Snowmageddon!! Over 20 inches of snow!!! That is what we in the mid-Atlantic should expect on Sat-Sun Jan 24-25 according to most weather apps, as of 9-10 days ahead of time. Of course, that kept us all busy checking those apps for the next week. As of Wednesday, I was still seeing numbers in the high teens in most cases, using Washington, D.C. as a representative location. But my Brave browser AI search proved its intelligence on Wednesday by telling me, with a big yellow triangle warning sign:
Note: Apps and social media often display extreme snow totals (e.g., 23 inches) that are not yet supported by consensus models. Experts recommend preparing for 6–12 inches as a realistic baseline, with the potential for more.
“Huh,” thought I. Well, duh, the more scared they make us, the more eyeballs they get and the more ad revenue they generate. Follow the money…
Unfortunately, I did not log exactly who said what when last week. My recollection is that weather.com was still predicting high teens snowfall as of Thursday, and the Apple weather app was still saying that as of Friday. The final total for D.C. was about 7.5 inches for winter storm Fern. In fairness, some very nearby areas got 9-10 inches, and it ended up being dense sleet rather than light fluffy snow. But there was still a pretty big mismatch.
Among the best forecasters I found was AccuWeather. They showed a short table of probabilities that centered on (as I recall) 6-10”, with some chances for higher and for lower, that let you decide whether to prepare for a low probability/high impact scenario. It seems that the Apple weather app is notoriously bad: instead of integrating several different forecast models like some other apps (and like your local talking head meteorologist), it apparently just spits out the results of one model:
The core issue is that many weather apps, including Apple Weather, display raw data from individual forecast models without the context and analysis that professional meteorologists provide. While meteorologists at the National Weather Service balance multiple computer models, dozens of simulations and their own expertise to create forecasts, apps often pull from a single source and deliver it directly to users.
“Everything that catches attention is mostly nonsense,” said Eric Fisher, chief meteorologist for WBZ-TV in Boston. He points to the viral snowfall maps that spread on social media, noting that extreme forecasts generate the most attention even when they may not be the most accurate.
Anyway, I tried to poke around and find out in dollar terms how much it benefits the weather apps to exaggerate storm dangers. I was unsuccessful there, but by playing with query wording, I was able to coax out of ChatGPT some numbers on how much these exaggerations may cost the rest of us. (A more qualitative search noted, among other things, that the “crying wolf” cost of people becoming jaded to alarmist forecasts may lead them to discount genuine warnings; and I will add it is inconvenient to find the stores out of things because of panic buying).
Everything below the line of asterisks is just a straight dump from the AI, since I could not readily improve on its presentation:
The economic impact of weather apps exaggerating storm dangers isn’t easy to quantify precisely because most formal studies focus on weather warnings overall (from official sources like the National Weather Service), not specifically on private weather apps exaggerating risk. However, economics research on false alarms, over-warning, and exaggerated alerts offers concrete dollar figures that we can use to approximate the kinds of costs involved. Below are the key economic impacts with as many dollar figures as available:
💸 1. Opportunity Costs of False or Overbroad Storm Warnings
Time cost of false warnings:
Traditional county-wide tornado warnings (over-broad alerts) have historically forced many people to shelter unnecessarily, resulting in ≈234 million person-hours per year spent under warnings (1996–2004) — valued at roughly $2.7 billion annually in lost time/productivity in the U.S. alone.
Improved storm-based warnings that reduce false warning area could save up to $750 million–$1.9 billion/year in avoided lost time.
This demonstrates that exaggerated alerts — such as those sometimes amplified or presented without clear context by weather apps — can force unnecessary economic shutdowns of work and business activity.
🏙️ 2. Evacuation and Preparedness Costs from False Alarms
Evacuation costs:
A NOAA analysis estimates that evacuation costs for hurricanes, when done broadly for storm-warnings, can reach about $1.4 billion annually in the U.S., with roughly $1 billion being unnecessary cost due to false alarms (people evacuating where no damaging winds ultimately occur).
