Meaning System Science

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

Status: Monograph A2 | November 2025

Abstract

Meaning System Science (MSS) is the general theory that explains how interpretation functions in human and artificial systems, treating interpretation as a functional coordination behavior rather than a language-bound or belief-bound phenomenon. MSS defines meaning-systems as bounded interpretive environments with explicit boundaries and membership conditions so interpretation can be evaluated without shifting the unit of analysis midstream, and by treating interpretive events as the minimal observable unit of interpretive cycles. MSS presumes a stated unit and boundary for analysis. Systemhood admissibility at that boundary is specified by System Existence Theory (SET) and is not defined within MSS. It distinguishes single-system behavior from coupled-system behavior and treats cross-boundary interaction as a primary source of instability when interfaces are underspecified. MSS identifies the variables that determine interpretive reliability, specifies their structural relationships, and defines the proportional conditions under which interpretation remains stable, becomes inconsistent, or reorganizes.

The Physics of Becoming formalizes the governing law of these relationships. Proportionism specifies the stance required to analyze systems defined by relationships rather than isolated components. Together, these elements satisfy the criteria for a general theory: defined variables, a governing relationship, cross-scale applicability, and predictive structure.

This monograph establishes the scientific identity of Meaning System Science and clarifies why contemporary environments require structural interpretation governance.

I. Introduction

Interpretation enables coordination in individuals, organizations, institutions, cultures, and artificial agents. Decisions, policies, instructions, and model outputs depend on shared assignment of reference and action relevance. When interpretive conditions remain stable, coordination requires limited corrective effort. When those conditions become uneven, even routine work requires repeated clarification.

Interpretation is treated here as a functional system behavior, not as a synonym for language, belief, or subjective impression. A system interprets when it differentiates environmental states in a way that modulates future action under constraint. This function does not require reflection or linguistic representation. Nervous systems increase speed, integration capacity, and predictive control, but they do not originate the function. Human and artificial interpretation are analyzed as higher-order realizations of the same system class within a stated boundary and membership condition.

Earlier environments reduced the visible cost of interpretive instability. Information moved slowly, channels were fewer, and organizational structures changed less often. Informal correction could compensate for divergence because interpretive demand typically remained within local correction capacity.

Contemporary environments reverse that balance. Inputs arrive across multiple channels at different times and resolutions. Roles, tools, and workflows shift frequently. Artificial intelligence increases output volume and plausibility while reducing the cost of high realism variation. Under these conditions, systems face more competing interpretations than their correction pathways can resolve. Interpretive instability becomes a performance constraint.

Interpretation has rarely been treated as a scientific object. Many disciplines examined components of interpretive behavior, such as language, signaling, structure, contradiction, and regulation. What was missing was a unifying account of how these components interact as a system, and how stability depends on their relationships rather than on any single domain.

Meaning System Science resolves this gap by treating interpretation as a bounded system behavior governed by identifiable variables and predictable relationships.

II. Definition of Meaning System Science

Meaning System Science is the study of how systems generate, transmit, regulate, and update meaning. It treats meaning as a structural process shaped by measurable conditions rather than as sentiment or cultural impression. A meaning-system is any bounded environment in which interpretation shapes coordinated behavior through shared reference, signals, pathways, and correction processes.

MSS explains how systems maintain continuity, absorb variation, and reorganize when demands exceed structural capacity. Its scientific identity rests on five commitments.

• Meaning is structural. Interpretation emerges from variable interaction, not from language alone, intent alone, or culture labels.

• Meaning is proportional. Reliability depends on the relationship among variables, not on the magnitude of any single one.

• Meaning is patterned. Systems exhibit repeatable interpretive behavior because the stabilizing conditions are general.

• Meaning is analyzable. Interpretive behavior can be measured, modeled, and diagnosed from artifacts and outcomes.

• Meaning is multi scale. The same architecture applies across individuals, teams, organizations, institutions, cultures, and artificial agents.

MSS does not replace existing disciplines. It integrates their contributions into a model capable of describing interpretive behavior under contemporary complexity.

II.1 The Minimal Architecture of Interpretation

Regardless of context or scale, systems cannot interpret without five structural conditions.

• Truth Fidelity (T): the system’s promised reference to observable reality. T is evaluated by the system’s reference artifacts, verification rules, and the reliability of its reference checks.

• Signal Alignment (P): the degree to which signals reinforce verified conditions. P is evaluated by signal to reference consistency, signal discipline under throughput, and the ratio of reinforcement to contradiction.

• Structural Coherence (C): the clarity and continuity of pathways through which interpretation routes for decision and correction. C is evaluated by role clarity, handoff integrity, closure authority, and the stability of decision pathways across time.

• Drift (D): the rate at which inconsistencies accumulate when T, P, and C lose proportion relative to demand. D is evaluated by unresolved contradiction volume over time, escalation recurrence, exception growth, and repeated re interpretation of settled conditions.

