, relative to each and every policy alternative.  There is a clear majority consensus that they want the floor median, relative to every possible alternative, and no institutional manipulation (in a unidimensional space, at least) can steer them away from what they know that they want.  In other words, if a restaurant refuses to offer what a majority of its customers actually want and if the latter know where to find it, there is no good reason to expect that the restaurant would be able to keep those customers.

Now, there is no guarantee that these customers would necessarily be lost to a competitor, but there is no requirement that they should keep eating out.  This has, of course, happened before–when the turnout was in decline between mid-1970s and 1990s, when all attention was focused on what’s wrong with the customers who stopped eating out.  Perhaps the problem was the restaurants that changed their recipes, rather than the customers?  In a sense, this offers a slightly different explanation of why and how politics polarized over that exact time period (although with a bit of the chicken and the egg causality in play):  the restaurants polarized, because they decided to offer sharply contrasting recipes.  The customers who did not like the new combination dropped out, while those who did began to pay more.  This has a perfect analogue in the evolution of the beer industry since 1990s (which I had blogged about in the past, but can’t seem to find), where the industry began to shift towards craft beer that can be sold to s relatively small set of consumers at a high markup and began to de-emphasize mass produced swill that may be hated by no one but not especially beloved by many and could only be sold at low markup if to large audiences, like Bud Lights.  Unlike the politics industry, the beer producers are not constrained to offering just one brand:  Bud Light remains, despite its shrinking market size, remains the best selling beer in United States.  Anheuser-Busch recognizes that not everyone will pay a premium for a beer that they like–either because they don’t really like a certain taste, they don’t care for spending too much money on beer, they had an odd taste, or because they don’t know (or any other reason).  They need an outlet and A-B can money off of that outlet.

In principle, there is no good reason that a political party cannot offer a craft beer and Bud Light at the same time.  Anheuser-Busch is not a “beer” company.  It is a profit-making company, and beer is simply how it makes money.  E.E. Schattschneider said, and I paraphrase, that the Democratic Party is not a “liberal” party.  It is a coalition that seeks to win elections.  “Liberalism” is simply how the Democrats win elections, much the way Anheuser-Busch makes money through the selection of craft beers that it offers.  But the idea of a “craft beer” being sold by Anheuser-Busch, the seller of the Bud Light, generates backlash.  The idea of “liberalism” (or any other concretely defined set of policy or a collective reputation, narrative, or such) being brought by the party of Jim Crow generates a far bigger backlash.  Popular control, or more accurately, disproportionate influence from the more active parts of the elecrotate, exerted through the primary process, multiplies the impact of this backlash.  A homogeneous, exclusionary menu for the entire chain is necessarily the result, with the due consequences in less competitive markets.

The bigger consequences of the polarization are felt among the consumers/voters, however.  With the Bud Light taken off the market, a large segment of the consumers who may not necessarily fit neatly into the resulting spectrum of beer tastes (i.e. who have “no taste”), who may be cheap and underinformed, but are nevertheless justifiably discontented, is the consequence.  One might even say that, without meaning to be contemptuous, that these are alcoholics, who ARE in fact dependent beer to survive.  (This is meant in all due seriousness:  beer may be a luxury good that people might be able to do without.  Government policy is not.  For all libertarian wet dream, huge segments of society are crucially dependent on a set of functioning government policy that appropriately meets their needs at the reasonable price, and this is literally a matter of life and death for the needy populations, as many of these political Bud Light drinkers are.)  In the end, both Republicans and Democrats alike are shrugging their collectively shoulders saying “let them drink craft brew,” when they cannot afford either of their brands.  (I note with some irony that the localities that gave rise to Trump voters were also the hotbed of the Whiskey Rebellion, back in the days of the Washington administration.  Some things don’t change.)

The real problem for the parties is not simply that the Bud Light drinkers are not merely rejecting the beers that they offer, however.  They may not necessarily know what their taste in beer “really” is, but they do not, with justification, trust that the beer companies would even try to meet their needs.  When the consumers trust the corporation, they might be willing to be led, to gamble on what the corporation offers, even at a price.  Thus the insiders can set the agenda and profit on the margins because the masses, even if not perfectly happy, will not rise against them:  hey, let’s try this slightly expensive new beer from A-B, instead of Bud Light.  Without this trust, the customers are lost–they will not tolerate your agenda-setting:  the bastards at A-B got rid of Bud Light!  I’ll never drink their crap again, even if they bring back Bud Light.

