Data integrity presupposes invariance because preserving accuracy across the data lifecycle is the preservation of intended content under storage, transmission, and processing operations.

Data integrity presupposes invariance because the integrity guarantee names a feature -- the data's intended content and internal rules -- that must remain unchanged under the family of transformations data undergoes (writes, reads, transmission, archival, processing). Checksums, signatures, and constraints are the mechanisms that verify invariance under each operation. Without invariance's joint commitment to preserved feature and preserving operations, there is no formal sense in which data is or is not corrupt; integrity is engineered invariance with detection and recovery. Invariance supplies the prerequisite condition: Properties unchanged under transformation. Data Integrity operates against that background: Accuracy and consistency preserved. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Data leakage presupposes a validation boundary separating information legitimately available at decision time from information reserved for calibration or evaluation.

The failure is defined by an invalid performance claim: a model, forecast, examination, audit, or blinded assessment is scored as though its decision were made without information that crossed into fitting or calibration. Validation supplies the held-apart reference and the claim of out-of-sample competence; without that separation, the same information flow is ordinary input rather than leakage.


Time leakage is the model-evaluation species in which future or held-out information enters fitting, feature construction, calibration, or validation. Information outside the artifact's declared as-of boundary enters its construction and makes the reported historical or prospective evaluation invalid. The artifact is a statistical or computational model pipeline and the output is contaminated performance, calibration, or deployment evidence. Applies to temporal, look-ahead, and future-derived leakage. Train-test contamination among contemporaneous records can be Data Leakage without a later-to-earlier information channel.


A data_structure is the arrangement-for-use trade — privileging some operations cheap at the structural cost of penalizing others (the no-neutral-arrangement invariant). It presupposes a trade_offs frame; the operation-profile is the trade made concrete. Trade-offs supplies the prerequisite condition: Balancing competing priorities. Data Structure operates against that background: An arrangement of information that makes some operations cheap at the structural cost of others. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Deadlock's essential commitment is a circular waiting dependency, which is precisely a closed loop returning to origin in the dependency graph, the structure cycle names. Cycle supplies the genus: A closed path in a network that returns to its origin, opening return, foreclosing ordering, and creating a loop invariant. Deadlock preserves that general structure while adding its differentia: Mutual blocking processes. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Deadweight loss typically presupposes price elasticity because the magnitude of welfare lost from a price distortion depends on demand and supply responsiveness.

Deadweight loss is the reduction in total surplus that results when a market distortion prevents mutually beneficial transactions, measured as the welfare triangle. The size of that triangle is determined by how responsive quantity is to price — the more elastic supply and demand, the larger the deadweight loss for a given tax or price wedge. Price elasticity supplies the responsiveness parameter that calibrates the magnitude. Deadweight loss as a quantified welfare claim typically presupposes elasticity, though qualitative existence-of-distortion arguments can be made without it, hence typical.


Deadweight loss presupposes Pareto efficiency because the welfare benchmark from which the loss is measured is the no-Pareto-improvement-available allocation.

Deadweight loss is the reduction in total surplus relative to a benchmark welfare allocation that distortions prevent the market from reaching. The benchmark is the competitive-equilibrium allocation with no market failures — precisely the Pareto-efficient frontier where no change can make anyone better off without making someone worse off. Pareto efficiency supplies exactly that benchmark: the set of allocations from which no Pareto improvement is available. Deadweight loss is meaningful only against this benchmark, presupposing Pareto efficiency as the reference point from which welfare losses are measured.


Deadweight loss is the specific shape allocation takes when distortions prevent mutually beneficial transactions, leaving surplus uncaptured by any party.

Deadweight loss is the inefficiency particularization of allocation: it names the structural shortfall between the realized assignment of resources and the welfare benchmark of competitive equilibrium. Where allocation names the bare assignment of limited supply to competing claims generally, deadweight loss specifies the case where the assignment forecloses mutually beneficial trades, producing a loss in which no party captures the foregone surplus — a particular form of misallocation distinguished from transfers, which merely shift surplus between parties.


Blowback is a self-referential return path: a signal the deceiver authored re-enters the deceiver's own decision loop through a shared channel — a specialized self-reference pathology where the system poisons itself. is-a a reflexivity/self-reference failure.


Presupposes an injected (misleading) signal into a shared channel; built on a signaling act whose return is the defect. Signaling supplies the prerequisite condition: Revealing hidden information. Deception Blowback operates against that background: A misleading signal injected into a shared channel to deceive an adversary returns through an unintended path to confuse the deceiver's own decision loop, allies, downstream systems, or future selves, at a cost the original calculation never scored. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


'Logical decidability is one instance of the computability partition' — decidability concerns a formal system's mechanical settleability; computability is the broader medium-neutral boundary. computability is the genus. decidability_computability is the survivor. Computability supplies the genus: The in-principle boundary between problems an effective procedure can solve and those none can. Decidability Computability preserves that general structure while adding its differentia: A class of yes/no questions admits a finite procedure that always terminates with the correct answer. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Decision presupposes constraint because selecting one alternative from a set requires that the admissible set be defined by binding restrictions.

Decision is the act of selecting one alternative from a set under conditions of constraint, uncertainty, or trade-off. The set itself is constituted by constraint — a condition that restricts admissible configurations to those satisfying it, producing the feasible set from which selection draws. Without constraint there is no bounded choice set, no closure of options, and no meaningful selection. Constraint supplies the binding restriction that defines what counts as an admissible alternative; decision then ranges over that set and commits to one element, so it presupposes constraint as the substrate of its choice space.


A decision presupposes reversibility and irreversibility because every selection carries an implicit commitment to a position on the reversal-cost dimension.

A decision is the moment when deliberation collapses into commitment, and the structure of that commitment is defined by where it sits on the reversibility dimension: whether the chosen path can be unwound, at what cost, and within what window. Without the prior distinction between reversible and irreversible action, a decision would have no commitment dimension — no closing-off of alternatives, no resource locking, no preserved or sacrificed flexibility. The reversibility property is what gives decisions their consequential weight.


Each gate is a `decision` (resolve advance/terminate/revert); the prime is the structured sequence of gate decisions. a decision is the 'building block' of which stage-gate is a sequence. After the cognitive_science frame is stripped away, the retained structural roles are those of Stage Gate Process: Partition a long commitment into evidence-gated stages with escalating commitment and a funnel of kills. Decision adds the local frame and commitments expressed in its identity: Committing to one alternative from a set under uncertainty and trade-off, collapsing open deliberation into a chosen path and foreclosing the others. The parent pattern remains recognizable without that vocabulary, while the child is the framed realization of it. That preservation test establishes decomposition rather than taxonomic subsumption.


'the broad prime under which this one is a sharp special case' — the specific competitive failure where the binding mechanism is TEMPO (one decision cycle forced to react to another's). Competition supplies the genus: Rivalrous pursuit of a scarce prize where one party's gain is another's loss. Decision Cycle Subordination preserves that general structure while adding its differentia: A slower actor's decision cycle becomes forced to respond to a faster actor's tempo, and responding faster deepens the subordination rather than escaping it. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


A relational PATHOLOGY between two actors' decision cycles — it presupposes decision-making and adds the cross-actor tempo dynamic decision alone does not contain. Decision supplies the prerequisite condition: Committing to one alternative from a set under uncertainty and trade-off, collapsing open deliberation into a chosen path and foreclosing the others. Decision Cycle Subordination operates against that background: A slower actor's decision cycle becomes forced to respond to a faster actor's tempo, and responding faster deepens the subordination rather than escaping it. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Decision Fatigue presupposes Decision: the phenomenon is a degradation pattern across a sequence of choice acts.

Decision fatigue is the empirical claim that the quality of choices in a sequence declines as the sequence continues, with later acts showing more default-reliance, impulsivity, and error. The phenomenon is defined over a sequence of Decision events: there must be repeated acts of selecting one alternative from a set before any degradation pattern can be observed or named. Decision fatigue therefore presupposes Decision as the unit it counts and the act whose quality degrades.


Decision Fatigue is a kind of bias: depletion produces a systematic, direction-consistent drift toward defaults and impulsive choices.

