TheHyperneticPrince

Based on my paper published in Systems: https://www.mdpi.com/2079-8954/14/2/197 TLDR: Starting with an old cybernetic insight, I develop the Hypernetic Law of Experience (HLE) to describe a regime of optimization-induced collapse that shows up across domains. I propose that such disparate phenomanae as recursive training collapse in AI, economic bubbles, and...

Summary Mesa-optimizers are typically described as internal agents that emerge unpredictably inside trained models. This post argues that they are neither mysterious nor unique to machine learning. Instead, mesa-optimizers arise from the classical geometry of principal–agent problems instantiated within recursive optimization architectures. When a learning system reinforces internal routines on...