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.

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.

First, yes, the commenter is correct, this is grim:

The tragedy of needlessly lost lives is, of course, bad enough to despair, but it’s made that much worse that false information created to ostensibly (and obviously) prevent a Muslim man from being credited with the kind of heroism normally reserved for films* is so casually distributed through major social media channels. Putting despair aside (easier said than done), I’m not interested in only shaming twitter et al for promulgating false narratives that always seem to conveniently fit into Grok’s preferred narratives of white/western supremacy. I’m more interested in thinking about how our processing of information will evolve.

There is always selective pressure in labor and life for those who better adapt to a changing technological and information landscape, and there’s no shortage of change happening right now. Some of it falls into classic “resist the propaganda” tropes. Don’t believe what you see on TV has evolved to don’t believe what you learn from the internet→ social media→AI→??? Once again, easier said than done, and I think it is more nuanced than that. It’s not just about information insulation and nihilism, it’s about cultivating the ability to better intuit when you are being misled.

Is there a subreddit? Of course there is a subreddit:

The comments are interesting because they are collectively sussing out specific, tangible clues that this is or isn’t AI. The convenient lack of license plates is both evidence of an error (if the state requires front license plates) and one of selective deception (the left car has their plate cropped out rather than blurred out). There is also the uncanny over-simplicity of the setting. No other people, debris, trash cans, mailboxes, etc. The absolute perfection of the cars outside of the region immediately surrounding the point of collision.

We have intuitive tools at our disposal, likely borne out of the same cogntive sources of the “uncanny valley” that haunts certain animation. We may have evolved to avoid predators that used mimicry to approach and infiltrate. These skills are ancient and innate, though. They are not inherently honed to combat AI-generated and distributed deception. We will have to evolve. And, as alluded to earlier, this is going to show up in far more than our politics.

There’s lots of hype around training students to work with AI. That’s all well and good, but I’m not sure how different those tools are than the ones that we honed to search with Google, to write and debug our own code, or to simply write effectively. What about the skills to evaluate and credit inputs? To discern the product of narrow expertise from distilled generalizations i.e. to discern new workflow and products from recycled “AI slop”. How much of a manager’s job is to simple assess whether the task was completed sufficiently or half-assed 70% of the way there? A lot of it? Most of it? The thing about half-assing it is that you are only incentivized to do it when avoiding 50% of the toil is worth the risk of getting caught. What happens when you can avoid 95% of the toil? Basic economics says you’re going to half-ass it a lot more unless the probability of getting caught or the punishment increases. What that means is that if management doesn’t get better at identifying 5%-assed AI slop from employees they’re going to have to start firing employees when they do get caught. In a world with high separation costs, that’s not an attractive option. Which means tilting the balance of decision-making back towards “actually doing the work” will fall to improved managerial oversight and monitoring. There’s no shortage of handwringing over escalating C-suite salaries. It will be interesting to how people respond to wage scales rebalancing towards middle management.

The most cliched thing to ask for in a job applicant has long been “attention to detail” or that they be “detail oriented”. I’m not sure if that is now obsolete or more important than ever. It’s not just about attention, per se. It’s evaluation, perhaps even cynicism. And it’s not because AI is evil or corrupt or even wrong. It’s just overconfident, and that overconfidence is catnip for anyone who wants to believe their work for the day is done at 9:05am. If you want to be in charge, you’re going to have to get really good at sussing out the little signs that what you’re looking at wasn’t produced for your task, but the average of all similar tasks. Can you look quickly and closely? You’re the boss, you’re busy, but so you better be good at it. The AI is in the details.

*And seriously, Ahmed al Ahmed is a hero. A movie hero. A crawling through the air ducts to fight the bad guys hero. Unarmed, he tackled a man actively firing a rifle at innocents and in the process saved a number of lives we will never know. He was shot twice. He’s real. I am in awe.

