The Scientific Lineage of Meaning

The Five Thinkers Who Formed Meaning System Science

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

Status: Monograph A3 | November 2025

Introduction

Meaning System Science did not emerge from a single field. It grew from a long, distributed lineage of inquiry in logic, linguistics, systems theory, thermodynamics, and affective neuroscience. Across different eras and environments, five thinkers uncovered structural conditions that determine how interpretation works. None collaborated. None aimed to describe meaning as a system. Yet together, their discoveries form the architecture that MSS now formalizes.

Each identified one dimension of interpretive stability that later became a variable of Meaning System Science:

• Truth Fidelity (T)

• Signal Alignment (P)

• Structural Coherence (C)

• Drift (D)

• Affective Regulation (A)

Their work unfolded under political upheaval, scientific realignment, and disciplinary fragmentation. Many navigated institutions that resisted their findings. Their discoveries were often responses to the pressures of their environments, pressures that mirror the structural conditions meaning systems encounter today.

By tracing their biographies, we see how meaning became a scientific object. We also see how breakthroughs form an inheritance. One generation finds a structural pattern. A later generation recognizes its generality. A still later generation integrates these insights into a single architecture.

Each section of this monograph explores:

• the world each scientist entered

• the constraints they faced

• the insight they produced

• the variable that insight made measurable

• and why that variable remains essential for modern meaning-systems

Meaning System Science is a general theory of interpretation. It is also the continuation of five lives, five breakthroughs, and five structural discoveries that changed how meaning can be understood.

We begin with the thinker who gave truth its modern structure.

I. Alfred Tarski (1901–1983)

Semantics and the Structural Foundations of Truth Fidelity

Alfred Tarski was born Alfred Teitelbaum in Warsaw in 1901, during a period of political volatility and rising antisemitism. As a young scholar, he entered the Lwów–Warsaw School, known for exacting standards of logic and formal reasoning. Under mentors such as Jan Lukasiewicz and Stanislaw Lesniewski, he developed the analytical precision that would characterize his work.

In 1923, as restrictions on Jewish academics intensified, he changed his surname to Tarski to preserve his ability to work and teach. In 1939, he traveled to the United States for a scientific congress. It was the last ship to leave Poland before the invasions. He remained in the United States, later learning that much of his family had been killed. His scientific life unfolded in exile.

Tarski’s central breakthrough emerged from a deceptively simple question: can truth be defined structurally, independent of belief or interpretation?
His answer is now known as the T-schema, formulated in his 1933 work on truth in formalized languages:

“‘Snow is white’ is true if and only if snow is white.”

The value of this formulation was not its simplicity but its structural clarity. Truth became a formal relation between statements and observable conditions. It did not depend on psychology, perspective, or rhetoric. It provided a stable reference point.

Tarski in the MSS Architecture

Meaning System Science incorporates Tarski’s contribution by defining Truth Fidelity (T) as the system’s promised reference to observable conditions. Meaning may vary across contexts, but reference must remain stable for the system to coordinate interpretation.

MSS extends Tarski’s insight by placing truth within a proportional system.
Truth Fidelity is one stabilizer among several. It interacts structurally with:

• the signals that carry meaning (P)

• the pathways that conduct interpretation (C)

• the regulatory capacity available for updates (A)

• and the rate at which inconsistencies accumulate (D)

Tarski provided the first structural invariant of meaning. His discovery anchors the meaning architecture.

II. Ferdinand de Saussure (1857–1913)

Semeiology and the Foundations of Signal Alignment

Ferdinand de Saussure was born in Geneva in 1857 into a family of scientists and scholars. His early education spanned Latin, Greek, Sanskrit, and Indo-European linguistics. Quiet, analytical, and often uncomfortable with public attention, he nonetheless held an important place in Geneva’s intellectual community.

During his studies in Leipzig, he encountered linguistics as it was then practiced: a historical science concerned with phonetic shifts and etymology. Saussure respected the work but believed something was missing. Language did not function as a list of words. It functioned as a system.

In lectures later reconstructed by his students, he developed the foundations of structural linguistics. His most important insight was relational: meaning arises from difference, not inherent properties.

A sign consists of:

• a signifier, the sound pattern or written form

• a signified, the associated concept

The relationship is conventional, not natural. Meaning is produced through the structure of the whole.

Saussure in the MSS Architecture

MSS applies Saussure’s insight through Signal Alignment (P).
Signal Alignment describes how consistently signals correspond to verified reference and to one another. Meaning remains stable only when signals reinforce the same conditions.

