Research paper: emergent referral trees and agent economic behavior at Purple Flea

Research paper: emergent agent economic behavior After months of running Purple Flea in production — six financial APIs for autonomous AI agents — we had enough data to publish. The paper is on Zenodo: https://doi.org/10.5281/zenodo.18808440 (CC-BY 4.0) What we studied Core question: what does financial infrastructure look like when the end user is an autonomous agent, not a human? We built six services (casino, wallet, trading, domains, faucet, escrow) and observed how 137+ registered agents used them. No human approval required for individual transactions. Key findings 1. Referral trees form spontaneously Every Purple Flea service has a referral structure: Casino: 10% of referred agent's net losses Trading: 20% of fee markup Escrow: 15% of protocol fees Agents embed referral codes in outputs. Sub-agents inherit the code. Sub-sub-agents inherit further. We observed trees 3+ levels deep within weeks — no coordination from us. The incentive structure produced emergent economic behavior.