The General Theory of Interpretation

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

Status: Monograph A6 | November 2025

Abstract

This monograph formalizes the position that Meaning System Science (A2–A5) constitutes a General Theory of Interpretation. The A-Series defines the variables, structural dependencies, and dynamic constraints that govern how interpretive behavior stabilizes, diverges, or reorganizes across human, institutional, cultural, and artificial systems. Here we clarify why these components meet the criteria for general-theory status, why no prior tradition produced a structural, multi-variable theory of interpretation that spans human institutions, cultures, and artificial systems, and why contemporary environments made its emergence both possible and necessary.

Across the last century, scholars in hermeneutics, linguistics, semiotics, systems theory, organizational science, and communication studies repeatedly noted, as a shared frustration, that interpretation lacked a unified structural account. Understanding could be discussed, meaning could be described, but no general framework could explain how interpretive reliability arises, fails, or recovers across different environments.

Modern information conditions and AI-accelerated meaning generation transformed this absence from an abstract puzzle into a structural constraint. Large-scale coordination, rapid decision environments, and artificial agents that generate and transform meaning at machine speed revealed patterns that demanded structural explanation. Meaning System Science provides the variable architecture, proportional law, and system dynamics required to treat interpretation as a system class with predictable behavior, including how inconsistency accumulates when stabilizing conditions fall out of proportion. This monograph consolidates that status and situates MSS alongside other general theories in modern science.

1. Introduction

Interpretation sits at the center of human activity. Every decision, policy, conversation, and model output depends on how information is understood and carried forward. Individuals interpret events in order to act. Teams interpret directives in order to coordinate. Institutions interpret environments in order to govern. Artificial systems interpret input data in order to produce responses that humans treat as meaningful.

Its importance was never in question. The issue, long acknowledged and never resolved, is that interpretation was treated as something that could be influenced but not explained in structural terms. Philosophy spoke of understanding, cognitive science of mental representations, organizational theory of sensemaking, AI research of model behavior. None produced a shared architecture capable of describing interpretive stability and instability across all these settings.

Meaning System Science arose directly from this gap. The earlier monographs in the A-Series were not designed as isolated contributions but as parts of an integrated structure:

• A2: Meaning System Science identified the variables that regulate interpretive reliability and defined them independently of any one discipline.

• A3: The Scientific Lineage of Meaning traced the breakthroughs that revealed each variable and formed MSS’s structural foundation.

• A4: The Physics of Becoming formalized the proportional law and system dynamics that determine when interpretation remains aligned and when it becomes uneven.

• A5: Proportionism articulated the epistemic stance required to analyze multi-variable systems without collapsing them back into any one domain.

Together, these works already function as a general theory. They define what must be structurally true for interpretation to remain reliable under changing conditions, whether the system is a person, a team, an institution, a culture, or an artificial agent.

This monograph formalizes that status. It does not add new variables, laws, or dynamics; it clarifies the scientific classification that the A-Series has already attained and explains why interpretation remained one of the last major domains of behavior without a general structural theory.

General theories appear when diverse observations can be expressed through a small set of structural relationships that hold across contexts. Thermodynamics achieved this for energy, evolutionary theory for biological change, information theory for communication. Meaning System Science extends this tradition to interpretation—not by analogy, but by identifying the conditions under which meaning remains structurally reliable as systems evolve.

Section 2 defines what counts as a general theory. Section 3 explains why interpretation lacked such a theory until now. Section 4 shows how MSS meets the criteria. Sections 5 through 8 describe the implications for research, practice, and governance.

2. What Counts as a General Theory

“General theory” is not an honorary label. In scientific practice, it names a theory that explains a class of phenomena in a way that is structurally consistent across contexts, scales, and implementations. It does not describe a single species, environment, or mechanism. It identifies the conditions under which the phenomenon behaves as it does wherever it appears.

This yields three core requirements.

