Thermodynamics of Evolution and the Origin of Life
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Abstract (arXiv)
We outline a phenomenological theory of evolution and origin of life by combining the formalism of classical thermodynamics with a statistical description of learning. The maximum entropy principle constrained by the requirement for minimization of the loss function is employed to derive a canonical ensemble of organisms (population), the corresponding partition function (macroscopic counterpart of fitness) and free energy (macroscopic counterpart of additive fitness). We further define the biological counterparts of temperature (biological temperature) as the measure of stochasticity of the evolutionary process and of chemical potential (evolutionary potential) as the amount of evolutionary work required to add a new trainable variable (such as an additional gene) to the evolving system. We then develop a phenomenological approach to the description of evolution, which involves modeling the grand potential as a function of the biological temperature and evolutionary potential. We demonstrate how this phenomenological approach can be used to study the "ideal mutation" model of evolution and its generalizations. Finally, we show that, within this thermodynamics framework, major transitions in evolution, such as the transition from an ensemble of molecules to an ensemble of organisms, that is, the origin of life, can be modeled as a special case of bona fide physical phase transitions that are associated with the emergence of a new type of grand canonical ensemble and the corresponding new level of description
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 4 of its 82 references that are in the map. First posted on arXiv 28 Oct 2021.
Builds on
4 of 82 references.
References · 4
- 2020The world as a neural networkcited by 18
- 2021Towards a Theory of Evolution as Multilevel Learningcited by 17
- 2020Towards a theory of machine learningcited by 14
- 2020Emergent Quantumness in Neural Networkscited by 10
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 1.
7 papers.
- 2024Dataset-learning duality and emergent criticalitycited by 5
- 2024Emergent field theories from neural networkscited by 4
- 2025Geometric Learning Dynamicscited by 2
- 2022Bio-inspired Machine Learning: programmed death and replicationcited by 1
- 2025Molecular Learning Dynamicscited by 1
- 2023A Theory of Intelligencescited by 0
- 2026Geometric framework for biological evolutioncited by 0
- 2026Geometric framework for biological evolution16 Mar 2026
- 2025Geometric Learning Dynamics20 Apr 2025
- 2025Molecular Learning Dynamics14 Apr 2025
- 2024Emergent field theories from neural networks12 Nov 2024
- 2024Dataset-learning duality and emergent criticality27 May 2024
- 2023A Theory of Intelligences23 Aug 2023
- 2022Bio-inspired Machine Learning: programmed death and replication30 Jun 2022