Purpose
The population simulator makes model ecology dynamics visible. It does not run real models. It teaches how carrying capacity, selection pressure, mutation rate, niche count, and resource budget change the shape of a population.
Toy ecology
Browser-localPopulation pressure simulator
Run a deterministic toy model of mutation, scoring, diversity, no-op, and retirement. This is explanatory, not predictive.
Run the simulation to see the generation summary.
Reading the output
- Champions are the best known artifacts for a niche.
- Specialists are useful but not universal.
- Challengers are candidates still collecting evidence.
- Retired artifacts are no longer active but remain in lineage.
- No-op rate rises when budget is tight or candidates are too similar.
PROCEDURE simulate_population(settings)
population <- INITIALIZE(settings.initial_size, settings.niches)
FOR generation IN 1..settings.generations
candidates <- FORK(population, settings.mutation_rate)
evidence <- FIGHT(candidates, settings.selection_pressure)
population <- KEEP_CHAMPIONS_SPECIALISTS_AND_DIVERSE_CHALLENGERS(evidence)
retired <- FLEE_REDUNDANT_OR_COSTLY(population, settings.budget)
no_op <- COUNT_REJECTED_LOW_MARGIN_ACTIONS(evidence)
END FOR
RETURN SUMMARY(population, retired, no_op)
END PROCEDURESource reports used for this guide
These reports are preserved verbatim in the site archive. The guide above is an editorial synthesis and may narrow, qualify, or reorganize claims from the source material.
Project evaluationsEvaluation of ModelBreeder.com v2.2 Philosophy ImplementationProject evaluation · 34.8 KB
Evolutionary AIDesigning the “Perfect” Evolutionary AI SystemEstablished and emerging methods · 46.5 KB
Evolutionary AIPerfect Evolutionary AI: Definition, Design, and ImplicationsConceptual synthesis · 29.4 KB
Core synthesisTeleodynamic Evolution of AI Ecosystems — Uploaded EditionConceptual synthesis · 15.3 KB