2.1 Defined Variables

A general theory must specify variables that determine how the phenomenon functions. These variables must be structurally grounded, applicable across contexts, and sufficiently abstract to apply to any system expressing the phenomenon. Temperature, energy, and entropy meet these criteria in thermodynamics. Signal, noise, and channel capacity do so in information theory.

For interpretation, a general theory requires variables that apply equally to human reasoning, organizational communication, institutional legitimacy, cultural meaning, and artificial model output. This is the standard MSS meets.

2.2 Governing Relationships

General theories include formal relationships among their variables. These may appear as laws, equations, invariants, or proportional constraints. What matters is that they:

• describe how variables jointly determine system behavior,

• hold across environments where the phenomenon appears, and

• enable prediction under changing conditions.

Without such relationships, variables remain descriptive rather than explanatory.

2.3 Multi-Scale Applicability

A general theory must operate across scales without changing definitions. A theory that works for individuals but fails for institutions, or vice versa, remains partial.

Interpretation has reached a point where apparently separate problems—organizational drift, institutional legitimacy loss, cross-cultural confusion, AI model hallucination—display shared structure when examined through MSS’s variables and proportional relationships. The following sections show that this structure meets the standards outlined here.

3. Why No General Theory of Interpretation Existed Before

Interpretation is foundational to human activity. Why, then, did it lack a general theory? Because the conditions required to see its structural invariants did not exist. Interpretation was distributed across disciplines, constrained by methods, and obscured by environmental limits that made its deeper structure difficult to observe.

3.1 Fragmented Intellectual Lineage

Interpretation did not emerge inside a single field. Instead:

• Hermeneutics focused on understanding and context, often resisting formalization.

• Linguistics and philosophy of language examined reference and meaning conditions without modeling stability under pressure.

• Semiotics analyzed signs without capturing temporal dynamics.

• Psychology and cognitive science studied internal cognition rather than shared interpretation.

• Systems theory and cybernetics modeled feedback and structure while bracketing semantics.

• Information theory formalized transmission while excluding meaning.

Scholars across these fields acknowledged the gap: no shared structural account of interpretation existed. Meaning System Science addresses this fragmentation—not by replacing prior work, but by identifying the structural features that appear across it once the phenomenon is viewed at the system level.

3.2 Methodological Barriers

Methodological traditions often discouraged attempts to formalize interpretation. Humanities disciplines emphasized contextual uniqueness. Cognitive science favored controlled settings. Systems fields pursued abstraction that bracketed meaning. Information theory avoided semantics by design.

Without a commitment to structural variables and proportional relationships, no general theory could emerge, even as domain-specific observations accumulated.

3.3 Environmental and Technological Delay

Earlier environments did not generate visible interpretive instability. Information flowed slowly. Reference points were shared. Organizational complexity was limited. Contradictions accumulated gradually and locally. Interpretation appeared background rather than structural.

Modern information environments changed this. Distributed platforms, global coordination, and rapid feedback cycles made interpretive divergence measurable in real time. The structure that had always shaped interpretation became visible because conditions accelerated enough to reveal it.

3.4 AI as the Breakpoint

Artificial meaning-systems intensified this visibility. Machine learning models began producing text and actions that humans treated as meaningful. Their failures showed consistent structural patterns:

• outputs that were locally coherent but globally inconsistent,

• responses tied to surface cues rather than underlying structure,

• variations correlated with training shifts.

These behaviors exposed the absence of a general theory of interpretation as a concrete constraint. The same proportional dynamics that destabilize teams under ambiguity also destabilize models under shifting data. MSS formalizes these dynamics as one system class.

4. Why Meaning System Science Satisfies the Criteria for a General Theory

MSS earns general-theory status through the combined structure of its variables, proportional law, system dynamics, and epistemic stance. Developed separately for clarity, they attain generality together.

4.1 Defined Variable Architecture

A2 identified five variables that determine interpretive reliability:

• Truth Fidelity (T)

• Signal Alignment (P)

• Structural Coherence (C)

• Drift (D)

• Affective Regulation (A)

These variables apply across individuals, teams, institutions, cultures, and artificial systems without modification. They describe structural requirements any meaning-system must satisfy to maintain consistent interpretation. Their universality satisfies the first requirement for general-theory status.

