On the thermodynamics of prediction under dissipative adaptation
Kai Ueltzh\"offer

TL;DR
This paper explores how thermodynamic principles underpin the emergence of complex, predictive information processing systems from simple dissipative structures, highlighting the role of efficiency and hierarchy in this transition.
Contribution
It combines thermodynamic and information-theoretic perspectives to explain the evolution from simple dissipative structures to complex, predictive systems.
Findings
Dissipated heat bounds non-predictive information.
High efficiency drives systems towards predictive capabilities.
Hierarchy of dissipative systems facilitates complexity emergence.
Abstract
On the one hand, the dissipated heat of a thermodynamic work extraction process upper bounds the non-predictive information, which the associated system encodes about its environment. Thus, emergent information processing capabilities can be understood from the perspective of a pressure towards high thermodynamic efficiency. On the other hand, the second law of thermodynamics plays a crucial role in the emergence of complex, self-organising dissipative structures. Such structures are thermodynamically favoured, because they can dissipate free energy reservoirs, which would not be accessible otherwise. Thereby, they allow a closed system to move from one meta-stable state to another meta-stable state of higher entropy. This paper will argue, that these two views are not contradictory, but that their combination allows to understand the transition from simple self-organising dissipative…
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Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics · Statistical Mechanics and Entropy · Neural dynamics and brain function
