The implication here is that many of the social beliefs we hold today are very different from what people held 50 years ago, and (possibly, therefore) it’s not radical to still hold those beliefs today. The Tweet above doesn’t specify exactly what those beliefs are, but we can use survey data to dig into what those might be. Thankfully, one of the greatest social surveys out there was first conducted in 1972, exactly 50 years ago: the General Social Survey.
What exactly did a normal person believe around 1972, according to the GSS?
Some eighteen months ago, I wrote here on “Money as a Social Construct“. Most civilizations over the millennia have found it expeditious to move from simple, immediate barter of physical objects like cows to some system involving “money”. But what is money? Wikipedia gives the following standard definition:
Money is any object or record that is generally accepted as payment for goods and services and repayment of debts in a given socio-economic context or country. The main functions of money are distinguished as: a medium of exchange; a unit of account; a store of value.
For convenience, the “thing” used as money is best if it is portable and durable and of limited amount. Gold and silver have historically served these purposes. Even though these are physical objects, their actual value in usage (e.g. how much gold does it take to buy a cow) is arbitrary. Its value in usage is whatever is agreed upon by the users.
For this system of money to work, the key players all have to believe in the value of the gold coins. Thus, money is a mainly social construct, an article of mutual faith. If people lose faith in the value of some form of non-commodity money, it will in fact become valueless.
We have moved from useful commodities like cows, to gold coins and bars, to printed dollar bills redeemable in gold, and now to fiat currencies not formally tied to any physical objects. And in the twenty-first century, most “money” is not even tangible printed bills, but is in the form of digital entries in accounts “somewhere”.
Trillions of dollars’ worth of transactions take place every year, on the supposition that the dollar you deposit in a major bank will be there next week or next year. At my own personal level, nearly all of my life savings exists in the form of investments in stocks or bonds of corporate entities, which are held in accounts that I only ever access from my computer. Thus, I rely on on-going functional, reasonably honest government to enforce rules on the stewardship of those funds at multiple levels. So I am betting everything on the supposition that law and order prevail.
Well, in war sometimes “law and order” do break down and the normal rules of stewardship are over-ridden. Such has been the case with Russian foreign reserves. The central banks of major nations hold assets in the form of accounts at other central banks. Russia, as a big net exporter, has accumulated reserves of dollars and other currencies at the central banks of various nations in the West. In the wake of Russia’s invasion of Ukraine, the Western banks froze some 630 million of Russian assets held in these banks. There has even been discussion of redeploying these assets to pay for assistance to Ukraine.
(Sadly, as I noted in How Overzealous Green Policies Force Europe to Bankroll Putin’s Military, these seemingly dramatic fund seizures and SWIFT sanctions are annoying but not crippling for Russia. Europe is still funneling billions of euros a month to Russia, because Europe has made itself utterly dependent on Russian natural gas due to prematurely chopping its own nuclear and coal power generation and banning the fracking process that has unlocked such enormous oil and gas production in the U.S.)
It is understandable why the West has taken such a step, in view of the unjustified Russian attack on Ukraine, and the ongoing atrocities such as the bombing of a maternity hospital and a clearly-marked children’s shelter. However, this action may lead to worldwide reappraisals of what is money and how net export nations choose to store their monetary surpluses.
The Wall Street Journal ran a piece called, “If Russian Currency Reserves Aren’t Really Money, the World Is in For a Shock.” It is suggested that central banks may be motivated to accumulate more of their reserves in the form of physical gold, held in their own countries, which cannot be confiscated by some outside forces. Or we may even go back to using “cows” as a store of value, with central banks gaining title to piles of useful commodities such as wheat or nickel or palladium.
Good hockey players skate to where the puck is heading. I bought into a fund of corn futures yesterday. After posting this article, I think I will log into my brokerage account and buy some shares in a fund holding physical gold.
It’s spring break and that means catching up on both research and my social network. It also means college basketball. I remain firmly in the camp that college athletes should be paid for their incredibly high-value labor and, in turn, recapture a huge share of the surplus currently enjoyed by schools and coaches. What I am beginning to rethink, however, is the way that “professionalization” can and will play out.
