Phenomenology

Phenomenology is the philosophical method and research tradition that takes *first-person conscious experience* — things as they appear to a subject — as the primary evidentiary and analytical object, developing disciplined practices (especially *the phenomenological epoché*, bracketing natural-attitude commitments about objective existence) to describe *the structures of experience as experienced*, while suspending judgment about ontological status. The essential commitment is that subjective experience has investigable structure — *intentionality, horizon, temporal flow, embodiment* — irreducible to third-person behavioral or physiological accounts. Every phenomenological claim specifies: (1) *the experiencing subject*[^husserl-1913] <!-- FACT-D19-175 --> (*transcendental ego* in Husserl; *embodied subject* in Merleau-Ponty); (2) *the intentional content, the noema*[^husserl-1900-1901] <!-- FACT-D19-176 --> (what experience is about); (3) *the conscious act, the noesis* (the experiencing itself); and (4) *the methodological epoché*[^heidegger-1927] <!-- FACT-D19-177 --> (bracketing assumptions to reveal experience's structural layers). Husserl (1900–1913) founded transcendental phenomenology; Heidegger (1927) inaugurated hermeneutic existential phenomenology by grounding consciousness in *being-in-the-world*; Merleau-Ponty (1945) foregrounded *the lived-body embodied perspective*[^merleau-ponty-1945] <!-- FACT-D19-178 -->, rejecting Cartesian dualism; Sartre[^sartre-1943]<!-- FACT-D19-179 --> extended the tradition into existential consciousness-as-nothingness. Modern applications (cognitive science, HCI, psychiatry, ethnography) trace genealogy to this core: *phenomenology as transcendental-or-hermeneutic study of consciousness structures from within lived experience*.

Interpretation

Phenomenology presupposes interpretation because describing structures of lived experience requires reading meaning from how things appear to a subject.

Phenomenology presupposes interpretation because the disciplined description of experience-as-experienced -- intentionality, horizon, temporal flow, embodiment -- is itself the recovery of meaning from presented appearances under a methodological framework (the epoche, eidetic reduction). Without interpretation's structured movement from a presented medium toward a constrained reading, the phenomenological epoche has no procedure: bracketing the natural attitude requires deciding what counts as the structure of appearance versus the natural-attitude residue. Phenomenology is interpretation turned reflexively on first-person experience.


Pipeline

A pipeline is a *sequence of stages through which work items or data flow in an ordered manner*, often allowing *concurrent processing of different stages to increase throughput* <!-- FACT-D30-076 -->[^ramamoorthy-1966]. The essential commitment is *staging*: dividing a workflow into discrete, separable steps such that each stage accepts outputs from the prior stage and produces inputs for the next, enabling overlap and parallelism without requiring true simultaneity within a single stage <!-- FACT-D30-077 -->[^ramamoorthy-1966].

Decomposition

A pipeline is a specialization of decomposition that breaks a workflow into ordered stages whose outputs feed the next.

A pipeline is a specialization of decomposition. Specifically, it instantiates the breaking-a-whole-into-recombinable-parts pattern with the additional commitment that the parts are sequenced stages and the recombination is a directed flow: each stage accepts the prior stage's output and produces input for the next. Like other decompositions, it assumes independent analysis of pieces yields the whole; the pipeline subclass enables concurrent processing of different stages on different items, trading staging overhead for throughput gains through overlap.

Iteration

A pipeline presupposes iteration because work items advance through repeated stage transitions, each consuming the previous stage's output.

A pipeline is a sequence of stages through which work items flow, with each stage accepting outputs from the prior stage and producing inputs for the next. This presupposes iteration: the repeated application of a step with state carried between rounds and progress measured across rounds. Each stage transition is an iteration step where the work item's state advances; the pipeline's throughput depends on the per-stage iteration consuming the previous stage's output. Without iteration's structure of repeated application with state passed forward, staging collapses into a single monolithic operation rather than an ordered flow.