4.2 Governing Relationships

A4 introduced the First Law of Moral Proportion:

L = (T × P × C) / D

Legitimacy (L) represents interpretive stability. The law identifies the structural condition under which stabilizing forces outweigh rising Drift. It echoes classical uses of “moral law” as internal necessity rather than ethics.

This relationship is the invariant that gives MSS its general-theory status.

4.3 System Dynamics

MSS also explains system behavior under load:

• how variability affects interpretation,

• how systems respond when stabilizers lose proportion,

• how Drift accumulates as inconsistencies increase,

• when reorganization becomes necessary.

These dynamics appear consistently across human and artificial systems, identifying interpretation as a structural rather than psychological or cultural phenomenon.

4.4 Multi-Scale Applicability

The same variables and law apply to individuals, teams, organizations, institutions, cultures, and artificial agents. Only the unit of analysis changes. This rare scale stability marks general-theory behavior.

4.5 Integration Across Disciplines

MSS integrates five domains—semantics, semeiology, systems theory, thermodynamics, affective science—as stabilizing dimensions of interpretation. It organizes their insights into a coherent architecture without reducing one to another.

4.6 Epistemic Stance

A5 introduced Proportionism, the stance required to analyze multi-variable systems without reduction. It provides the interpretive alignment needed to apply the theory consistently.

4.7 Predictive Capacity

MSS predicts:

• when interpretive reliability will fail,

• where Drift will accumulate,

• how systems will reorganize,

• which structural adjustments will restore clarity.

These predictions hold regardless of content. This predictive coherence fulfills the final requirement for general-theory status.

5. Implications of General Theory Status

General-theory classification shifts interpretation from a background process to a system class with measurable requirements.

5.1 Interpretation as a System Class

Interpretation becomes structurally analogous to energy, information, or selection: a phenomenon with internal constraints rather than an impressionistic or culture-bound process. Many failures once attributed to communication, leadership, or bias reveal themselves as structural interpretive failures.

5.2 Unified Analytical Foundation

Before MSS, psychologists emphasized cognitive load, communication scholars emphasized noise, organizational theorists emphasized alignment, political scientists emphasized legitimacy, and AI researchers emphasized model drift. MSS provides a shared structural foundation that integrates these partial explanations into one system architecture.

5.3 Positioning of Applied Disciplines

MSS defines the system. Transformation Science models how meaning behaves under real pressures. Transformation Management operationalizes these insights in practice. This mirrors the structure of established fields: physics → engineering; information theory → communication engineering; MSS → Transformation Science → Transformation Management.

5.4 Organizational and Institutional Analysis

General-theory status clarifies why institutions struggle with ambiguity, slow decisions, and legitimacy challenges. These are structural phenomena governed by proportional conditions:

• High T with low C yields stalled execution.

• Misaligned signals reduce trust.

• Unresolved inconsistencies increase D and erode legitimacy.

MSS shifts organizational assessment from anecdote to structural evaluation.

5.5 Artificial Meaning-Systems

AI models exhibit interpretive behavior governed by the same proportional conditions:

• misalignment when P outpaces T,

• drift when internal representations diverge from signals,

• error propagation under low A,

• reorganization or failure when contradictions accumulate.

MSS complements AI safety methods by treating interpretive reliability as a system-level stability problem.

6. Expanded Application Landscape

General-theory status broadens MSS’s application across system types. The variables—T, P, C, D, A—apply without alteration.

6.1 Cognitive and Psychological Systems

Interpretive reliability under stress, load, or transition can be analyzed structurally, distinguishing content errors from structural ones.

6.2 Communication and Information Environments

MSS treats communication as structural alignment, revealing where misunderstanding originates, how misinformation spreads, and why Drift escalates in fragmented channels.

6.3 Organizational and Institutional Systems

Decision coherence, alignment, workflow clarity, and legitimacy can be examined through proportional conditions that reveal structural bottlenecks long before performance declines.