This rethinking began with the the realization that my enjoyment of the product is largely insensitive to the presence of great players. The gap between NBA and NCAA basketball, in terms of quality of play, is so great that I simply don’t watch the sports in the same way. I consume the NBA the way I do Denis Villeneuve films: enjoying an artform in its closest approximation to perfection at the bleeding edge of innovation. NCAA basketball, in contrast, is a soap opera for genre aficionados. It’s Battlestar Galactica for sports fans.
There is a floating, ever-changing cast of characters supporting a handful of recurring leads. Clans and sub-clans. Rises and falls. Tragic failures and heroic redemption arcs. And, much like the latest show about wizards or post-apocaplyptic alien invasion survivors on the SciFy channel, the enjoyment of this product doesn’t require high level precision or execution. Quite frankly, the show is more enjoyable when the actors aren’t famous or especially elite; it keeps me squarely focused on the shlocky fun, rather than getting distracted by any urge to pick apart the film composition, story logic, or actor subtext. College basketball, in much the same way, keeps me squarely focused on the drama of gifted athletes doing their best to help their team achieve success in a limited window before moving on to the rest of their lives. Trying to get a little slice of glory now, while their knees will allow for greatness, before getting on with the endless particulars of adult life later.
Which brings me back to the eventual professionalization of college sports with athlete compensation. Schools will find themselves faced with a decision of whether they should spend money on the very best athletes or try to compete with less expensive players. Athletes will have to decide where the best opportunities to develop their professional game are, and how much of their human capital investment portfolio they want to dedicate to sports. What might the equilibrium look like?
We can coarsely reduce the pool of athlete’s into three categories: all-in on athletics, those looking to purely subsidize secondary education, and those aiming for a mix of both. Currently schools capture the most rents from the pure athletics all-ins, who dedicate nothing but the bare minimum to schooling while maximizing their athletic preparation. The all-ins will often be the best players, who get the most media attention and contribute the most to winning glory, attracting applications from young fans and donations from nostalgic alumni. You might expect that compensation would shift the most suprlus to them. We have to consider, however, the possibility that a proper market for elite college athletic labor would provide the prices needed to accelerate the formation of pre-professional academies and player futures contracts. The very best 18-year old basketball players may find it far more lucrative to take a $120K in income and full-time coaching today in exchange for 2% of future professional earnings.
At the same time, college basketball may similarly learn the true nature of their collective good: that it is, in fact, a zero-sum competition where the total amount of talent isn’t nearly as important for earnings as they think. While a small number of schools absorbing all of the top talent might be exciting for covers of no longer existent sports magazines, in reality 120 teams competing for a less skewed distribution of talent more predominantly interested in subsidizing the full cost of college (i.e. tuition, lost wages, etc) may actually make for more drama, which means more ratings, which means more money. Why try to compete with the academies for 1 year of the next Lebron when those same resources, will get you 5 good players for 4 years? Combined with the fact that this bundle of athletes will place greater value on (nearly) marginally costless scholarships, teams looking to compete in the long-term with a maximimally effcient allocation of resources could shift the competitive equiibrium could actually shift away from the top talent.
Sports are fun when they are played at the highest level. They are also fun, however, when a little chaos is injected into the drama. It’s great when Steph Curry casually hits shots 40 feet from the basket, when Lebron James or Nikola Jokic make Matrix-esque passes through impossible angles. But it’s also great watching players struggle at the edge of far more human limitations to a find to win on the biggest stage of their lives while wearing the jersey of one of hundreds of colleges. The highest drama includes players making shots, but sometimes it needs players to dribble off their foot, too.
We don’t have to limit earnings to capture that glory. We don’t have to take money from young people whose particular talents put them in the sliver of the human population whose greatest earning potential might be age 20. We don’t need to appeal to platitudes or false nostalgia to explain why they’re being compensated with something better than money. We can just pay them. Some things will change, but I think you’ll be shocked to see how little the experience of college basketball will change. College sports will remain largely the same, but it will be a bit less shady, a bit less hypocritical. It will place greater value on, and care for, the players they have directly invested in.