Decision fatigue produces a systematic — not random — shift in choice patterns as a sequence progresses: later choices reliably tilt toward defaults, status-quo options, and impulsive selections. The displacement is in a consistent direction and persists across the sequence rather than averaging out across additional acts. That is the defining structure of Bias: a persistent offset of process output from the unbiased target. Decision fatigue specializes bias to sequential decision-making under deliberative-resource depletion.


Decision fatigue is a specialization of cognitive resource depletion in which the depleting capacity is sustained deliberative choice.

Decision fatigue is a specialization of cognitive resource depletion in which the consumed capacity is specifically that for effortful deliberative choice. It inherits the general depletion pattern of time-dependent performance decay under sustained use, reversible by rest, and distinguishes itself by tying the consumption to extended sequences of decisions and by registering its signature in choice-quality markers: more defaults, more impulsivity, more status-quo bias, more errors late in the sequence than early. The substrate is the same depleting resource; the specialization fixes its expenditure to decision-making.


Deep time presupposes time because its content is the cognitive frame adopted for timescales of millions to billions of years.

Deep time presupposes time because its content is a re-framing of the temporal dimension at scales vastly beyond biographical or historical horizons — geological, evolutionary, cosmological. It inherits time's structural commitment to ordered succession with measurable duration and irreversible direction, particularized to the very-long-timescale regime where processes invisible on human timescales become dominant and human intuition systematically underestimates duration. Deep time is time read at log-scale.


Defamiliarization presupposes a habituated, stimulus-specific loss of orienting response that an estranging re-presentation can interrupt.

The familiar object's repeated inconsequential presentation has already reduced deliberate attention even though the perceiver remains capable of responding. Without that habituated automatism there is nothing for estrangement to break and no recovery of attention that distinguishes the technique from merely presenting something novel.


Defeat in detail is a specific mechanism within competition — a globally weaker party beats a globally stronger but DISTRIBUTED one via local superiority and sequential engagement (concentration latency as the binding variable). One way to win a competition, not competition itself. it is 'one way to win a competition, not competition itself.'. Competition supplies the genus: Rivalrous pursuit of a scarce prize where one party's gain is another's loss. Defeat In Detail preserves that general structure while adding its differentia: A globally weaker attacker beats a globally stronger but distributed adversary by achieving local superiority and engaging the parts sequentially before they can combine. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Defect requires a localized deviation's consequence to spread through the regular structure's coupling channels and alter a macroscopic property.

A deviation that remains causally inert or wholly local does not satisfy the prime's disproportionate-consequence identity. Propagation supplies the source, medium, pathway, speed, attenuation, and reach structure; Defect adds regularity, localization, stress concentration, bulk-versus-defect diagnosis, and engineering leverage.


Defense in depth specializes redundancy: it layers barriers FOR failure ABSORPTION UNDER ATTACK, with INDEPENDENCE OF failure MODES load-bearing and an optimizing adversary seeking the correlated breach. Plain redundancy duplicates for reliability against RANDOM failure. 'defense in depth specifically denotes layering for failure absorption under attack' — the adversarial specialization of stacked-multiples. Redundancy supplies the genus: Duplicate critical components. Defense In Depth preserves that general structure while adding its differentia: Stacking multiple independent protective layers between threat and asset so that only a correlated breach across all layers produces total loss. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Degrees of freedom is effective dimension specialized to the count of independent parameters remaining after constraints are imposed.

Dimension counts independent coordinates needed to specify a configuration. Degrees of Freedom inherits that invariant and fixes how it is obtained: start from candidate parameters and subtract binding constraints, yielding the effective dimension of admissible variation.


Degrees of freedom presupposes decomposition because the count of independent parameters is read off the system's decomposition into independent coordinates after constraints.

Degrees of freedom quantifies the number of independent parameters required to specify a system's complete state, computed as unconstrained parameters minus constraints. This presupposes decomposition: breaking a whole into constituent parts whose independent analysis and recombination reconstitute it. The state is decomposed into a set of independent coordinates (mechanical generalized coordinates, statistical sample components, mechanism joints), each contributing one dimension to state space; constraints reduce the effective count. Without decomposition's structure-preserving partition into independent pieces, there is no notion of independent parameters to count.


Degrees of freedom presupposes constraint because counting independent parameters only becomes meaningful once binding restrictions on configurations are specified.

Degrees of freedom counts the independent parameters needed to specify a system's state after constraints are imposed — formally, unconstrained parameters minus constraints. The count is meaningful only against a specified set of binding restrictions that prune the feasible set: holonomic constraints in mechanics, statistical constraints after estimation, kinematic constraints in mechanism design. Without constraints as a first-class structural object defining the admissible subset, the dimensionality reduction that degrees of freedom measures would have nothing to subtract from and no operational content.


Deixis is a specialization of Pragmatics, retaining the parent's defining structure while adding the child's specific commitments.

Pragmatics supplies the genus: What a signal means in context is the joint product of its literal content and patterned contextual machinery, not just what the signal literally encodes. Deixis preserves that general structure while adding its differentia: Context-dependent meaning. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Delegation of authority presupposes authority because there must be a legitimate decision-making power before any of it can be transferred to subordinates.

Delegation is the structured transfer of a right to decide from a principal to an agent within specified limits, with the principal retaining ultimate responsibility. The transfer can only occur if such a right exists in the first place — a recognized, legitimate power to make binding decisions. Authority supplies precisely that: the socially recognized capacity to create obligations, vested in a role rather than in raw capability. Without an underlying authority to allocate, there is nothing for delegation to assign or for accountability mechanisms to track back to.


The Delphi Method is Iteration specialized to anonymous expert elicitation, with each round's feedback becoming input to the next.

The Delphi Method repeatedly elicits expert judgments, summarizes responses and rationales, returns controlled anonymous feedback, and uses that output as input to the next round until a stopping criterion is met. Removing the expert-panel, anonymity, judgment-elicitation, and foresight frame leaves Iteration's portable structure: repeated steps, carried state, output-to-input updating, progress assessment, and a termination rule.


The Delphi Method is the specific shape aggregation takes when distributed expert judgment is collapsed into a consensus through structured, anonymized iterative rounds.

The Delphi Method is the particularization of aggregation to the setting of distributed expert opinion: many individual judgments are collapsed into a unified summary that retains the consensus signal while suppressing personal-influence and dominance distortions. Where aggregation names the deliberate loss of granular detail to retain chosen features generally, Delphi specifies that the feature retained is the convergent expert estimate while the detail suppressed is identity-linked bias, anchoring, and groupthink artifacts that contaminate unstructured pooling.


Demand is 'desire made cost-responsive and quantified into a curve' — preference is the unconditioned ranking (the input), demand is the schedule preference generates against a budget. It presupposes preference. Preference supplies the prerequisite condition: Agent's ordering over a choice set on some evaluative dimension. Demand operates against that background: A schedule relating quantity sought to generalized cost, with slope, elasticity, and substitution structure. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Denormalization presupposes Trade-offs, whose structure must already obtain for the child mechanism to be meaningful or operational.

Trade-offs supplies the prerequisite condition: Balancing competing priorities. Denormalization operates against that background: Deliberately re-introduce controlled redundancy against a canonical form, trading a synchronization burden for access-side wins. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Density is a relation OF a subset IN a host under a closeness notion (closure-equals-host); it is a specialized property within the set-and-membership apparatus — a set whose closure fills another. Set and Membership supplies the genus: Groups and categorizes elements. Dense Set preserves that general structure while adding its differentia: A smaller set reaches arbitrarily close to every point of a larger one. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


How a system's dependency WEIGHT is distributed across providers; it presupposes a dependency structure and characterizes the shape of that reliance (a graph-weight property). Dependency supplies the prerequisite condition: Directed relation in which one element relies on another being present, prior, compatible, or supplied, with a specifiable failure mode if the condition is unmet. Dependency Distribution Concentration operates against that background: How a system's dependency weight is distributed across providers — concentrated or spread — is a structural property that bounds its fragility independent of its own defenses. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Derivative Amplification is a specialization of Propagation, retaining the parent's defining structure while adding the child's specific commitments.

