CHRIS HAY

IDEAS · SYSTEMS · OBJECTS / LONDON · 2026

What keeps an evolving world alive?

Variation keeps the world populated. An arms race is another question.

ABOUT THIS NOTE +

Predator–prey coevolution and whether it produces an arms race have a long history in artificial life. Nolfi and Floreano asked this question of coevolving robots in 1998. Here I am examining what the particular rules of my small world sustain. Both species survived to 10,000 ticks in 5/6 worlds at 1% swaps and 6/6 at 2%. None of the eleven surviving worlds passed either registered categorical-coupling screen.

N-CELL80-02PARTIALLY SUPPORTEDRECORDED 2026-09-09DRAFT · V0.1REFERENCE DRAFTFOLLOW ↓
WATCH / ONE POPULATED WORLD

A world with
something living in it.

Can an evolving world keep both its food-eaters and its predators alive long enough for their interactions to matter?

BEFORE YOU PRESS PLAY

Two ways to make a living in the same world.

This world begins with 60 grazers and 10 predators. Grazers gain energy from food growing on the grid. Predators gain energy by killing grazers. Both spend energy and can reproduce; if either population disappears, that relationship ends.

Newborns inherit their parent’s species, programs and numerical settings. The settings can mutate, and each eligible program has a 2% chance of being swapped at birth. That changes how organisms behave. The experiment asks whether these conditions keep both species present over 10,000 ticks—10,000 steps of the world’s clock.

Pale circles mark grazers, gold diamonds mark predators, and green squares contain food. A square may hold several organisms, so larger marks show a more crowded location. Select a square to see who is there.

WHAT TO WATCH
  1. 01 / PLAYRead the landscape.

    Watch organisms move and food disappear and regrow. The replay starts after the first tick.

  2. 02 / THE COUNTSFollow both species.

    Use the separate grazer and predator totals to see whether each population persists.

  3. 03 / THE ENDPOINTAre both still here?

    Jump to the final tick. Survival in this one world is the first observation; the comparison across worlds comes next.

The population traces show grazers in gold and predators in blue. Rising and falling numbers alone do not tell us whether either species evolved in response to the other.

EX-9 / SEED 42 / 48 × 48 / 2% SWAPSRECORDED WORLD STATES
TICK0

Eat. Move. Reproduce. Survive.

Predators + grazers

Recorded grid: 48 by 48, 60 living organisms at tick 0. Use the recorded-state download for the complete data.

60alive

50grazers

10predators

0 births · 10 deaths · 10 predation kills / cumulative

GrazersPredatorsFood

The preview is a recorded frame. Play loads 9.3 MB of history.

Select a square to inspect its organisms. Arrow keys work when a world has focus.

One recorded 2% world, sampled every twenty ticks. Its complete 10,000-tick history hash matches the closure experiment. This shows coexistence; the arms-race screen did not pass. Squares are actual grid locations; marks group organisms sharing a tile, growing with occupancy. In the lineage view, mixed tiles prioritize program 33, then 37; inspect a tile for every program present. Positions are not interpolated. Download recorded states ↓ · Replay provenance ↗

AFTER THE REPLAY / WHAT WE LEARNED

The relationship survived. An arms race remains unestablished.

Both species were still present at the end of this replay. Across the wider comparison, they survived in all six worlds at 2% program swaps and five of six at 1%. These conditions gave evolution a populated world in which interactions could continue.

An evolutionary arms race would require a stronger pattern: a change in one species followed by an evolutionary response in the other. I tested the surviving worlds for that pattern. None of the eleven passed either of the registered screens.

The replay lets us see coexistence. The measurements below establish how often it lasted and what the response tests found. Survival for 10,000 ticks does not establish indefinite survival, and the screens do not capture every possible form of coevolution.

01 / KEEP THE WORLD POPULATED

Still alive.
Still an open question.

At low but nonzero program-swap rates, predators and grazers survive together in eleven of twelve worlds. Select one to inspect its recorded endpoint.

