The Five Sciences of Meaning

A Foundational Essay in Meaning System Science

Authors: Jordan Vallejo and the Transformation Management Institute™ Research Group

Status: Foundational Essay No. 001 | November 2025

Introduction

Meaning System Science did not appear spontaneously. It emerged from the recognition that multiple scientific disciplines were studying different aspects of meaning without naming the underlying system. Over the last century and a half, five fields—semantics, semiotics, systems theory, thermodynamics, and affective science—have each developed rigorous accounts of truth, signals, interaction, pressure, and interpretation. What they lacked was a unifying architecture that treated these as components of one coherent system.

This essay traces the historical development of those fields, the structural insights each produced, and the scientific rationale for treating them as parts of a single meaning system.

I. Semantics: Meaning as Logical Structure

Modern semantics took shape in late nineteenth-century philosophy of language. Gottlob Frege’s 1892 paper Über Sinn und Bedeutung (“On Sense and Reference”) argued that meaning is structured: expressions have both a sense (Sinn) and a reference (Bedeutung), and the same reference may be presented under different senses. This moved meaning out of purely psychological territory and into logical form.

Alfred Tarski’s 1944 paper “The Semantic Conception of Truth” extended this structural stance. He treated truth as a formally definable relation between language and world and showed that the stability of meaning depends on explicitly specified truth conditions, not on speaker intentions.

From the standpoint of Meaning System Science, semantics contributes three pillars:

• Meaning can be treated as a formal structure, not merely a feeling or intuition.

• Truth fidelity is definable and analyzable within that structure.

• Coherence is a functional property of a system of statements, not just a stylistic preference.

Semantics, in other words, provides the first clear view of meaning as an object with internal architecture.

II. Semeiology (Semiotics): Meaning as Signal and Relation

Where semantics focused on the internal structure of meaning, semiotics focused on how meaning is carried and differentiated.

Ferdinand de Saussure’s posthumously published Course in General Linguistics (1916) argued that the basic unit of language, the sign, consists of a signifier (sound pattern) and signified (concept), and that its value arises from relational differences within a system of signs rather than from any inherent connection to objects. Saussure explicitly proposed a general “science of signs” (semiology) in which linguistics would be one part.

Independently, Charles Sanders Peirce developed a triadic account of the sign as a relation between sign, object, and interpretant, treating semiosis as an ongoing process in which meaning is generated and transformed. His Collected Papers present signs as participating in a genuine triadic relation that cannot be reduced to simple pairings.Semiotics adds three key insights:

• Meaning is transmitted through structured signals.

• Signals and interpretations are systemically related, not arbitrary on each use.

• Meaning can degrade, shift, or diverge as signs move through different contexts and interpreters.

Where semantics frames meaning as architecture, semiotics frames it as behavior: how meaning moves and transforms

III. Systems Theory: Meaning in Networks of Interaction

In the mid-twentieth century, scientists in biology, engineering, and the social sciences began noticing recurring patterns in how complex wholes behave. Ludwig von Bertalanffy’s General System Theory (1968) proposed that many systems—biological, mechanical, social—share common organizational principles and should be studied in terms of relations and wholes rather than isolated parts.

Norbert Wiener’s Cybernetics: Or Control and Communication in the Animal and the Machine (1948) further integrated information, control, and feedback, arguing that communication and regulation obey similar laws in machines and organisms.

Systems theory contributes three central ideas relevant to meaning:

• Systems maintain stability or lose it based on feedback quality and timing.

• Drift arises when interactions and inputs exceed the system’s regulatory capacity.

• Any account of meaning in organizations must consider networks of interaction, not isolated messages or individuals.

Systems theory did not explicitly define “meaning,” but it supplied a model of how patterns of interaction stabilize or destabilize behavior, a model that MSS applies to meaning itself.

IV. Thermodynamics: Meaning Under Load and Dissipation

Thermodynamics emerged in the nineteenth century as a theory of heat, work, and energy, but its conceptual framework soon became a model for how ordered systems behave under increasing load. Rudolf Clausius’ 1865 formulation of the second law established entropy as a measure of the dispersion of energy in isolated systems. Ludwig Boltzmann’s statistical mechanics (1872) extended this, showing that ordered macrostates tend to move toward more probable, less ordered configurations unless work is exerted to maintain structure.

In the twentieth century, the field expanded beyond classical physics. Norbert Wiener’s cybernetics explicitly linked thermodynamic concepts to information and communication, arguing that noise, uncertainty, and signal degradation follow generalizable laws of organization and control (Wiener, 1948). Ilya Prigogine’s work on nonequilibrium systems later demonstrated that complex systems under sustained flow can reorganize into new forms of order, what he called “dissipative structures” (Nicolis & Prigogine, 1977).

Meaning System Science draws on this tradition the same way biology, information theory, and cybernetics have: by using thermodynamics as a structural model rather than a literal physical mechanism. MSS does not treat meaning as energy or equate entropy with psychological disorder. Instead, it uses thermodynamic principles to describe patterns that appear reliably across ordered systems, including those that are informational and interpretive.

Three structural parallels are especially pertinent:

First, systems under sustained load incur rising costs to maintain ordered states. As inputs multiply, whether they are energetic, informational, or communicative, the system must allocate more regulatory effort to prevent disorder. Meaning systems in organizations exhibit the same requirement: clarity, coordination, and shared understanding must be actively maintained as volume and velocity increase.

Second, drift behaves analogously to entropy in that unstructured dispersion becomes more likely when complexity or noise rises. This mirrors Shannon’s insight that uncertainty increases as channels saturate and signal-to-noise ratios decline. Thermodynamics provides a precise vocabulary for describing why meaning becomes harder to stabilize under high informational demand.