Propagation supplies the genus: The systematic spreading of a signal, effect, or state from a source through a medium or network, where the medium's structure governs how fast it moves, how it attenuates, and which paths it follows. Derivative Amplification preserves that general structure while adding its differentia: In a serial chain where each upstream stage responds to the rate of change of its downstream neighbour, variation amplifies geometrically with chain length. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Design for implementation presupposes trade-offs because constraining design to producibility necessarily worsens some functional dimensions to improve others.

Design for implementation systematically constrains design decisions to fit the realities of production, assembly, operation, and end-of-life processes — meaning the design cannot pursue ideal functional performance alone. The discipline operates by accepting compromise on one valued dimension (e.g., maximum performance, minimum part count, ideal geometry) to improve another (manufacturability, maintainability, supply-chain robustness). This is exactly the trade-off structure: improving one dimension within a feasible set requires worsening another. Without recognition that valued dimensions are multidimensionally coupled, the entire discipline collapses into single-objective optimization.


Design for implementation presupposes constraint because the discipline is precisely the inclusion of production and operational limits as binding restrictions on design choices.

Design for implementation is the systematic practice of treating manufacturing capabilities, supply-chain limits, assembly sequences, and operational costs as binding restrictions on the admissible set of design configurations. Without constraint's machinery of treating a binding restriction as a first-class structural object, there would be no way to convert downstream realization limits into upstream design rejection rules: a configuration violating production feasibility would be no different from a configuration meeting it. The parent prime supplies the binding-restriction structure that this discipline applies to implementation context.


Design for lifecycle adaptability typically presupposes modularity because the future changes it accommodates are absorbed at module boundaries.

Design for lifecycle adaptability deliberately structures systems so they can be modified, upgraded, or repurposed across operational life without excessive cost. The practical mechanism is typically modular structure: changes are absorbed by replacing or reconfiguring discrete modules without rippling through the whole. Modularity supplies the decomposition into independently revisable components with stable interfaces. Lifecycle adaptability rides on this organization to keep modification cost bounded, though some adaptability strategies use redundancy or parameterization without strict modularity, so the dependence is typical rather than strict.


Design for lifecycle adaptability is the specific shape adaptation takes when adaptability mechanisms are deliberately built into a system at design time.

Design for lifecycle adaptability is the engineered, anticipatory particularization of adaptation: rather than a system changing in response to sustained environmental change after the fact, the designer pre-embeds modular interfaces, redundancy, excess capacity, and reconfiguration paths so that future modification is supported. Where adaptation names the modification of a system to fit new conditions generally, the design-for-adaptability variant fixes the timing — the adaptive capacity is engineered in upfront, before the conditions that will exercise it are known.


Design patterns is the specific shape abstraction takes when recurring solution structures are named and extracted for reuse across instances.

Abstraction is the purpose-relative retention of structure that keeps what is load-bearing for a use across many concrete instances. Design patterns is the particular shape this move takes in design and architecture: recurring problem-solution shapes are extracted from many concrete instances, named, and codified so the abstracted structure can be reused in new situations. It is a structurally-particularized instance of abstraction whose specific projection retains the generalizable problem-solution structure and discards the particulars of any one application.


Design prototyping is the specific shape approximation takes when a tractable physical or interactive surrogate stands in for the eventual full product.

Approximation is the deliberate substitution of a tractable surrogate for an intractable target, with a controlled and named error the use case can tolerate. Design prototyping is the particular shape this move takes in engineering and design: the eventual full product is the intractable target, the prototype is the simpler tangible surrogate, and the bounded fidelity gap is what the learning purpose can absorb. It is a structurally-particularized instance of substitution-under-controlled-error whose specific machinery is materialized partial embodiment for the sake of feasibility and form learning.


Design prototyping typically presupposes iteration because the prototype's purpose is to feed learning into successive rounds of design refinement.

Design prototyping creates partial, testable versions of a design to learn about feasibility, form, and function before full commitment. The prototype's value is realized through iteration: each round produces evidence that feeds the next design cycle, with state carried between rounds and progress measured across them. Iteration supplies the repeated-application-with-state-carryover structure that prototyping rides on. Some single-shot prototypes serve as one-time confidence checks without iterative cycles, so the dependence is typical, though the canonical use case is iterative refinement.


The deliberate ASYMMETRIC editing of an affordance set so the misuse path is costly while the legitimate path stays free; affordance is the raw material, this is the design move performed ON affordances. 'affordance is the material; this is the design move'. Affordance supplies the prerequisite condition: An action possibility offered by the fit between an agent and its environment. Designed-Out Misuse operates against that background: Affordances and defaults are arranged so the misuse path is costly or impossible while the legitimate path stays easy, preventing harm before enforcement has to act. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Removing the route-design frame leaves inference of latent preference from a durable trace of costly observed choices rather than stated reports.

Desire paths add a prescribed route, an inscribing substrate, repeated traversal, and a redesign response to the portable revealed-preference inversion from behavior to what users value or need.


Determinism presupposes causality because its claim is precisely that the present state plus laws fix a unique successor via the productive cause-effect connection.

Determinism presupposes causality because its content — that state plus laws fix exactly one successor — operates on the cause-effect relation: laws are the productive connection from prior state (cause) to next state (effect), and modal robustness is the counterfactual invariance of the law itself. It inherits causality's four-part structure and particularizes it to the case where the law is single-valued rather than probabilistic. Without causality's productive connection, the deterministic trajectory has nothing to specify.


Deterrence is coercion in the RESTRAINT-FORCING direction (costs conditional on a proscribed act). Explicit genus-to-species. Coercion supplies the genus: Shaping another agent's choice by manipulating the costs and threats attached to their options, so the agent itself 'chooses' the coercer's preferred action — the common parent of forcing an action (compellence) and forcing restraint (deterrence). Deterrence preserves that general structure while adding its differentia: Preventing an action not by blocking it but by arranging consequences so the target's own cost-benefit calculation makes the action unattractive. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Develops_from is a relation but a highly-specified one — one continuant through directed, qualitatively-distinct, rule-governed stages — adding the four commitments (continuant identity, directed ordering, qualitative change, generative rule) the bare relation lacks. A typed directed identity-preserving edge as against an unlabeled link. Relation supplies the genus: Describes associations or dependencies. Develops-From Relation preserves that general structure while adding its differentia: A later entity came to be by stage-wise transformation of a continuing predecessor, the same continuant passing through directed, qualitatively distinct stages under a generative rule. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


A diagnostically inert signal is signaling specialized to a message that detects an event while omitting actionable diagnostic content. Signaling supplies the genus: Revealing hidden information. Diagnostically Inert Signal preserves that general structure while adding its differentia: A signal that announces a failure but carries none of the content needed to act on it, splitting detection-completeness from recovery-completeness. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Self-reference is the PRECONDITION; the diagonal flip is what extracts the impossibility. It presupposes a self-modeling system that can name its own analyzers, then engineers a flip object. Reflexivity (Self-Reference) supplies the prerequisite condition: Self-referential systems. Diagonal Impossibility operates against that background: In a self-modeling system, a putative total analyzer is refuted by constructing an object engineered to make the analyzer's verdict on it contradict the property it decides. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Dialectic is a kind of interpretation: structured exchange between positions recovers meaning that no single vantage can reach.

Dialectic uses structured question-answer-clarification across multiple positions to expose assumptions, pressure claims, and converge on understandings unreachable from a single vantage point. The end product is a constrained reading of the matter under examination, answerable to the exchange's evidence and the framework that scopes available answers. That is the activity of Interpretation — recovering meaning from a representational substrate under a framework that makes some readings available and others not. Dialectic specializes interpretation to multi-voice procedural inquiry.


Dialectic presupposes inquiry-based learning because its elenchic question-answer-clarification cycle is the inquiry stance applied through structured dialogue.

Dialectic presupposes inquiry-based learning because the multi-interlocutor exchange depends on the stance that understanding arises by formulating questions, testing claims against evidence and counter-position, and revising in light of what the exchange surfaces. Without inquiry's commitment to investigation-driven knowing -- problematic situation, evidence-gathering, explanation-construction, revision -- the elenchus collapses to mere debate. Dialectic specializes the inquiry stance by routing the investigation through structured dialogue between competing positions rather than a solo investigator's confrontation with a phenomenon.