• Affective Regulation (A): the system’s capacity to sustain interpretation and correction quality under load. A is evaluated by correction throughput under pressure, recovery time after conflict or disruption, and the stability of decision quality as load increases.

Each variable is necessary.

• Without T, interpretations lose promised reference.

• Without P, action-relevant signals do not converge on shared reference conditions.

• Without C, interpretation cannot route through stable pathways for decision and correction.

• Without D, the system cannot distinguish isolated inconsistency from accelerating accumulation.

• If A is insufficient, correction capacity saturates under load and proportional recovery time increases.

Together, these variables are sufficient as a minimal explanatory architecture when the meaning-system boundary and membership conditions are explicitly defined. Common influences such as complexity, incentives, culture, trust, and role ambiguity are analyzable as drivers or manifestations of one or more variables within a specified system-object.

II.2 Meaning System Boundary and Membership Conditions

MSS requires an explicit definition of the meaning-system being analyzed. A meaning-system is not a topic, a channel, or a culture label. It is a bounded environment whose interpretive behavior can be evaluated from artifacts, signals, pathways, and outcomes.

• Boundary specifies what is inside the system for purposes of interpretation, correction, and accountability. At minimum, the boundary declares the decision or coordination domain being governed, the authoritative artifact set used to establish reference, and the time window over which continuity is evaluated.

• Membership conditions specify who or what participates in the interpretive loop and under what rules their inputs count. Membership is a structural criterion that determines which agents, roles, models, or subunits are authorized to generate, transmit, modify, or finalize meaning within the boundary.

Boundary and membership do not add variables beyond T, P, C, D, and A. They define the system-object to which variable claims apply. Without a declared system-object, claims about interpretive behavior become non comparable because the unit of analysis can shift without being noticed.

Interpretive Events provide the minimal unit within the declared system-object. An interpretive event is a bounded interpretive cycle in which a reference condition becomes decision relevant, signals are evaluated, a response pathway is selected, and the cycle resolves into a closure outcome or remains open. Drift is evaluated across event series through recurrence, divergence, and non closure, rather than inferred from impressions or narrative summaries.

Monograph A4 extends object definition by formalizing identity preservation under change and by specifying coupled-system interface dynamics.

III. The Five Sciences of Meaning

MSS integrates five scientific domains by treating each as a partial view of a shared architecture.

1. Semantics contributed disciplined treatment of truth conditions and reference, which MSS inherits as Truth Fidelity (T) defined as promised reference to observable reality.

2. Semeiology contributed the study of how signals convey and modulate interpretation across modalities, which MSS inherits as Signal Alignment (P) defined as signal reinforcement of verified conditions.

3. Systems theory contributed models of pathways, roles, and interdependence, which MSS inherits as Structural Coherence (C) defined as the routability and continuity of interpretation for decision and correction.

4. Thermodynamics contributed constraint and load behavior, which MSS applies to interpretive work by defining drift as a rate and by treating correction throughput as a capacity that can saturate.

5. Affective science contributed models of regulation and integration under pressure, which MSS inherits as Affective Regulation (A) defined as correction quality stability under load.

MSS treats these domains as partial views of a single system behavior. The explanatory target is not language alone, signaling alone, structure alone, or regulation alone. Interpretive reliability emerges from their proportional relationship inside a declared meaning-system boundary with explicit membership conditions. When those relationships remain in proportion relative to demand, interpretation converges and closes with limited corrective work. When they do not, the first observable failures tend to appear in constraint specification and closure routing rather than in any single component domain.

IV. Why Interpretation Fails Without a Structural Theory

Interpretive failure in contemporary systems rarely begins as a conflict of intent. It begins as a coordination problem created by unequal update rates across stabilizers. Verification updates slowly because it requires checkable reference. Signals update quickly because signal production has low marginal cost. Structure shifts in discrete changes. Regulatory bandwidth does not scale linearly with throughput. When these rates diverge, inconsistencies stop resolving locally and begin accumulating as drift.

The earliest break usually appears as one of two signatures. Constraint Failure (KF) occurs when evaluation constraints are missing, ambiguous, or unenforceable in practice, so the system cannot determine what would count as valid. Closure Failure (CF) occurs when correction cannot route to an authoritative decision, so inconsistencies persist across handoffs and downstream actions.

Without a structural theory, KF and CF are often treated as generic communication or process problems, and effort targets the wrong lever. A structural theory shifts response from persuasion to diagnosis: classify KF versus CF, then restore proportional capacity where the cycle fails so interpretation can converge and close under load. That diagnostic posture is the origin of the Institute’s instruments. LDP 1.0 formalizes variable-level identification, the 3E Standard™ specifies disciplined execution requirements once the failure mode is known, and the 3E Method™ provides a redesign sequence for restoring proportional capacity in live systems.

V. The Physics of Becoming

The Physics of Becoming provides the governing relationship that connects the variables to an evaluable outcome: Legitimacy (L), defined as the degree to which a declared meaning-system produces shared interpretation reliably enough to coordinate action.