There’s no getting around the fact that UBI experiments are not producing the kind of results many expected, myself very much included. Now, to be clear, this is in Finland, which has a quite robust social safety net, but precise zeros from a sample of 2,000 unemployed subjects is not something that can be ignored either. If you asked me five years ago where a new UBI might, at the margin, have a zero effect, I would have picked a Nordic country, but still…

“Companion” (2025, written and directed by Drew Hancock) is a perfect example of a film that doesn’t get much of a chance these days in theaters, but is creative, entertaining, and best consumed without information or presumption going in. It’s not a “twist” or “paradigm shift” film. You will piece together many, but not all, of the reveals a half-step before they are revealed. In short, it is an excellent film currently streaming on HBO Max. It’s also part of the ever-growing evidence that the post-Hollywood sweet spot may in fact be low-ish budget projects ($10 million in this case), filmed far from LA, with talented and competent actors, but without tabloid-level stars. If that means we’re getting a second wave of Friedkinesque 70s filmmaking with a smidge of CGI and 80% less actor (and civilian) endangerment, I am all for it. What might be a crash for studios, agents, and publicists could be another golden age for creatives (writers, directs, actors, editors, set designers, etc) and film-goers.

But don’t put too much on my amateur prognosticating. And certainly don’t read a review or even watch a trailer. Just give it 90 minutes of your life.

[HT to Patton Oswalt who recommended “Companion” in an interview with Tom Papa.]

There is a broad consensus that healthcare in the United States is suboptimal. Why it is suboptimal is, of course, a subject of much debate, but that’s not what I am curious about at the moment. When people argue against the merits of the status quo, the superior systems of western Europe, Canada, the UK, and (occasionally) Singapore are mentioned. But if you look at most of those countries, the rate of satisfaction is, by the standards of most goods we consume, quite poor. In the survey reported below, 30% of US consumers are satisfied with their healthcare, compared to 46% of Canadians. The high water mark of “non-city states whose data I actually believe” is Belgium at a whopping 54%, and this is a survey conducted before the Covid-19 pandemic!

So while I have no doubt that improvements can be made in any system, there’s perhaps an under-discussed obstacle that may be unavoidable in any democracy: there is no stable political equilibrium because voters will never be happy with the status quo.

Here’s my simply reasoning.

1. The wealthier we get, the more expensive healthcare will get. Healthcare is example 1A of Baumol’s curse in the modern world. No matter how much our economies grow, the cost of labor will grow commensurately, meaning healthcare will keep getting more expensive until we find a significant capital substitute for labor. (This is not a cue for AI optimists to chime in, but yes, I get it. We’ve been waiting for a “doc in a box” for a long time, and if the speed with which I got a Waymo is any indicator, we’ve got a ways to go before Docmo get’s real traction.)
2. The wealthier we get, the more we value our lives. With that greater valuing comes greater risk aversion, and a greater willingness to pour resources into healthcare. If the labor supply of sufficiently talented and trained doctors can’t keep up, then wealth inequality is going to have a lot to say about how access to healthcare quality is distributed. Yes, there are positive spillovers as wealthiest individuals dump resources into healthcare, but are those spillovers enough to overcome envy?
3. Citizens in wealthy countries are deep, deep into diminishing returns on healthcare expensitures. Combine that with growing risk aversion, and you’re got yourselves something of a resource trap, where you’re chasing a riskless, decision-perfect, healthcare experience that you can’t afford and likely doesn’t even exist.
4. Fully socialized medicine a la the UK is of course an option, but the perils of connecting your entire healthcare system to the vicissitudes of politics is something being keenly felt since Brexit. Put bluntly, I always struggle with the idea of making healthcare wholly dependent on voters who will happily vote for anything so long as it doesn’t increase their taxes…

If economic growth allows for greater health, but that greater health itself pushes your baseline expectations for health farther out, then you’re on something akin to a hedonic treadmill— one where cost disease keeps increasing the incline. If the world getting better means that increased demand for healthcare will always outstrip increases in our ability to supply it, that it will always be too expensive and overly distributed to those wealthier than us, and if and when we do socialize healthcare voter demands will, again, outstrip their willingness to be taxed for it…I don’t see a clear path to satisfied consumers.

Maybe this is just me projecting, but I don’t have a hard time imagining that I’d be complaining about the quality of health care I’m receiving no matter what country I lived in, though I’d be willing to try out “Billionare in Singapore” if anyone wants to support a one household experiment.