Differential_decay is the child that enriches generic decay: two COUPLED signals fade at DIFFERENT rates, so the survivor carries a meaning the pair never asserted (often a sign flip) — a CONTENT transformation, where signal_decay_and_fadeout is a MAGNITUDE reduction. generic decay reduces magnitude, differential decay reshapes content. Signal Decay and Fadeout supplies the genus: Signals, influence, or effects systematically weaken over time or space. Differential Decay preserves that general structure while adding its differentia: Two coupled signals decay at different rates, so the surviving component carries forward a meaning the original pair never intended — often with a sign flip. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Differentiated instruction is the specific shape adaptation takes when teaching tailors content, process, and product to learner variation.

Differentiated instruction is the structurally-particularized form adaptation takes in the pedagogical case: the system (the classroom and teacher's practice) modifies internal structure (content, process, product, environment) in response to sustained variation in learner readiness, interests, and profiles, preserving effective instruction across the distribution. It inherits adaptation's commitment to internal modification that improves fit to changed conditions, particularized to the case where the changing conditions are within-classroom learner diversity rather than external environment.


Differentiated instruction is a specific pedagogy that tailors content, process, product, and environment to individual learner profiles within one classroom.

Differentiated instruction is a specialization of pedagogy whose distinctive move is calibrating the instructional encounter to the readiness, interests, and learning profiles of individual learners rather than delivering a uniform program. It inherits pedagogy's general commitment that an instructional agent deliberately structures another agent's engagement with content to cause durable capability change, and adds the operating principle that the structuring must vary across learners within a shared class because uniform delivery predictably underserves both ends of the readiness distribution and fails to leverage classroom diversity.


Diffusion is a specialization of propagation in which the spreading mechanism is the aggregate of random or gradient-driven movements of microscopic constituents.

Diffusion specializes propagation by fixing the transmission mechanism as the aggregate stochastic or gradient-driven movement of many microscopic constituents, with no central directing agent. Where propagation names the systematic spreading of a signal or effect through a medium or network generally, diffusion specifies that the spread arises from microscopic randomness producing macroscopic flux down concentration gradients, governed quantitatively by Fick's law — a particular shape propagation takes when the medium is a stochastic substrate and the influence moves via collective random-walk dynamics.


Diffusion is a specialization of flow in which net transport arises from microscopic stochastic motion down a concentration gradient.

Diffusion is a specialization of flow in which the transport mechanism is the aggregate of microscopic random walks rather than coherent bulk motion, and the driving gradient is in concentration of the transported quantity itself. It inherits the general flow commitment of directional transfer of a conserved quantity from source to sink along a gradient, and specializes by fixing the mechanism to stochastic redistribution that yields Fick's-law proportionality between flux and gradient, with no central agent directing the movement.


Diffusion presupposes gradient because the net flux it describes is driven by, and proportional to, the gradient of the diffusing quantity.

Diffusion presupposes gradient because its constitutive law, Fick's, ties flux to the negative of the concentration gradient: there is no net diffusion in the absence of a gradient, and the gradient supplies both direction (down-slope) and magnitude (proportional to steepness) of macroscopic transport. Gradient supplies the general apparatus of pointwise direction-of-steepest-change in a scalar field; diffusion translates that local field-structure into a quantitative rule for net transport via the diffusion coefficient. Without a gradient, microscopic random motion produces no macroscopic flux.


Dimensional analysis presupposes constraint because dimensional homogeneity is a binding restriction on which equations among physical quantities can be admissible.

Dimensional analysis requires every physical equation to be dimensionally homogeneous: every additive term and both sides of any equality must share identical dimensional signatures. This presupposes constraint: a condition restricting admissible configurations to those satisfying it, with the feasible set as a first-class object. Dimensional homogeneity is a binding restriction on the space of candidate equations: any expression violating it is not admissible regardless of other merit. The Buckingham pi theorem then quantifies how this binding constraint reduces the number of free parameters, exactly the constraint-reduces-feasible-set move.


Dimensional analysis presupposes invariance because dimensional homogeneity requires that physical laws hold unchanged under unit-system changes.

Dimensional analysis presupposes invariance because dimensional homogeneity -- the requirement that both sides of a physical equation share the same dimensional signature -- is exactly the claim that the equation's truth is preserved under the named family of unit-system transformations. The Buckingham pi theorem then exploits this invariance: the dimensionless groups are the invariants of the unit-change group, and the count (n minus k) is the dimension of the invariant subspace. Without invariance's joint specification of preserved feature and preserving operations, dimensional reasoning has no formal warrant.


Dimensionality Reduction is a kind of approximation: a low-dimensional surrogate stands in for high-dimensional data with controlled loss.

Dimensionality reduction maps high-dimensional data into a lower-dimensional representation chosen to preserve the structural features that matter for downstream tasks — variance, neighborhoods, predictive information — while discarding redundant or noisy dimensions. The low-dimensional representation is a tractable surrogate for the intractable original, with an explicit error measure tied to the downstream criterion. That is the defining shape of Approximation, here specialized to data representation where the surrogate is a lower-dimensional projection or embedding.


Dimensionality reduction is a specialization of compression in which redundancy in a high-dimensional representation is removed by projecting onto a lower-dimensional latent structure.

Dimensionality reduction is a specialization of compression in which the redundancy being exploited is dimensional: high-dimensional data lies on or near a low-dimensional manifold, and a transformation projects it onto that lower-dimensional representation while preserving variance, distances, or predictive information. It inherits the general compression commitment that redundant structure can be eliminated without losing what matters and that the choice between lossless and lossy reduction is governed by which features must be preserved. The specialization fixes the redundancy to dimensional correlations rather than symbol-level statistics.


Diminishing Incremental Gains is a kind of trade-off: each new unit of input buys less output, raising the relative cost of further improvement.

Diminishing incremental gains says successive units of an input produce smaller increments of output once a threshold is passed, so the marginal cost of each next unit of benefit rises along the concave curve. Continuing to improve on the output dimension therefore requires giving up more on the input side — time, money, effort — than for any prior unit. That is the structure of a Trade-off, here driven by concavity in the input-output relationship rather than fixed-frontier opposition between two outputs.


Diminishing incremental gains presupposes nonlinearity because concave saturation of returns is one of nonlinearity's signature shapes.

Diminishing incremental gains presupposes nonlinearity because its structural claim, that each successive unit of input produces less output than the one before, is precisely a statement of concavity: a nonlinear input-output relation in which scaling does not preserve proportionality. Nonlinearity supplies the general space of relationships in which superposition fails and qualitative features like thresholds and saturation become possible; diminishing gains picks out the concave-saturating subfamily of that space as a domain-general heuristic across learning, utility, exercise, and effort.


Diminishing returns is the specific shape nonlinearity takes when a production function with a fixed factor exhibits eventually-negative second derivative in the variable input.

Diminishing returns is the structurally-particularized instance of nonlinearity in a production-economic setting: a production function with at least one fixed factor exhibits an eventually-negative second derivative in the variable input, so marginal product declines past a threshold. It carries forward nonlinearity's general commitment that scaling does not preserve proportionality and that concavity and saturation are structural features, and gives this shape the specific economic content of an empirically robust law about marginal, average, and total product under input fixity.


The law of diminishing returns is a specialization of diminishing incremental gains restricted to production with at least one fixed input.

The law of diminishing returns is a specialization of diminishing incremental gains restricted to the technological setting of a production process with at least one fixed factor, where the marginal product of a variable input eventually declines. It inherits the general concave input-output pattern that each successive unit yields a smaller increment, and specializes by fixing the domain to production economics, the cause to the fixed-factor constraint, and the variables to marginal, average, and total products. The broader heuristic of diminishing gains covers learning curves, utility, and effort; the law isolates the production-function instance with formal microeconomic predictions.


A DAG is a specific KIND of network/graph — directed edges plus a global no-return (acyclicity) constraint. A specialization of the general network with the added direction + acyclicity invariants.


