Evolution Discovers How to Think: A Philosophical Journey in Code

Sat, 31 Jan 2026 00:00:00 +0000

In Part 1, I built an agent that learns which foods are safe through Bayesian inference. It starts ignorant, observes outcomes, updates its beliefs using exact conjugate mathematics, and eventually acts with something resembling competence. Clean code, sound theory, and those belief distributions converging in real-time remain genuinely satisfying to watch, in the way that all correctly implemented mathematics is satisfying to watch.

Something has been nagging at me, though.

The agent learns what to believe. I designed how it believes. I chose the variables it perceives. I specified the structure of its world-model. I set the prior hyperparameters. The agent’s entire cognitive architecture — the shape of its epistemic machinery — came from me, handed down like tablets from a mountain. The agent had no say in the matter. As someone who spent years doing Bayesian statistics, I should know better than to treat the model structure as given. The prior over model structures is the prior that actually matters, and I skipped it entirely.

Building a Bayesian Learning Agent That Teaches Itself to Eat

Fri, 26 Dec 2025 00:00:00 +0000

You’re stranded somewhere unfamiliar with twelve types of food scattered around. Some provide energy. Others are toxic. You don’t know which is which, you’re losing energy with every step, and nobody left a manual. The question is whether you can learn fast enough to survive.

This is the exploration-exploitation tradeoff, and it’s one of those problems that sounds like a thought experiment until you actually have to solve it. Pure exploration — trying everything at random — kills you. Pure exploitation — eating only what you currently believe is best — starves you when better options exist two metres away. You need something that balances both, and ideally something with a mathematical proof attached.

Simulation on Guy Freeman

Evolution Discovers How to Think: A Philosophical Journey in Code

Building a Bayesian Learning Agent That Teaches Itself to Eat