Tulips, Traffic Jams, and Tempests (Part 2): The Properties of Complexity

In the first installment of this series, I discussed some well-known phenomena that are emergent effects of complex systems, and gave a general definition of complexity.  In this installment, we’re going to delve a little deeper and look at some common properties and characteristics of complex systems.  Understanding such properties helps us understand what are the types of complex systems and what kinds of tools we have available to study complexity, which will be the topic of the third installment of the series.

There are four common properties that can be found in all complex systems:

  • Simple Components (Agents)
  • Nonlinear Interaction
  • Self-organization
  • Emergence

But what do these mean, and what do they look like?  Let’s examine each in turn.



One of the most interesting things about complex systems is that they aren’t composed of complex parts.  They’re built from relatively simple components, compared to the system as a whole.  Human society is fantastically complex, but its individual components are just single human beings—which are themselves fantastically complex compared to the cells that are their fundamental building blocks.  Hurricanes are built of nothing more than air and water particles.  These components are also known as agents.  The two terms are interchangeable, but I prefer agents in general and that will be the term used throughout the rest of this post; the usual distinction among those who use both terms is that agents can make decisions and components cannot.  But computer simulations show that even when agents can only make one or two very simple deterministic responses with no actual decision-making process beyond “IF…THEN…,” enough of them interacting will result in intricate complexity.  We see this in nature, too—an individual ant is one of the simplest animals around, driven entirely by instincts that lead it to respond predictably to encountered stimuli, but an ant colony is a complex system that builds cities, forms a society, and even wages war.  The wonder of complex systems is that they spring not from complexity, but from relative simplicity, interacting.  But there must be many of them—a single car on a road network is not a complex system, but thousands of them are, which leads us to our next property.



For complexity to arise from simple agents, there must be lots of them interacting, and these interactions must be nonlinear.  This nonlinearity results not from single interactions, but from the possibility that any one interaction can (and often does) cause a chain reaction of follow-on interactions with more agents, so a single decision or change can sometimes have wide-ranging effects.

In technical terms, nonlinear systems are those in which the change of the output is not proportional to the change of the input—that is, when you change what goes it, what comes out does not always grow or shrink proportionately to that original change.  In layman’s terms, the system’s response to the same input might be wildly different depending on the state or context of the system at the time.  Sometimes a small change has large effects.  Sometimes a large change is absorbed by the system with little to no effect at all.

This is important to understand for two reasons.  First is that, when dealing with complex systems, responses to actions and changes might be very different than those the actor originally expected or intended.  Even in complex systems, most of the time changes and decisions have the expected result.  But sometimes not, and when the system has a large number of interactions, the number of unexpected results can start to have a significant impact on the system as a whole.

The other reason this is important is that nonlinearity is the root of mathematical chaos.  Chaos is defined as seemingly random behavior with sensitive dependence on initial conditions—in nonlinear systems, under the right conditions, prediction is impossible, even theoretically.  One would have to know with absolute precision the starting conditions of every aspect of the system, and considering that the uncertainty principle means that it’s physically impossible to do so according to the laws of physics, perfect prediction of a complex system is impossible: to see what happens in a complex system of agents interacting in a nonlinear fashion, you must let it play out.  Otherwise, the best you can do is an approximation that loses accuracy the further and further you get from the starting point.  This sensitivity to initial conditions is commonly simplified as the “butterfly effect,” where even small changes can have large impacts across the system as a whole.

In short, the reason the weather man in most places can’t tell you next week’s weather very accurately isn’t because he’s bad at his job, but because weather (except in certain climates with stable weather patterns) literally cannot be predicted very well, and it gets harder and harder the further out you try to do so.  That’s just the nature of the system they’re working with.  It’s remarkable they’ve managed to get as good as they have, actually, considering that meteorologists only began to understand the chaotic principles underlying weather systems when Lorenz discovered them by accident in 1961.  Complex systems are inherently unpredictable, because they consist of a large number of nonlinear interactions.