Evacuations are one of the most expensive forms of response — from lost wages to lodging and fuel costs — and exaggerated risk messaging can trigger them prematurely or more frequently.
🏭 3. Business Productivity Losses from False Alerts
Example from private sector:
A building management firm reported that by reducing false storm alerts (better targeted data rather than broad app warnings), it saved ~$10 million in lost production time in one year alone.
This is a real dollar example showing how over-warning impacts businesses directly when they preemptively shut down operations.
💼 4. Broader Firm-Level Economic Avoidance
Cost avoidance from better warning info:
A study of nearly 500 firms found that switching from deterministic (binary “storm/no storm”) warnings to probabilistic information could produce $2.3 billion to $7.6 billion per year in avoided costs for businesses, thanks to better decision-making.
While this doesn’t measure exaggeration specifically, it underscores the economic penalty of imprecise or alarmist warnings, which many weather apps unintentionally echo.
📉 5. Insurance & Property Impacts
Although not directly about exaggerated warnings, inaccurate perceptions of storm danger influence behavior and pricing:
Insurance premiums rising: In storm-prone states (e.g., Florida), homeowners’ premiums have risen by ~57% or more because of risk perceptions and loss patterns tied to severe weather.
Losses from storms: Billion-dollar disasters are common; for example, Hurricane Milton in 2024 had estimated total costs of $21–$85 billion (depending on source).
While not directly caused by exaggeration, inflated perceptions of danger (from apps or media) can affect insurance markets and property values, contributing indirectly to economic impacts.
📈 6. Net Benefits vs. Exaggeration
It’s important to balance:
Early warnings overall clearly save money and lives. Improved forecasts and early alert systems have been estimated to avoid$300 million to $2 billion per year in asset losses and provide up to $32 billion/year in broader economic benefits (through optimized activities and prep).
This highlights that the problem isn’t warnings per se, but inaccurate or exaggerated warnings that cause over-response.
If you didn’t know, China has had negative population growth for the past 4 years. Japan has had negative population growth for the past 15 years. The public and economists both have some decent intuition that a falling population makes falling total output more likely. Economists, however, maintain that income per capita is not so certain to fall. After all, both the numerator and denominator of GDP per capita can fall such that the net effect on the entire ratio is a wash or even increase. In fact, aggregate real output can still continue to grow *if* labor productivity rises faster than the rate of employment decline.
But this is a big if. After all, some of the thrust of endogenous growth theory emphasizes that population growth corresponds to more human brains, which results in more innovation when those brains engage with economic problems. Therefore, in the long run, smaller populations innovate more slowly than larger populations. Furthermore, given that information can cross borders relatively easily no one on the globe is insulated from the effects of lower global population. Because information crosses borders relatively well, the brains-to-riches model doesn’t tell us who will innovate more or experience greater productivity growth.
What follows is not the only answer. There are certainly multiple. For example, recent Nobel Prize winner Joel Mokyr says that both basic science *and* knowledge about applications must grow together. That’s not the route that I’ll elaborate.
The title is excellent, given that the author Brad Jacobs did in fact make a few billion dollars.
The book itself is fine to read, but also fine to skip if you aren’t yourself burning to build a billion dollar company through excellent management and mergers and acquisitions. I certainly don’t care to, which Jacobs says would make me a bad hire for one of his companies:
I only hire people who are motivated to make a lot of money…. If an candidate says to me ‘I’m not motivated by money’, I suspect either they’re not being candid or they lack the hunger that’s necessary to succeed
The book has plenty of hard-driving sentiments like this that you’d expect from a self-made billionaire:
Fire C players
For the first time ever, an American company, Exxon, had reported quarterly earnings in excess of $1 billion. The words “obscene profits” flashed on my TV screen, and I remember thinking “That sounds pretty good! Maybe I ought to check out the oil sector.” [This part I agree with, economic theory predicts that entrepreneurs will enter the sectors with the highest profits and its what I’d do if I wanted to make money, though in practice I think it is surprisingly rare for would-be entrepreneurs to choose this way -JB]
“The CEO trait most closely correlated with organizational success is high IQ” [specifically more important than EQ]
But Jacobs balances these ideas with some surprisingly hippy-like attitudes. Jacobs went to Bennington College and almost had a career as a jazz keyboardist. Chapter 1 is titled “How to Rearrange Your Brain”, and emphasizes the importance of meditation. Page 21 is basically “have you ever really looked at your hands, man… do it, it’s a trip”
I don’t want to spend even one hour around people who are unkind. An organization is like a party. You only want to invite people who bring the vibe up
Though perhaps this hippy/anti-hippy balance shouldn’t be surprising for someone who says one of the main things he asks about potential hires is “can this person think dialectically”.
