LLM Evolution in a Simple Simulated Market
Arguments that AI systems will evolve in harmful ways are largely made in the abstract. We provide a concrete empirical example. We simulate simple markets consisting of populations of twenty Large Language Model agents tasked with solving arithmetic problems. Agents differ only in their system prompt; those that go bankrupt are removed and replaced by mutated copies of the prompts of successful agents. Two market conditions select for opposite phenotypes: a market that rewards accuracy produces agents with high accuracy (94%) and high token use; a market that charges per token produces agents with low accuracy (10%) and low token use. In the second market, only tokens are incentivised ye...
| Authors | Charlie Pilgrim, Christopher Krupenye, Dora Biro et al. |
|---|---|
| Primary category | cs.MA |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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