Is This the End of the Largest Refugee Crisis in the Americas?

James BaileyLeave a comment

Our 2024 post on the Venezuelan election provides context for this week’s dramatic events:

Venezuela held an election this week; President Maduro says he won, while the opposition and independent observers say he lost. Disputed elections like this are fairly common across the world, but where Venezuela really stands out is not how people vote at the ballot box- it is how they vote with their feet.

Reuters notes that “A Maduro win could spur more migration from Venezuela, once the continent’s wealthiest country, which in recent years has seen a third of its population leave.”

This makes Venezuela the largest refugee crisis in the history of the Americas, and depending on how you count the partition of India, perhaps the largest refugee crisis in human history that was not triggered by an invasion or civil war.

Instead, it has been triggered by the Maduro regime choosing terrible policies that have needlessly and dramatically impoverished the country

Plus some foreshadowing:

I hope that the Venezuelan government will soon come to represent the will of its people. I’m not sure how that is likely to happen, though I guess positive change is mostly likely to come from Venezuelans themselves (perhaps with help from Colombia and Brazil); when the US tries to play a bigger role we often make things worse. But what has happened in Venezuela for the past 10 years is clearly much worse than the “normal” bad economic policies and even democratic backsliding that we see elsewhere. 

Here’s an update on the chart I shared then, showing that the diaspora has continued to swell:

I hope that Venezuela will soon become the sort of country people don’t want to flee. I don’t necessarily expect that it will, but it’s not now a crazy hope:

Liberal Democratic Institutions Generally Improve After US Military Intervention (Post-Cold War)

Jeremy Horpedahl3 Comments

With the arrest of Venezuelan President Maduro, the US is potentially attempting to remake the institutions of yet another country. I say potentially because, as of now, all that has happened is that Maduro was removed. His VP stepped in to replace him, and it appears that, for now, the rest of the structure of government is in place.

Nonetheless, any time the US intervenes in the affairs of another country, it brings back the old debates about regime change, nation building, exporting democracy, etc. Many want to discuss the legal and moral implications of these actions — and these are certainly worth discussing! — but as social scientists we should also ask “does it work?”

For example, one excellent paper on regime change via CIA covert intervention is from Absher, Grier, and Grier. They look at five cases during the Cold War in Latin America of CIA-sponsored regime change, and find moderate declines in income and large declines in democratic institutions. Not a good case for regime change and exporting democracy!

But what if we look at more recent interventions — post-Cold War — and look at direct military interventions by the US, rather than covert CIA operations or indirect funding of factions within a country. This is more in line with what might be happening in Venezuela right now (if regime change is ultimately what the US military pushes for). Using a list from Chris Coyne’s book After War (table 1.1) as a starting point for the relevant cases, and then using data from the V-Dem Liberal Democracy Index, we have seven cases since 1990 to examine (note: I have added Libya to Coyne’s list, which I believe is the only new addition of explicit military intervention since he created the list):

The first thing you might notice is that relevant to their starting position (pre-US military intervention), all except one of these countries saw improvements in their V-Dem Liberal Democracy Score after 25 years (or whatever the end point is for those more recent than 25 years). Some of the improvements — such as Libya, Somalia, and Iraq — are quite small, around 0.1 points on the 1-point scale. But other improvements — especially Kosovo and Bosnia — are quite large, around 0.3 points on the 1-point scale.

The one decline is Afghanistan, though you will note that during the occupation (which lasted a very, very long time, until 2021) their liberal democracy score did improve slightly, about as much as Iraq. I should also note that if we didn’t use my 25-year cut-off, Haiti would also have slipped back to roughly where they were in 1993, with a large decline happening since 2020.

For reference on this scale: the US scores 0.75 in 2024, the best scoring country is Denmark with 0.88, the World average is 0.37 (or 0.29 weighted by population), and the European average is 0.62 (or 0.56 weighted by population).

So while the improvements in Kosovo and Bosnia are impressive, they still fall below the average score in Europe. And those examples point to another problem with my simple analysis: we don’t have the counterfactual of what their score would be without US intervention. That kind of sophisticated analysis is what the above-mentioned Absher paper does (using synthetic control), but it’s more than I can do in a short blog post. Nonetheless, we should note that while we can’t say that US intervention caused these improvements, things didn’t get worse in most countries (as many critics of intervention assume always happens) — Afghanistan being the notable exception after the US ended the occupation.

Now that I’ve got the causation caveat out of the way, we should note a few more limitations of my analysis. First, perhaps the V-Dem Liberal Democracy Index isn’t the best one to use. Our World in Data has seven democratic measurement sets to choose from, and even from V-Dem there are others we could have used. I think Liberal Democracy best captures what we are usually talking about in terms of “does it work?” but you could use another measure. However, glancing through the other available measures, such as Polity, I don’t think the picture would be radically different with another measure of liberal democracy. 25 years is also somewhat arbitrary of a cut-off, though in Coyne’s book he uses 20 years, so I’m going beyond that.