Modularity

A pipeline presupposes modularity because the discrete separable stages with well-defined interfaces are exactly modular components.

A pipeline divides a workflow into discrete, separable stages, each accepting outputs from the prior stage and producing inputs for the next, enabling overlap and parallelism. This presupposes modularity: decomposition into discrete, largely self-contained components with stable interfaces that define what each provides and what it depends on. Each pipeline stage is a module whose interface to neighbours is the stage's input and output types. Without modularity's commitment to clear boundaries and stable interfaces, stages could not be designed, tested, replaced, or run concurrently in isolation from one another.


PK/PD Modeling (Pharmacokinetics / Pharmacodynamics)

PK/PD modeling is the coupled mathematical framework for characterizing what the body does to a drug (pharmacokinetics, PK — absorption, distribution, metabolism, excretion) and what the drug does to the body (pharmacodynamics, PD — the concentration-response relationship at the site of action), with the coupling providing a dose→concentration→effect pipeline that is the central quantitative apparatus of clinical pharmacology, as Rowland and Tozer (2010) develop in their canonical treatment of clinical pharmacokinetics and pharmacodynamics.<!-- FACT-D47-091 -->[^rowland-tozer-2010] The essential commitment is that drug response is not simply a function of dose but of the time course of drug concentration at the effect site, which is itself a compartmental-distribution-and-elimination function of dose; explaining and predicting clinical response therefore requires modeling both legs of the pipeline together, and many clinical phenomena (delayed effect, hysteresis between concentration and response, tolerance development) are only intelligible in the combined framework. Every PK/PD articulation specifies (1) the PK model — typically a one-, two-, or three-compartment model with absorption, distribution, and elimination rate constants; or physiologically-based pharmacokinetic (PBPK) models that explicitly represent organs and blood flow; (2) the PD model — direct concentration-effect relationship (often Hill-equation sigmoidal), indirect-response models where drug affects the rate of production or elimination of a response variable, turnover models, and tolerance/sensitization sub-models; (3) the coupling between PK and PD — effect-compartment lag, hysteresis, the relationship between plasma and effect-site concentrations; and (4) population-level extensions — population PK/PD analyzing between-subject variability, covariate effects (age, renal function, genetics), and individualized dose recommendations. PK/PD modeling is the quantitative backbone of drug development, therapeutic drug monitoring, individualized dosing, and regulatory decision-making.

Dose-Response Relationship

PK/PD modeling presupposes dose-response relationship because the pharmacodynamic half of the model is precisely the concentration-to-effect mapping the parent prime names.

PK/PD modeling couples the pharmacokinetic dose-to-concentration mapping with the pharmacodynamic concentration-to-effect mapping, and this second half is structurally the dose-response relationship: how response magnitude depends on input intensity with characteristic potency, efficacy, slope, and ceiling parameters. Without dose-response's machinery — the quantitative mapping from input magnitude to response magnitude with its characteristic shape parameters — PK/PD would have no model of how concentration translates into clinical effect, and the dose-concentration-effect pipeline would terminate at concentration.

Flow

PK/PD modeling presupposes flow because pharmacokinetic transport of drug across body compartments is structurally a flow of matter through a network.

PK/PD modeling characterizes how drug concentration moves through body compartments — absorption, distribution, metabolism, excretion — which is structurally a flow process: a conserved quantity transported through a network along gradients, governed by rate and continuity equations. Without flow's machinery of directional transport with conservation, rate, and channel structure, the compartmental pharmacokinetic representation would have no transport-dynamics framework to deploy. Flow supplies the conserved-quantity transport substrate that pharmacokinetic modeling instantiates for drug molecules in physiological compartments.

Therapeutic Window

PK/PD modeling presupposes therapeutic window because its dose-to-effect pipeline serves the goal of staying within a usable dose range.