Complex systems do not have central control.  Rather, the agents interact with each other, giving rise to a self-organized network (which in turn shapes the nonlinearity of the interactions among the agents of the network).  This is a spontaneous ordering process, and requires no direction or design from internal or external controllers.   All complex systems are networks of connected nodes—the nodes are the agents and the connections are their interactions—whether they’re networks of interacting particles in a weather system or networks of interacting human beings in an economy.

The structure of the system arises from the network.  Often it takes the form of nested complex systems: a society is a system of human beings, which is a system of cells, each level of which is itself a complex system.  Mathematically, the term for this is a fractal—complex systems tend to have a fractal structure, which is a common feature of self-organized systems in general.  Some complex systems are networks of simple systems; others are networks of complicated systems; many are networks of complex sub-systems and complicated sub-systems and simple sub-systems all interacting together.  A traffic light is a simple system; a car is a complicated system; a human driver is a complex system, the traffic system is a network of many individual examples of all three of these sub-systems interacting as agents.  And it is entirely self-organized: the human beings who act as drivers are also the agents who plan and build the road system that guides their interactions as drivers, by means of other complex systems such as the self-organized political system in a given area.



Emergent properties, as discussed in part one of this series, are those aspects of a system that may not be determined merely from isolating the agents—the system is greater than the sum of its parts.  An individual neuron is very simple, capable of nothing more than firing individual electrical signals to other neurons.  But put a hundred billion of them together, and you have a brain capable of conscious thought, of decision-making, of art and math and philosophy.  A single car with a single driver is easy to understand, but put thousands of them on the road network at the same time, and you have traffic—and its own resulting emergent phenomena like congestion and gridlock.  Two people trading goods and services are simple, but millions of them create market bubbles and crashes.  This is the miracle of complexity: nonlinear networks of relatively simple agents self-organize and produce emergent phenomena that could not exist without the system itself.

Some common emergent properties include information processing and group decision-making, nonlinear dynamics (often shaped by feedback loops that dampen or amplify the effects of behaviors of individual agents), hierarchical structures (such as families and groups which cooperate among themselves and compete with each other at various levels of a social system), and evolutionary and adaptive processes.  A hurricane, for example, is an emergent property in which many water and air molecules interact under certain conditions and with certain inputs (such as heat energy from sunlight), enter a positive feedback loop that amplifies their interactions, and become far more than the sum of their parts, until the conditions change (such as hitting land and losing access to a ready supply of warm water), at which point they enter a negative feedback loop that eventually limits its growth and later dictates its decline back to nonexistence.  Adam Smith’s “Invisible Hand” is an emergent property of the complex systems we call “economies,” in which individual actions within a nonlinear network of agents are moderated by feedback loops and self-organized hierarchical structures to produce common goods through self-interested behavior.  Similarly, the failures of that Invisible Hand such as a speculative bubbles and market crashes are themselves emergent behaviors of the economic system, that cannot exist without the system itself.



Now that we’ve established the common properties of complex systems, in the next article we’ll look at a couple different types, what the differences are, and what tools we can use to model them properly.

On Nazis and Socialists

I commonly run into the argument that the Nazis were clearly left wing, because “Socialism is right there in their name.”  It’s getting old, because it ignores literally everything else about them.  Bottom Line: yes, they were socialists, but no, they were not leftists.

Part of the problem is that there’s no good accepted narrow definition of socialism–it ranges from Marxist-style Communism to Soviet-style command economies to Scandinavian-style public welfare states. A few months ago the American Economics Association’s Journal of Economic Perspectives published a paper trying answer the question of whether modern China is socialist, and it was fascinating because first they had to establish a working definition of socialism. Even today, there’s serious ongoing debate about that in academic economics circles.

But in the broad sense, Nazis were socialist, in that the government controlled the economy towards its own goals–the Reich ran the factories and mines and basically the entire supply chain and directed how resources and products would be used at the macro level.