Does this change signal a fundamental transformation of the beer industry, eh, parties?  I don’t know.  It may be true that having the Bud-Light consumers provides the big breweries with a bit of fallback position, a cash cow:  there are many, if heterogeneous Bud Light drinkers who will drink their swill as long as it is cheap, which will generate revenue with which A-B can buy out craft breweries, one at a time.  But there seems no logical reason that there cannot be a coalition of craft breweries that don’t cater to the Bud Light consumers, who can even garner additional profit from their lack of association with the cheap swill (which seems to underlie the present HRC campaign’s strategy of assembling only “respectable” voters, while abandoning the working class whites, including many of the Sanders voters.)  Indeed, if true, this direction of the Democratic Party would imply a simple continuation of the present trend, not a transformation.  Trump as the Republican nominee is indeed representing a change, but not necessarily a profitable one:  there is a good reason why A-B is de-emphasizing Bud Lights–because it is not as profitable.  Working class whites, like Bud Light drinkers, are “cheap.”  They don’t vote too often, and as such, don’t yield profit the way “ideological” voters do.  Like Bud Light ads with bikini-clad women, Trump might generate a bit of PR magic, but it can only go so far.  If the price of this shift, as it seems at the moment, is a backlash against the craft brews currently offered by the GOP, it can only demolish its electoral profits.

 

Information and Correlated Equilibrium

The correlated equilibrium is a much underappreciated application of game theory in situations involving uncertainty.  As such, it is of huge import to me, but apparently not to those who want to know about “strategic advantages” and such nonsense in situations without much subtlety.

The heart of correlated equilibrium is the presence of exogenously generated “signals” that provide “instructions” for the players to follow through.  So, the signal might be, if “full moon, buy” or “if green light, go.”  (This is, in a sense, why and how “astrology” works.”  Not necessarily because movement of the stars necessarily “cause” people to behave certain way, but because they can coordinate people.  If you don’t believe me, try and see if people show up to work at most workplaces on Sundays.)  The efficacy of the signals vary depending on the specifics of the game, however.  In Prisoners’ Dilemma type games, there is no point in cooperating, unconditionally, so the signals are irrelevant.  In pure coordination games, the more precise the signal is, the better.  If you want to meet others at some locale, knowing the city is more useful than knowing the state, knowing the block, better than the city, knowing the address even better than knowing the block.  In Hawk-Dove games (aka “chicken” or “battle of the sexes” games), however, things are a bit more complicated.

In Hawk-Dove games, you’d rather “lose” than wind up in the “bad” outcome.  (in contrast, in Prisoners’ Dilemma, you’d rather wind up in the “bad” outcome than lose.)  The benefit of the correlated equilibrium is that, by providing the signals with just enough uncertainty, you can help the players avoid the “bad” outcome and improve their net welfare.  What happens is that, if one player gets the signal to “defect,” the other player will always get the signal to “cooperate.”  In other words, the player is certain to win when he follows the signal.  When the player is given the signal to “cooperate,” however, the other player may have gotten the signal to cooperate or to defect.  So the player might wind up either losing or end up in the “meh” outcome.  Because the player does not know what signal the other side has gotten, he is discouraged from cheating since, if he does, there is a significant probability that the “bad” outcome, which he wants to always avoid, might occur–if the other player has been given the signal to “defect” and is following it. As such, then, the signals (or rather, the process that generates the signals) effectively become the “institutions” shaping the game.  The signals can be rigged:  as long as the players find it to their advantage to follow the signal rather than ignoring it altogether, the mixture of “cooperate” and “defect” for each player can be anything, as long as following the signal makes them better off than not following the signal.

The key feature that makes correlated equilibrium honest is the presence of uncertainty.  If you have been given the signal to cooperate, you do not want to defect because you do not know if the other player has been told to cooperate or defect.  What happens if that uncertainty is eliminated?  There is nothing you can do if the other player has been told to defect:  if the game is working as designed, he is always supposed to win if he does defect and there is nothing you can do to change the outcome.  BUT you can always defect if he has been told to cooperate.  The other expects to either lose or wind up in the “meh” outcome.  You can ensure that he always loses, at least until he figures it out.  In other words, if you gain the due “informational advantage,” you can force the other player into getting just two outcomes–“defect” will always get him a “win” but “cooperate” will ensure that he loses.

This subverts the rationale for correlated equilibrium:  just playing a regular old mixed strategy, without paying attention to the signals, would yield a better payoff for the other player than following the signal (and predictably losing). Conceivably, the other player might adopt a more complex strategy in which he mixes strategy only if he is given the signal to cooperate, in order to shake out your taking advantage of your ill-gotten information.  While the math becomes a bit messy, it is fairly easy to show that, in course of eliminating the gains from the ill-gotten information, the average payoff for both sides becomes smaller than under the “ignorant” correlated equilibrium.  The bottom line is that, regardless of what happens, the neat “institution” that served everyone reasonably well falls apart once people know “too much,” at expense of everyone.