Finally, I want to stress even more than on the causation point: none of these improvements mean the intervention passes a cost-benefit test. There was much destruction of lives and property in all of these cases, the use of US tax dollars, and some other harms to the US and international law (e.g., restrictions of civil liberties in the US from the War on Terror). I do not want to suggest that this means the interventions were worth the cost, merely that they did not fail on this one measure of improving democracy. It is also not a prediction that future interventions, such as in Venezuela, will succeed. Instead, I wrote this post because it goes against my priors (I would not have guessed improvements in 6/7 cases).

Is the Silver Bubble Bursting?

Scott Buchanan2 Comments

This is a five-year chart of the silver ETF SLV:

By most standards, this pattern looks like we entered a bubble a few months ago: speculative froth, unjustified by fundamentals. Economic history is replete with such madness of crowds. It is accepted wisdom on The Street that these parabolic price rises seldom end well. I lost a few pesos buying into the great gold bubble of 2011. All sorts of justifications were given at the time by the gold bugs on why gold prices ought to just keep on rising, or at least reach a “permanently high plateau” (in the famous words of Irving Fisher, just before the 1929 crash). Well, gold then proceeded to go down and down and down, losing some 60% of its value, until the price in 2015 matched the price in 2009, before the great bubble of 2010-2011.

Today, similar justifications are proffered as to why silver is going to the moon. There is a long-standing deficit in supply vs. demand; it takes ten years for a new silver mine to get productive; China has started restricting exports; Samsung announced a breakthrough lithium battery that can charge in six minutes, but requires a kilogram of silver; AI infrastructure is eating all the silver. These narratives seem to feed on each other. As the silver price moved higher in the past month, out came yet wilder stories that ricochet around the internet at high speeds: the commodities exchanges have run out of physical silver to back the paper trades; and the persistent claim that “they” (shadowy paper traders, central banks, commodity exchanges, the deep state, etc.) are “suppressing” silver and gold prices by means of shorting (which makes no sense). Given this popular shorting myth, it was with great glee that the blogosphere breathlessly spread the bogus story that some “systematically important bank” was in the process of being liquidated because it got squeezed on its silver short position.

The extreme price action at the very end of December (discussed below) was like rocket fuel for these rumors. Having bought a little SLV myself so as to not feel like a fool if the silver rally did have legs, I spent a number of hours as 2025 turned to 2026 trying to sort all this out. Here are some findings.

First, as to  the medium term supply/demand issues, I refer the reader to a recent article on Seeking Alpha by James Foord. He shows a chart showing that silver demand is increasing, but slowly:

He also notes that as silver price increases, there is motivation for more recycling and substitution, to compensate. He concludes that the current price surge is not driven by fundamentals, but by paper speculation.

The last ten days or so have been a wild ride, which merits some explanation. Here is the last 30 days of SLV price action:

Silver prices were rising rapidly throughout the month, but then really popped during Christmas week, reaching a crescendo on Friday, Dec 26 (blue arrow), amid rumors of physical shortages on the Shanghai exchange. To cool the speculative mania, the COMEX abruptly raised the margin requirements on silver contracts by some 30%,  from $25,000 to $32,500, effective Monday, Dec 29. I think the exchange was trying to ensure that speculators could make good on their commitment, and the raise in margin requirement would help do that. (Note, the exchange is liable if some market participant fails to deliver as promised and goes BK).

Anyway, this move forced long speculators to either post more collatoral or to liquidate their positions, on short notice. Blam, the price of silver dropped a near record amount in one day (red arrow). For me, a little minnow caught in the middle of all this shark tank action, the key part is what came after this forced decline. Was the bubble punctured for good? Should I hold or fold?

As shown above, the price has traded in a range for the past week, with violent daily moves. Zooming out to the a one-year view, it looks like the upward momentum has been halted for the moment, but it is unclear to me whether the bubble will deflate or continue for a while:

I sold about a quarter of my (small) SLV holding, hoping to buy back cheaper sometime in the coming year. Time will tell if that was a good move.

Usual disclaimer: Nothing here is advice to buy or sell any security.