EX-9 / SIX WORLDS AT EACH SWAP RATE · 10,000 TICKS
1%PROGRAM SWAPS
2%PROGRAM SWAPS

○ Grazer◇ PredatorFilled = alive at the endpoint

SELECTED WORLD / SEED 42

1,667 grazers

741 predators

Neither coupling screen passed. Label p = 0.9799; circular p = 0.9709.

Numerical mutation remained active. Each screen required p < 0.05/12 (about 0.00417). Select a world to inspect its endpoint; these symbols are not population sizes. Recorded results ↗

02 / WHAT NEEDS TO CHANGE?

Keep the numbers moving.
Lose the predators.

NUMERICAL-ONLY RETEST / SIX SEEDS · 10,000 TICKS
Full mutation
6/6
Both species alive at the endpoint.
Numerical mutation only
0/6
Predators extinct in every seed.

This is the historical matched full-versus-numerical-only test, separate from the later low-swap matrix above.

Numerical mutations alter settings such as reproduction thresholds. Program swaps replace a gene used for movement or deciding when to reproduce. Changing only the numbers did not sustain the predators in these worlds.

The earlier mutation-off controls had already lost predators in all ten worlds across two otherwise robust configurations, measured at 3,000 ticks. Adding a pause after feeding did not rescue them.

What the comparison establishesREAD +

Access to program swapping matters to persistence under these conditions. The comparison does not isolate every population-level mechanism involved. Once one world has millions of births and another has collapsed, the histories differ in much more than a single visible trait.

Neither this test nor a 10,000-tick endpoint establishes indefinite coexistence.

Coexistence isn’t coevolution.

03 / LOOK FOR A RESPONSE

Two populations change.
Are they answering each other?

THE REGISTERED ARMS-RACE TEST
  1. OBSERVETrait changes.

    Sustained changes in categorical role programs.

  2. COMPARETwo nulls.

    Label permutations and circular shifts.

  3. IF BOTH PASSCausal replay.

    Disrupt the proposed response at a traced birth.

The final stage was not triggered. None of the eleven surviving low-swap worlds passed either screen.

A population curve rises and falls for many reasons. An arms-race claim needs evidence that one side’s evolutionary change provokes a response on the other.

The original coupling screen failed. Removing program swaps could not test whether a high swap rate was obscuring a response, because it also removed the predators. The lower, nonzero rates supplied surviving worlds in which the same question could be asked.

04 / KEEP THE LIMIT IN VIEW

The world continues.
The signal is absent.

EX-9 / finite-horizon survival and categorical coupling
Swap rateBoth species aliveworldsWorlds testedSurvivors passing either screen
1%560
2%660

10,000 ticks. Each screen used p < .05/12. Numerical mutation remained active. Complete results

The detector examines a particular categorical pattern. It does not measure every kind of coevolution, including continuous numerical-trait responses. Grazers in this world have no direct predator-sensing channel.

My conclusion is therefore bounded: the ecology stayed populated, but a traced arms race remains unestablished.

OPEN

Can a useful change make the next advance possible?

A populated world gives evolution somewhere to happen. The final note asks whether its improvements build on one another.

THE NOTE / AT READING PACE

Read the complete noteOPEN +

A long-running question

Predator–prey coevolution and whether it produces an arms race have a long history in artificial life. Nolfi and Floreano asked this question of coevolving robots in 1998. Here I am examining what the particular rules of my small world sustain.

Nolfi & Floreano (1998) — Coevolving predator and prey robots: do “arms races” arise in artificial evolution?

THE NOTE

The predators disappear when I stop the mutations.

That happened in all ten worlds in the original control comparison: five seeds in each of two configurations. With mutation enabled, both species survived in all ten to the 3,000-tick endpoint.

The same worlds could sustain predators and grazers. Their fate depended on whether inherited variation continued to arrive.

It was a result about what kept this ecology populated. Understanding what kind of evolution was happening required another test.

A world with consequences

Cell80's grazers seek food. Predators seek grazers. Organisms spend energy, acquire it, reproduce and pass on their programs and numerical settings.

