Why Biology is Different
summary
Claims that diffusive influence systems are almost surely asymptotically periodic yet can encode chaos and Turing machines on fractal, measure-zero Cantor sets; constructs a coding tree to represent phase-space evolution and a flow-tracker algorithm that assembles tensorized trees; analyzes products of stochastic matrices via a new analytic invariant called S-energy; proves chaos can occur with non‑positive Lyapunov exponents and shows mixed time‑scale interactions cause loss and recovery of memory, so renormalization must be dynamics‑driven rather than fixed-grid.
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chapters (18)
- 0:00Intro
- 1:00Are the right variables
- 1:47Big data
- 2:18Linear
- 4:00Program vs Data
- 7:02Scaling 1905
- 9:01Downward Causation
- 10:41Agents
- 12:22Diffusion
- 16:30Ising Model
- 18:42Lattice
- 20:27Decoupling
- 24:27Algorithm
- 30:14S Energy
- 32:32Intuition
- 34:39Flow Tracker
- 36:47Summary
- 40:32Challenges
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