P.S. Tuesday, Jan 6, 2025, after market close: I wrote this last night (Monday, Jan. 5) when silver was still rangebound. SLV was about $69, and spot silver about $76/oz. But silver ripped higher overnight, and kept going during the day, up nearly 7% at the close to new all time high. It looks like the bubble is alive and well, for now. Congrats to silver longs…

Where do we find papers to read?

mdmakowsky5 Comments

I was going to write a long post this week but time got short, so I went looking for new papers to skim through, put a few in my reading list, and then share one here. But Bluesky is bereft of new papers and Twitter isn’t even 3% of what it used to be. NBER working papers? Of course, but I’d desperately love to not have to resort to sharing the same working paper series that everyone else depends on and I don’t get to be a part of. Which is petty, yes, but it would nonetheless be great to tap other veins. I haven’t really figured out how to properly channel the SSRN digests that can feel at times like an entirely uncurate deluge. At the moment too much of my research diet is based in my personal network.

Are there accounts on bluesky I should be following? Or a particularly good SSRN digest? Or a substack I should be subscribing to? Or a Cuban coffee shop where cool social scientists hang out and share dope new papers?

Hit me up.

Summary of You Wouldn’t Steal a Car

Joy Buchanan1 Comment

I have a new working paper with Bart Wilson titled: “You Wouldn’t Steal a Car: Moral Intuition for Intellectual Property” 

This quote from the introduction explains the title:

… in the early 2000s… the Motion Picture Association of America (MPAA) released an anti-piracy trailer shown before films that argued: (1) “You wouldn’t steal a car,” (2) pirating movies constitutes “stealing,” and (3) piracy is a crime. The very need for this campaign, and the ridicule it attracted, signals persistent disagreement over whether digital copying constitutes a moral violation.

The main idea:

In contemporary economies, “idea goods” comprise a substantial share of value. Our paper examines how norms evolve when individuals evaluate harm after the taking of nonrivalous resources such as digital files.

We report experimental evidence on moral evaluations of unauthorized appropriation, contingent on whether the good is rivalrous or nonrivalrous. In a virtual environment, participants produce and exchange two types of resources: nonrivalrous “discs,” replicable at zero marginal cost, and rivalrous “seeds,” which entail positive marginal cost and cannot be simultaneously possessed or consumed by multiple individuals. Certain treatments allow unauthorized taking, which permits observation of whether participants classify such actions as moral transgressions.

Participants consistently label the taking of rivalrous goods as “stealing,” whereas they do not apply the same term to the taking of nonrivalrous goods.

To test the moral intuition for taking ideas, we create an environment where people can take from each other and we study their freeform chat. The people in the game each control a round avatar in a virtual environment, as you can see in this screenshot below.

In the experiment, “seeds” represent a rivalrous resource, meaning they operate like most physical goods. If the playerin the picture (Almond) takes a seed from the Blue player, then Blue will be deprived of the seed, functionally the equivalent of one’s car being stolen.

Thus, it is natural for the players to call the taking of seeds “stealing,” Our research question is whether similar claims will emerge after the taking of non-rivalrous goods that we call “discs.”

The following quote from our paper indicates that the subjects do not label or conceptualize the taking of digital goods (discs) as “stealing.”

Participants discuss discs often enough to reveal how they conceptualize the resource. In many instances, they articulate the positive-sum logic of zero-marginal-cost copying. For example, … farmer Almond reasons, “ok so disks cant be stolen so everyone take copies,” explicitly rejecting the application of “stolen” to discs.

Participants never instruct one another to stop taking disc copies, yet they frequently urge others to stop taking seeds. The objection targets the taking away of rivalrous goods, not the act of copying per se. As farmer Almond explains in noSeedPR2, “cuz if u give a disc u still keep it,” emphasizing that artists can replicate discs at zero marginal cost.

We encourage you to read the manuscript if you are interested in the details of how we set up the environment. The conclusion is that it is not intuitive for people to view piracy as a crime.

This has implications for how the modern information economy will be structured. Consider “the subscription economy.” Increasingly, consumers pay recurring fees for ongoing access to products/services (like Netflix, Adobe software) instead of one-time purchases. Gen Z has been complaining on TikTok that they feel trapped with so many recurring payments and lack a sense of ownership.

In a recent interview on a talk show called The Stream, I speculated that part of the reason companies are moving to the subscription model is that they do not trust consumers with “ownership” of digital goods. People will share copies of songs and software, if given the opportunity, to the point where creators cannot monetize their work by selling the full rights to digital goods anymore.

A feature of our experimental design is that creators of discs get credit as the author of their creation even when it is being passed around without their explicit permission. Future work could explore what would happen if that were altered.

Related Reading

“An Experiment on Protecting Intellectual Property” (2014) with Bart Wilson. Experimental Economics, 17:4, 691-716.