That said, the Nazis explicitly rejected what we’ve come to think of as the “left-right” spectrum in favor of what political theorists call a “third way,” which married leftist-style government control of the economy to right-wing-style government control of social lives in a militaristic fascism focused on directing all social and economic aspects of the country towards the needs of the Fatherland. Nationalism (right) + Socialism (left) = National Socialism. Funny how that works. Thus, it’s a great straw man, because BOTH sides can legitimately point to aspects of Nazism and say “See?! They were the other side!” When the reality is they were neither.

Note: neo-Nazis, on the other hand, generally ignore the economic aspects of National Socialism in favor of the eugenicist racism, conservative nativism, and militaristic nationalism, and ARE legitimately classified as right-wing extremists.

The more you know.

Opinions, Assholes, and Believability

My next post was going to be a continuation of my introduction to complexity, and I promise that I’ll get around to that eventually, but a few days ago I was made aware of an exchange on Facebook that got me thinking, and I’d like to take a moment to lay out my thoughts on the matter.

I personally did not witness this exchange, but a friend of mine took a screenshot of the first part of the conversation (before the original commenter apparently deleted the thread).  First, some context: this occurred after a firearms industry page (Keepers Concealment, a maker of high quality holsters) shared a video of Ernest Langdon demonstrating the “Super Test,” a training drill that requires a shooter to fire rapidly and accurately at various ranges.  Ernest Langdon is indisputably one of the best handgun shooters in the world.  That’s an objective fact, and he has the competition results and measurable skills to prove it.  He is ranked as a Grand Master in the US Practical Shooting Association, a Distinguished Master in the International Defensive Pistol Association, and has won 10 National Championship Shooting titles and 2 World Speed Shooting titles.  All of which explains why when some nobody on Facebook (who we shall refer to as “Mr. Blue” as per my color-coded redacting) made this comment, quite a few people who know who Ernest Langdon is raised their collective eyebrows:

Opinions Screenshot

Mr. Blue, who as mentioned is a nobody in the shooting world with exactly zero grounds to critique Ernest Langdon, still for some reason felt the appropriate response to this video of the one the best shooters to have ever walked the face of the earth was to provide unsolicited advice on how he could improve.  Then, when incredulous individuals who actually know what they’re talking about point out exactly how arrogantly stupid that response is to this particular video, another person, Mr. Red, chimes in to claim that if we accept no one is above reproach, then “it’s fair for people (even those who can’t do better), to critique what they see in the video.”  To which I want to respond: no, it is not.

I agree entirely with Ray Dalio, the founder of Bridgewater Associates—the world’s largest hedge fund—when he says, “While everyone has the right to have questions and theories, only believable people have the right to have opinions. If you can’t successfully ski down a difficult slope, you shouldn’t tell others how to do it, though you can ask questions about it and even express your views about possible ways if you make clear that you are unsure.”  What that means is not that you can’t form an opinion.  It means that just because you have the right to HAVE an opinion doesn’t mean you have the right to express it and expect for anyone to take it seriously.  Just because you happen to be a breathing human being doesn’t make you credible, and the opinions of those who don’t know what they’re talking about are nothing more than a waste of time that serves only to prove that you’re an idiot.  Like the old saying says, “Better to remain silent and be thought a fool than to speak and remove all doubt.”

But Mr. Red’s comment goes to an attitude that lies at the heart of stupidity: the idea that everyone’s opinion is equally valid and worth expressing, and all have a right to be heard and taken seriously.  This certainly isn’t a new phenomenon: Isaac Asimov wrote about a cult of ignorance in an article back in 1980: “The strain of anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that ‘my ignorance is just as good as your knowledge.’”  But new or not, it very much drives the willingness of ignorant nobodies to “correct” and “critique” genuine experts.  Mr. Blue has no idea of the thousands of hours of training Ernest Langdon has put into perfecting his grip and recoil management and trigger control, the hundreds of thousands of rounds of ammunition he’s put down range to hone his technique and become one of the best in the world at what he does.  Mr. Blue has put nowhere near that amount of time and effort into his own training—I know this, because if he had he’d also be one of the best shooters in the world, instead of some random nobody on Facebook.  But despite that vast gulf of experience and expertise, Mr. Blue still thinks he can and should provide unsolicited advice on how Ernest Langdon can be better.  And then doesn’t understand why others are laughing at him, and another commenter rides to the rescue, offended at the very notion people are dismissive of the critique of a nobody.

