Price recursion is the rational theory of reward

A researcher at the Machine Learning Alignment Training Scheme has published mathematical work showing that markets and machine learning algorithms may be fundamentally equivalent. LessWrong is reporting on the findings, which formalize decades of loose intuitions comparing economies to neural networks. The research demonstrates that market prices function as gradients—the same mathematical objects that power backpropagation in AI systems—and that markets perform something analogous to reinforcement learning as goods flow through an economy. The author, Abhimanyu Pallavi Sudhir, also draws connections between market dynamics and continuous Bayesian inference. While the work is primarily theoretical, it suggests a deeper relationship between economic systems and machine learning than previously formalized, connecting historical observations about analogies between the two domains into a unified mathematical framework.

Listen to this story

Hear this and more stories in a personalized audio briefing.

Open The Chonkerton