I think there is a huge set of implication from this line of thinking.  The promise of “data science” and “predictive analytics,” literally, is to make everything predictable:  give us enough data, we will help you figure out exactly what to expect, conditional on the situation.  We all live in a universe where we respond to a whole bunch of signals, of course.  We know that, if we see, say, someone who is a Democrat, he’d act in a certain fashion, in a particular set of circumstances.  But there is enough uncertainty that his behavior cannot be predicted with precision:  so, in the lingo of correlated equilibrium, there is some probability that he might “defect” or “cooperate,” and since we want to avoid the “bad” outcome where we both “defect,” we should just “cooperate.”  By knowing precisely what “signal” he has gotten out of the situation on hand, however, we can predict if he will “defect” or “cooperate” and choose accordingly and benefit in the short run–that is, until he gets wise and changes his behavior so as to make our information advantage irrelevant, to the disadvantage of us both.  In other words, the more information there is, the harder it is to sustain a “let’s agree to disagree because we don’t know any better”–because “we don’t know any better” does not apply.

I was thinking about this when I was reading this blog post.  More information makes the world increasingly predictable and eliminates the uncertainty in which “cooperation” can take place.  So called “alpha” in investing, depending on the informational asymmetries falls apart not just because of the simple textbook arbitrage, but because your competitors can better anticipate your moves and make countermoves that nullify your potential gains rather than hold back, deterred by their uncertainty as to how you might respond (so, it may not be TOO different from the textbook arbitrage in abstract.)  The room for tacit cooperation for mutual profit shrinks.  Market purists might think this a welcome development:  they see the universe as a zero sum game where the profits that do not accrue to the firms/investors somehow automatically to the consumers.  But here, it is not obvious where consumers benefit, especially if one considers the likely next step where investors find ways to ignore the information, or, at least, nullify the advantages that their competitors accrue from them.  In the application to the politics, the problems seem much starker:  the uncertainty that provided cover for many politicians from across the political divide could cooperate has been taken away, forcing them to take up the “obvious” and “confrontational” stances, ironically with increasingly more of their “real” activities shrouded in secrecy.  Not exactly an obviously “positive” development, I would think.

 

Two Faces of Institutions

The DNC email scandal has been generating a lot of controversy–at least in the Twitterverse.  A lot of talk has been focused on the apparently hypocrisy of the DNC, acting in contravention of the letter of the DNC bylaws requiring fairness–notwithstanding the fact that, from the beginning, it was obvious that the mainstream Democrats did not like Sanders and his movement, who, not inaccurately, were outsiders to the Democratic Party (1/3 to 40% of the Sanders voters being independents and all that).  Equally, a lot of talk has been over the probable fact that not a whole lot had been done, explicitly, by the DNC to overtly rig the outcome in favor of the Clintons.

I think these are mistaken.  The role of institutions is not simply to serve as the tools that can be rigged by those who wield them, subtly or overtly, to win the immediate outcome.  They are to serve as focal points to rally all potential members of the coalition around.  While the two might, in practice, be more similar than not, this implies exactly the opposite use of institutions.

If the goal is to use institutions as weapons to beat one’s “enemies” with, then there is no point in sticking to the spirit of the rules.  The letter of the institutions may be adhered to, but there is enough flexibility for a clever lawyer to find loopholes around. The Clintons and their allies can revert to their old habit of arguing over what “is” means, if a favorable definition would net them an advantage over their rivals, like Sanders and his supporters.

But, if the goal of institutions is to serve as focal points, inflexible adherence to the spirit as well as the letter of the rules is critical.  Perhaps the potential allies are lost, confused, or uncertain.  If so, they need to learn where the party is and find the path to where the party is leading them.  To paraphrase Shakespeare, the party’s rules must be as constant as the North Star to provide as reliable a guidance as possible, even if maintaining that constancy requires considerable sacrifice.  That sacrifice may be worth while if the allies thus gained can provide valuable aid.

Of course, the premise behind the second perspective is the existence of uncertainty and the faith that, if sufficient goodwill and constancy can be shown, the “lost” partisans will appreciate it and find their way themselves.  A useful analogy might be to that of a lighthouse.  The lighthouse serves to provide the path for the mariners who are uncertain about where they are headed, but are skilled enough that, if they do, they can find the way themselves.  In order for the lighthouse to function properly, its light and location needs to be both well-known and constant–with the knowledge known to all that a great deal of effort is being made to keep things as constant as possible.  Once the lighthouse becomes a tool of manipulation, the mariners would no longer trust them.  They are not incompetent:  they are skilled enough that they may still find their way on their own, but at greater peril to themselves.  Knowing that they’d been tricked, however, will destroy their faith in the institutions of the lighthouse in the long run, and with it, any advantage that the lighthouse keepers to extract advantages therefrom.  In other words, strategic manipulation of the lighthouses may frustrate the mariners who are enemies of the lighthouse keepers, but, in the long run, it will simply ensure that the mariners will be mortal enemies, since they will not, probably, be killed off merely by the evil lighthouse tricks.