Discounting is the specific shape time value of money takes as the quantitative technique for converting future cash flows into present-value equivalents.

Discounting is the operational particularization of time value of money: it provides the formula and procedure (C/(1+r)^t or C·exp(-rt)) for converting future cash flows into present-equivalent amounts using a discount rate that encodes time preference, opportunity cost, and risk adjustment. Where time value of money names the foundational principle that a unit of currency today is worth more than the same unit later, discounting fixes the quantitative apparatus — the rate, the compounding scheme, the present-value computation — that operationalizes the principle for decision analysis.


Discounting is the specific shape time preference takes when its psychological weighting is operationalized as a discount-rate formula converting future cash flows to present value.

Discounting is the structurally-particularized instance of time preference in finance and economic analysis: the psychological weighting of present over future is turned into an explicit discount rate r and a formula C divided by one plus r to the t (or continuous exponential) that converts a future cash flow into its present-value equivalent. It carries forward time preference's general commitment that delayed outcomes count for less than immediate ones, and gives this idea its specific quantitative shape in NPV, IRR, and discounted-cash-flow analysis.


Discounting is a technique that achieves commensurability across time by converting future values to present value; a mechanism, not a genus-instance. Commensurability supplies the prerequisite condition: Diverse values expressed in common metric enabling comparison. Discounting (Present Value) operates against that background: Present value calculation. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


'feedback's goal-directed specialisation — feedback structured around an explicit target and a signed gap that the action is keyed to.' A strict subspecies of feedback (which has no target). Feedback supplies the genus: Outputs influence inputs. Discrepancy-Driven Correction preserves that general structure while adding its differentia: Iteratively close the signed gap between a target and an observation. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Discreteness is one constitutive pole of the bundled discrete-versus-continuous comparison and supplies quantization's separated admissible states.

The current node explicitly compares countable separated values with a continuum and then analyzes mechanisms that produce quantized levels. Discreteness is therefore an internal constituent, not merely a neighboring concept: removing separated states removes both one side of the comparison and the defining output of quantization.


Continuity is the other constitutive pole of the bundled comparison and supplies the continuum against which imposed quantization is defined.

The current identity includes a direct discrete-versus-continuous contrast and engineered quantization from continuous signals. Continuity is therefore part of the live bundle. This edge records the source as it exists while a separate identity audit decides whether the bundle should be narrowed and renamed Quantization rather than pretending the already-canonical poles are absent.


Discreteness is a specific kind of boundary where the demarcation produces isolated points with no intermediate values between them.

Discreteness is a specialization of boundary. The general pattern marks a demarcation between an entity and what is outside, with the demarcation criterion governing membership and the permeability governing crossings. Discreteness instantiates this with the demarcation isolating each element from its neighbours, formally captured by the discrete topology in which every singleton is open and every point is isolated. The boundary between any element and any other is impermeable: there are no intermediate values. It is boundary maximized to the per-element scale, enabling counting, enumeration, and the combinatorial toolbox.


Discreteness presupposes set and membership because identifying separated states requires the prior availability of distinct elements satisfying a membership criterion.

Discreteness presupposes set and membership because its defining property, separated identifiable states with positive minimum spacing or isolated points, makes sense only against the prior availability of a collection of distinct elements that membership picks out. Set supplies the general apparatus of collection-as-first-class-object with a well-defined inclusion criterion; discreteness then characterizes the substructure of the set as one in which elements are individuated without intermediate values. Without set and membership, there is no enumerable collection on which discreteness's combinatorial reasoning, counting, and finite-state machinery can operate.


Discretion presupposes authority because the designed delegation of judgment within a rule's gap requires legitimately empowered decision-making in that space.

Discretion presupposes authority because the rule system's deliberate leaving-open of a gap and assignment of the choice within it to a situated agent requires that the agent hold legitimate power to make binding decisions in that space. Without authority's recognized right to bind, the discretionary choice is mere opinion that others may ignore. Discretion is authority operating in the rule's silences: the rule fixes the boundary, and the authority delegated to the agent fills the interior with case-specific judgment that binds through the agent's recognized standing.


Diseconomies of scale is the specific shape scale takes when growth past some size makes per-unit performance worse rather than better.

Scale names the general fact that system properties and governing laws vary as size or aggregation level changes, so that the system at one scale may be qualitatively different from the system at another. Diseconomies of scale is the particular shape this pattern takes in cost-output relations: beyond some band, coordination overhead and internal friction rise faster than added output, turning the size-efficiency curve unfavorable. It is a structurally-particularized instance of the band-specific-ontology pattern where the band-crossing is felt as an upturn in average cost.


Diseconomies of scale is the extension of diminishing marginal contribution into per-unit-cost reversal past a threshold scale. Diminishing Incremental Gains supplies the genus: Reduced benefit per unit. Diseconomies of Scale preserves that general structure while adding its differentia: Rising per-unit cost once scale grows past a point. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Disjointness is empty intersection under a fixed identity criterion — a relation among collections within the set-and-membership apparatus. A specialized set-relation. Set and Membership supplies the genus: Groups and categorizes elements. Disjointness preserves that general structure while adding its differentia: Two or more populated collections share no element. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Dislocation Motion is typically a specialization of Propagation, retaining the parent's defining structure while adding the child's specific commitments.

Propagation supplies the genus: The systematic spreading of a signal, effect, or state from a source through a medium or network, where the medium's structure governs how fast it moves, how it attenuates, and which paths it follows. Dislocation Motion preserves that general structure while adding its differentia: Large-scale change of an ordered medium achieved by propagating a localised defect rather than rearranging every unit at once. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Dispersion is the property-indexed differentiation of rates within a propagating bundle — a specialization of propagation ('not propagation as such'). Propagation supplies the genus: The systematic spreading of a signal, effect, or state from a source through a medium or network, where the medium's structure governs how fast it moves, how it attenuates, and which paths it follows. Dispersion preserves that general structure while adding its differentia: A co-launched bundle separates because propagation rate depends on a per-component property. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Disruptive innovation is the specific trajectory-crossing case within the broad Schumpeterian creative_destruction — an INITIALLY INFERIOR entrant on a cheaper steeper curve crossing the incumbent's value curve. creative_destruction also includes SUSTAINING displacements that are not disruptive. The inversion test (was the entrant initially worse on the headline metric?) distinguishes them. 'creative destruction is the broader category and disruptive innovation a specific mechanism within it.'. Creative Destruction supplies the genus: Replacement through innovation. Disruptive Innovation preserves that general structure while adding its differentia: An initially inferior entrant on a cheaper, steeper trajectory crosses the incumbent's value curve and displaces it, because the terms of competition shift rather than the entrant winning on the old ones. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Dissipation presupposes irreversibility because the unrecoverable conversion of order into heat is the defining one-way process irreversibility names.

Dissipation is the systematic conversion of organized energy or structural order into thermalized, un-recoverable form through interactions with many degrees of freedom. This rests on irreversibility: the structural preclusion of restoring a process's prior state without a costly compensating change in the environment. Dissipation cannot be a coherent pattern unless the conversion has a privileged direction; the entropy-increase clause that makes friction, viscosity, and Joule heating dissipative is exactly the thermodynamic structural feature irreversibility specifies as its mechanism.


Distortion is a transformation's DEVIATION from a reference faithful one; it presupposes a transformation. Transformation supplies the prerequisite condition: A rule-governed mapping that restructures an input into a different output, holding certain invariants fixed while altering others. Distortion operates against that background: Systematic, mapping-induced deviation of an output from a faithful rendering of its input. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


A systematic, rule-governed, non-random deviation of output from faithful input is precisely bias's systematic-directional-error signature applied to signal transformation. Bias supplies the genus: Systematic, directional error distinct from random noise. Distortion preserves that general structure while adding its differentia: Systematic, mapping-induced deviation of an output from a faithful rendering of its input. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Distributional assumption presupposes statistical inference because the commitment to a distribution family is meaningful only within the inferential reasoning it enables.