A large portion of my favorite art and artists frame themselves as “anti-capitalist”. Now, I know I am repeatedly on the record saying that the terms “capitalism” and “socialism” have been stripped of most meaning at this point, with limited ability to communicate any useful information save group signaling or mood affiliation (i.e. everything I don’t like is an exemplar of late-stage capitalism or crypto Marxist socialism), but the language is used enough within the art I like that it’s worth pondering a moment.

Now, I think it’s a bit of a trap to impose strong interpretations on how artists interpret themselves or their art. It’s their art, not mine. I also think it’s a memeable offense to try to “gotcha” artists who sell their art in the marketplace as capitalists who doth protest too much.

Yeah, I get it, everyone has to eat, even if you would prefer to live in a socialist utopia. I do think there is more worth untangling, though, and as an act of good faith I will spoil part of my conclusion. I don’t think there is anti-capitalism art anymore than there is pro-capitalism art. Rather, I think there is art and there is propaganda.

Art is one of many luxuries yielded by the remainder of time not subsumed by the needs of survival. A society that builds within itself a marketplace that rewards specialization, innovation, and efficiency will find itself suffused in art. I’m sure there are things beyond relationships, purpose, and art that make life worth living, but I can’t think of any. If in pursuing your purpose you can find art and build community, well, that’s a life well-lived.

So does art depend the marketplace or is it an act of rebellion against it? Can it be both? I think it can. The true threat to art is not the intercession of commerce but service to power. Art is characterized more than anything by a direct, if parasocial, relationship between the artist and their audience. Service to power corrupts that relationship, demanding service to an intentionally unobserved third party. The perceived communication from the artist is now an act of deception, surrepticiously communicating the preferred messaging of the third-party. Beyond just robbing the artist of their integrity, it undermines the confidence an audience can have in all the art it consumes. Propaganda is, in this manner, a negative externality, polluting everything that art can and does provide in our lives.

When writers and other artists complain about the interventions of private equity, they are complaining about a couple things. First, and probably foremost, it is often the insistence on a revenue model (high risk, high growth) that simply does not translate to the current media landscape. It’s a bad model and makes for bad business. It should not be ignored, however, that one of the failures that a high-growth revenue model brings to a media context is a necessary subserviance to power. Service to large equity stakes (i.e. evil rich people), yes, but also service to regulatory authorties, cultural authorities, anyone and everyone that might derail your path to the hearts of the largest common denominator, to the other side of a dreamed of (and likely wholly imaginary) tipping point beyond which the glories of power law scaling will turn your tens of millions into hundreds of billions.

When a story teller places you in a dystopian future where the vagaries of a galactic-scale marketplace lead to the devaluing of life on wholesale planetss in service to the profits of preexisting conglomerates, corporations, and sultanates, it is often framed as anti-capitalist fiction. And that’s a natural summation: people are commodified, exchanged, and disposed of. But of course the power behind commodification comes with military bodies, royal lineages, and a sci-fi feudalism whose roots always trace back eventually coercive force. Commerce may be the engine producing the resources underpinning your evil army (soldiers gotta have something to eat, a way to get there, and something to shoot), but in the end the big bad evil empire is always pointing a gun.

Conversely, when a comedian both sets up shop Austin, TX and underpins a genre of comedy that frames itself as “anti-woke”, it is not disappinting to most artists because it is “pro-capitalism”. It’s disappointing because once you scratch the surface, it becomes clear it is in service of power. It’s not aligning itself as anti-woke because there are three words that audiences will shame them for using. It’s doing so in the hopes that punching down on the same vulnerable, (often extremely) small minority populations targeted by other locuses of political and media power, they can acquire the same kind of most-favored nation status that have lifted the careers of others whose mediocre talents were insuffient to garner an audience on their own.