The Clintons and their allies have been quite clear, I think, in their view that they do not view Sanders and his supporters to be a valuable ally so far in the campaign.  The so-called “Rendell strategy” has focused on winning over affluent and educated segments of the Republican electorate, rather than the poor and the working class. The behavior of the DNC is consistent with this worldview:  Sanders and his people are not, ultimately, welcome (enough) in the Democratic Party that they control.  Maybe they can get away with it this time, with the Trump campaign in total disarray, but this subverts the ability of the Democrats to expand the electorate in the long run (Even today, will they be able to get away with it?  If they do lose all of the non-Democratic Sanders supporters to the degree that a significant portion of them do indeed choose Trump, they will have lost even that bet, even if that seems rather unlikely now).  Lost trust will take a long time, if ever, to recover.  Rules are rules for a reason:  by adhering closely to them, even if they could get away with bending them out of shape, the elites can induce the masses to rely on them as focal point–because everyone will be there, so to speak.  If the rules are only for the little people, why should the little people abide by them if the only people they will find are other little people?

 

Elections, Risk, and Strategery

This would be an interesting question even if it lacked political context, but the electoral context adds even more:

Generically, the Democrats have a numerical advantage over the Republicans in U.S:  There are more potential Democrats, but their turnout is more variable.  The only paths that the Republicans have for winning the race is to gamble, and there are two ways to gamble.

First, the Republicans might gamble on the Democrats getting unlucky:  they might hope that their voters turn out with the usual reliability, but somehow, Democrats get unlucky and their voters don’t turn out.  This was basically Mitt Romney’s approach in 2012, and this was not as unjustified in retrospect as might have seemed then:  the midterm elections of 2010 and 2014, if anything, underscore how unreliable the Democratic electorate is.  But, for 2012, with Barack Obama at the top of the ticket generating more reliable turnout among the less reliable segments of the Democratic voters, they were probably going to be as reliable as possible, and they were exactly that in practice.

Second, the Republicans might gamble on their own “lucky” shot.  Instead of getting fewer votes with certainty, they might change their approach and gamble on a chance of getting more votes, with the downside of falling apart completely.  This is what Donald Trump’s campaign is all about:  he runs a respectable chance of outperforming Romney among the working class whites and is risking a big fat chance of losing a lot of the regular, reliable Republican votes–and he’s gambling that he won’t.  So he will probably do much worse than Romney (in terms of the mean) but has a decent chance that he would do better.  But, again, the Republicans are facing the wrong candidate.  Unlike Barack Obama, Hillary Clinton is about as most unreliable vote getter among possible Democratic candidates as possible:  she will get most of the establishment Democrats, but enthusiasm for her among the liberals, the youth, ethnic minorities, etc. is low.  They will not vote Republican…but the real risk is that they will vote third party if they vote at all–posing a significant risk for the Democrats in lost votes.  This is exactly the scenario where the reliable but few regular Republican electorate probably stood an excellent chance.  Instead, they are gambling with a Trump.

As a generic game, the game of war (the card game) with uncertainty, featuring trade off between mean and the variance, does not really have a clear solution:  it is really just a variation on the matching pennies game. Of course, the parties did not necessarily choose their candidates “strategically.”  Still, it is fascinating how mismatched the Republican and Democratic candidates were and still are, in 2012 and again in 2016.

Why Betting Markets Should Never be (Wholly) Trusted.

Andy Gelman is usually right about most things, but here, he manages to be both right and seriously, nay, fundamentally wrong.

The problem with betting markets is the same problem with the price system in all (asset) markets identified by Stiglitz and Grossman in 1970s: the price system conveys information cheaply, while real expertise is expensive.  If the information conveyed by the markets becomes too perfect, people simply underinvest in the real information.  And the underinvestment continues until the market information is just wrong enough to justify investing again.  This is further compounded by the fact that, in the end, polls are themselves expensive and unreliable as conveyors of information.  So reliance on the markets, imperfect as it might be, is bound to weigh in even than asset prices in financial markets.  An echo chamber is likely to form and to sustain itself.  Unpredictability in politics is inevitable and no amount of data can eliminate it wholly.  By trusting too much in markets or polls, we are setting ourselves up for mistakes.