This is the same mindset that leads to people who barely graduated high school presuming to lecture the rest of us on why the experts are wrong on politics, on science, on economics, on medicine.  This is the mindset that leads to anti-vaccination movements bringing back measles outbreaks in the United States.  This is the mindset Sylvia Nasar described when she wrote “Frustrated as he was by his lack of a university education, particularly his ignorance of the works of Adam Smith, Thomas Mathus, David Ricardo, and other British political economics, [he] was nonetheless perfectly confident that British economics was deeply flawed.  In one of the last essays he wrote before leaving England, he hastily roughed out the essential elements of a rival doctrine.  Modestly, he called this fledgling effort ‘Outlines of a Critique of Political Economy.’”  The subject she was writing about?  Friedrich Engels, friend and collaborator of Karl Marx, and co-author of Das Kapital.  Is there any wonder that the system they came up with has never worked in practice?

While the conversation that inspired this line of thought was in the shooting world, I see it all the time in many, many different fields.  Novice weightlifters “critiquing” world record holders.  Undergraduate students “critiquing” tenured professors in their area of expertise.  Fans who’ve never stepped into a cage in their lives expounding upon what a professional fighter in the UFC “did wrong” as if they have the slightest idea what it’s like to step into the Octogon and put it all on the line in a professional MMA fight.  People with zero credibility believing they have the standing to offer unsolicited advice to genuine, established experts.  This isn’t to say that experts are infallible, or that criticism is always unfounded.  But to have your opinion respected, it must be believable, and if you lack that standing you’d damn well better be absolutely certain your criticism is well-founded and supported by strong evidence, because that’s all you have to go on at that point.  Appeal to authority is a logical fallacy, but unless you’ve got the evidence to back up your argument, the benefit of the doubt is going to go to the expert who has spent a lifetime in the field, versus the nobody who chooses to provide unsolicited commentary.

When you have an opinion on a technical subject, and you find yourself moved to express it in a public forum, please, just take a second and reflect.  “Do I have any standing to express this opinion and have it be believable, or is it well-supported by documented and cited evidence in such a way that it overcomes my lack of relative expertise?  Do I have a right for anyone to pay attention to my thoughts on this subject?  Or am I just another ignorant asshole spewing word diarrhea for the sake of screaming into the void and pretending I matter, that I’m not a lost soul drifting my way through existential meaninglessness, that my life has purpose and I’m special?”  Don’t be that guy.

Opinions and assholes, man.  Everyone’s got ‘em, and most of them stink.

Voodoo Economics (Well, It’s Complicated #3)

  • Feldstein (1986)
  • Feldstein and Elmendorf (1989)
  • Garrison and Lee (1992)
  • Engen and Skinner (1992)
  • Slemrod (1995)
  • Auerbach and Slemrod (1997)
  • Mendoza et al. (1997)
  • Padovano and Galli (2001)
  • Gale and Potter (2002)
  • Desai and Goolsbee (2004)
  • Gale and Orszag (2005)
  • Eissa (2008)
  • Mertens and Ravn (2010)
  • Huang (2012)
  • Favero and Giavazzi (2012)
  • Yagan (2015)
  • Mertens (2015)
  • Zidar (2015)
  • Gale and Samwick (2017)

All of these studies, meta-studies, and papers have one thing in common: they all look at the effect of tax cuts on supply-side economic growth, either in the US specifically or in developed countries in general, by assessing empirical data.  They look at both narrow framed cuts like the Bush cuts in 2001/2003, and at broad framed reforms like the Reagan cuts in 1981 and 1986.  And ALL of them find the empirical data shows effectively no statistically significant correlation between tax cuts and supply-side economic growth.  None.  Zip, zero, zilch, nada.

The theory of supply side economics, also known as top down economics, investment-side economics, trickle-down economics, or Reaganomics, is elegant.  It sounds good.  It fits the rational models of Chicago school neoclassical economists to a T. In short, it says tax cuts stimulate economic growth by freeing up capital for investment and spending.