Art is inherently, maybe even necessarily, anti-power, because there is no room in the relationship with it’s audience for the interests of a third party. Art needs patrons, yes. The marketplace is a boon to the production and consumption of art. But when the movies, television, writing, music, video games, dance, etc of our lives is compelled to serve the interests of anyone but the artist and the audience, people sniff it out. They rebel. They blame. Whether they blame the market, the government, or religious authorities, well, that just depends on the current framing of power. And honestly, I’m not sure the framing matters all that much. It might show up in the artist’s statement next to the installation or in their AMA on reddit, but most people don’t read those, they already know who to be mad at. And besides, the art already exists. They don’t need intervention from any third-parties, maybe not even the artists.

Sports gambling is entering it’s first series of major crises since widespread legalization. While there is the typical handwringing around the intersection of vice and broad entertainment, there is also the added dimension of the role that insider information can and should play within any speculative market. Those arguments, conducted earnestly, are of course completely valid, but I think they are not giving enough attention to a key distinction in the online incarnation of sports gambling.

Speculating on sports outcomes produces the same elicitation and aggregation of information as a more traditional speculative market, such as commodities futures or stock equity markets. Information, acted upon through purchase, reveal each individual’s beliefs about the true value of a contract paid upon the conclusion of a sporting event or the price of agricultural commodity at a given date and time. The market exchange of these contracts aggregates these beliefs into a collective piece of information in the form of a market price. Some contract holders get richer, some poorer, and the broader world benefits from the distillation of private information into public prices. The problems within sports gambling stem from the second channel through which entertainment is provided and paid for: random outcome generation.

Sports match outcomes are something you speculate on. Random outcomes are something you gamble on. Yes, there is random chaos in sports the same way there is random weather in agriculture. There is no speculating on a roulette wheel, however, that’s a pure gamble. I believe the major sports leagues and the online gambling companies they partner with have made a grievous error allowing their sites to offer (nearly) pure gambles.

Think about how much casinos invest in the integrity of their games as pure and fair gambles. Dice are rigorously inspected and routinely replaced. Roulette wheels are engineered with astounding tolerances. Card games occur under multiple layers of scrutinous observation. Manipulation under such conditions is sufficiently costly such that it is almost never worth undertaking.

How do you go about making similar investments in monitoring 6 inches of horizontal manipulation of the first pitch of a baseball game? Of a marginal player taking himself out of a game injured a few minutes early? The answer is you largely can’t. So now you have human roulette wheels who can decide what number they land on. Which brings us to the second, closely related problem in the new regime of sports gambling: inframarginal game outcomes. Once a game is probabalistically decided before its official conclusion, teams will often play their substitutes to finish out the formality in order to rest their main players and protect them from injury. These players typically earn smaller salaries, often over far shorter careers, with less scrutiny over their quality of play. These are the exact players for whom a couple hundred thousand dollars may be worth incurring a small amount of risk. The product of their play in terms of success (i.e. scoring, hitting, etc) is still highly conditional on their ability relative to their opposition, but the play itself (i.e. shooting, swinging, pitching choices, fouling, etc) is entirely within their control. It may be less purely random, but it is nonetheless sold to gambling customers as fair.

Whether the outcomes in question are quasi-random outcomes or merely inframarginal, what matters is that they are not joint products of competition. To significantly manipulate these outcomes does not require the explicit or implicit coordination of multiple individuals across competing teams. Yes, one player can tilt the odds, but if you are looking to make significant money manipulating sports gambling, you can’t just tilt the odds a few percentage points. There’s a reason the Black Sox Scandal of 1919 involved eight players (seven if you consider Buck Weaver innocent, which I do).

As I love to point out, coordination across individuals is very difficult. Crimes involving coordination are, in turn, far easier to monitor. Online gambling massively reduced the transaction costs in sports gambling, opening the door for orders of magnitude increases in the number and variety of bets that could be taken. There’s obviously demand for pure gambling alongside outcome speculation, and that demand could now be met through random and inframarginal in-game player outcomes.

The danger, of course, is that few of these events are truly inframarginal. Every pitch and available player counts towards the outcome. Enough manipulation by enough players will graze away the integrity of the core product. The subsidy of lower end players through gambling will change how they approach their careers and how management approaches their employment. Fans will react accordingly as well, adjusting how they view outcomes. We’re already seemingly hardwired to view everything as causal and conspiratorial, overestimating bias in refereeing and player preferences. This will only stoke those fires further.