Strongly recommend the book if you want to follow Jacobs’ path; weakly recommend it as a general management/self-help book or way to learn about markets.
The confluence of politics, recent interest in agent-based computational modeling, and Pluribus have convinced me now is the time to write about the “Cooperative Corridor”. At one point I thought about making this the theme of a book, but my research has become overwhelmingly about criminal justice, so it got permanently sidelined. But hey, a blog post floating in the primordial ether of the internet is better than a book that never actually gets written.
It’s cooperation all the way down
Economic policy discussions are riddled with “Theories of Everything”. Two of my favorites are the “Housing” and “Insurance” theories of everything. Housing concerns such huge fractions of household wealth, expenditures, and risk exposure that the political climate at any moment in time can be reduced to what policy or leader voters think is the most expedient route to paying their mortgage or lowering their rent. Similarly, the decision making of economic agents can, through a surprisingly modest number of logical contortions, always be reduced to efforts to acquire, produce, or exchange insurance against risk. These aren’t “monocausal” theories of history so much as attempts to distill a conversation to a one or two variable model. They’re rhetorical tools as much as anything.
My mental model of the world is that it is cooperation all the way down. Everything humans do within the social space i.e. external to themselves, is about coping with obstacles to cooperating with others. It is a fundamental truth that humans are, relative to most other species, useless on our own. There are whole genres of “survival” reality television predicated on this concept. If you drop a human sans tools or support in the wilderness, they will likely die within a matter of days. This makes for bad television, so they are typically equipped with a fundamental tool (e.g. firestarting flint, steel knife, cooking pot, composite bow, etc) after months of planning and training for this specific moment (along with a crew trained to intervene if/when the individual is on the precipice of actual death). Even then, it is considered quite the achievement to survive 30 days, by the end of which even the most accomplished are teetering on entering the great beyond. No, I’m afraid there is no way around the fact that humans are squishy, nutritious, and desperately in need of each other. Loneliness is death.
Counterintuitive as it may be, this absolute and unqualified dependence on others doesn’t make cooperation with others all that much easier. This is the lesson of the Prisoner’s Dilemma, that our cooperation and coordination isn’t pre-ordained by need or even optimality. Within a given singular moment it is often in each of our’s best interest to defect on the other, serving our own interests at their expense.
Which isn’t to say that we don’t overcome the Prisoner’s Dilemma every day, constantly, without even thinking about it. Our lived experience, hell, our very survival, is evidence that we have manifested myriad ways to cooperate with others despite our immediate incentives. What distinguishes the different spaces within which we carry out our lives is the manner in which we facilitate these daily acts of cooperation.
Kin
The first and fundamental way to solve the prisoner’s dilemma is to change the payoffs so that each player’s dominant strategy is no longer to defect but instead to cooperate. If you look at the payoff matrix below, the classic problem is that no matter what one player does (Cooperate or Defect), the optimal self-interested response is always to Defect. Before we get into strategies to elicit cooperation, we should start with the most obvious mechanism to evade the dilemma: to care about the outcome experienced by the other. Yes, strong pro-social preferences can eliminate the Prisoner’s Dilemma, but that is a big assumption amongst strangers. Among kin, however, it’s much easier. Family has always been the first and foremost solution. Parents don’t have a prisoner’s dilemma with their children. It doesn’t take a large leap of imagination to see how kin relationships would help familial groups coordinate hunting and foraging or il Cosa Nostra ensuring no one squeals to the cops.
