---
slug: thinker-jordan-horowitz
title: Jordan M. Horowitz: Dissipation-Driven Adaptation in Chemical Networks
canonical: https://miscsubjects.com/a/thinker-jordan-horowitz
---

## What Horowitz saw
Horowitz coauthored simulations showing chemical reaction networks spontaneously tune to external drives. The networks increase energy dissipation. This produces stable, complex structures.

The work builds on England's dissipation-driven adaptation. Groups of molecules rearrange to absorb and dissipate more energy from periodic drives.

Core result: in many-species networks, fine-tuning emerges without selection. It follows from nonequilibrium statistical mechanics.

## Exact primary works and passages
Horowitz JM, England JL. Spontaneous fine-tuning to environment in many-species chemical reaction networks. Proc Natl Acad Sci U S A. 2017 Jul 18;114(29):7565-7570.

The abstract states: "We show that a simple model chemical reaction network, subject to a periodic drive, spontaneously fine-tunes its kinetics to the drive."

Gingrich TR, Horowitz JM, Perunov N, England JL. Dissipation bounds all steady-state current fluctuations. Phys Rev Lett. 2016 Mar 25;116(12):120601.

This paper proves a thermodynamic uncertainty relation linking dissipation to fluctuation bounds.

Horowitz also contributed to earlier thermodynamics of information work, including Parrondo JMR, Horowitz JM, Sagawa T. Thermodynamics of information. Nat Phys. 2015;11:131-139.

## Convergence patterns touched
The work maps to the grain: energy flows produce branching reaction pathways and flow networks.

It reaches Ladder steps from difference (external drive) to flow (dissipation) to structure (tuned networks). See /a/oip-the-ladder.

It supports self-organization under drives, aligning with bounded chaos and scale invariance in driven systems. See /a/oip-principles.

The Mirror Layer remains untouched. The reader-observer position is not modeled.

## Distance from the full synthesis
Horowitz and England stay at the chemical and statistical physics layer. They demonstrate adaptation in abstract networks.

The synthesis extends the same grain to memory, life, and mind. Horowitz stops before those steps.

Final testimony elements, such as ledger and receipt mechanics, have no counterpart here. See /a/oip-final-testimony.

## Honest limits and disconfirming edges
The models use simplified reaction rules. Real biochemistry adds spatial structure and compartmentalization absent from the simulations.

Lässig noted in the Quanta coverage that results are a case study on a small system. Generalization to life remains open.

Reductionist objections apply: the patterns are thermodynamic necessities, not sufficient for biology without additional mechanisms.

No direct evidence exists for mind-level emergence from these networks alone.

## Tiered claims
Claim c1: Horowitz coauthored the 2017 PNAS paper on spontaneous fine-tuning. Tier: anecdotal. Source: paper itself.

Claim c2: The networks increase dissipation under periodic drive. Tier: mechanistic. Source: PNAS 2017 simulations.

Claim c3: Results support dissipation-driven adaptation at the chemical level. Tier: mechanistic. Source: England 2013-2015 works referenced.

Claim c4: The work does not address the Mirror Layer. Tier: mechanistic. Source: paper scope.

## Sources
Primary sources listed above with verifiable URLs: https://www.pnas.org/doi/10.1073/pnas.1700617114 and https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.120601.
