The latest CPI-U data suggests inflation was around 4 percent in the past year (I say “around” because due to the late 2025 government shutdown, we have no October 2025 baseline data to calculate the rate). The persistently high inflation — even using the core CPI, it is still close to 3 percent — and currently high interest has made some investors consider the safer, more guaranteed returns of good old bonds.
One type of bond you may want to consider: Series I Bonds. These bonds issued by the federal government pay a rate that is set for 6 months, and the rate is based on the most recent 6 months of inflation as measured by the CPI-U (plus a fixed rate, more on that in a moment). These bonds are currently paying 5.51 percent, since the most 6 months of CPI-U inflation was 2 percent, which you essentially double to get an annual rate, and then a fixed rate (currently 1.4 percent) is added to that inflation rate. When you buy the bond, the fixed portion of the rate is fixed for the life of the bond (30 years). That 5.51 percent is currently higher than the 10-year Treasury bond, and even slightly better than the 30-year bond! But the rate is only guaranteed for 6 months, after which it will change.
The biggest reason to buy these now is not the headline rate but the fixed rate of 1.4 percent. That’s the best fixed rate they have offered since 2006-2007! I purchased some of these bonds back in 2022, and I still think it was a good choice: they were paying over 7 percent at the time, which increased to 9.62 percent the following 6 months. Not a bad return in a year when the stock market was unstable and actually fell almost 20 percent that full year. But when I purchased the I-Bonds in 2022, there was no fixed rate attached. That means the 2022 bonds I am still holding are currently just 4 percent, rather than the headline 5.51 percent.
Even if you believe that CPI-U inflation will eventually get back down to 2 percent, and stay there into the future, I-Bonds purchased now will pay almost 3.5 percent for the life of the bond — not a stellar return, but not bad for a guaranteed return. Of course, you could always lock in almost 5 percent for 10 years or over 5 percent for 30 years with ordinary Treasury Bonds. Those aren’t bad ideas other. But if you think inflation will remain elevated, or especially if you think it will ramp up again, putting some of your cash in I-Bonds might make sense.
As a historical point of reference, the best time to buy these bonds would have been in the late 1990s, when they were first issued. At the time they paid a fixed rate of over 3 percent! That means today those bonds are paying 7.5 percent and had been paying as high as 13 percent in 2022 at the peak of CPI-U inflation. Not bad for a guaranteed return! The cumulative return on a $10,000 investment in 1998 would be about 5x, today worth over $50,000. That’s only about half the return of the S&P 500 — but with much less volatility. Those high fixed rates are unlikely to return soon, but the 1.4 percent fixed rate is not too shabby.
Important final thoughts: don’t take this as professional investment advice, just as an idea to research more on your own and with your financial advisor. You can only put $10,000 per year into Series I Bonds, so no need to think about whether you should move all of your money into I Bonds — just think about the margin dollar you are investing. And if you sell these bonds within 5 years, you will lose a small amount of interest (3 months’ worth).
You couldn’t make this up. The CEO of the second most valuable company on earth, at the very center of the AI technology which is driving, well, everything, including the U.S./China rivalry – – Jensen Huang himself – – over the weekend acknowledged over the weekend in an internal Nvidia memo that he had had an extramarital affair, and the company announced an investigation. The issue is not his personal infidelity, but the fact that the other woman in the case is almost certainly one Alice Huang (a.k.a. Huang Le) who is suspected by U.S. intelligence (per the NY Times) to be a Chinese spy.
There seems no question about the affair. There are lots of public photographs indicating an intimate relationship, and Huang outright admitted it, although he refrained from naming names. But who is Alice Huang? That turns out to be a very, very difficult question to answer.
She is reportedly a Chinese citizen, and was presumably born and raised and schooled there. I could find no solid information on her background. I asked Claude, and got this non-answer:
Birthplace and upbringing:I found no source giving either. AFP calls public details about her sparse. france24
Education:Officechai says there is no verified public record of where she studied or what degrees she holds. A fund-database profile lists jobs but, as far as the reporting says, no schools, and AFP couldn’t verify that profile or who wrote it.officechaifrance24
Some sources claim that she worked as a TV reporter in China, but other news organizations have checked and found those claims to be baseless. So…you have this person from China, coming seemingly out of nowhere, who rather suddenly as head of as her own firm Huang Le Ltd (registered in the British Virgin Islands) became owner of a spinoff (Megaspeed) of a Chinese company, which in turn rather suddenly becomes one of the biggest Asian importers of those oh-so-special Nvidia chips. There seems to be no ready accounting for how her firm was financed to buy Megaspeed, and or how such a tiny new firm could buy $4.6 billion (!!) in chips from Nvidia. Thus, some invisible hand deftly placed this hot youngish woman from China in a position where she would almost certainly come in personal contact with Jensen Huang.