The problem is that little to no evidence supports it actually working that way in the real world.  At all.  The closest we get is Romer and Romer (2010), which supports the idea that demand will increase in the short term in response to unexpected tax cuts, but stops short of any evidence demonstrating actual long term growth, especially on the supply side.

As Gale and Samwick (2017) puts it: “The argument that income tax cuts raise growth is repeated so often that it is sometimes taken as gospel.  However, theory, evidence, and simulation studies tell a different and more complicated story.  Tax cuts offer the potential to raise economic growth by improving incentives to work, save, and invest.  But they also create income effects that reduce the need to engage in productive economic activity, and they may subsidize old capital, which provides windfall gains to asset holders that undermine incentives for new activity.  In addition, tax cuts…not accompanied by spending cuts…will typically raise the federal budget deficit.  The increase in the deficit will reduce national saving…and raise interest rates, which will negatively affect investment.  The net effect of the tax cuts on growth is thus theoretically uncertain and depends on both the structure of the tax cut itself and the timing and structure of its financing.”

If you want to argue that taxes are a moral evil, fine.  I’m not going to delve into the philosophy of social choice theory here.  That’s your call.  But if you want to support your philosophical argument by saying the economics are on your side, that “basic economics” tell us tax cuts are always good for the economy by boosting growth, that I’ll refute all day every day, because it just ain’t true.

The simplistic story told by “supply side economics” advocates is pure political bullshit, unsupported by evidence or theory in a complex and nuanced reality, no matter how many Thomas Sowell books you’ve read.  In the face of the real world, it’s complicated.

Tulips, Traffic Jams, and Tempests (Part 1): An Introduction to Complexity

In the early 1600s, during the Dutch Golden Age, tulips—a flower which had been introduced to Europe less than a century before—had become a status symbol, a luxury item coveted by all who wanted to flaunt their wealth.  At the same time, the Dutch were busy inventing modern financial instruments.  This became a dangerous combination when, in the mid-1630s, speculators entered the tulip market and futures prices on tulip bulbs—a durable commodity, given their longevity—began to skyrocket.  At its peak, in early 1637, single bulbs of the most coveted varietals traded for prices 10-15 times the annual salary of a skilled craftsman (roughly the equivalent of $500,000 to $800,000 today).  Even common varietals could sell for double or triple such a craftsman’s salary.  And then, in February 1637, almost overnight, prices dropped by 99.9999%, the market collapsed, the contracts were never honored, and tulip trading effectively stopped.  It’s generally considered the first recorded example of a speculative bubble.  For centuries, theorists have argued various explanations, from outside forces (a bubonic plague outbreak led traders to avoid a routine auction in Haarlem), to rational markets (prices matching demand and never separating wildly from the intrinsic value of the commodity), to legal changes in the futures and options market about the structure of contracts (meaning futures buyers would no longer be obligated to honor the full contract).  The Tulip Mania is one of the most famous stories in economics, and no one really knows why it happened in the first place.

Driving home from work, I (and probably most of you) often notice a curious phenomenon, which most of us just take for granted at this point.  Every evening at rush hour, my commute slows down.  Even when there’s no accident blocking a lane or two, even when the on-ramps are metered to ensure there aren’t dozens of cars trying to merge into the lane at once, even when there’s no dangerous weather, even when everyone is theoretically trying to get home as fast as they safely can, the cars around me on the highway are moving well below the speed limit.  We call this phenomenon “congestion” or a “traffic jam,” and everyone has just learned to deal with it.  Scientists have tried to model traffic for decades, with everything from fluid dynamics to phase theory.  Economists have likened it to “tragedy of the commons” models.  But no one has been able to produce a good mathematical model that matches empirical observations and can explain where it comes from in the first place in the absence of external triggering events.