Which, at least to me, would be a little more fun.
There is a new paper in Journal of Economic Perspectives. Its author, Dan Sichel, studies the price of nails since 1695 (image below). Most of you have already tuned off your attention by now. Please don’t do that: the price of nails is full of lessons about economic growth.
Indeed, Sichel is clear in the title in the subtitle about why we should care — nail prices offer “a window into economic change”. Why? Because we can use them to track the evolution of productivity over centuries.
Take a profit-maximizing firm and set up a constrained optimization problem like the one below. For simplicity, assume that there is only one input, labor. Assume also that a firm is in a relatively competitive market so as to remove the firm’s ability to affect prices so that, when you try to do your solutions, all the quantity-related variables will be subsumed into a n term that represent’s the firm share of the market which inches close to zero.
If you take your first order conditions and solve for A (the technological scalar). You will find this this identity
What does this mean? Ignore the n and consider only w and p. If wages go up, marginal costs also increase. From a profit-maximizing firm’s standpoint trying to produce a given quantity, if prices (i.e. marginal revenue) remained the same, there must have been an increase in total factor productivity (A). Express in log-form, this means that changes in total factor productivity are equal to αW – αP. This means that, if you have estimates of output and input prices, you can estimate total factor productivity with minimal data. This is what Sichel essentially does (and Douglas North did the same in 1968 when estimating shipping productivity). All that Sichel needs to do is rearrange the identity above to explain price changes. This is how he gets the table below.
The table above showcases the strength of Sichel’s application of a relatively simple tool. Consider for example the period from 1791 to 1820. Real nail prices declined about 0.4 percent a year even though the cost of all inputs increased noticeably. This means that total factor productivity played a powerful role in pushing prices down (he estimates that advances in multifactor productivity pulled down nail prices by an average of 1.5 percentage points per year). This is massive and suggestive of great efficiency gains in America’s nail industry! In fact, this efficiency increases continued and accelerated to 1860 (reinforcing the thesis of economic historians like Lindert and Williamson in Unequal Gainsthat American had caught up to Britain by the Civil War).
I know you probably think that the price of nails is boring, but this is a great paper to teach how profit-maximizing (and constrained optimization) logic can be used to deal with problems of data paucity to speak to important economic changes in the past.
Last week I went to Disney World for the first time. The decorations live up to the hype. The whole enterprise down to the efficient parking systems was impressive.
Galaxy’s Edge is a new Star Wars themed area in Hollywood Studios
In his book The Decadent Society, Ross Douthat argues that following the Apollo mission, Americans underwent a period of economic stagnation, demographic decline, and intellectual and cultural repetition. I think he makes good points, and every American should grapple with his proposition.
He specifically mentions Disneyland on page 36-37:
But has anything that fits this description happened since the moon mission? … There has been a growth in what [David] Nye calls “the consumer’s sublime” of Disneyland and Las Vegas. … But the hyperloop is a blueprint, Las Vegas is a simulacrum…
Has Douthat been to Orlando recently? Walt Disney was not complacent, and neither are the Disney employees who continue to carry out his vision. Orlando is a place where Americans have built stuff in the past few decades instead of trying to veto all progress.
Perhaps it is a decadent society that overvalues the Disney World pilgrimage. My parents never took me, so I am proof that you can have a good childhood without it. However, to build this zone and enjoy it seems like a perfectly legitimate peacetime activity for a country. People desire to stroll down a safe, beautiful, clean, walkable street with their families. The problem is that so many Americans can only do that for a few days per decade and empty their savings to Disney for the privilege.
There is a pernicious idea that respectable Americans live in towns that look just like 1950 and they do tourism at sites that look like 1850. Walt Disney obviously did not think that way. On Twitter, @EliDourado and @mnolangray are agitating every day to build more better stuff. We don’t need Donald Duck on every corner, but we could create cities that serve families better.
One surprise I found inside of the Tomorrowland zone of Magic Kingdom is an old ride called The Carousel of Progress.