Third, under sufficiently intense or sustained flow, systems often reorganize. Prigogine’s dissipative structures show that when old configurations can no longer handle load, new structures form that redistribute work and stabilize activity. Organizations under meaning strain follow a comparable pattern: they reorganize workflows, decision pathways, and governance structures when prior forms can no longer absorb the interpretive demands of the environment.

These parallels are conceptual rather than physical, but they are grounded in a century of interdisciplinary scholarship in which thermodynamic reasoning has been used to illuminate non-physical systems without overstepping scientific boundaries. For MSS, thermodynamics offers a framework for explaining why meaning requires ongoing effort, why it degrades in high-pressure environments, and why structural reconfiguration becomes necessary as interpretive demands outpace capacity.

This makes thermodynamics a necessary component of the five-science architecture: it provides the model of load, dissipation, and systemic reorganization that meaning systems reliably exhibit in conditions of modern complexity.

V. Affective Science: Meaning as Regulated Interpretation

Affective science brings the interpreter into view. William James’ 1884 article “What Is an Emotion?” argued that emotional experience is closely tied to bodily changes, suggesting that interpretation and physiology are tightly linked rather than cleanly separable.

Antonio Damasio’s Descartes’ Error (1994) later made a detailed case that emotion and feeling are integral to rational decision-making; damage to neural systems supporting emotion leads to impaired reasoning, not “pure” rationality. Lisa Feldman Barrett’s 2006 paper “Solving the Emotion Paradox: Categorization and the Experience of Emotion” framed emotions as constructed through conceptualization, emphasizing that affect and prior knowledge shape how situations are interpreted.

From the perspective of MSS, affective science shows that:

• Meaning-making depends on regulated physiological and cognitive resources.

• Under stress, attentional capacity narrows and interpretive nuance declines.

• Systems that demand high interpretive complexity without adequate regulation produce chronic ambiguity, overreaction, or withdrawal.

Even when structure and signals are formally sound, meaning can fail because the interpreter is overextended. Affective science explains why meaning systems have capacity limits.

VI. Convergence: From Separate Domains to a Single System

When these five scientific lineages are considered together, they outline a single underlying system:

• Semantics: the structure of meaning.

• Semiotics: the movement and differentiation of meaning through signals.

• Systems theory: the interaction and feedback conditions under which meaning stabilizes or drifts.

• Thermodynamics: the pressure and dissipation dynamics affecting ordered states.

• Affective science: the human regulatory capacity that constrains interpretation.

Historically, these fields developed in different institutional and intellectual contexts, with distinct vocabularies and methods. They did not converge because they disagreed on fundamentals; they remained separate because no overarching framework treated meaning as a multi-variable system that obviously required all five dimensions.

Meaning System Science provides that framework. It takes as its object the behavior of meaning within complex systems and treats truth fidelity, signal behavior, structural coherence, thermodynamic load, and affective regulation as interdependent variables rather than isolated subjects of different disciplines.

VII. Why Unification Became Necessary in the Twenty-First Century

For much of the twentieth century, it was possible to treat these disciplines as parallel but independent. The volume and speed of information, the complexity of global organizations, and the reach of digital networks had not yet made interpretive limits a pervasive structural concern.

In the twenty-first century, that changed. Information density increased dramatically. Organizational structures became more interdependent. AI systems began producing and mediating signals at scales that outpaced unaided human interpretation. Under these conditions, meaning no longer behaved as a purely local or psychological phenomenon; it exhibited recognizable patterns of drift, overload, and reorganization across entire systems.

In this environment, it became practically and theoretically necessary to treat meaning as a scientific object governed by identifiable structural, systemic, thermodynamic, and regulatory constraints. MSS is the response to that necessity: it integrates the five sciences of meaning into a single explanatory architecture.

Conclusion: Meaning as a Scientific Object

The five sciences of meaning reveal that meaning is not merely a matter of private experience, isolated sentences, or local culture. It is a structured, transmitted, systemic, energetically constrained, and bodily regulated field.

• Semantics shows that meaning has internal form.

• Semiotics shows that meaning is carried and differentiated by signals.

• Systems theory shows that meaning participates in feedback loops that can stabilize or drift.

• Thermodynamics shows that meaning is vulnerable to pressure, load, and dissipation.

• Affective science shows that meaning depends on finite interpretive capacity.

Meaning System Science does not supersede these disciplines; it unifies them. It names the system that their findings collectively describe and provides a framework for analyzing how meaning behaves under real-world conditions.

Meaning, in this light, is not an undefined background condition. It is a scientific object: a multi-variable system whose behavior can be studied, modeled, and, within limits, designed.

Afterword

While this essay traces the scientific lineages that form the foundation of Meaning System Science, its application relies on a specific interpretive stance. Proportionism provides that stance. It examines how the core variables described by the five sciences—truth fidelity, signal behavior, structural coherence, thermodynamic load, and affective regulation—must hold proportion for meaning to remain stable within real systems.

Proportionism is not a sixth science but the orientation that makes the five sciences intelligible as one integrated discipline. It interprets their findings jointly, clarifying how meaning becomes strained when any variable overwhelms the others and how coherence is restored when they return to proportion. Proportionism serves as the practical bridge between the scientific architecture described here and the methods used to analyze drift, diagnose misalignment, and design meaning-stable systems.

Citation

Vallejo, J. (2025). The Five Sciences of Meaning. Transformation Management Institute™ Research Library, Foundational Essay 001.