Triadic Conceptual Structure of the Maximum Entropy Approach to Evolution

Many problems in evolutionary theory are cast in dyadic terms, such as the polar oppositions of organism and environment. We argue that a triadic conceptual structure offers an alternative perspective under which the information generating role of evolution as a physical process can be analyzed, and propose a new diagrammatic approach. Peirce’s natural philosophy was deeply influenced by his reception of both Darwin’s theory and thermodynamics. Thus, we elaborate on a new synthesis which puts together his theory of signs and modern Maximum Entropy approaches to evolution. Following recent contributions to the naturalization of Peircean semiosis, we show that triadic structures involve the conjunction of three different kinds of causality, efficient, formal and final. We apply this on Ulanowicz’s analysis of autocatalytic cycles as primordial patterns of life. This paves the way for a semiotic view of thermodynamics which is built on the idea that Peircean interpretants are systems of physical inference devices evolving under natural selection. In this view, the principles of Maximum Entropy, Maximum Power, and Maximum Entropy Production work together to drive the emergence of information carrying structures, which at the same time maximize information capacity as well as the gradients of energy flows, such that ultimately, contrary to Schroedinger’s seminal contribution, the evolutionary process is seen to be a physical expression of the Second Law.

💡 Research Summary

The paper challenges the conventional dyadic framing of evolutionary theory—most often expressed as a binary opposition between organism and environment—by proposing a triadic conceptual structure rooted in Charles S. Peirce’s semiotics. In Peirce’s model a sign (representamen), its object, and an interpretant form a three‑part relation. The authors map these components onto three kinds of causality identified by Peirce: efficient cause (the physical transfer of energy and matter), formal cause (the self‑organizing pattern of the system), and final cause (the long‑term goal of the system). By aligning efficient, formal, and final causes with modern physical principles—Maximum Entropy (MaxEnt), Maximum Power, and Maximum Entropy Production (MEP)—they construct a unified framework that treats evolution as a physical optimization problem governed by the Second Law of Thermodynamics.

The first pillar of the framework is the Maximum Entropy principle. In a situation of limited information, MaxEnt selects the probability distribution that maximizes uncertainty, thereby ensuring that a system explores the widest possible set of microstates compatible with its macroscopic constraints. When applied to evolution, this principle implies that natural selection favors phenotypic and genotypic configurations that maximize informational capacity, i.e., those that can encode the greatest variety of responses to environmental gradients.

The second pillar, Maximum Power, stems from the work of engineers such as H.T. Odum and states that systems evolve toward configurations that extract and dissipate the greatest possible power from their surroundings. In the triadic view this corresponds to the efficient cause: the physical conduit that channels energy from the environment into the system.

The third pillar, Maximum Entropy Production, posits that, among all admissible steady states, a nonequilibrium system will settle into the one that produces entropy at the highest rate. This is interpreted as the final cause: the evolutionary “goal” of maximizing the dissipation of gradients, which in turn drives the emergence of structures that are both highly dissipative and highly informative.

To illustrate how the three pillars interact, the authors turn to Robert Ulanowicz’s analysis of autocatalytic cycles. In a prebiotic chemical milieu, simple reaction networks can acquire autocatalytic feedback, thereby amplifying the flow of matter and energy through the network. The authors argue that such a cycle functions as a Peircean sign: the chemical reactions constitute the representamen, the surrounding energy gradient is the object, and the autocatalytic network itself acts as an interpretant—a physical inference device that “reads” the gradient and converts it into internal fluxes.

Within this interpretation, efficient causality supplies the external energy flux, formal causality provides the stable network topology (the autocatalytic pattern), and final causality pushes the system toward configurations that maximize both power extraction and entropy production. Consequently, the autocatalytic cycle simultaneously maximizes information capacity (by supporting a larger repertoire of internal states) and gradient dissipation (by channeling more energy). This dual optimization exemplifies how the triadic structure can reconcile the apparently opposing tendencies of information accumulation and entropy increase.

The authors further contend that this triadic synthesis overturns Erwin Schrödinger’s famous claim that life represents “negative entropy” (negentropy). Instead of viewing life as an exception to the Second Law, they argue that evolutionary dynamics are a concrete expression of it: natural selection steers systems toward states that both store information and accelerate entropy production. In this sense, evolution is not a fight against the Second Law but a sophisticated, self‑organized mechanism that fulfills it.

Beyond the theoretical exposition, the paper outlines several implications. First, it offers a new diagrammatic language for representing evolutionary processes that makes explicit the three causal layers, facilitating interdisciplinary communication between biologists, physicists, and semioticians. Second, by treating interpretants as physical inference devices, the framework opens the door to quantitative models of adaptation that can be calibrated with thermodynamic measurements (e.g., power fluxes, entropy production rates). Third, the triadic approach may be extended to complex adaptive systems outside biology, such as ecological networks, economies, and artificial intelligence, wherever information processing and energy dissipation co‑evolve.

In conclusion, the paper proposes that evolution should be understood as a triadic, thermodynamically grounded process in which Maximum Entropy, Maximum Power, and Maximum Entropy Production operate together. This perspective not only reconciles the emergence of information‑rich structures with the inexorable increase of entropy but also provides a fertile conceptual bridge between Peircean semiotics and modern statistical physics.