A distributional assumption is a structural commitment to model uncertain quantities under a specific probability-distribution family, made in service of drawing conclusions from samples about populations or future outcomes. Without statistical inference's machinery — reasoning from finite observations to underlying processes with explicit accounting for sampling variability and model uncertainty — there would be no purpose to imposing distributional structure on data. Statistical inference supplies the reasoning context that makes the distributional-assumption choice a load-bearing modeling decision rather than mere descriptive labeling.


A distributional assumption presupposes probability because it commits to a specific probability distribution shape for uncertain quantities.

A distributional assumption presupposes probability because the commitment to normal, exponential, power-law, or any other shape family is a commitment within probability's apparatus -- the assignment of coherent numerical weights over outcomes obeying additivity, normalization, and conditioning. Without probability's framework of sample space, events, and measure, there is no distribution to assume, no parametric structure to impose, and no model risk to bear. The assumption is a chosen restriction within the infinite-dimensional space of probability distributions probability theory provides. Probability supplies the prerequisite condition: Quantifies uncertainty and likelihoods. Distributional Assumption operates against that background: Commitment to assume uncertain quantities follow specific distribution. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Distributional Assumption is a specialization of Assumption, retaining the parent's defining structure while adding the child's specific commitments.

Assumption supplies the genus: A proposition treated as true for the purposes of some reasoning without being currently demonstrated within it, forming the load-bearing layer between what is given and what is concluded. Distributional Assumption preserves that general structure while adding its differentia: Commitment to assume uncertain quantities follow specific distribution. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


Distributional_effects is the critical recognition of what the aggregation operation conceals — the vector behind the scalar; it presupposes aggregation as the collapsing step. Aggregation supplies the prerequisite condition: Deliberately collapsing many items into a single summary, choosing which information to discard to gain tractability. Distributional Effects operates against that background: An aggregate outcome conceals systematically heterogeneous, unit-level changes. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Divergence-convergence presupposes variation strategies because the divergence phase systematically generates the variety the convergence phase then selects from.

Divergence-convergence is the cyclical macro-structure of disciplined design: a divergence phase deliberately expands the space of candidate framings and concepts, followed by a convergence phase that evaluates and narrows. The divergence phase is itself a variation strategy — controlled injection of variety into a process to surface alternatives. Variation strategies supplies the general practice of using variation as a deliberate tool to surface options and escape local optima. Divergence-convergence presupposes that practice as the engine of its divergence half, on which the subsequent convergence selection then operates.


Divergence-convergence in the design process presupposes iteration because its expand-then-narrow phases recur cyclically with each cycle building on the previous one.

Divergence-convergence is structured as a recurring cycle: a divergence phase generates breadth, a convergence phase narrows to a chosen candidate, and the process then re-opens to another divergence phase at finer grain. Without iteration's machinery — repeated application of a process with state carried between rounds and a notion of progress measured across them — there would be no way to organize the design process as cumulative cycles; the expand-and-narrow moves would collapse into a single non-recurring sequence rather than a productive macro-structure.


Operates to reduce the variance of a total outcome over multiple exposures, each with its own uncertainty; presupposes risk (a known distribution of outcomes to manage). Risk supplies the prerequisite condition: Exposure to a known distribution of possible outcomes. Diversification operates against that background: Spreading exposures across positions whose failure modes are uncorrelated reduces total-outcome variance; correlation, not count, drives the benefit. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Division of labor presupposes specialization because partitioning a joint task into narrow assigned roles depends on each performer narrowing function.

Division of labor is the system-level partitioning of joint production into distinct sub-tasks assigned to distinct performers whose specialized outputs are re-integrated. This rests on specialization: components narrowing to distinct partial functions rather than remaining general-purpose, trading self-sufficiency for higher collective performance through narrowing, complementarity, and integration. Smith's pin-factory analysis is the same observation in both directions. Without specialization's narrowing-plus-complementarity structure, the partitioned sub-tasks could not yield the productivity gain that distinguishes division of labor from mere parallel duplication of the whole task.


Dose-response relationship presupposes nonlinearity because the characteristic curves are sigmoidal with thresholds, saturation, and ceilings rather than proportional.

Dose-response relationship presupposes nonlinearity because the canonical dose-response curve is structurally nonlinear: it exhibits thresholds below which response is negligible, sigmoidal rise through a sensitive range, and saturation at a ceiling effect. The function's clinically meaningful shape parameters (potency, efficacy, slope, threshold, ceiling) are precisely the features of a nonlinear input-output relation. Without the prior commitment that scaling inputs need not scale outputs proportionally and that thresholds and saturation are structural rather than accidental, dose-response would collapse to a trivial linear scaling.


Dose-response relationship is a specialization of function (mapping) that assigns response magnitudes deterministically to dose levels.

Dose-response relationship is a specialization of function. Specifically, it instantiates the rule-assigning-each-input-exactly-one-output structure with dose as domain element and response magnitude as codomain element, capturing the Paracelsus insight that toxicity is a quantity-function rather than a substance-property. It commits to determinism in the function-theoretic sense -- same dose, same expected response in the specified system -- and is parameterized by characteristic shape features (potency, efficacy, slope, threshold, ceiling) that distinguish it from a mere correlation or noisy relation.


'double counting is aggregation, just aggregation that has gone wrong at a specific place' — it presupposes the aggregation operation and is the failure where overlapping buckets are summed without subtracting |A n B|. Presupposes-parent, not is a specialization of. Aggregation supplies the prerequisite condition: Deliberately collapsing many items into a single summary, choosing which information to discard to gain tractability. Double Counting operates against that background: Including the same underlying unit more than once in an aggregate because overlapping buckets are summed without subtracting their intersection. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Downward Causation presupposes Emergence: it requires a higher level whose properties are not reducible to lower-level constituents.

Downward causation claims that higher-level structures causally influence the behavior of their lower-level constituents. That claim requires the higher level to exist as a causally relevant level of organization with properties not trivially reducible to lower-level descriptions — exactly what Emergence supplies. Without emergent higher-level structure there is no upper terminus from which downward influence could flow. Downward causation presupposes emergence as the structural precondition that establishes the levels between which downward influence is asserted to run.


Downward Causation presupposes Layering: it requires a stratified architecture in which higher strata can influence lower ones.

Downward causation asserts that influence flows from higher strata back onto their lower-level constituents. That assertion requires a stratified architecture in which strata are distinguishable, each with its own descriptive vocabulary and operational regime — the structure supplied by Layering. Without a layered organization there is no higher stratum from which influence could descend and no lower stratum to receive it. Downward causation presupposes layering as the structural substrate that makes upward and downward causal directions meaningfully distinct.


Downward causation presupposes causality because it asserts genuine cause-effect influence flowing from higher levels back to lower-level constituents.

Downward causation presupposes causality because the claim that wholes causally constrain or shape their parts requires the full four-component causal relation -- antecedent, consequent, productive connection, and modal robustness -- to hold across the hierarchical boundary. Without causality's apparatus, downward influence collapses to mere description or correlation. The controversial commitment of the prime is precisely that the higher level satisfies cause-component requirements (counterfactual sensitivity, productive linkage) with respect to lower-level dynamics, not just that aggregate descriptions are explanatorily useful.


The downward context edge (macro reshapes micro situations) — 'the other half'. After the philosophy frame is stripped away, the retained structural roles are those of Micro Macro Linkage: A two-way bridge by which micro units produce macro regularities and macro conditions reshape the micro situation. Downward Causation adds the local frame and commitments expressed in its identity: Higher-level influence. The parent pattern remains recognizable without that vocabulary, while the child is the framed realization of it. That preservation test establishes decomposition rather than taxonomic subsumption.


A phase transition/bifurcation near criticality is the mechanism (second generator) dragon-king theory invokes; the prime is the statistical SIGNATURE and diagnostic built around it. Part-to-whole: presupposes the critical-transition mechanism but adds the bimodal-tail test and precursor families. Tipping Points (or Phase Transitions) supplies the prerequisite condition: Abrupt state change. Dragon King Theory operates against that background: The largest events in some complex systems come from a distinct mechanism — synchronization or bifurcation near criticality — leaving them above the power-law tail and partly predictable. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Dunning-Kruger effect is a specialization of bias in which low-competence individuals systematically overestimate their own competence.