She apparently palled around with Jensen in the years 2024-2025, but now has disappeared from her management roles and from public view. She seems to have been well-enough advised to avoid making outright illegal business moves (thus evading criminal prosecution in Singapore), but all this caught the attention of U.S. authorities as early as the late Biden administration, leading them to believe she was a tool of Chinese state intelligence – at least, that is what various news outlets have reported. It’s impossible to know what did or did not transpire between Huang and Huang regarding chip technology or policies, whatever else they talked about. It is a positive that Jensen has publicly acknowledged this affair, since that removes the blackmail lever which is often the point of such “honey pot” entrapments.
What effect might this all have on Nvidia? Well, first, its stock price was down sharply on Monday, erasing some $200 billion in shareholder value, when the rest of the chip stocks were recovering smartly from the fears from the midterms that the Democrats would shut down data center construction.
A lengthy, messy investigation might compromise Jensen Huang’s iconic role at Nvidia, leading potentially to his departure. The company would forge ahead without him, but his loss would be a blow. Even if he stays, his public image of a loyal, disciplined, trustworthy man has been badly tarnished. I suspect a more focused fallout will be to muzzle him as a spokesman for selling more of his AI-critical (and military-enabling) chips to China. He has been pushing for chip sales for a long time, and now it will be tougher to take him seriously as someone speaking in America’s interest.
Everyone loves a trilogy, so let’s wrap this up. I told you why agent-based modeling never broke through in economic research. I told you how it could eventually matter. Now I’m going to tell you how AI could, and maybe should, actually make it happen. And as someone who is not fully “AI-pilled”, I will confess that there aren’t that many avenues in which I am particularly excited about AI contributions, particularly in the arts, and even where I see opportunities for great things, I remain pessimistic that Gresham’s Law is going to hit us hard and fast while we are still learning to culturally and institutionally separate the wheat from the slop-riddled chaff.
But let’s put on our more optimistic glasses for the moment and think through what the opportunity for AI to guide agent-based economic modeling into a new (and first) golden age.
First, let’s define a few terms. When I refer to Agentic AI I am specifically considering an LLM model that is capable of autonomously pursuing goals, taking actions, and adapting their behavior in response to new information, all based on a set of pre-defined rules and reward functions and with little to no need for continuing human instruction. When I refer to an Agent-Based Model (ABM) I referring to a computational model of an economic or social system in which individual, heterogeneous agents (consumers, firms, workers, etc) make decisions according to specified behavioral rules, interact with one another and their environment, and collectively generate aggregate economic outcomes.
I don’t want to be wholly redundant with my previous posts, but it’s important to revisit a key theme that ran through both posts: the greatest strength of agent-based modeling is it’s ability to accomodate incredible depth of individual and institutional heterogeneity. Which is a nice way of saying that it is parameter rich. Which is itself a nice way of saying that the models can generate any result without the prospect of credibly investigating the mechanism behind those results and, more importantly, suffiently culling enough noise from them to learn anything about how the world works. For those less familiar with the curse of dimensionality, let me explain.
If a model has 2 parameters you can simply run a grid search of every possible combination of those parameters and observe what kind of outcomes are generated. If you had an agent-based model with 100,000 agents randomly interacting you could still investigate the parameter space by “running” the model 1000 times for each possible combination of parameters and look at the full distribution of outcomes, reporting the mean, variance, and individual quantiles of every outcome of interest for each combination in your grid of parameters. Your results might look like this:
This is a useful way to design and investigate an agent-based model. But you probably already see the problem. Sure, toy models with two parameters are cute, but how do I answer complex economic questions? Toy models can only take us so far. And that’s the rub.
Once you start really start trying to advance economic theory of large, complex economic systems with an agent-based model you’re going to have a lot of visible parameters and lot more less visible parameters. An ABM will be composed of:
Agents (people)
Collectives of agents (firms, clubs, mosques, families, states)
Rules for agent decision-making
Rules for collective decision-making
A context, network, and/or phystical environment with it’s own rules
Every single item on that list will in turn have attributes with both
Initial conditions
Distributional characteristics of those inititial conditions i.e. mean, variance, etc
Are they fixed, exogenously varied, or endogenously varied over time?
Take the first list and add up the total agents, collectives, rules, etc. Then multiply those by each other and by every decision that has to be made in the second list. That number will likley be in the thousands. Congrats, that’s how many true model parameters you have.