“You Wouldn’t Steal a Car: Moral Intuition for Intellectual Property,” with Bart Wilson (SSRN)

Joy on The Subscription Economy (EWED)

The Anthropic Settlement: A $1.5 Billion Precedent for AI and Copyright (EconLog)

Tariffs Are Not Smart Industrial Policy

Zachary Bartsch2 Comments

Economists overwhelmingly see tariffs as clearly welfare-reducing. Tariffs on imports result in higher prices, fewer imports, less consumption, and more domestic production. In fact, it is the higher prices that solicit and make profitable the greater domestic production. We don’t get the greater domestic output at the pre-tariff price. We can show graphically that domestic welfare is harmed with either export or import tariffs. The basic economics are very clear.

However, the standard model of international trade makes a huge assumption: Peace. That is, the model assumes that there are secure property rights and no threats of violence. All transactions are consensual. This is where the political scientists, who often don’t understand the model in the first place, say ‘Ah ha!. Silly economists…’ They proceed to argue for tariffs on the grounds of national security and the need for emergency manufacturing capacity. But is an intellectual mistake.  

Just as economists have a good idea for how to increase welfare with exchange, we also have good ideas about how to achieve greater or fewer quantities transacted in particular markets. This is not a case of economists knowing the ideal answer that happens to be politically impossible.  Rather, if it pleases politicians, economists can provide a whole menu of methods to increase US manufacturing, vaccine manufacturing, weapons manufacturing… Heck, we can identify multiple ways to achieve more of just about any good or service. Let the politicians choose from the menu of alternatives.

The problem with tariffs is that they reduce consumer welfare a lot, given some amount of increased production in the protected industry. Importantly, this assumes that the tariffs aren’t hitting inputs to those industries and are only being applied to direct foreign competitors. The below argument is even stronger against imperfectly applied tariffs, like the US tariffs of 2025.

What’s the alternative?

The alternative is a more focused tack. If the government wants more missile or ship production, then what should it do? There’s plenty, but here’s a short list of more effective and less harmful alternatives to tariffs:

Continue reading →

How Good Were 2025 Forecasts?

James BaileyLeave a comment

Last January I shared a roundup of forecasts for the year from markets and professional economists. Were they any good? Here was their prediction for the US economy:

WSJ’s survey of economists reports that inflation expectations for 2025 were around 2% before the election, but are closer to 3% now. Their economists expect GDP growth slowing to 2%, unemployment ticking up slightly but staying in the low 4% range, with no recession. The basic message that 2025 will be a typical year for the US macroeconomy, but with inflation being slightly elevated, perhaps due to tariffs.

The verdicts (based on current data, which isn’t yet final for all of 2025):

Inflation: Nailed it exactly (2.7%)

GDP: We’re still waiting on Q4, but 2025 as a whole is on track to be a bit above the 2.0% forecast.

Unemployment: 4.6% as of November 2025, a bit above the 4.3% forecast

Recession: Didn’t happen, making the 22% chance forecast look fine

So the professional forecasters were probably a bit low on GDP and unemployment, but overall I’d say they had a good year. What about prediction markets?

For those who hope for DOGE to eliminate trillions in waste, or those who fear brutal austerity, the message from markets is that the huge deficits will continue, with the federal debt likely climbing to over $38 trillion by the end of the year. This is one reason markets see a 40% chance that the US credit rating gets downgraded this year.

While the US has only a 22% chance of a recession, China is currently at 48%, Britain at 80%, and Germany at 91%. The Fed probably cuts rates twice to around 4.0%.

Deficits: Nailed it, the federal debt is currently around $38.4 trillion.

US Credit Downgrade: It’s hard to score a prediction of a 40% chance of a binary event happening, but in any case Moodys downgraded the US’ credit rating in May, so that all three major agencies now rate it as not perfect.

The Fed: Cut rates a bit more than expected.

Foreign Recessions: China and Britain avoided recessions. Germany had a recession by the technical definition of Kalshi’s market, but not really in practice (FRED shows -0.2% Real GDP growth in Q2 followed by 0.00000% growth in Q3). Britain avoiding recession when markets showed an 80% chance was the biggest miss among the forecasts I highlighted.

Overall though, I’d say forecasters did fairly well in predicting how 2025 turned out, in spite of curveballs like the April tariff shock.

If you think the forecasters are no good and you can do better, you have more options than ever. Prediction markets are getting more questions and more liquidity if you’re up for putting your money where your mouth is; if you don’t want to put your own money at risk, there are forecasting contests with prizes for predicting 2026.

2025 in Data

Jeremy Horpedahl1 Comment

By almost any measure, 2025 was a great year for the United States.

Despite inflation remaining elevated and the damage from new tariffs, the economy did well. Inflation-adjusted median earnings are higher than a year ago, though only by about 1.3%. While most prices are still rising, one bright spot for affordability is that home prices are falling in much of the country (according to Zillow estimates).