The financial crisis recession that started in late 2007 was very different from the 2020 pandemic recession. Even now, 15 years later, we don’t all agree on the causes of the 2007 recession. Maybe it was due to the housing crisis, maybe due to the policy of allowing NGDP to fall, or maybe due to financial contagion. I watched Vernon Smith give a lecture in 2012 in which he explained that it was a housing crisis. Scott Sumner believes that a housing sectoral decline would have occurred, and that the economy-wide deep recession and subsequent slow recovery was caused by poor monetary policy.
Everyone agrees, however, that the 2007 recession was fundamentally different from the 2020 recession. The latter, many believe, reflected a supply shock or a technology shock. Performing social activities, including work, in close proximity to others became much less safe. As a result, we traded off productivity for safety.
The policy responses to each of the two were also different. In 2020, monetary policy was far more targeted in its interventions and the fiscal stimulus was much bigger. I’ll save the policy response differences for another post. In this post, I want to display a few graphs that broadly reflect the speed and magnitude of the recoveries. Because the recessions had different causes, I use broad measures that are applicable to both.
Silicon Valley venture-backed tech startups have had a wildly successful twenty years, coming to dominate the markets. But tech remains a relatively small sector in terms of the total number of businesses and employees, and by many measures entrepreneurship and small business have been in relative decline in the US during the 2000’s.
Covid accelerated many pre-existing trends, like the shift to remote work. But it reversed other trends, and seems to have led to a revival in entrepreneurship broadly.
This wasn’t all good at first- in 2020, the share of “necessity entrepreneurs” also reached record highs. These are people who start a business because they can’t get the job they want, not because they expect their business to be wildly successful. But in 2021, the rate of new entrepreneurs remained high while the share of “necessity entrepreneurs” and “opportunity entrepreneurs” returned to their normal balance.
Another good sign is that the share of businesses surviving at least a year is also at record levels:
This semester I am participating in a reading group with undergraduate students that focuses on the history and prospects for capitalism and socialism. Lately we have been reading Joseph Stiglitz, who has long argued that China’s transition to a market economy has gone much better than the former Soviet Union. Gradual transition is superior to “shock therapy,” according to Stiglitz.
There’s an extent to which this is true. If we just look at economic growth rates since, say, 1995, China has clearly outpaced Russia.
It’s hard to know exactly what year to start, since GDP figures for former planned economies immediately after transition aren’t reliable, but the start date is mostly irrelevant for everything I’ll say here (please play around with the start year in the charts to see if I’m cherry-picking years). 1995 seems a reasonable enough year to start for reliable post-transition starting point.
As we see above, while Russia has had a rough doubling of GDP per capita since 1995 (respectable, and yes, it’s all adjusted for inflation!), China has soared almost 600%. Wow! But this is something of a cheat. Despite all that growth, average income in China is still lower than Russia: only about 60% of Russia in 2020. China started from a much lower level, meaning that faster growth, while not guaranteed, is at least easier to achieve. In fact, if we go back to 1978, when China’s first reforms began, GDP per capita in the Former USSR was about 6 times as high as China (that’s according to the latest Maddison Project estimates, which will always be speculative for non-market economies, but are the best we have).
Furthermore, Russia hasn’t really transitioned to a democracy either. China clearly hasn’t, but no one doubts that. But despite having the outward symbols of democracy (elections, a legislature, etc.), Russia still scores low on most indexes of democracy and civil liberties. For example, Freedom House scores them at 19/100, a little better than China (9/100), but nothing like Western Europe.
So, did the quick transition to market economies fail? Not so fast. While it did fail in Russia, in most of Eastern Europe and the eastern part of the former USSR seems to have been a major success. Take a look at this chart, which shows the former Soviet Republics in and near Europe (I exclude Central Asian FSRs).
The saying that “The first casualty of war is the truth” has been credited to anti-war Senator Hiram Warren Johnson in 1918 and also to the ancient Greek dramatist Aeschylus. We have seen this played out dramatically with Russia’s invasion of Ukraine. From the Ukrainian side have come the predictable overinflated estimates of the enemy’s losses, and perhaps understated reporting of their own casualties. Also, on the first day or two of the war there was a raunchy defiant response of Ukrainian defenders to a “Russian ship” that was demanding their surrender; as far as I know that exchange was for real, but the initial report by Ukraine that all the heroic defenders were killed was not true. Maybe I am biased here, but these sorts of excesses are stretching some core truth, not trampling over it roughshod.