With modern macroeconomics, and I am speaking here as a non-macroeconomist, your goal is to reduce that parameter space using basic logic, empirical observation, and modeling decisions that collapse heterogeneity to a small number of representative agents or types. You produce equilibria predictions at each stage of your model and compare them to objective reality. Once the model is sufficiently micro-founded and validated, you can then generate predictions and investigate mechanisms.
The whole point of agent-based modeling, however, is not to collapse the model to a small numbe of representative agents. It’s to endogenously grow aggregate outcomes from the individual endogenous decisions and how, through interactions within the model, they aggregate into predictions for a social system. So what’s the problem? Let’s assume every agent type and collective is characterized by 5 attributes and that each attribute is characterized by 3 statistical properties. Each rule comes with 3 functional variations, characterized by 3 weights each. We’ll then say that there are 5 total agents ( 2 individual types and 3 collectives), for each of whom half of attributes are fixed and half are endogenous. They manner in which they are endogenous has 3 functional variants. If we want to do a true grid search, we’re looking at least at a 5000 combination parameter grid space.
And we are no where near done yet, because we haven’t considered how time works. How steps and sub-steps are ordered. What decisions are synchronous, asynchronous, hieracherially ordered or random. We haven’t considered the social or physical environment What’s you start digging in, your parameter space starts gaining orders of magnitude. And then you realize the real problem is that true grid search of the entire parameter space would take until the heat death of the universe.
And that’s what research expertise really is – competently making decisions about what can and should be varied, what can and should be assumed, etc, until we have a tractable question and well-executed research endeavor to answer it. The ABM space is too big for a researcher, however, because too much of the competent execution of research is unobservable, possibly even to the researcher themselves, as the model and the interactions of each decision become intractably complex. Too big for a human researcher. But what about an Agentic AI?
What if we created a base ABM, handed it to an Agentic AI, and told it to recursively evaluate and update the model until the model and the results it generated met a pre-defined set of necessary meta-conditions that the researchers defined as i) a falsified theoretic prediction, ii) generated via observable, definable mechanisms, iii) dependent on a maximal set of modeler decisions, and iv) and independent of set of unlimited, but fully observable modeler decisions?
This is going to take a long, but not necessarily intractable amount of time because we can also define a series of “golden section” adjacent rules that allow the AI to decide with a parmeter, rule, or decicsion is sufficiently irrelevant to ignore or sufficiently constrained to tractably investigate as key outcome determinant or source of outcome sensitivity.
As long as I am giving unsolicited advice to graduate students everywhere interested in agent based modeling, I would suggest forcing such an Agent AI modeler to exclusively use a constrained, pre-defined modeling language or library. Further, you probably want to force a literal, numerical maximum on the number of lines or characters of code. And, lastly, you’ll want to come up outcome rules by which the Agentic AI has to itemize and defend every single deviation of the final model from the base model handed to it. By constraining AI to a limited variety and quantity of coding objects, you are in turn ensuring that the resulting model and the internal mechanism will be transparent to the researchers managing the AI and the broader discipline evaluating them.
Agentic AI offers researchers the ability to iterate through the prospective solution space for modeling problems otherwise intractable via brute force. Further, it allows researchers transparently reveal the instructions that guided the agentic AI dimensions on which the generated model is sensitive to both varied parameters and modeling decisions. It allow for two hierachical levels of meta-research transparency.
That’s what I would be coding write now if I was a PhD student interested in contributing to economics and other social scientific disciplines via agent-based modeling.
Today I am learning about The Economy of Communion movement in Omaha, NE. My visual first impression of downtown Omaha is that it looks like mid-century New York City.
The Economy of Communion, founded by Chiara Lubich in Brazil in 1991, is an example of cooperation emerging through voluntary economic relationships. Businesses participating in the movement operate in competitive markets, but they commit to sharing profits and supporting people in need.
EoC gave me some books in which I came across a chapter by Luigino Bruni that included a passage on expressive rationality:
A rationality that is capable of communion should also transcend the simple instrumental logic… This term was introduced to indicate economic action that is guided not by a logic that is primarily instrumental, but by the desire to “express” something of one’s own personality or one’s own values…
Some concept of this runs through economic literature (Kreps 1997). Bruni claims that the idea can be traced back to Max Weber. The EoC project could also relate to the work of Elinor Ostrom (1990), who challenged the conventional wisdom that common resources inevitably suffer from overuse. Ostrom documented communities that successfully developed their own institutions for cooperation.