The Dunning-Kruger effect is a specialization of bias. The general bias pattern is systematic, signed displacement of outputs from a true value, persisting beyond noise. Dunning-Kruger specializes by giving the displacement a specific shape: self-assessment of competence is systematically displaced upward at low competence and modestly downward at high competence, with the same metacognitive deficit producing both the incompetence and the failure to recognize it. The same systematic-rather-than-random-displacement logic of bias applies, with self-assessment as the estimating process and the competence gap as the specific cause of the offset.


Dunning-Kruger effect presupposes metacognition because the self-assessment failure it names is a failure of monitoring one's own competence.

The Dunning-Kruger effect is the pattern in which low-competence individuals overestimate their competence because they lack the metacognitive apparatus to recognize their lack. The effect is constitutively a failure of second-order monitoring: judging one's own performance, identifying gaps, calibrating confidence. Metacognition supplies precisely that monitoring capacity — representing and evaluating one's own cognitive processes. Without metacognition as a structural commitment of cognitive systems, there would be no self-assessment operation to fail and no double-curse mechanism in which the missing competence and the missing self-awareness coincide.


Dynamic programming is the specific shape optimization takes when problems exhibit optimal substructure and overlapping subproblems.

Dynamic programming is the specific shape optimization takes when the problem has optimal substructure (the optimal solution can be assembled from optimal solutions to subproblems) and overlapping subproblems (the same subproblems recur many times). Optimization in general is the search over a feasible set for an element maximizing or minimizing an objective subject to constraints. DP instantiates this by exploiting recursive structure: the search is decomposed across stages or subproblems, results cached, and assembled bottom-up or top-down. Bellman's principle of optimality is what licenses the recursive decomposition into a polynomial-time procedure.


Dynamic programming presupposes recurrence because its decomposition into overlapping subproblems is expressed as a Bellman recurrence relation.

Dynamic programming solves problems by decomposing them into overlapping subproblems whose solutions are stored and reused, exploiting optimal substructure. This presupposes recurrence: the structural property by which a pattern, condition, or value reappears across iterations, classically formalized as recurrence relations. The Bellman equation is precisely such a recurrence: the optimal value at stage n is a function of optimal values at smaller subproblems. Both memoization and tabulation are mechanisms for exploiting the reappearance of the same subproblem; without recurrence as the structural reason that the same subproblems recur, DP's caching strategy yields no speedup.


Dynamic programming is the specific shape decomposition takes when subproblems overlap and optimal substructure lets cached solutions compose into the full answer.

Dynamic programming is the specific shape decomposition takes when the whole is a decision problem with optimal substructure and overlapping subproblems. The breaking-into-recombinable-parts pattern that decomposition names manifests here as identifying subproblems whose optimal solutions combine into the parent's optimum; the overlap means the same parts recur, making memoization or bottom-up tabulation transform exponential recursion into polynomial computation. Optimal substructure is the reversibility-and-recombinability condition decomposition presupposes, sharpened for optimization contexts. After the operations_research frame is stripped away, the retained structural roles are those of Decomposition: Breaking a whole into parts that can be analyzed independently and recombined to reconstitute the whole, making complexity tractable through divide-and-conquer. Dynamic Programming adds the local frame and commitments expressed in its identity: Solve via subproblem reuse. The parent pattern remains recognizable without that vocabulary, while the child is the framed realization of it. That preservation test establishes decomposition rather than taxonomic subsumption.


Echo chamber is constituted by a self-reinforcing positive-feedback loop plus loss of the corrective negative-feedback signal. Feedback supplies the prerequisite condition: Outputs influence inputs. Echo Chamber operates against that background: A bounded community whose input filter and internal reinforcement gradient amplify shared beliefs and strip out the corrective signal that would moderate them, while members experience the environment as complete rather than filtered. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Stage-ordered occupancy through a sequence of states; succession is the specialization where the current stage modifies its own substrate to gate the next. State and State Transition supplies the genus: Captures system condition and evolution. Ecological Succession preserves that general structure while adding its differentia: Stage-ordered change in which the current occupants themselves modify the substrate, determining which stage can come next through facilitation, inhibition, or tolerance. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Each successional stage modifies its own substrate to determine which stage comes next — a path-dependent, order-locked sequence. Path Dependence supplies the genus: Outcomes are shaped by the specific historical sequence of past choices, which lock in consequences and foreclose alternatives that persist despite present incentives to change. Ecological Succession preserves that general structure while adding its differentia: Stage-ordered change in which the current occupants themselves modify the substrate, determining which stage can come next through facilitation, inhibition, or tolerance. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Economies of scale are a specialization of increasing returns in which average cost per unit falls as production scale grows.

Economies of scale are a kind of increasing returns specialized to the supply side: as the cumulative scale of production grows, average cost per unit declines because fixed costs spread, specialization deepens, larger equipment becomes viable, and learning accumulates. It inherits the general pattern that the marginal benefit of additional input rises with the cumulative state variable, producing self-reinforcing advantage, and supplies the specific case where the accumulating variable is production volume and the increasing-returns mechanism operates through declining unit cost rather than rising user utility.


Economies of scale is the specific shape scale takes when growth lowers per-unit cost through fixed-cost spreading, specialization, and learning.

Scale names the general fact that system properties and governing laws vary as size or aggregation level changes, so the system at one scale may be qualitatively different from at another. Economies of scale is the particular shape this pattern takes when expansion lowers average cost per unit through fixed-cost spreading, deeper specialization, larger-equipment viability, bulk purchasing, and accumulated learning. It is a structurally-particularized instance of band-specific behavior in which the favorable band makes growth self-reinforcing and can reshape industry competitive structure.


Economies of scope is the specific shape synergy and antagonism takes when shared resources across heterogeneous outputs make joint production cheaper than separate production.

Economies of scope is the cost-synergy particularization of synergy and antagonism: joint production of distinct outputs produces a combined cost lower than the additive baseline of stand-alone production, because a shared indivisible resource is amortized across heterogeneous uses. Where synergy and antagonism names deviations from a baseline combination rule generally, scope economies specify the baseline (additive cost) and the direction of deviation (sub-additive), with the interaction being the amortization mechanic of the shared substrate.


Economy of force is a specialized allocation rule: drive non-decisive efforts to their minimum-viable floor so freed mass concentrates at the decisive point. It is allocation (assign limited supply across competing claimants) plus a decisiveness gradient + minimum-viable threshold + reserve.


An ecotone is a boundary GIVEN DEPTH — it presupposes a boundary between two regimes and adds a band of measurable extent, gradient, exchange, and zone-specific generative structure. It is not an is-a of boundary (a boundary is a curve; the ecotone is the opposite — a generative band) so composition, not subsumption.


The edge effect is the band of phenomena a STEEPENED gradient drives at a regime boundary; it presupposes gradient (the rate-of-change) as its load-bearing ingredient. 'The gradient is an ingredient, not the prime.'. Gradient supplies the prerequisite condition: Distribution and change over space/time. Edge Effect operates against that background: Where two regimes meet, a thin high-gradient band forms a distinct third regime with its own resident phenomena, absent from either interior. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


The band forms AROUND a boundary line of two abutting regimes; presupposes boundary as the reference locus (file explicitly band-not-line, but the line is the reference the band forms around). Boundary supplies the prerequisite condition: Defines system limits. Edge Effect operates against that background: Where two regimes meet, a thin high-gradient band forms a distinct third regime with its own resident phenomena, absent from either interior. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Editorial independence is the STRUCTURAL condition that secures credible (impartial-seeming) judgment regardless of the evaluator's disposition — it relocates impartiality from a disposition to an institutional firewall ('s central move). Impartiality supplies the prerequisite condition: Treatment that depends only on relevant features and is invariant under the identity of the party involved. Editorial Independence operates against that background: An evaluative judgment institutionally insulated from the parties it affects. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Effect size presupposes comparison because magnitude is read off the relation between two or more co-considered quantities.

Effect size quantifies the magnitude of a relationship or difference in substantive, interpretable units, separately from statistical significance. This presupposes comparison: placing items under a shared frame, selecting a dimension along which they are co-considered, applying an alignment rule, and reading off a relation. An effect size is precisely such a reading: it requires comparands (typically treatment and control, or two conditions), a shared scale, and an alignment rule that makes the difference commensurable. Without comparison's operation of generating relational information, the magnitude has nothing to be the magnitude of.