Every summer, when the water in the north Atlantic is warm enough, and the winds are just right, and the atmospheric pressure is just right, sometimes—about a dozen times a year between June and November—a storm that, at any other time would remain just a storm, picks up speed and begins cyclonic motion.  And if the conditions are just right (and no one is quite sure what “just right” means), that cyclone will develop into a hurricane.  These massive tempests are to the original storm what the Great Chicago Fire was to the lantern that first lit the flames.  While the normal storm would have made some people wet and maybe knocked some trees over, hurricanes can cause widespread death and destruction among whatever’s in their paths, whether it’s fishing villages in the Caribbean or the New Orleans metropolis.  And, much like the Tulip Mania or traffic jams, while scientists have gotten reasonably good at identifying risk factors, no one is really sure what causes an ordinary storm to become a hurricane.  It requires the perfect combination of the right factors in the right place at the right time.  We can identify the (mostly) necessary conditions, but even when all of them are present, often a hurricane never appears.  Sometimes one appears even when they aren’t all there.  And yet, despite this apparent randomness, it happens like clockwork, a dozen or so times a year in the same six month timeframe.

Why do we care?  What do Dutch tulip markets, highway congestion, and tropical cyclones have in common?  The answer is all are natural features of what we call “complex” systems.  In this series of articles, we’ll look at what complex systems are and how they differ from complicated systems.  Markets, urban commutes, and weather patterns are all examples of different types of complex systems, and sometimes complex systems inherently exhibit unpredictable, wild, seemingly inexplicable behavior like bubbles and crashes, congestion and slowdowns, and out of control feedback loops.  Not because anyone wants them, or because they design for them, or they screwed up and designed the system badly.  But because that’s the nature of complexity.

Complexity is a difficult term to define, even though it’s been widely used in various scientific disciplines for decades.  In the next article of this series we’ll look at the defining characteristics of a complex system.  But for now, we’ll stick to the broad overview.  Complexity is the state in which the components of a system interact in multiple ways and produce “emergence,” or an end state greater than the sum of its parts.  Cars, buses, a multi-lane highway, public transportation, on- and off-ramps, surface streets, traffic lights, pedestrians, and so on are the components of the system.  They all interact in many different ways in a densely interconnected and interdependent system—what happens in one area can have wide-ranging affects across multiple areas of the system as a whole.  And thus, even though everyone hates traffic jams and everyone just wants to get home as efficiently as possible, the traffic jam nonetheless appears, like clockwork, every evening at rush hour.  Congestion is an emergent property of the commuting system.  It is more than the sum of its parts, completely different that the pieces making it up, the cars and the roads and so on.  That’s complexity, in a nutshell.

Contrast this to the other major type of systems, which we call “simple” and “complicated.”  A simple system is something like a simple machine.  A pendulum is a simple system.  A lever is a simple system.  In these, the system is the sum of its parts.  It allows us to do things we could not do without the system, but it is additive.  There are limited interactions, and they operate by well-defined rules.  A complicated system is just the extension of this, composed of many simple systems linked together.  Whereas the defining feature of a complex system is interconnectivity, a complicated system is defined by layers.  Hierarchical systems like military organizations are complicated systems: they may be very difficult to work through and figure out what goes where, but when you figure it out, you can see all the relationships and know what effects an action in one area will have elsewhere.  Many engineering problems deal with complicated systems, and thus humans have become quite skilled at understanding these types of systems: we use mathematical tools like differential equations and Boolean logic, and can distill the system into its essential components, which allows us to manipulate the system and solve problems.  It may be difficult and take an awful lot of math and ingenuity, but at the end of the day, the problems are solvable with such tools.

Complex problems, however, are not solvable with the traditional tools we use to address complicated systems, because by their very nature they work in fundamentally different ways.  As I already mentioned, they are defined not by the components and layers, but by the interconnectivity and interdependency of those components.  The connections matter more than the pieces that are connected, because those connections allow for emergent properties greater than the sum of the parts.  They allow for butterfly effects and feedback loops and inexplicable changes.  Complex systems are not all the same—complexity can occur in deterministic physical systems like weather patterns and ocean currents, or in nondeterministic social systems like ecosystems and commodities markets and traffic patterns, and even in deterministic virtual systems like computer simulations.  Because, again, what matters for complexity is the connectivity, not the components.

