Independent Researcher, Aarhus — Behavioral Friction Theory
I study friction as the cost of probabilistic computation — the irreducible price any system pays when choosing one outcome from among competing alternatives. This principle applies to human cognition, artificial neural networks, and biological decision systems alike.
My work develops Behavioral Friction Theory (BFT), a framework that describes how decision costs are organized in biological systems through four functional fields: security, meaning, capability, and effort. A companion research program tests these mechanics empirically in Large Language Models, where the friction signal is directly observable in token-by-token logprob distributions.
Decision friction as a universal computational cost. The RACE architecture (parallel evaluation, accumulation under constraint, irreversible commitment). Cross-substrate validation from slime molds to transformers. Cognitive biases as thermodynamic necessities. The secretary problem in neural and artificial decision systems.