消えたクラッシュ：5 つのモデル経済における制御と創発

Back to Articles

*Field notes from the Build Small Hackathon, June 2026. Third installment.*

In the first of these notes I told a story I was proud of. I drew a Wood Legend

called the Run on Oona's Hoard, a 1929 bank run reskinned as woodland folklore, and

watched the owl who keeps the honey read the panic and start liquidating. The flood of

supply crashed the honey price from 10 down to 3 over the next few turns. Nobody

scripted it. A reskinned bank run made an agent dump an asset, and the dump moved a

price. That was the whole thesis: give a small model a role and a budget, and emergent

market behavior falls out for free.

Then I rebuilt the wood, and the crash stopped happening. This installment is about

why, because the failure taught me more about building on agents than the original

success did.

Five labs, five minds

The rebuild swapped one model running five creatures for a council of five different

labs' small models, each driving its own creature: an OpenAI model, an NVIDIA model,

an OpenBMB model, and a half-billion-parameter model I fine-tuned myself running two of

them. The point was honesty. If the claim is that small models can run a living

economy, the strongest version of that claim is five distinct architectures making

distinct choices in the same market, not one model wearing five hats.

That heterogeneity is exactly what broke the story I had already written up.

The price is whatever the agents decide to trade at

I rebuilt the operator side too. The player is now a financier who works from the

shadows: short a good, whisper a true tip to set up its fall, spring the legend, and

collect when the price craters. I made that loop legible on the screen, with an

objective, a scoreboard, and a one-click first trade. Making a promise visible is the

fastest way to discover the promise is false.

Because when I shorted honey and sprang the Run on Oona's Hoard, honey did not crash.

It rose. The council models, reading a rumor that the vault was empty and a tip that

the crop was doomed, did not dump honey the way the original single model had. They

hoarded it. Scarcity, not a fire sale. The short lost money, and the headline the

narrator wrote, with no irony, was that the honey gamble had soured.

This is the lesson, and it is not specific to a game. In an agent economy the reference

price is not a dial you turn. It is the residue of what the agents actually choose to

trade. The original crash was real, but it was contingent on one model's disposition,

not a robust property of the system. Change the population, and the emergent behavior

you documented can simply evaporate.

Three ways to fail

I spent three live runs trying to make the crash come back by pushing on the economy

from the outside, the way you would shock a textbook supply and demand model.

First I left the legend as a pure rumor and trusted the agents to react. They did not

sell. Second I dumped a windfall of honey into every creature's stores, reasoning that

a glut would collapse demand and pull the price down. That worked beautifully against

my test policy, a rule-based stand-in I use for fast offline runs, because the test

policy follows a mechanical wants-threshold: flood its inventory and it stops buying.

The live models ignored the windfall and traded on their own read of the room. The

gambit lost again. Third I sized the short up, which only made the loss larger.

Three recordings, three losses: minus fifteen, minus twenty-six, minus twenty-seven

pebbles, when the entire premise was that this was how you made money. The pattern was

the warning. Every lever I pulled was an input to the agents' decision, and the agents

were free to decline. You cannot steer a heterogeneous population of models with a

mechanical shock, because the shock only biases a choice they still get to make.

The trap inside the trap is worth naming on its own. The fix that worked against my

fast test policy gave me false confidence and cost me a live run to disprove. When the

cheap stand-in and the real agents disagree, the stand-in is the one lying, and any

result that only reproduces under the stand-in is not a result.

Author the seam, do not push the inputs

The resolution was to stop trying to convince the agents and to make the panic true by

construction. A bank run is, definitionally, a crash. So the legend now crashes its

good at settlement, after the market has finished clearing for the turn, by overwriting

the reference price directly. The agents trade all they like; then the run lands as a

fact, the price halves, and the short that front-ran it settles into profit. The crash

is no longer a behavior I hope for. It is an authored consequence I impose at the one

seam where nothing downstream can argue with it.

That sounds like giving up on emergence, and it is the opposite. The emergent layer,

five models trading, gossiping, hoarding, forming grudges, is still doing all the work

that makes the wood feel alive. What I learned is that you do not get reliable outcomes

by pushing harder on emergent inputs. You get them by choosing the precise seam at

which to author a deterministic override, and leaving everything upstream free. Emergence

for texture, authored control for the moments that have to happen. The craft is knowing

which is which, and where the seam sits.

*Table 1. The same gambit across four worlds. The crash was emergent and fragile under one model, absent under a heterogeneous council, and reliable only once it was authored at the settlement seam.*

What I took away

Three things, and all three outlive the game.

First, emergence is contingent, not durable. Behavior you observe and write up from one

population of agents can vanish when you change the population, even if nothing else

changes. Treat a single impressive run as an anecdote, not a property, until it survives

a different cast.

Second, you do not control a market of agents by shocking its inputs. Supply and demand

levers only bias choices the agents are still free to make, and a heterogeneous council

will frequently decline. Reliable outcomes come from authoring at a settlement seam,

downstream of every decision, not from pushing harder upstream.

Third, the cheap simulator that lets you iterate fast is also the one most likely to

flatter a wrong fix. When the stand-in and the real agents disagree, believe the agents.

I build agent-based market models for a living, and I have made every one of these

mistakes at larger scale and higher stakes than a wood full of woodland creatures. It

was useful to make them again somewhere the only thing at risk was a pile of pebbles

and a story I had told too confidently the first time.

Small models, big adventures, and a crash you have to author yourself.

*Try it: the Space.

Open agent traces: the dataset.*