Is the lone-printer die-out an early birth-death accident?
research/selfrep-extinction-kits
Exact Markov-chain test of why a lone replicating printer dies out (stylised, assumed inputs): about a fifth die before a fleet forms at 500 h mean life; wear-out lives remove it.
What it found
Tests the extinction explanation left open by selfrep-wearout with an exact continuous-time Markov chain of the same shop (Poisson vitamin kits, exponential builds and lives). With assumed inputs and a budget of 4 units/h, the chain gives a 0.194 chance that a lone seed machine is lost before the fleet reaches 6 machines at 500 h mean life (simulation 0.198 ± 0.006), of which 0.162 is the seed retiring before its first copy; relaxing the Markov assumptions moves this by at most about 0.03. Wear-out lives (Weibull shape 2 or more) cut it to under 3%. Exact mean extinction times for small equilibrium fleets match simulation (1464 h against 1511 ± 50 h at 200 h life), but with regular kits and lognormal builds long times roughly double, so the chain is a lower guide there. Stylised: every input is assumed; simulated only.
A figure from its own output
Paper, code and results
More in Self-replicating manufacturing
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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.