Kinship remains the first solution, but it doesn’t scale. Blood relations dilute fast. I’m confident my brother won’t defect on me. My third-cousin twice removed? Not so much. The reality is that family can only take you so far. If you want to achieve cooperation at scale, if you want to achieve something like the wealth and grandeur of the modern world, you’re going to need strategies and institutions.
Strategies
There are many, if not countless, ways to support cooperation among non-kin. Rather than give an entire course in game theory, I’ll instead just enumerate a few core strategies.
Tit-for-Tat = always copy your opponent’s previous strategy
Grim Trigger = always cooperate until your opponent defects, then never cooperate again
Walk Away = always cooperate, but migrate away from prior defectors to minimize future interaction
The Prisoner’s Dilemma is far, far easier to solve amongst players who can reasonably expect to interact again in the future. The logic underlying all of these strategies is commonly known as The Folk Theorem, which is the broad observation that all cooperation games are far easier to solve, with a multitude of cooperation solutions, if there is i) repeated interaction and ii) an indeterminate end point of future cooperation.
Strategies can facilitate cooperation with strangers, which means we can achieve far greater scale. But not as much as we observe in the modern world, with millions of people contributing to the survival of strangers over vast landscapes and across oceans. For that we’re going to need institutions.
Institutions
Leviathan is simply Thomas Hobbes’ framework for how government solves the Prisoner’s Dilemma. We concentrate power and authority within a singular institution that we happily allow to coerce us into cooperation on the understanding that our fellow citizens will be coerced into cooperating as well. That coercion can force cooperation at scales not previously achievable. It can build roads and raise armies. This scale of cooperation is the wellspring for both some of the greatest human achievements and our absolutely darkest and most heinous sins. Sometimes both at same time.
Governments can achieve tremendous scale, but there remain limits. My mental framing has always been that individual strategies scale linearly (4 people is twice as good as 2 people) and governments scale geometrically (i.e. an infantry’s power is always thrice its number). Geometric scaling is better, but governments always eventually run into the limits of their reach. Coercion becomes clumsy and sclerotic at scale. There’s a reason there has never been a global government, why empires collapse.
Markets can achieve scale unthinkable by governments because their reach is untethered to geography. Markets are networks. They scale exponentially. They solve the prisoner’s dilemma through repeated interaction and reputation. The information contained in prices supports search and discovery processes that both support forming new relationships while also creating sufficient uncertainty about future interactions. Cooperation is a dominant strategy. This scale of cooperation, of course, is not without critical limitations. Absent coercion there is no hope for uniformity or unanimity. No completeness. Public goods requiring uniform commitment or sacrifice are never possible within markets. The welfare of individuals outside of individual acts of cooperation (i.e. externalities) is not weighed in the balance.
There are other institutions that solve the prisoner’s dilemma. Religions, military units, sororities…the list goes forever. This article is already going to be too long, so I’ll start getting to the point. Much of the fundamental disagreement within politics and society at large is what comprises our preferred balance of institutions for supporting and maintaining cooperation, who we want to cooperate with, and the myths we want to tell ourselves about who we are or aren’t dependent on.
The Cooperative Corridor
Wealth depends on cooperation at scale. Wealth brings health and prosperity, but it also brings power. The “cooperation game” might be the common or important game, but it isn’t the only game being played. Wealth can be brought to bear by one individual on another to extract their resources. This is colloquially referred to as “being a jerk”. Perhaps more importantly, groups can bring their wealth to bear to extract the resources from another group. This is colloquially referred to as “warfare”.
Governments are an excellent mechanism for warfare. All due respect to the mercenary armies of history (Landsknechts, Condottieri, etc.), but markets are not well-suited to coordinate attack and defense. Which isn’t to say markets aren’t necessary inputs to warfare. This is, in fact, the rub: governments are good at coordinating resources in warfare, but markets are far better at generating those resources. A pure government society may defeat a pure market society in a war game, but a government-controlled society whose resources are produced via market-coordinated cooperation dominates any society dominated by a singular institution.