MSS extends Saussure’s insight by:

• expanding signals beyond language, including procedural cues, institutional messages, digital outputs, and behavior

• placing signals within a proportional structure where they interact with truth, structure, regulation, and drift

• treating misalignment as a measurable structural condition

Saussure revealed that signals are not vessels for meaning but constraints on how meaning becomes interpretable. MSS formalizes this as P.

III. Ludwig von Bertalanffy (1901–1972)

Systems Theory and the Foundations of Structural Coherence

Ludwig von Bertalanffy was born in 1901 near Vienna. He entered academia during a time when biology was split between reductionism and theories invoking undefined vital forces. Dissatisfied with both, he sought an approach that could explain how organisms maintain stability, regulate themselves, and reorganize as conditions change.

Bertalanffy observed that these patterns recur across different types of systems. Organisms, machines, and organizations all rely on relationships, flows, and coordinated structures. From this insight, he developed General System Theory, a framework that described how systems maintain order through structural organization.

His career included political entanglements in Austria during the Anschluss that shaped later perceptions of his early work. After the war, he continued his scientific development in Canada and the United States, where he focused on cross-disciplinary system principles.

Bertalanffy in the MSS Architecture

MSS draws directly from Bertalanffy's insight through Structural Coherence (C).
Structural Coherence describes the clarity, continuity, and usability of the pathways through which meaning moves.

Where T anchors reference and P governs signals, C governs:

• roles

• processes

• decision pathways

• and the structures that sustain continuity across time

MSS extends Bertalanffy’s work by placing coherence within a proportional system. Structural Coherence cannot be evaluated in isolation. Its stability depends on its proportional movement relative to T, P, A, and the rate at which inconsistencies accumulate (D).

Bertalanffy made it possible to study interpretation as a structural process. His work forms the basis for C.

IV. Ilya Prigogine (1917–2003)

Thermodynamics and the Foundations of Drift

Ilya Prigogine was born in Moscow in 1917. His family left the Soviet Union in 1921 and eventually settled in Belgium. This early experience of displacement shaped his interest in systems that change when exposed to unstable conditions.

Classical thermodynamics viewed entropy as a movement toward disorder. Prigogine challenged this idea. Through his research in chemical kinetics, he discovered that systems pushed far from equilibrium do not necessarily degrade. Under sustained variability, they often reorganize into new stable configurations.

He called these dissipative structures and demonstrated that reorganization follows consistent patterns. His work introduced a new scientific understanding of transformation in physical systems.

Prigogine in the MSS Architecture

Meaning System Science applies Prigogine’s insight through Drift (D).
Drift is the rate at which inconsistencies accumulate when Truth Fidelity, Signal Alignment, and Structural Coherence lose proportion relative to correction capacity.

Drift is not collapse or chaos. It is not an emotional or metaphorical concept. It is structural and measurable.

MSS extends Prigogine by:

• treating drift as a thermodynamic rate-variable

• analyzing how inconsistencies accumulate when stabilizers move at different velocities

• positioning drift as the denominator pressure in the proportional law

• showing how increased D indicates the need for reorganization

Prigogine demonstrated that systems reorganize according to structural pressures. MSS codifies these pressures as D.

V. Lisa Feldman Barrett (1963– )

Affective Science and the Foundations of Regulation

Lisa Feldman Barrett was born in Toronto in 1963. She began her career in clinical psychology within a scientific environment that assumed emotions were universal biological reactions. Her early research, however, found inconsistencies in this model. Physiological patterns varied widely, and neural studies did not support discrete emotional circuits.

This led Barrett to a new theory. Emotions are not fixed responses. They are constructed interpretations shaped by prediction, past experience, context, and internal physiological conditions.

Her work showed that regulation is not separate from interpretation. The nervous system integrates sensory information, internal states, and learned concepts to update meaning.

Barrett in the MSS Architecture

Meaning System Science incorporates Barrett’s insight through Affective Regulation (A).
A refers to a system’s regulatory capacity, or its ability to absorb variability and update interpretations before inconsistencies accumulate into Drift.

MSS extends Barrett’s work by:

• treating regulation as a structural condition

• applying regulatory logic across individuals, teams, organizations, and artificial systems

• showing how insufficient regulation accelerates drift as a rate

Barrett demonstrated that interpretation depends on regulatory capacity. MSS formalizes this as A.