And because complex problems are not solvable with the tools we use to solve complicated problems, we often get unexpected results, causing even worse problems despite our best intentions.  This fundamental misunderstanding of how complex systems work has led to everything from inner city gridlock to economic collapse.  Researchers have only been studying complexity for about three decades now, but it has revolutionized understanding in fields ranging from computer science and physics to economics and climatology.  It’s amazing what you can do when you start asking the right questions.

In the next article, we’ll look at the characteristics of complex systems and a couple different types of them.  Then we’ll look at the tools we use to understand them.  And finally, since I’m an economist and this is my blog, we’ll look at the relatively new field of complexity economics and try to understand some the lessons learned about how markets actually work.

On Rationality (Economic Terminology, #1)

As an economist, I often find myself talking past people when trying to explain complicated economic theories.  Surprisingly, this is less because of the in-depth knowledge required, and far more because we aren’t using the same terminology.  Many words used in economic contexts have very different meanings than their common usage.  Utility and value, for one.  Margin, for another.  And perhaps the most common source of confusion is the concept of rationality.

In common usage, “rational” basically means “reasonable” or “logical.”  The dictionary definition, according to a quick google check, is “based on or in accordance with reason or logic.”  Essentially, in common usage a rational person is someone who thinks things through and comes to a reasonable or logical conclusion.  Seems simple enough, right?

But not so in economics.  Traditional economic theory rests on four basic assumptions–rationality, maximization, marginality, and perfect information.  And the first of those, rationality, is the single biggest source of confusion when I try to discuss economic theory with non-economists.

To an economist, “rational” does not in the slightest sense mean “reasonable” or “logical.”  A rational actor is merely one who has well-ordered and consistent preferences.  That’s it.  That’s the entirety of economic rationality.  An economically rational actor who happens to prefer apples to oranges, and oranges to bananas, will never choose bananas over apples when given a choice between the two.  Such preferences can be strong (i.e., always prefers X to Y) or weak (i.e., indifferent between X and Y), but they are always consistent.  And those preferences can be modeled as widely or narrowly as you choose.  It could just be their explicit choices among a basket of goods, or you could incorporate social and situational factors like altruism, familial bonds, and cultural values.  They can be context dependent–one might prefer X to Y in Context A, and Y to X in Context B, but then one will always prefer X to Y in Context A and Y to X in Context B. It doesn’t matter: what their preferences actually are is irrelevant, no matter how ridiculous or unreasonable they might seem from the outside, so long as they are well-ordered and consistent.

This isn’t to say preferences can’t change for a rational actor.  They can, over time.  But they’re consistent, at the time a decision is made, across all time horizons–if you give a rational actor the choice between apples and bananas, it doesn’t matter whether they will receive the fruit now or a day from now.  They will always choose apples, until their preferences change overall.

An irrational actor, then, is by definition anyone who does not have well ordered and consistent preferences.  If an actor prefers apples to bananas when faced with immediate reward, but bananas to apples when they won’t get the reward until tomorrow, they’re economically irrational.  And the problem is, of course, that most of us exhibit such irrational preferences all the time.  For proof, we don’t have to look any further than our alarm clocks.

A rational actor prefers to get up at 6:30 AM, so he sets his alarm for 6:30 AM, and wakes up when it goes off.  End of story.  An irrational actor, on the other hand, prefers to get up at 6:30 AM when he sets the alarm, but when it actually goes off, he hits the snooze button a few times and gets up 15 minutes later.  His preferences have flipped–what he preferred when he set the alarm and what he preferred when it came time to actually get up were very different, and not because his actual preferences have changed at all.  Rather, he will make the same decisions day after day after day, because his preferences aren’t consistent over different time horizons.  The existence of the snooze button is due to the fact human beings do not, in general, exhibit economically rational preferences.  We can model such behavior with fancy mathematical tricks like quasi-hyperbolic discounting, but they’re by definition irrational in economic terminology.