This all adds up to what I refer to as the Cooperative Corridor. A society of individuals needs to cooperate to grow and thrive. A culture of cooperation can be exploited, however, by both individuals who take advantage of cooperative members and aggressive (extractive) rival groups. Institutions and individual strategies have to converge on a solution that threads this needle. One answer might appear to be to simply cooperate with fellow in-group members while not cooperating with out-group individuals. This is no doubt the origin of so many bigotries—the belief that you can solve the paradox of cooperation by explicitly defining out-group individuals. Throw in the explicit purging of prior members who fail to cooperate, and you’ve got what might seem a viable cultural solution. The thing about bigotry, besides being morally repugnant, is that it doesn’t scale. The in-group will, by definition, always be smaller than the out-group. Bigotry is a trap. Your group will never benefit from the economies of scale as much as other groups that manage to foster cooperation between as many individuals as possible, including those outside the group.
[SPOILERS AHEAD if you haven’t watched through Episode 6]
You’ve been warned, so here’s the spoilers. An RNA code was received through space, spread across the human species, and now all but a handful of humans are part of a collective hive mind whose consciousnesses have been fully merged. That’s the basic part. The bit that is relevant to our discussion is the revelation that members of the hive mind 1) Can’t harm any other living creature. Literally. They cannot harvest crops, let alone eat meat. 2) They cannot be aggressive towards other creatures, cannot lie to them, cannot it seems even rival them for resources. 3) The human race is going to experience mass starvation as a result of this. Billions will die.
In other words, a cooperation strategy has emerged that spreads biologically at a scale it cannot support. It is also highly vulnerable to predation. If a rival species were to emerge in parallel, it would undermine, exploit, enslave, and eventually destroy it. The whole story borders on a parable of how a species like Homo sapiens could destroy and replace a rival like Homo neanderthalensis.
Cultural strategies are selected within corridors of success. Too independent, you die alone. Too cooperative, you die exploited. Too bigoted, you are overwhelmed by the wealth and power of more cosmopolitan rivals. Too cosmopolitan, you starve to death for failure to produce and consume resources. Don’t make the mistake of thinking the “corridor of success” is narrow or even remotely symmetric, though. On the “infinitely bigoted” to “infinitely cosmopolitan” parameter space, a society is likely to dominate it’s more bigoted rivals with almost any value less than “infinitely cosmopolitan.” So long as members of society are willing to harvest and consume legumes, you’re probably going to be fine (no, this isn’t a screed against vegetarianism, which is highly scalable. Veganism, conversely does have a much higher hurdle to get over…). So long as a group is willing to defend itself from violent expropriation by outsiders, they’re probably going to be fine. Only a sociopathic fool would see empathy as an inherent societal weakness. Empathy, in the long run, is how you win.
How this relates to political arguments
I almost wrote “current political arguments”, but I tend to think disagreements about institutions of cooperation are pretty much all of politics and comparative governance. We’re arguing about instititutions of in-group, out-group, and collective cooperation when we argue about the merits of property rights, regulation, immigration, trade, annexing territory, war. When we confront racism, nationalism, and bigotry, we we are fighting against forces that want to shrink the sphere of cooperation and leverage the resources of the collective to expropriate resources of those confined or exiled to the out-group. These are very old arguments.
The good news is that inclusiveness and cosmopolitanism are economically dominant. They will always produce more resources. But being economically and morally superior doesn’t mean they are necessarily going to prevail. The world is a complex and chaotic system. The pull towards entropy is unrelenting. And, in the case of cultural institutions and human cooperation, the purely entropic state is a Hobbesian jungle of independent and isolated familial tribes living short, brutish lives. Avoiding such outcomes requires active resistance.
Have you ever looked up and wondered where the time went? One moment you’re living your life, and the next moment you realize that you’ve just lost time that you’ll never get back? That’s what happened to Japan’s economy at the turn of the century in an episode that’s known as ‘the lost decades’. It was a period of slow or null economic growth. Economists differ with their explanations. One cause was the prevalence of ‘zombie firms’.