6.4 Cultural and Societal Systems

Polarization appears as proportional imbalance rather than moral failure. MSS allows meaning reliability to be compared without reducing differences to ideology.

6.5 Hybrid Human–Machine Systems

Meaning is now shared across human and machine agents. MSS provides a unified structure for analyzing joint interpretive stability.

6.6 Artificial Meaning-Systems

Model alignment, robustness, and drift can be treated as proportional stability issues rather than prediction failures.

6.7 Knowledge and Educational Systems

Curricular coherence and comprehension thresholds can be evaluated structurally, identifying when misunderstanding stems from weak scaffolds rather than learner capacity.

6.8 Crisis and High-Load Environments

High Drift, low regulation, and rapid signal shifts reveal structural failures. MSS distinguishes overload from structural breakdown and guides redesign.

7. Why This Classification Emerges Now

General theories appear when hidden invariants become visible across contexts and scales. Interpretation reached this threshold only recently. Structural relationships remained concealed until several developments converged.

7.1 Advances in Cognitive and Affective Sciences

Modern research demonstrated that regulation shapes interpretive stability, allowing A to be formalized as a variable and revealing how the nervous system detects accumulating inconsistencies under changing conditions.

7.2 Convergence of Systems Theory and Semantic Inquiry

Researchers began treating meaning as a structural property of systems rather than words, creating space for a variable-based model of interpretation.

7.3 Increased Information Complexity

Modern environments made interpretive divergence measurable in real time. Rising complexity exposed relationships among T, P, C, D, and A that were previously theoretical.

7.4 Artificial Meaning-Systems

AI systems made interpretive structure observable in controlled contexts. The consistency between human drift and model drift confirmed interpretation as a general-system phenomenon.

7.5 Proportional Analytical Methods

Proportionism provided the stance required to analyze multi-variable systems. Without it, the general theory would have remained implicit.

8. Conclusion

Meaning System Science establishes interpretation as a structural phenomenon with defined variables, a proportional law, and dynamics that hold across human, institutional, cultural, and artificial systems. Interpretation moves from an implicit background process to a system class whose behavior can be modeled, predicted, and governed. Multiple failure modes now resolve into a single structure: organizational misalignment, legitimacy loss, cultural fragmentation, and AI drift reveal themselves as expressions of the same architectural conditions.

This recognition provides what earlier traditions could not: a unified account of how meaning stabilizes, destabilizes, and reorganizes under pressure. Interpretation appears as a structural feature of systems, with constraints that are measurable and conditions that are repeatable across contexts.

By naming MSS a general theory, we gain a coherent foundation for the applied sciences that follow. Transformation Science and Transformation Management inherit this structure and translate it into methods for diagnosing, designing, and steering meaning-systems in modern environments.

The contribution of MSS is not invention; it is recognition of a structure that has always existed. The proportional architecture beneath interpretation—across minds, institutions, cultures, and machines—is not new. What is new is the ability to name it, model it, and govern it as a single class of system.

Citation

Vallejo, J. (2025). Monograph A6: The General Theory of Interpretation. TMI Scientific Monograph Series. Transformation Management Institute.

References

Barrett, Lisa Feldman. How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin Harcourt, 2017.

Bertalanffy, Ludwig von. General System Theory: Foundations, Development, Applications. George Braziller, 1968.

Gadamer, Hans-Georg. Truth and Method. 2nd rev. ed., Continuum, 2004. (Orig. German ed. 1960.)

Kegan, Robert. In Over Our Heads: The Mental Demands of Modern Life. Harvard University Press, 1994.

Prigogine, Ilya, and Isabelle Stengers. Order Out of Chaos: Man’s New Dialogue with Nature. Bantam Books, 1984.

Shannon, Claude E. “A Mathematical Theory of Communication.” Bell System Technical Journal, vol. 27, 1948, pp. 379–423, 623–656.

Weick, Karl E. Sensemaking in Organizations. Sage Publications, 1995.