The S&P 500 total return is over 18% in 2025. GDP has grown at an annualized rate of about 2.5% for the first three quarters of 2025, and will probably be around 3% in the 4th quarter — not a blockbuster rate of growth, but continuing improvement for our already record high GDP of 2024.

The unemployment rate did tick up slightly, from 4.2% last November to 4.6% currently. This is definitely an indicator to watch over the next few months, but it is still well below average.

But even outside of the economy, there is plenty of good news in the data. Crime rates are plummeting. The murder rate fell something like 20%, as well as every major category of crime (violent crime overall is down 10%). This are some of the largest one-year drops in crime the US has ever seen.

Homicides aren’t the only category of deaths that are falling in 2025. For most categories of death as tracked by the CDC, there is a long lag (6 months or more) before all of the deaths are categorized. So we can’t look at complete 2025 data yet. For example, drug overdoses have increased massively in recent years, especially during the pandemic. But after plateauing in 2021-23, drug ODs started falling in 2024 and have continued to fall in early 2025. For the 12 months ending in April 2025, drug OD deaths were 26% lower than the prior 12 months. If we look at just the first 5 months of the year, 2024 was 20% lower than 2023, and 2025 was another 20% lower than 2024. For the first five months of 2025, ODs are basically back down to the same level as 2018 and 2019. Motor vehicle deaths also increased during the pandemic, but they are down 8% in the first half of 2025, essentially back down to 2018-19 levels.

Was it all good news? No, you can certainly find some data to be pessimistic about. For example, despite the efforts of DOGE and other attempts to cut federal government spending, over $2 trillion was added to the national debt in 2025, up 6 percent from the end of 2024 and surpassing $38 trillion. And as I mentioned above with the unemployment rate, there is some evidence the labor market may be weakening.

Not all is rosy as we head into 2026, but 2025 was a year filled with many positive trends on the economic front and in society more generally. May your new year be prosperous and healthy!

Review of MUGFA (Aerogarden type) Countertop Hydroponic Units

Scott Buchanan1 Comment

Last year about this time, as the outside world got darker and colder, and the greenery in my outdoor planters shriveled to brown – – I resolved to fight back against seasonal affect disorder, by growing some lettuce and herbs indoors under a sun lamp.

After doing some reading and thinking, I settled on getting a countertop hydroponics unit, instead of rigging a lamp over pots filled with dirt indoors. With a compact hydroponics unit there is no dirt, no bugs, it has built-in well-designed sun lamp on a timer, and is more or less self-watering.

These systems have a water tank that you fill with water and some soluble nutrients. There is a pump in the tank that circulates the water. There is a deck over the tank with typically 8 to 12 holes that are around 1 inch diameter. Into each hole you put a conical plug or sponge made of compressed peat moss, supported by a plastic basket. On the top of each sponge is a little hole, into which you place the seeds you want to grow.

A support basket with a dry (unwetted, unswollen) peat moss grow sponge/plug in it.

As long as you keep the unit plugged in, so the lights go on when they should, and you keep the nutrients solution topped up, you have a tidy automatic garden on a table or countertop or shelf.

The premier countertop hydroponics brand, which has defined this genre over the past twenty years, is Aerogarden. This brand is expensive. Historically its larger models were $200-$300, though with competition its larger models are now just under $200.  Aerogarden tries to justify the high cost by sleek styling and customizable automation of the lighting cycles, linked into your cell phone.

I decided to go with a cheaper brand, for two reasons. First, why spend $200 when I could get similar function for $50 (especially if I wasn’t sure I would like hydroponics)? Second, I don’t want the bother and possible malfunction associated with having to link an app on my cell phone to the growing device and program it. I wanted something simple and stupid that just turns on and goes.

So I went with a MUGFA brand 18-hole hydroponics unit last winter. It is simple and robust. The LED growing lights are distributed along the underside of a wide top lamp piece. The lamp has a lot of vertical travel (14“), so you could accommodate relatively tall plants. The lights have a simple cycle of 16 hours on, 8 hours off. You can reset by turning the power off and on again; I do this once, early on some morning, so from then on the lights are on during the day and the evening, and off at night.  The water pump pumps the nutrient solution through channels on the underside of the deck, so each grow sponge has a little dribble of solution dribbling onto it when the pump cycle is on. I snagged a second MUGFA unit, a 12 hole model, when it was on sale last spring. The MUGFA units come complete with grow sponges/plugs, support baskets/baskets for the sponges, nutrients (that you add to the water), clear plastic domes you put over the deck holes while the seeds are germinating, and little support sticks for taller plants. You have to buy seeds separately.