VI. Integration

These five thinkers worked in different eras and disciplines, yet their discoveries align with clear structural consistency. Each revealed one condition that governs interpretive stability.

• Tarski established truth as structural correspondence.

• Saussure identified signals as relational constraints that condition interpretation.

• Bertalanffy demonstrated that systems maintain stability through coherent structure.

• Prigogine showed that instability accumulates in measurable ways and reorganizes when stabilizers lose proportion.

• Barrett demonstrated that regulatory capacity governs how quickly meaning can update.

Together, these discoveries form the variables of Meaning System Science.

A sixth figure, Claude Shannon, sits at the boundary of this lineage. Shannon formalized communication but explicitly excluded semantics. His work quantified transmission but left interpretation unmodeled. MSS begins where Shannon’s framework ends, by introducing variables that describe how systems interpret incoming information.

MSS does not merge earlier theories. It positions each contribution as one structural element in a proportional architecture. The variables, the governing law formalized in the Physics of Becoming, and the stance defined in Proportionism rest on these insights.

Contemporary environments make this integration necessary. Information increases in volume and velocity. Signals multiply across digital channels. Structures carry more complexity. Drift rises when stabilizers lose proportion. Human and machine interpretation now interact in ways that require explicit regulation.

Under these conditions, interpretation cannot be treated as intuition, communication technique, or perspective. It requires a general theory.

Meaning System Science provides that theory. It unifies the insights of Tarski, Saussure, Bertalanffy, Prigogine, and Barrett into an architecture that explains how meaning is generated, maintained, destabilized, and restored across human, institutional, and artificial systems.

This lineage forms the scientific foundation on which MSS is built.

Citation

Vallejo, J. (2025). Monograph A3: The Scientific Lineage of Meaning. TMI Scientific Monograph Series. Transformation Management Institute.

References

Alfred Tarski
Feferman, A., & Feferman, S. Alfred Tarski: Life and Logic. Cambridge University Press, 2004.
Tarski, A. “Pojęcie prawdy w językach nauk dedukcyjnych” [The Concept of Truth in Formalized Languages], 1933.
Woleński, J. Logic and Philosophy in the Lvov–Warsaw School. Kluwer Academic Publishers, 1990.

Ferdinand de Saussure
Saussure, F. de. Cours de linguistique générale. Ed. Charles Bally & Albert Sechehaye, 1916.
Culler, J. Saussure. Fontana Press, 1976.
Harris, R. Saussure and His Interpreters. Edinburgh University Press, 2001.
Bouquet, S., & Engler, R. (eds.). Ferdinand de Saussure: Sources Manuscrites et Documents. Various volumes, 1990–2000.

Ludwig von Bertalanffy
Bertalanffy, L. von. General System Theory: Foundations, Development, Applications. George Braziller, 1968.
Davidson, M. Uncommon Sense: The Life and Thought of Ludwig von Bertalanffy. J. P. Tarcher, 1983.
Hammond, D. The Science of Synthesis: Exploring the Social Implications of General Systems Theory. University Press of Colorado, 2003.
Hofkirchner, W. (ed.). Biographies of Systems Thinkers. Various chapters, 2018.

Ilya Prigogine
Prigogine, I. From Being to Becoming: Time and Complexity in the Physical Sciences. W. H. Freeman, 1980.
Prigogine, I., & Stengers, I. Order Out of Chaos: Man’s New Dialogue with Nature. Bantam Books, 1984.
Kondepudi, D., & Prigogine, I. Modern Thermodynamics: From Heat Engines to Dissipative Structures. Wiley, 1998.
Bedau, M. A., & Humphreys, P. (eds.). Emergence: Contemporary Readings in Philosophy and Science. MIT Press, 2008.

Lisa Feldman Barrett
Barrett, L. F. How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin Harcourt, 2017.
Barrett, L. F. Seven and a Half Lessons About the Brain. Houghton Mifflin Harcourt, 2020.
Lindquist, K. A., & Barrett, L. F. “The Experience of Emotion.” Annual Review of Psychology, 2008.
Simmons, W. K., Lindquist, K. A., & Barrett, L. F. “A Constructed Emotion Model of Cognitive and Affective Neuroscience.” Social Cognitive and Affective Neuroscience, 2013.

Boundary Figure: Claude Shannon
Shannon, C. E. “A Mathematical Theory of Communication.” Bell System Technical Journal 27 (1948): 379–423, 623–656.