Organized crime famously offered a “numbers game” prior to state lotteries. Desperate for a credibly random outcome, a common mechanism was to use the middle three digits of the number of shares traded on the NYSE as the winning number. There are no shortage of lotteries now, but there obviously remains latent demand, and customers clearly enjoy bundling gambling with a product far more entertaining to consume than scratching off a ticket. Pro sports was unable to deny the profits from exactly such a bundling, but the cross contamination with their core product may prove to be of greater cost.

I’m not businessman, just a lowly economist and sports fan, but if I were running a $11.3 billion per year firm, I would be far more risk averse.

Universities continue to turn down the “Trump Compact”. The intitial nine schools targeted with an “invitation” were from a seemingly curated list of elite institutions, though some are perhaps notably less wealthy or more aspirational than the others. I can’t help but think there was some attempt to create a prisoner’s dilemma situation, where one more eager or fearful university might start a domino effect by committing first. That has not occurred.

What I do expect at some point in the coming weeks is a broadened offering of the compact to schools across the country. I expect messaging that specifically targets large public universities in states with Republican-controlled state legislatures that will be leveraged to pressure schools to sign on to the compact in hopes of currying favor with the administration and their voter base. I expect several schools to sign.

Here’s why I think that would be a grave mistake.

The compact comes with promises of “most-favored” status for applicants to federal grants through institutions such as the NIH, NSF, and Department of Defense. The thing is, they can promise that all they want. They don’t actually have that much influence over the review process. They’ll no doubt work to tip the scales on a few grants and promote them heavily, but the media coverage will vastly outweigh the dollars being shifted by the compact. It will, as always, be theater first and governance last.

But let’s say your school does procure several grants. Perhaps you’re a school that has in the past carried $20 to $30 million in active grants from the NIH and NSF, amounting to roughly $5 million per year in operating expenses. That sounds like a lot, but it’s not. Johns Hopkins University, by comparison, had $843 million in just NIH grants active in 2023. If you’re operating with $5 million a year in grant money, you have an office of sponsored projects, an Internal Review Board for human subjects research, and maybe an office for industry sponsorship. That maybe amounts to 15 to 20 personnel. What happens if the Trump administration comes through, putting its thumb on the scale for you, doubling or tripling your active grants within two years?

Chaos. Institutional chaos.

Sponsored research requires capital, personnel, and resource management. It requires legal compliance, doubly so if you’re spending federal money. It requires experienced leadership and management that know how to check boxes, file reports, track money, review protocols, and continuously train ever-churning research personnel.

But hey, that’s the point, you might be saying. We want to be ambitious and grown, we want to hire new and experienced personnel. We want to grow into an important research institution and this is our big chance! Be careful what you wish for. It’s one thing to incrementally grow over years and decades. It’s a whole other thing to try to do it in reaction to a sudden influx of money. Which, to be clear, isn’t just money. It’s an obligation. An expectation to produce scientific contributions on the US taxpayers’ dime. Obligations come with many things, but patience with incompetence borne of growing pains isn’t one of them.

But none of that is the problem. The real problem. The trap.

The trap is that this money isn’t going to stick around. This regime isn’t permanent. They aren’t invested in any way in scientific public goods or even science as a conept. This is, again, theater. They will move on to other things the instant it fails to the get the traction they want. They will lose elections, political tides will turn, etc. And what your institution will be left with is the reputation you earned.

And what will that reputation be? One of compliance with an anti-science, anti-public health, anti-intellectual regime. Further, you will judged on the fruits of that compliance. At the margin, it will be science that was undersupported, delayed in launch, stalled in execution, and eventually delivered short of expectations. You will have sold your reputation for a ticket on a ride you weren’t tall enough to be on yet. Grants will dry up, returning to previous levels or worse, leaving you with a bloated staff you no longer need, trying to find ways to lay off employees with all the protections of state government labor regulations.

There is no getting rich quick in academic research. There’s only avenues of over-reaching impatience ending in tears.