Images above from Amazon , for 12-hole model

I have made a couple small modifications to my MUGFA units. The pump is not really sized for reaching 18 holes, and with plants of any size you’re likely not going be stuffing 18 plants on that grow deck. Also, the power of the lamp for the 18-hole unit (24 W) is the same as the 12-hole unit; the LEDs are just spread over a wider lamp area. That 24W is OK for greens that don’t need so much light, but may only be enough to grow a few (mini) tomato plants. For all these reasons, I don’t use the four corner holes on the 18-hole unit. Those corner holes get the least light and the least water flow. To increase the water flow to the other 14 holes, I plugged up the outlets of the channels on the underside of the deck leading to those four holes. I cut little pieces of rubber sheeting, and stuffed them in channel outlets for those holes.

The 12-hole unit has a slightly more pleasing compact form factor, but it has a minor design defect [1]. The flow out of the outlet of each of the 12 channels under the deck is regular, but not very strong. Consequently, the water that comes out of each outlet drops almost straight down and splashes directly into the water tank, without contacting the grow sponge at that hole. The waterfall noise was annoying. The fix was easy, but a little tedious to implement. I cut little pieces of black strong duct tape and stuck them under the outlet of each hole, to make the water travel another quarter inch further horizontally. Those little tabs got the water in contact with the grow sponge basket. The picture below shows the deck upside down, showing the water channels under the deck going to each hole. There is a white sponge basket sticking through the nearest hole, and my custom piece of black duct tape is on the end of the water channel there, touching the basket. (In order to cover the exposed sticky side of the duct tape tab that would be left exposed and touching the basket, I cut another, smaller piece of duct tape to cover that portion of the tab, sticky side to sticky side.). This sounds complicated, but it is straightforward if you ever do it. Also, many cheap knock-off hydroponics units don’t have these under-deck flow channels at all. With MUGFA you are getting nearly Aerogarden type hardware for a third the price, so it is worth a bit of duct tape to bring it up to optimal performance.

12-hole MUGFA deck, upside down with one basket;  showing my bit of black duct tape to convey water from the channer over to the basket.

Some light escapes out sideways from under the horizontal lamps on these units. As an efficiency freak, I taped little aluminum foil reflectors hanging down from the back and sides of the lamp piece, but that is not necessary.

To keep this post short, I have just talked about the hardware here. I will describe actual plant growing in my next post. But here is one picture of my kitchen garden last winter, with the plants about 2/3 of their final sizes:

The bottom line is, I’ve been quite satisfied with both of these MUGFA units, and would recommend them to others. They provided good cheer in the dark of winter, as well as good conversations with visitors and good fresh lettuce and herbs. An alternate use of these types of hydroponics units is to start seedlings for an outside garden.

ENDNOTE

[1] For the hopelessly detail-obsessed technical nerds among us – – the specific design mistake in the 12-hole model is subtle. I’ll explain a little more here.        Here is a picture of the deck for the 18-hole model upside down, with three empty baskets inserted. The network of flow channels for the water circulation is visible on the underside. When the deck is in place on the tank, water is pumped into the short whitish tube at the left of this picture, flows into the channels, then out the ends of all the channels. (Note on the corner holes here, upper and lower right, I stuck little pieces of rubber into the ends of the flow channels to block them off since I don’t use the corner holes on this model; that blocking was not really necessary, it was just an engineering optimization by a technical nerd).

 Anyway, the key point is this: the way the baskets are oriented in the 18-hole model here, a rib of the basket faces the outlet of each flow channel. The result is that as soon as the water exits the flow channel, it immediately contacts a rib of the basket and flows down the basket and wets the grow sponge/plug within the basket. All good.

The design mistake with the 12-hole model is that the baskets are oriented such that the flow channels terminate between the ribs. The water does not squirt far enough horizontally to contact the non-rib part of basket or the sponge, so the water just drips down and splashes into the tank without wetting the sponge. This is not catastrophic, since the sponges are normally wetted just by sitting in the water in the tank, but it is not optimal. All because of a 15-degree error in radial orientation of the little rib notches in the deck. Who knows, maybe Mugfa will send me a free beta test improved 12-hole model if I point this out to them.

Part II: Why agent-based modeling could happen in economics. Eventually.

mdmakowsky2 Comments

Three years ago I ruminated on why agent-based modeling never got any real traction in economics. It got a suprising amount of attention and I continue to receive emails about it to this day. I took care to explicitly punt on what the value-add of agent based models could and/or may yet be.

“ So why should economists give agent-based modeling another shot? That’s another post for another day. …

Well, today is that day, in no small part because this excellent thread led to a new batch of emails about my old post. Now, to be clear, that post was based on a solid decade of experience writing, presenting, and publishing papers built around agent-based models. This endeavor is far more speculative. I have a bit of prickly disdain for the genre of forecasting you find on “I’m not unemployed, I’m an Entrepreneur and Futurist” LinkedIn profiles, so I’ll ask you to indulge even more glibness than usual. With the cowardly caveats now out of the way, let’s get into it.