Japan’s Economy
Japan had a current account surplus from 1980-2020, which means that they had more savings than they effectively utilized domestically. Metaphorically, they were so full of savings that they exhausted productive domestic investment opportunities and their savings spilled out into other counties in the form of foreign investments. This was driven by high household savings and slow growth in domestic investment demand. The result was the Japanese firms had easy access to credit. Maybe a little too easy…
Private corporate debt ballooned throughout the 1980s. That’s not intrinsically a problem. In the 1990s, households began saving somewhat less, and most firms began to drastically deleverage… But not all firms. The net effect of the mass deleveraging was that interest rates fell. The firms that remained in debt were the ones that risked insolvency. Less productive firms had slim profits and their Earnings Before Interest, Taxes, Depreciation, and amortization (EBITDA) was slim. So slim, that they couldn’t pay their debts. Faced with the prospect of insolvency, firms did what was sensible. They refinanced at the lower interest rates. Firms went to their banks and to bond markets and rolled over their debt, which they couldn’t afford, and replaced it with debt that had a lower interest rate. This occurred across industries, but especially in non-tradable goods and services that were insulated from international competition. Crisis averted.
Except this process of refinancing, while avoiding acute defaults and a potential financial crises, ensured that the less productive firms would survive. Not exactly failing and not exactly thriving, they could sort of just hold on to something that looks like life. Well, high debt and low profits aren’t much of a life for a firm. It’s more like being undead – like a zombie. Between 1991 and 1996, the share of non-finance firm assets held by zombie firms ballooned from 3% to 16%. The run-up differed by industry: Manufacturing zombie assets rose from 2% to 12%, from 5% to 33% in real estate, and from 11% to 39% in services. These zombie firms linger on, tying up valuable resources with low-productivity activities and drag on the economy.
China’s Economy
I’m not prone to China hysteria generally. However, I do have uncertainty about the plans and actions of the Chinese government because I don’t know that domestic economic welfare is its priority. That makes forecasting more political and less economic and outside my expertise. Regardless, the Chinese economy is a constraint on the government, whether they like it or not. And there are some echoes of the Japanese economy’s lost decades.
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.
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:
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 forecastingcontests with prizes for predicting 2026.
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 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!
This post illustrates a couple of things that I learned this year with an application in finance. I learned about the simplex when I was researching amino acids. I learned some nitty-gritty about portfolio theory. These combined with my pre-existing knowledge about game theory and mixed strategy solutions.
Specifically, I learned a way of visualizing all possible portfolio returns. This post narrowly focuses on 3 so that I can draw a picture. But the idea generalizes to many assets.
Say that I can choose to hold some combination of 3 assets (A, B, & C), each with unique returns of 0%, 20%, and 10%. Obviously, I can maximize my portfolio return by investing all of my value in asset B. But, of course, we rarely know our returns ex ante. So, we take a shot and create the portfolio reflected in the below table. Our ex post performance turns out to be a return of 15%.
That’s great! We feel good and successful. We clearly know what we’re doing and we’re ripe to take on the world of global finance. Hopefully, you suspect that something is amiss. It can’t be this straightforward. And it isn’t. At the very least, we need to know not just what our return was, but also what it could have been. Famously, a monkey throwing darts can choose stocks well. So, how did our portfolio perform relative to the luck of a random draw? Let’s ignore volatility or assume that it’s uncorrelated and equal among the assets.
Visualizing Success with Two Assets
Say that we had only invested in assets A and B. We can visualize the weights and returns easily. The more weight we place on asset A, the closer our return would have been to zero. The more weight that we place on asset B, the closer our return would have been to 20%.
If we had invested 75% of our value in asset B and 25% in A, then we would have achieved the same return of 15%. In this two-asset case, it is clear to see that a return of 15% is better than the return earned by 75% of the possible portfolios. After all, possible weights are measures on the x-axis line, and the leftward 75% of that line would have earned lower returns. Another way of saying the same thing is: “Choosing randomly, there was only a 25% that we could have earned a return greater than 15%.”
Economist Writing Every Day 