And that’s why behavioral economics is now a major field–at some point between Richard Thaler’s Ph.D research in the late 1970s and his tenure as the President of the American Economics Association a couple years ago, most economists began to realize the limitations of models based on the unrealistic assumption of economic rationality.  And so they began to start trying to model decision making more in keeping with how people actually act.  Thaler last year predicted that “behavioral economics” will cease to exist as a separate field within three decades, because virtually all economics is now moving towards a behavioral basis.

In future editions of this series, we’ll look at other commonly misunderstood economic terms, including the other three assumptions I mentioned: marginality, maximization, and perfect information.

What Is Antistupid?

There are things which cannot be taught in ten easy lessons, nor popularized for the masses; they take years of skull sweat. This be treason in an age when ignorance has come into its own and one man’s opinion is as good as another’s. But there it is…The world is what it is—and doesn’t forgive ignorance.” -R.A. Heinlein, Glory Road

What is Antistupid?

This blog has been, conceptually, years in the making.  It is just an extension of a Quixotic quest I have been upon for most of the last decade—namely, a quest against stupidity, in all its forms, wherever I find it.

What do I mean by stupidity?  At its core, I suppose my definition of stupidity would center on laziness of thought.  Whether that takes the form of willful ignorance, deliberate rejection of empirical evidence and logic and the scientific method, a devotion to unthinking dogma, a refusal to confront one’s own cognitive biases, a preference for echo chambers and “truthiness” over verifiable facts, or any other version of lazy thought, all would qualify.  Stupidity is not just ignorance.  Stupidity is not just being wrong.  Stupidity is laziness.

This is not to say those guilty of stupidity are themselves inherently stupid.  I firmly believe the vast majority of people are innately intelligent and capable of critical thought and reason.  Studies of IQ test results have shown a general increase in scores for generation after generation, known as the Flynn Effect—a result not yet well understood, but fairly damning of the conclusion that people are just stupid.  If I thought people themselves were irredeemably stupid, there would be no point railing against stupidity.  It would be as much a waste of time as railing against the weather.

Rather, I believe that people are lazy.  That reason and objective assessment of the facts are much harder than emotion and heuristic thought processes, and we tend to default to the latter without deliberate effort.  There’s some strong evidence for this belief, from various cognitive and social psychology studies such as those cited by Daniel Kahneman in his book “Thinking, Fast and Slow” (2011) and Duncan Watts in his “Everything Is Obvious—Once You Know the Answer” (2011).  Our brains work very efficiently, but the ways they work tend to lead us toward lazy thought patterns unless we work very hard to counter these tendencies.  And often, our upbringing and education just reinforces those tendencies rather than showing us a better way.

But despite all evidence to the contrary, despite long experience, I believe there’s merit in confronting this laziness, in shining light on stupidity and revealing it for what it is, and trying to guide those willing to listen back to the path of intelligent thought and nuanced reason.   In trying to show them a better way, a way that has, slowly and in fits and starts over the millennia, lifted mankind from the muck and filth of subsistence and grinding poverty to the heights of civilization and prosperity.  Because I have not yet lost hope for humanity, and much like a religious missionary preaching faith to the resistant heathens, even small and occasional victories make the struggle worthwhile.

This blog will tackle this challenge in multiple ways.  It will examine complicated and complex issues and try to reveal the nuanced realities underlying the oversimplifications.  It will look at and try to understand new and interesting ideas.  It will review books and articles and studies and try to place them in context.  It will challenge prevalent modes of thought and maybe even wax philosophic on occasion.  But most of all, it will strive to be a beacon in the dark, a guiding light for anyone struggling to make sense of the complex world around them, for anyone seeking refuge from the sea of popular stupidity around them.

I do not pretend to always be right.  In fact, I am routinely wrong, and do not expect that trend to change.  The difference between me (and those like me) and most people is simply this: I try to figure out when I’m wrong, and learn from it, and be less wrong in the future.  And, more importantly, when confronted with stupidity, we do not merely reject it out of hand, but seek to examine it, to learn from it, and to use it to strengthen our own understanding. That’s Antistupid.  So let’s tilt some windmills.