On the Russian side, perhaps because there is no even vaguely legitimate justification for their invasion, the lies have been simply ludicrous. Apparently, the Russian troops have been told that they are going there to rescue Ukrainians from the current regime which is a bunch of “neo-Nazis”. If Putin’s thugs had a sense of humor or perspective, they might have discerned the irony of characterizing the Ukrainian regime as “neo-Nazi” when the president (Zelenskyy) is a Jew, whose grandfather’s brothers died in Nazi concentration camps.
And the Russian lies go beyond ludicrous, to revolting and inhuman. Russian Foreign Minister Sergey Lavrov has dismissed concerns about civilian casualties as “pathetic shrieks” from Russia’s enemies, and denied Ukraine had even been invaded.
The Associated Press snapped a picture in the besieged city of Mariupol a few days ago which went viral, showing a pregnant woman with a bleeding abdomen being carried out on a stretcher from a maternity hospital which the Russians had bombed. The local surgeon tried to save her and her baby, but neither one survived. The Russian side put out a string of bizarre and contradictory stories, claiming that they had bombed the hospital because it was a militia base (a neo-Nazi militia, of course) but also that no, they didn’t bomb it, the hospital had been evacuated and the explosions were staged by the Ukrainians, and the bloody woman in the photos was a made-up model. Ugh. I find it chilling to observe a regime in operation where there is absolutely no respect for what the truth actually is; rather, lies are manufactured to serve whatever purpose will suit the regime.
I know that some of that goes on even with Western democracies, but we are still usually ashamed of outright lying, and stand discredited when exposed. But with hardcore authoritarian regimes, there does not seem to be even this minimal respect for integrity.
Freedom of speech becomes even more critical as cynicism about truth becomes more widespread in the world, even in our own political discourse. Putin is trying to suppress the truth within Russia, now with very harsh penalties (fifteen years in prison) for those disseminating information contrary to the party line. All he needs to do is deem such talk as “treasonous”, and into the clink you go.
I do worry about similar trends towards censorship within the West. In our case, it is not so much governments (so far) doing the censorship, but Big Tech. If Google [search engine and YouTube] / Facebook/Twitter disapprove of your content, they can label it “hate speech” or whatever, and your voice disappears from public discourse. But what gives the high priests of big tech the authority and the powers of moral discernment to rule on what discourse is permissible? Also, the algorithms of social media sites usually direct you towards other sites that reinforce your own point of view, so you rarely get exposed to why the other side believes what it does. However annoying it may be to see various forms of nonsense circulating on-line, the time-tested democratic response is to allow (nearly) all points of view to be fairly stated, and to trust in the people to figure out where the truth lies. Otherwise, the truth can become a casualty of culture wars, as it is in shooting wars.
I had the title of this post sitting in “Drafts” for a couple months now, but Kris and Paul have given me good reason to actually write about it. These thoughts are largely off the cuff, but they do come from experience.
What is Agent-Based Modeling?
This is not actually as straight-forward question as one might think. If you define it broadly enough as, say, any model within which agents make decisions in accordance with pre-defined rules and assigned attributes, then the answer to the overarching question posed by this post becomes: well, actually, economics has been producing agent-based models for decades, but that answer is as annoying as it is useless.
Instead, let’s start with a minimal definition of an agent-based model:
They are composed of n >3 agents making independent decisions
Agents are individually realized within the model.
Decisions are made in accordance with pre-defined rules. These rules may or may not evolve over time, but the manner in which they evolve are themselves governed by pre-defined rules (e.g. learning, mutation, reproduction under selective pressures, etc).
If we stop at this minimalist definition, then the answer becomes only marginally less trivial, as essentially any dynamic programming/optimal control model within macroeconomics would meet the definition. This leads to what I consider the minimal definiton of an agent-based model as a distinct subclass of computational model:
Agents within the model are characterized by deep heterogeneity.