What are the advantages of agent-based models?

Deep heterogeneity, replicability, scale, flexibility, and time. There are different ways to frame it, but it all boils down to the fact that a multi-agent computational model does not require collapsing to statistical moments or limited heterogeneity (i.e. 3 or fewer types of agent) in order to “converge” or compute. It is not reliant on the single run of human history in order to postulate counterfactuals – you can run the model millions of times and observe the full distribution of outcomes. The population is not limited to the scale of the sample or the population – it can be as large as you can computationally handle. How flexibile can it be? Literally everything but the ur-text of the model can be endogenous. And time? Again, how long you run the model is limited only by computational capacity coupled with your own patience.

Do note that everything I just listed is also a disadvantage.

Agent-based modeling can be a new class of “meta-analysis”

The science of observing, distilling, interpreting, and even managing the scientific project is generally speaking the domain of statisticians and historians of thought. Interestingly, it’s been my experience that historians of economic thought were some of the biggest early enthusiasts for agent modesl (I even wrote a paper with one). I think there is an opportunity, however, to borrow from the logic of applied statistics used in the meta analysis of literatures.

Meta-analysis in economics is pre-demominantly constituted by reviews of empirical literatures that conduct statistical analysis of the coefficients estimated in regression equations across multiple papers. Comparisons across data sets, geographic and temportal settings, and statistical identification strategies allow practicioners, policy makers, and the curious public to better internalize the state of the literature and what it is actually telling us. These are valuable contributions not just because a decades work can be reduced to a paper reduced to an abstract reduced to a title that showed up in a google search conducted by an intern at the think tank recommending policy to a lawyer with good hair who won an election fourteen years ago. They are valuable because they fight against the current wherein we all are drawn to cherry-pick the empirical results that confirm our priors, particularly those that have a political valence associated with them. Meta-analyses have also shown the peculiar biases introduced by the career incentives in all social sciences – the seminal figure being the sharp cutoff in published p-values at traditional 0.05 “statistical significance” threshold.

To reiterate: these papers are useful, but they are also limited by the necessity to find like for like papers whose results can be compared. A framing must be set upon in advance within which the authors of the meta-analysis can curate the contributions to be included and collectively evaluated. Only when the analysis is completed can the authors take a step back and try to adjudicate what the collective results are and how they reflect upon any relevant bodies of theory. It is an inherently atheoretical exercise. There’s a reason schools of thought are rarely (ever?) upended by a meta-analysis that successfully adjudicates between competing models. There’s always just enough daylight between data estimation and a given model to resist acquiescing to claims that any analysis is testing a models validity.

Agent-based modeling offers the opportunity for meta-analysis of models. In an artificial world with millions of agents, we can program behavior that corresponds with different theories of labor markets, households, crime, addiction, etc. We can model markets characterized by monopoly, monopsony, and competition born of everything from government fiat to specific elasticities of substitution between goods. Hey now, hold your horses. A model of everything is a model of nothing. Once you allow for too much complexity, there’s no room for inference. It’s just noise.

Yes, of course. You can’t model everything. But there is a greater opportunity to find when models are mutually incompatible. Incongruent. Is there a way to run an artificial city of a million agents to formulate a social scientific theory of everything? Absolutely not. But it would be interesting if a million runs of a million models shows that you can never have both a highly monopsonistic labor market and a income-driven criminal market because the high substitutability of cash across sources necessary in the criminal market allows for the kind of Coasean bargains that undermine monopsony. To be clear, I just made that up. But there’s room for as yet unseen cross-pollination across bodies of applied theory.

Pushing the Lucas critique all the way to the hilt

This is essentially a recursive version of modern macroeconomics where agents within the model learn the results being reported in the paper about the model they inhabit, changing their behavior accordingly. Wait, isn’t that just the definition of “equilibrium”? I mean, we already have the Lucas Critique. Yes, but we typically have very well-behaved agents in those models. What if they are a bit noisier in their heterogeneity? What if they took suboptimal risks, many failed, but some won? What if there was an error term in their perceptions of the world i.e. they ran incomplete regressions, observed the results, and then treated the results as a sufficient approximation of the truth? Essentially a behavioral world where agents are often smart but sometimes unwise? Where the churn of human folly and hubris undermined equilibrium while fueling both suffering and growth. A story of Schumpeterian economic growth told by the iterating arcs of Tolstoy and Asimov.

No, I said all the way

I’m not sure if what I just described is just the kind of advanced macroeconomics I am currently ignorant of or complete nonsense. Possibly both. To be clear, I’m deeply skeptical of the preceding paragraphs. One of the ironies of complexity science is that those who take it seriously know that overly complex theoretic ambitions are the death of good science. No, I think if you really want to apply agent-based methodologies within economics, it is best to go in the opposite direction. Simpler models let loose in larger, less constrained sandboxes.