Effect size presupposes scale because it quantifies the magnitude of an observed relationship in substantive units of measurement.

Effect size presupposes scale because reporting magnitude in interpretable units requires a chosen scale -- raw difference, standardized mean difference, odds ratio, variance explained -- against which the effect is sized. Without scale's commitment to specifying size, resolution, or unit of aggregation, there is no axis along which to express how large the deviation from zero is and no comparison across studies on a common dimension. Effect size is the scale at which a relationship is described, separated from the significance question of whether it differs from zero. Scale supplies the prerequisite condition: Properties change with size. Effect Size operates against that background: Magnitude of effect. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


'not predictive coding in general; efference copy is the specific self-versus-world attribution built from broadcasting a command copy to the perceiver.' predictive_coding (predict input, propagate error) is the genus; efference copy is the self-attribution specialization. Predictive Coding supplies the genus: A system predicts its input and propagates only the prediction error. Efference Copy preserves that general structure while adding its differentia: Routing an internal copy of a command to the perceiver so self-caused effects can be predicted and subtracted, leaving only the world-caused remainder. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association. The typical qualifier limits the claim to the characteristic route, not a constitutive requirement of every instance; exceptions must retain the child's identity through another mechanism.


Efficiency presupposes comparison because its verdict exists only relative to feasible alternatives evaluated under a shared input–output frame.

An arrangement is efficient only relative to actual or counterfactual alternatives aligned on the same outputs, resource dimensions, and acceptance criteria. Without Comparison's co-framing and relation read-off there is no dominance test, frontier, or removable slack—only an ungrounded praise word.


Efficiency presupposes constraints because they define the feasible alternatives against which resource use and result are judged.

Technology, resource, quality, safety, reliability, and regime constraints determine which alternatives are genuinely available. Remove or omit one and the frontier moves, so the same operating point may change from efficient to inefficient or the reverse.


Efficiency rebound is a counterresponse offset specialized to demand expansion induced by an efficiency-driven fall in effective unit cost.

It inherits a direct improvement, a coupled adaptive response, and graded offset accounting. It adds efficiency as the intervention, effective per-unit cost as the changed condition, activity volume as the response, and resource saving as the quantity being offset.


The rebound begins with a genuine efficiency gain that reduces resources required per unit of service.

Without an input-output improvement at fixed output there is no expected per-unit saving for expanded activity to consume. Efficiency supplies the direct gain; the rebound describes the aggregate response after unit cost falls.


The magnitude of efficiency rebound is typically governed by how strongly activity volume responds to the resulting fall in effective price.

Direct rebounds in market activities are naturally parameterized by price elasticity. Indirect, economy-wide, and shadow-price responses can occur without a single observable market-price elasticity, so the dependency is typical rather than strict.


The efficient market hypothesis presupposes financial arbitrage because the mechanism by which prices incorporate information is competitive arbitrage trading.

The efficient market hypothesis presupposes financial arbitrage because its central claim — that asset prices incorporate all available information so quickly and completely that no strategy yields systematic risk-adjusted excess returns — rests on a prior mechanism: aggressive arbitrage by informed traders impounds information into prices. Without arbitrage's structure of exploiting price discrepancies and thereby closing them, there is no force driving prices toward fundamental value. EMH inherits arbitrage's convergence dynamic and supplies the macro-level consequence: in the limit of frictionless competitive arbitrage, prices reflect information and arbitrage opportunities disappear.


Every Eigenvalue/Eigenvector pair is defined relative to a transformation whose invariant directions and scalar gains it records.

Eigenpairs are a fingerprint of an operator, not the operator itself. Every admitted case requires a transformation acting on a space before one can ask which nonzero directions it merely scales and by what factor. The eigen-structure adds the invariant directions, scalar gains, spectrum, dominant mode, and spectral gap.


Eigenvalue/Eigenvector structure strictly presupposes Linearity because scalar multiples and sums must be respected by the operator whose equation is Av = λv.

The live identity is eigen-structure for a linear transformation. Its invariant-direction equation, modal decomposition, basis expansion, and independent scalar gains all depend on homogeneity and additivity. Fixed Point is not the genus: an eigenvector with eigenvalue other than one changes magnitude, and a projective-direction reinterpretation changes the map and discards the eigenvalue component.


Elicitation channel contribution presupposes observability because it diagnoses how a constructed output channel limits recovery of an internal state from the outputs it produces.

The channel-crossing test asks which hidden-state distinctions survive and which recorded differences are attributable to the channel. Observability supplies the prerequisite condition: Infer internal state externally. Elicitation Channel Contribution operates against that background: The recorded representation of a system's unobserved state is a joint product of state and the elicitation channel, so the analyst's marginal of interest is recoverable only by controlling the channel. If the parent condition is removed, the child relation becomes undefined or loses the mechanism asserted by this edge; the parent can obtain independently, so the relation is presupposition rather than subsumption.


Elicitation channel contribution contains representation because the channel maps a target state into a recorded medium under a faithfulness claim whose systematic distortion is being diagnosed.

The answer, label, score, testimony, or model output is a channel-produced representation rather than the internal state itself. Representation supplies an internal constituent: Model complex ideas. Elicitation Channel Contribution requires that role within this mechanism: The recorded representation of a system's unobserved state is a joint product of state and the elicitation channel, so the analyst's marginal of interest is recoverable only by controlling the channel. Remove the parent-role and the child loses a required internal operation, even though the parent can exist outside the child. The child is therefore built from the parent rather than being a taxonomic kind of it.


Embeddability requires binding ambient and conflict Constraints that divide possible placements into admissible and forbidden sets.

Constraint supplies the hard predicate defining feasibility. Embeddability specializes the decision object to faithful placements of a substrate into a host and asks whether the admissible placement set is empty, without ranking the survivors.


Embeddability requires the class of structure-preserving injective Embeddings whose conflict-free subset is tested for non-emptiness.

Embedding supplies the possible faithful placements from substrate to host. Embeddability adds the ambient limits, a conflict predicate, the existential yes-or-no gate, a hardness landscape, and the remediation triangle when no admissible witness exists.


Embedding is 'the FAITHFUL INJECTIVE special case' of representation — representation models A by B in any (possibly lossy, many-to-one) way; embedding adds injectivity and structure-preservation. A specialization of representation. Representation supplies the genus: Model complex ideas. Embedding preserves that general structure while adding its differentia: A structure-preserving injection of one system faithfully into a richer one. The parent can occur without those added commitments, whereas removing the parent structure leaves no basis for classifying the child as this subtype. That asymmetry establishes subsumption rather than mere association.


The upward aggregation edge (micro produces macro regularities) — 'emergence is half the pattern'. After the systems_cybernetics frame is stripped away, the retained structural roles are those of Micro Macro Linkage: A two-way bridge by which micro units produce macro regularities and macro conditions reshape the micro situation. Emergence adds the local frame and commitments expressed in its identity: Complex patterns from simple rules. The parent pattern remains recognizable without that vocabulary, while the child is the framed realization of it. That preservation test establishes decomposition rather than taxonomic subsumption.


Emergent formalization is the specific shape emergence takes when informal usage patterns crystallize into formal grammatical structure over time.

Emergent formalization is the structurally-particularized form emergence takes in the diachronic-linguistic case: the lower-level constituents are token usages and chunking events in speech communities, the higher-level phenomenon is grammatical rule status, the sense of novelty is that the rule has properties (productivity, obligatoriness) absent from any single token, and the conditions are the frequency and conventionalization thresholds that grammaticalization requires. It satisfies emergence's four-part specification, particularized to language change.


Emic/etic analysis contains two coordinated viewpoints whose insider and outsider stations generate different access, occlusion, and bias profiles.

The emic scheme is positioned inside participants' competence and the etic scheme outside it under a comparative analytic framework. Remove positioned access from either description and the paired distinction collapses; the method adds distinct closure rules and treats divergence between the two views as evidence rather than error.