Agents exist within a finite environment which serves as a constraint in at least one dimension (lattice, sphere, network, etc).
Decisions are made sequentially and repeatedly over time
Now we’re getting farther into the weeds and beginning to differentiate from whole swaths of modern macroeconomics that either employ a “representative agent” or collapse agent attributes to the 1st and 2nd moments of distributions. But that doesn’t eliminate all of modern macro. If embracing heterogeneous agents in your models of macroeconomics, banking, etc, are of interest to you, there are scholars waiting to embrace you with open arms.
Which brings me to the final attribute that I believe fully distinguishes the bulk of the agent-based models and their advocates from modern economics:
Agent-based models exist as permanently dynamic creations, absent any reliance on equilibria as a final outcome, characterization, or prediction.
The departure from general or partial equilibria as outcomes or predictions is where the schism actually occurs and, I suspect, is where many purveyors found themselves with a research product they had a hard time selling to economists. Economics, perhaps more than any other social science, demands that theoretic predictions be testable and falsifiable. Agent-based models (ABMs) don’t always produce particularly tidy predictions that lend themselves to immediate validation. Which doesn’t preclude them from making a scientific contribution, but it puts them on unsteady footing for economists who are used to having a clear path from the model to the data.
OK, but really, why didn’t agent-based modeling happen?
As much as big, irreconcilable differences in scientific philosophy would make for a satisfying explanation, I suspect the most salient reasons are less sexy and, in turn, less flattering of the day-to-day realities of grinding out research in the social sciences. Here are a few.
Economics was already a “model” social science
One of the reasons mobile phones caught on faster in Africa than North America was an absence of infrastructure. The value add of going from “no phones” to “mobile phones” is far larger than going from “reliable land lines in every edifice” to “mobile phones”, making it easier to justify both investments in relevant infrastructure and bearing of personal costs. Such a thing occurred across the social sciences with regards to ABMs.
Rational choice and mathematical sociology always had a limited following. Evolutionary biologists were often alone in their mathematical modeling, computational biology barely existed, and cultural anthropologists were more excited about Marx’s “exchange spheres” than they were about formal models of any kind. For a PhD student in these fields, the first time they saw a Netlogo demonstration of an agent based model, they were seeing something never previously available to their field: the ability to formalize their own theories in a way fully exogenous to themselves. There would be no fighting about what their words actually meant, whose ideas they were mischaracterizing, what they were actually predicting. Their critics, be it journal referees or thesis committee members, would have no choice but to confront their theory as an independent entity in the world.
This advantage of formality, of independent objectivity, in agent-based modeling was not something new to economics. While critics have many (often correct) complaints about modern economics, it’s rare to air concerns that economics is insufficiently formal or mathematized.
Too many “thought leaders”, not enough science
Axtell and Epstein wrote their landmark book “Growing Artificial Societies” in 1996. In it they produced a series of toy simulation models within which simple two-good economies emerged. This wasn’t revolutionary in it’s predictions by any means (whole swaths of macro models were able to make comparable predictions for two decades prior), but the elegance through which minimalist computer code could produce recognizable markets emergent from individual agent decisions was just incredible. The potential to readers was immediately obvious: if we can produce such things from 100 lines of code, what could we simulate the fully realized power of modern programming?
What came next was…still more people evangelizing and extolling the power of ABMs to revolutionize economics. What didn’t come were new models. Forget revolutionary, its hard to even find models that were useful or at least interesting. The ratio of “ABMs are gonna be great” books and articles to actual economic models is disappointing at best, catastrophic to the field at worst.
There were a couple early models that got attention (the artificial Anastazi comes to mind), but after a few years everyone noticed that same 2-3 models were still be brought up as examples by evangelists, and none of them had meaningful economic content. As for the new models that did end up floating out there, there was also an oversupply of “big models”, with millions (billions) of agents and gargantuan amounts of code that intended to make predictions about enormous chaotic systems. Models, such as the Santa Fe Artificial Stock Market, tried to broadly replicate the dynamics of actually stock markets across a large number of dimensions. Such ambitions were greeted with skepticism by economics for a variety of reasons, not least of which the “curse of dimensionality”, which limits what you can learn about underlying mechanisms when the number of modeler choices exceeds your ability to test them or, for that matter, verify their internal coherence. For better or worse, these models felt akin to amateurs trying to predict a town’s weather 30 days out.