L = (T × P × C) ÷ D

Legitimacy is evaluated only within a specified boundary and membership condition. Changing the system-object changes what shared interpretation refers to, and therefore changes what L measures. L is not a moral verdict. It is a structural property of interpretive reliability inside a declared system.

The governing relationship clarifies a dependency that operational environments repeatedly expose. A system can show strong local verification, high signal activity, or clean process design and still produce incompatible interpretations when drift increases as a rate faster than correction throughput can reduce it.

Monograph A4 formalizes this relationship across coupled systems and specifies how drift transmits across interfaces when constraints and closure pathways are not mutually specified.

VI. Proportionism

A general theory requires disciplined inference. Proportionism specifies that stance. Interpretive outcomes must be read as relational behavior among variables rather than as a single domain story selected by habit or professional bias.

Proportionism constrains attribution. Claims about interpretive failure must be stated as variable-level hypotheses inside a declared system-object, with clear separation between symptom description and cause assignment. High signal volume is not, by itself, evidence of a signal problem. Process churn is not, by itself, evidence of a structure problem. Escalation fatigue is not, by itself, evidence of a regulation problem.

Proportionism also forbids two common analytic moves. Diagnosis may not skip boundary declaration, and diagnosis may not treat a symptom label as a cause.

Monograph A5 formalizes Proportionism, including rules for hypothesis ordering, competing explanations, and disciplined attribution in coupled systems.

VII. Meaning as a System Class

MSS treats interpretation as a system class defined by shared structural requirements rather than by substrate, mechanism, or content. A meaning-system belongs to this class when coordinated action depends on the same minimal architecture, T, P, C, D, and A, operating in proportional relation.

This classification makes meaning scientifically analyzable across scale without redefining the object each time. An individual updating a belief, a team coordinating across roles, an organization maintaining continuity through change, and an artificial agent producing stable outputs under constraint are different instantiations of the same system class because the stability requirements are invariant.

Interpretation is not uniquely human in this definition. It is a system behavior that arises wherever signals guide action through internal or distributed reference structures. Language, reflection, and culture can increase expressive power, but they are not prerequisites for membership in the class. What varies across biological, organizational, institutional, and artificial systems is the mechanism by which each variable is realized, monitored, and corrected.

VIII. Scientific Criteria

General theories require defined variables, a governing relationship, cross-scale scope, predictive structure, and explanatory sufficiency.

MSS specifies a minimal variable architecture, T, P, C, D, and A. It formalizes the governing relationship linking proportional stability to legitimacy within a declared system-object. It applies across meaning-systems as a system class. It predicts stability outcomes through proportional behavior rather than isolated factor strength. Its measurement posture is artifact-grounded, with evidence-gating and explicit uncertainty handling within declared boundaries and membership conditions.

IX. Applied Disciplines

Meaning System Science provides the foundation for two applied branches.

• Transformation Science (B Series) studies how meaning-systems reorganize when stabilizing variables shift unevenly during becoming. It models transformation through changes in interpretive event series, where tools, policies, structures, incentives, and AI agents alter the conditions under which interpretation converges and closes. Its focus is on signatures such as proportional strain, interface instability in coupled systems, correction throughput limits, and drift behavior as a rate.

• Transformation Management is the professional discipline of governing and executing change as meaning-system work. Practitioners declare boundaries and membership, reinforce evaluation constraints and closure pathways, identify variables that are out of proportion relative to demand, and redesign capacity where the cycle fails. The discipline operationalizes this posture through LDP 1.0, the 3E Standard™, and the 3E Method™.

X. Relation to the Institute’s Governance and Field Studies

The C series applies MSS to specific meaning-system domains where interpretive instability has become a primary governance problem, including AI systems, scientific institutions, and cultural production. The Interpretation Field Studies extend the same architecture into empirical case analysis, showing how variables and failure signatures present in live environments.

XI. Conclusion

Meaning System Science defines interpretation as a governable system behavior. It establishes the meaning-system as the unit of analysis, requires explicit boundary and membership conditions, and specifies a minimal architecture evaluable from artifacts, signals, pathways, and outcomes. Within that frame, interpretive reliability is explained through proportional relationships among T, P, C, D, and A, with drift treated as a rate that can be tracked rather than debated.

The Physics of Becoming links proportional stability to legitimacy inside a declared system-object. Proportionism constrains inference so diagnosis follows variable relationships rather than defaulting to single domain stories. Transformation Science models reorganization under change. Transformation Management operationalizes governance through diagnostics, standards, and method.

Meaning System Science is the general theory of interpretation in operational terms. It makes interpretive stability definable, diagnosable, and governable across human and artificial systems without shifting the object midstream.

Citation

Vallejo, J. (2025). Monograph A2: Meaning System Science. TMI Scientific Monograph Series. Transformation Management Institute.