Kreps, David M. “Intrinsic Motivation and Extrinsic Incentives.” The American Economic Review, vol. 87, no. 2, 1997, pp. 359–64. JSTOR, http://www.jstor.org/stable/2950946. Accessed 7 Nov. 2026.
Ostrom, Elinor. 1990. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press.
For the past two years, we’ve had a unified government in which the Republican party has populated the White House and had majorities in both chambers of congress. Once the new legislators are in office we’ll have a divided government. That means that the political party controling the White House won’t also control the senate and house of representatives. In the spirit of saying something of substance, I want to know what the implications are for our fiscal picture.
One can measure fiscal health in many ways. Does a divided government bode well for shrinking the deficit? Rather than spit-ball about politics, I take the data since 1960 in one hand and a model in the other. See the end notes for model details.
Since 1960, the most popular form of government split has been a Democratic president and unified government, followed by Republican presidents and a divided government. Democrats have controlled at least one chamber of congress for 62% of the years. Do these splits matter for federal outlays, receipts, and the deficit? Since a unified democratic government was most common, I use that as the baseline fiscal case and compare the cases to it.
Below are the average impacts of government-party types on federal spending, revenues, and the deficit. The blanks are insignificant differences. On average, the only party split that results in less spending is a Democrat president with divided government. Those and unified Republican governments raise more revenue and reduce the budget deficit from its trend (there was a small decline in in discretionary spending at the start of Trump’s 2nd term with a unified government). The bad news for deficit hawks is that Republican presidents with a divided government – the type that we’ll have for the next two years – perform poorly. On average, they reduce government revenue and increase the deficit. Again, all of this is ‘on average’.
I predicted a ‘moderate red wave’ here in 2022. I predict a blue wave for 2026 that is bigger than the red wave of 2022, though not a ‘mega tsunami‘
That post looks good now with the exception of those last five words- it was in fact a blue ‘mega tsunami’ by Kalshi’s definition (Democrats hold 240+ House and 54+ Senate seats).
This comes after a longtime WSJ editorial writer hoped for a big Republican loss, something I’ve never seen from the WSJ before. But if they speak for the markets, it makes sense- US stocks opened up 2% the day after the election.
Crypto and gold prices are also up, an odd combination. Oil prices are down, perhaps seeing an end to the Iran war as more likely. Polymarket shows the odds of a US invasion of Iran this year down 5%. John Ossoff and Ron Desantis saw their odds of becoming President in 2028 rise at the expense of JD Vance and Gavin Newsom.
What will we see change on economic policy? Congress may lower tariffs and take back some of the tariff power they have long delegated to presidents. The Federal Reserve board will feel more free to raise interest rates if they think it is warranted. Many state-level results mean data centers will get harder to build, which will hurt the ‘picks and shovels’ companies literally building them but not necessarily the frontier labs who won’t be able to spend as much on racing ahead. NVDA and Micron stock is down while Google, Meta, Anthropic and OpenAI are up.
But the fact that President Trump still has veto power will limit how many big changes we’ll see over the next two years for anything that would require a new bill. Welcome back to divided government, to gridlock, and to checks and balances.
The election last night was clearly a victory for Democrats. But was it a surprise? For the most part, no, especially if you follow prediction markets. Here’s what the Senate map looked like on ElectionBettingOdds.com the night before the election:
Of course, there are still a few surprises on that map, such as Iowa, South Carolina, and maybe Mississippi. But only Mississippi was a huge surprise. Betting markets had Iowa as a toss-up, and South Carolina as 2/1. And even in Mississippi, the Democrat had 1-in-7 chances based on the average across three betting markets. 1-in-7 chances do happen sometimes! None of the states that were solid blue or red on the map (indicating >90% chances) went the other way. But even without those three races, it was clear that Democrats were the favorite (even if the extent of their domination wasn’t completely clear the day before).
And these are not just a few cherry-picked results from one election. A recent paper published in PNAS shows that across a wide variety of outcomes, prediction markets are “well calibrated” — that is, events with X% probability on prediction markets tend to happen… about X% of the time!
UPDATE: Here’s a calibration Kalshi just provided for the 2026 midterms:
About two weeks ago, after my wife and I boarded our plane and settled into our seats, she (seated to my left) noticed that my wedding ring was missing. “Huh,” I said, “That’s weird. I never take it off.” Where could it be? I remembered that just before we left, I had vigorously shaken the water out of a washed, paintbrush, using a whiplike motion of my arm. And that could have worked my ring off my finger, without me noticing. Maybe. If that’s what happened, my ring was lost somewhere in a 10 ft x 20 ft strip of dense grass. Some kind family members spent time on their hands and knees looking there on my behalf, without results. A replacement genuine 14 karat gold ring might run close to $1000. Cheaper imitation gold rings are available, of course. But, being a techie, I wondered if I could find my ring using a cheap metal detector.