Bad models drove out good
The problem of too few good models was closely followed by the over-supply of bad models. Agent-based modeling, for good and for ill, is not a technique with high entry costs. A successful macroeconomic theorist is effectively a Masters-level mathematician, bachelors level computer programmer, and PhD economist. Netlogo programming can be learned in a week. You can get really good at programming agent-based models in a dedicated summer.
This isn’t unto itself a problem, but I can tell you this: in my first 5 years as an assistant professor, I was asked to review at least 100 papers built around agent-based models. I’m not sure if any of them were any good. I am sure that many of them were extremely bad. Most concerning is that I don’t think I learned anything from any of them. The costs of producing bad ABM papers is much lower than the costs of producing bad theory papers based on pure math. Bad science is often evolutionarlily selected for in modern science, a dynamic that in the case of ABMs was only amplified by a lower cost supply curve.
Now, here’s the thing: there was probably huge selection effects into what I interacted with. I doubt I was getting the best papers sent to me for review given my status in the field. But the quantity of bad papers was astonishing. They were just too easy too churn out. I suspect that some decent papers were lost in haystack of ad hoc pseudoscience and, in turn, some decent scientific careers probably got lost in the shuffle. More than once I had the thought “Editors are going to start rolling their eyes every time they see the term agent-based modeling if this what keeps coming across their desks.” Combined with the fact that ABMs are tricky to evaluate because you really need to go through the code to know what is driving the results, I think a lot good modelers got lumped in with the dreck.
[Not for nothing, it wasn’t uncommon for ABM papers to spend the bulk of the paper describing model outputs, while having nearly nothing about model inputs (i.e. rules, code, math, etc). These models were essentially black boxes that expected you to take their coherence on faith. I should note here that I haven’t really kept up with the field in the past few years. Hopefully transparency norms have improved, particularly in biological, ecological, and anthropological modeling, where ABMs have thrived to a far greater extent.]
The empirical revolution took hold of economics
I’ve save the biggest reason for last, but honestly I think it dwarfs the others.
The same rise in cheap computational power that gave rise to other forms of computational modeling, including ABMs, came along with the plummeting cost of data creation, storage, analysis, and access. By 2010 it was already increasingly clear that theory was taking a backseat in economics. Not because we were becoming an a-theoretic discipline (far from it), but because the marginal contribution of theory against the body of broadly accepted economic framings was small compared to those made by empirically testing the predictions of the existing body of theories against real data. The questions were no longer “How do we mentally organize and make sense of the world”, but instead “What is the actual measured effect of X on Y?” Theory gave way to statistical identification. Modeling technique gave way to causal inference.
Agent-based models are hard to empirically evaluate and test
Which gives way to a sort of subsidiary problem. It is more difficult for agent-based models to take advantage of the new data-rich world we live in. They don’t produce neatly direct predictions the way that microeconomic theories do, nor do they lend themselves to measured empirical validation in the same way as general equilibrium predictions of macroeconomic models. Empirical validation is by no means impossible, but it requires the matching of observed dynamics or patterns, which is generally a taller order. In this way, agent-based computational models are a bit of a throwback to the days of “high theory”, making for interesting discussion but of secondary importance when it comes to the assigning of journal real estate that makes and breaks careers.
Bonus story
I once presented my ABM paper on emergent religious divides, only to have an audience member become extremely upset, closing with the denouncement that “This isn’t agent-based modeling, this is economics!” That was my first exposure to the theme of ABMs as “antidote” to the hegemony of economics and all of its false prophecies. The idea that the destiny of ABMs was to unseat economics as the queen of the social sciences was probably an effective marketing strategy in many hallways, but not so much in economics departments (well, maybe at The New School).
So why should economists give agent-based modeling another shot?
Economist Writing Every Day 