Almost a decade ago Paul Smaldino and I wrote a paper about how groups collectively evolve separate strategies for internal and external cooperation. It’s a cool paper, I’m proud of it, and I kinda, sorta think its a major plotline in “Pluribus“. No, I don’t think the writers are aware of our paper. Yes, I know I sound like a crazy person, but I think the model we designed and explored is relevant to the story they are telling. Maybe next week I’ll lay out the parallels now that season one is complete.

Our paper is a simple story where i) evolutionary pressure on a couple of simple parameters for behavior at the individual level, ii) combined with parameters for how collective behavior emergers from individual pressure, can lead to iii) a world where a society of nice people can be, collectively, quite vicious. The evolutionary pressue is subtle, but also simple. Populations of uncooperative people fail to scale their resources and die off. Populations of cooperative people thrive until they are confronted by aggressive collectives that exploit and expropriate from them, killing them off. But if a group somehow evolves a culture in which members cooperate internally and externally on an individual level, while also being difficult to exploit collectively- if they thread that needle, they thrive.

I think there’s an opportunity for agent-based models within economics to do what we did in our model, but much bigger and much better. Framed as a question: why are the agents in our model only varying along simple parameters? Why aren’t they varying in the complexity of their behavior? Why aren’t they evolving their own rich, multi-layered strategies? Why aren’t they evolving strategies based on their own predictions for not just individual behavior, but how they think that behavior will change the landscape of resources and institutions in the collective? Why they are only playing the game we laid out, choosing amongst the strategies we gave them?

For me, the seminal moment when AI became something worth considering was not as far back as when computers beat players at chess or last week when LLMs were used to fabricate college application essays. It was in 2017 when AlphaGo Zero arrived at a level of play in Go that surpassed grand champions without any outside information besides the rules for the game. It was very specifically not an LLM as I understand them. It learned only by playing against itself. It created knowledge and insight strictly be internally iterating within a set of rules that evaluated success and failure.

We don’t know how to model an entire economy. Apologies to those interested in the Sante Fe Artificial Stock Market, but that’s always been too complex for my blood. So, again, we don’t know enough to make an agent-based model of an entire economy from the ground up, but we do know the rules of evolutionary success (survival and reproduction) and market success (resources and risk). We also have rules that we are comfortable imposing on emotional, sympathetic, and empathetic success (quantity and intensity of interpersonal relationships, observation of other’s success, the absence of suffering). Add in a few polynomial parameters for shape of utility, disutility, and you’ve got a context where agents will learn how to play whatever games you throw at them.

So why not simpy set the rules in place, build a million agents in a world of other agents forced to play games in a world of interactive games? The twist, of course, is that their strategies start as a blank slate.

Step 1: randomly match with another
Step 2: randomly choose to interact or not
Step 3: If you interact, randomly chooes to cooperate or not
Step 4: Go to 1

The question is, can you make the agents smart enough to update and add to those 4 lines of code in a manner that could evolve complex behavior, but not so rigid or intelligent that emergent strategies are obvious from the get go? Can you write a model where not only the strategies being played are endogenous, but the games themselves? There’s at least two people who already think the answer may be yes. And, yes, that paper is exceptionally cool, even if they consider their model outside the rubric of agent-based models.

Is this an AI thing? Because it sounds like an AI thing

Again, we find ourselves in a meta-enterprise relative to the field as it stands, only now we’re talking about game theory and evolutionary behavioral economics where the human contribution is at the meta level – the ur text of the model where rules and parameters serve as a substrate upon which something new can emerge. New, but replicable. Something that you can work backwards from, through the simulated history, to reverse engineer the mechanism underlying the outcomes.

Economics is riding high (as a science, at least. Less so as as policy advocates.) The credibility revolution and emphasis on causal inference placed it in an ideal position to make contributions in what is a golden age of data availability. Before all this, however, was an era of high theory, one where macroeconomists formed schools of thought and waged wars of across texts. It’s no dougbt too conveniently cyclical to predict a new era of high theory on the horizon, but that’s what agent-based models could offer. A new era of theory, only this time centered around microeconomics, where milllions of deeply heterogenous agents are brought into being in a sandbox of carefully selected rules and hard parameters, where those rules and parameters are varied across millions of runs, and the model is run millions of time in parallel, each run a wholly fabricated counterfactual history.

Will the model replicate and explain our world? Almost assuredly not. But the models and strategies the agents come up with? Those could be entirely new. And that’s what the next era of high theory needs more than anything else. Not just new models. New sources of models.

New models for inventing models.