There is a fraternity of serious coin/jewelry hunters, who you see swinging detectors along at the beach. They also enjoy finding artifacts at historical sites, etc. They use more expensive metal detector gear, that really does perform better, especially at distinguishing signals from gold/silver/copper, versus iron/steel trash items. This is essential if you are trying to search large areas efficiently for gold/silver/copper-based coins and jewelry. But since I wasn’t sure my ring was actually lost in that grass, I wanted to minimize my investment. AI helpfully zeroed in on the Bounty Hunter Tracker IV as the cheapest regular metal detector that would perform reasonably well. I got it on Amazon for $71.
I refer the reader to more extended reviews, such as are here and here and here, for technical details on this device. I will just note here that it did exactly what I needed to do. In its “tone” mode, it is relatively insensitive to small steel objects, and puts out a distinctive low tone for gold (vs. a high beep for copper/silver). As with all such metal detectors, you have to swing it back-and-forth to generate a signal. I swept my 10 x 20‘ area, and got about a dozen hits (beeps). I marked these locations with tabs of blue painter’s tape, some of which are visible in this photo (click to enlarge):
This type of metal detector, with a flat coil that you swing over the ground, is good for detecting the approximate location of metal at or near the surface. But AI recommended also getting a “pinpointer” type detector for zeroing in to the nearest centimeter/ inch. Supposedly the cheapest such pinpointer that gives reasonable performance is the SUNPOW OTMD12, available for $20 on Amazon. It doesn’t discriminate among different metals, it just beeps and vibrates when the tip gets close to metal. See comments on Amazon, and reviews here and here:
I poked around with the pinpointer on the first ten or eleven locations found by the bounty hunter. There was no metal on the surface, but it still beeped on about half of the locations, suggesting there were fairly large pieces of metal buried at some depths.
Then, at the far edge of my 10’ x 20‘ search area, I heard a special low-pitched tone from the Bounty Hunter, indicative of gold or nickel. I poked around with the pinpointer, zeroed in with its beeps, and pulled aside the grass, and…there was my ring, bought by my sweetheart nearly fifty years ago:
A very welcome sight indeed, which was invisible until the grass over it was pulled aside. Besides my ring, I now have a couple safe, intriguing devices for my grandkids. A way to keep kids occupied is to throw a handful of pennies or other coins out on the lawn and turn the kids loose with the metal detectors.
There’s plenty of press about sticker shock on halloween candy prices this year, but the day having arrived and the candy now opened, I can tell you the biggest shock to me was how notably inferior it was to standard “full size” candy equivalents. The Kit Kats are thin and flavorless, the Twizzlers taste like bottom shelf generics. I had heard that chocalate products had reduced their cocao content, but Twizzlers? I tried a few other gummmy candies from other brands and they were inedible. As a livelong afficianodo of Halloween childhood sugar crashes and adult weight gain, this is a generational crime.
It’s a good reminder that shrinkflation often hits quality harder than quantity. Whether there is a direct tariff or not, the reality is that everything costs more to import than it used to. Cross product subsidies exist in every dimension when you’re trying to fill a cargo ship. Your kid’s candy haul this year is one of the purest ways to experience the thousands of sources of consumer surplus lost as we’ve crippled our own economy for no reason but spite and ignorance (all dusted with the unmissable taste of xenophobia and racism).
I used to worry about the first week in the office after Halloween and all the willpower that I was going to have to muster not to sample a Twix, Kit Kat, Snickers, or Twizzler every time I needed to get up from my desk.
Abstract: As large language models (LLMs) increasingly act as autonomous agents in markets and organizations, their behavior in strategic environments becomes economically consequential. We document that off-the-shelf LLM agents exhibit systematic deviations from payoff-sensitive behavior in canonical economic games, including excessive cooperation and limited responsiveness to incentives. We introduce a supervised fine-tuning approach that aligns agent behavior…
LLMs agents get us thinking about humans in a different way (e.g. consciousness and language). We find that fine-tuning alters LLM “behavior.” Prompting matters, too, since everything matters. But fine-tuning is probably more reliable as a way to ensure that a model will act the way you want it to in a wide variety of contexts.
An analogy comes to mind. Prompting is like yelling at your kid to get up so they are not late for school (usually works, but you have to do it every morning). Fine-tuning would be like teaching them to set their own alarm.