The smaller prototype had suffered familiar collapses: predators exhausted the available prey and then starved. Making a larger world with more resources produced configurations where both populations survived.

Those were the configurations used for the mutation-off controls. Turning mutation off still lost the predators.

I also added a period of satiation after a predator fed. That improved some surviving populations but did not rescue the mutation-off worlds. A pause between meals was not enough to explain the difference.

What needs to vary?

There are two kinds of inherited change in these experiments.

Numerical mutations alter settings such as reproduction thresholds. Program swaps replace a gene used for a role such as movement or deciding when to reproduce.

A later experiment kept numerical mutation and removed program swapping. In all six tested seeds, predators were extinct by the 10,000-tick endpoint. The matched full-mutation worlds retained both species.

Changing the settings alone did not sustain those predator populations. Allowing the organisms to inherit different role programs did.

That narrows the question. The available program variation matters to the persistence of this ecology. It does not yet explain exactly how the many resulting changes combine to sustain the populations.

Survival is an observation. Response is a claim.

An evolutionary arms race would involve something more specific: changes on one side creating pressure for changes on the other, with further responses following.

Population curves alone cannot establish that sequence. Predator numbers can rise and fall as food becomes abundant or scarce. The most common programs can also change without one species having evolved in response to the other.

The registered test therefore looked at sustained changes in categorical traits across the two species. It asked whether their alternation was stronger than expected under randomized comparisons.

The original predator experiment did not pass that test.

One possible objection remained: perhaps the rate of program swapping was itself obscuring a response. Removing swaps entirely could not settle that, because it also removed the surviving predators.

Enough variation to keep asking

The follow-up tested lower, nonzero swap rates while keeping numerical mutation active.

There were now eleven surviving worlds in which to examine the coupling question.

None passed either of the two registered screens: a label-permutation comparison and a circular-shift comparison preserving within-species event clustering. Each used the fixed threshold of 0.05 divided by twelve. No causal arms-race replay was triggered.

Lower swap rates had kept the question testable. They had not produced the required signal.

What the living world tells us

Across these tested conditions, continued mutation—and specifically access to program swaps in the numerical-only comparison—can determine whether predators persist.

The claim stops at the measured horizons. Survival to 10,000 ticks does not establish indefinite coexistence. The categorical detector also does not cover every form of coevolution: it does not measure continuous numerical-trait responses, and grazers in this world have no direct predator-sensing channel.

The evidence supports a populated, changing ecology. A traced arms race remains unestablished.

That distinction matters because an active world makes progress easy to imagine. We see organisms moving, lineages changing and populations recovering. Each observation raises a question about what the system has learned to do.

In the first note, replay let me test whether one inherited change mattered to a particular event. Here, the question was whether variation could sustain the interactions in which further evolution might happen.

first note

The next question asks what those interactions can build.

Measurements, methods & provenanceOPEN +

The world viewers play spatial states exported by the Rust ecology engine. They do not run a new simulation in your browser. The other studies show recorded outcomes and an intervention diagram. Historical EX-4 and the later closure batch used different gene pools and assays.

The complete September closure batch contains 379 primary worlds plus verification runs. That is not the total across the earlier programme. First-step benefit counts direct offspring of a focal organism; the factorial assay counts total births in fresh founder populations.

Source reports and their file hashes are included with the evidence download. The EX-4 replay uses the historical gene library and reproduces the recorded birth and plurality event. The EX-9 replay matches the complete history hash of the selected closure world. Both show sampled end-of-tick states, without interpolating organism positions.

SOURCES & PROVENANCE

AUTHOR / CHRIS HAY · VERSION / 0.1

REFERENCE THIS DRAFT

An unpublished working record. These references identify the draft and omit a publication date. They become version-specific publication citations when the record is released.

Chris Hay. What keeps an evolving world alive? [Unpublished draft, version 0.1. First publicly recorded 2026-09-09]. https://chrishayuk.com/notebook/what-keeps-an-evolving-world-alive
DOWNLOAD