PK/PD modeling presupposes therapeutic window because the entire dose-to-concentration-to-effect pipeline it constructs is constructed in order to keep the patient in the operating range where intended effects are achieved without unacceptable toxicity. The window supplies the upper and lower bounds the pipeline must respect; PK/PD supplies the quantitative apparatus for predicting whether a given regimen will hold the time-course of effect-site concentration inside those bounds. Without the prior commitment to a clinically meaningful operating range, the modeling has no design target.


Platform Design

platform architecture
shared foundation
modular platform
ecosystem foundation

Platform Design is the practice of creating a *stable core foundation* — a set of standardized interfaces, shared services, reusable components, or computational infrastructure — on which multiple independent variations, applications, or specialized derivative systems can be built without each derivative requiring full system redesign. A platform is characterized by (1) the identification and engineering of a common substrate (hardware, software, organizational processes) that solves problems common to multiple use cases or applications, (2) the specification of explicit interfaces (APIs, physical connectors, data schemas, communication protocols) that allow external developers, vendors, or teams to build specialized applications on top without modifying the platform core, (3) the commitment to platform stability — the core abstractions remain fixed while applications and extensions evolve — which reduces coordination cost and enables parallel development of platform variants, (4) the recognition that the platform controls critical shared resources (processing, memory, storage, network, organizational decision authority) and must balance openness (enabling diverse applications) against control (maintaining platform integrity and quality), and (5) the economic insight that platform design shifts value creation from individual product design to ecosystem orchestration — value accrues not to the single best product but to the platform that attracts the most valuable applications and users. The deeper insight is structural: complex systems rarely succeed as monoliths; instead, they succeed as governed ecosystems where a stable core enables thousands of independent participants to innovate without synchronization. Platform design originated in operating systems (UNIX, Windows, iOS, Android) where the platform abstracts hardware variation and provides core services (file systems, process management, memory allocation, networking) enabling application developers to build without hardware expertise. The principle extended to hardware platforms (automotive, aviation, industrial equipment where a shared chassis or bus architecture enables multiple product variants), business platforms (marketplaces, franchises, supplier ecosystems), and organizational platforms (shared service centers, data platforms, technology infrastructure). The mechanism works because it inverts scalability: instead of central teams building individual products for each market, a platform team builds the foundation and the ecosystem builds the applications, making system complexity scale with ecosystem participation rather than central capacity<!-- FACT-D35-031 -->[^cusumano-gawer-2002].

Interface

Platform design is a constituent piece of interface; its standardized contracts between core and derivative systems are the platform's load-bearing structure.

Platform design builds a stable core foundation by engineering explicit interfaces — APIs, physical connectors, data schemas, communication protocols — that let independent applications connect to and reuse the core without modifying it. These interfaces are the platform's defining structural feature: they specify what is exposed and what is hidden, what crosses and what is guaranteed on each side. The interface prime supplies the asymmetric-visibility and structured-protocol component on which the entire platform pattern is built.

Modularity

Platform design presupposes modularity because the stable core and pluggable derivatives architecture is exactly modular decomposition with stable interfaces.

Platform design creates a stable core foundation on which independent derivative applications are built without redesigning the core, with explicit interfaces letting external builders add specialized layers. This architecture is constituted by modularity: discrete components with well-defined boundaries, stable interfaces that decouple platform from applications, and low coupling between core and derivatives. Modularity supplies exactly this decomposition principle. Platform design presupposes it as the structural pattern that makes a stable core with interchangeable derivatives possible at all, so the platform discipline is modularity in a particular configuration.


Polysemy

Polysemy is the condition in which a *single form* carries *multiple related senses* that share a conceptual core: (1) one signifier (a word, icon, label, code identifier) maps, at a single moment in time, to two or more **distinct but connected** signifieds; (2) the senses are **motivated** — speakers can articulate (often implicitly) why the senses belong together (extension by metaphor, metonymy, specialization, or abstraction)[^lakoff-1987]<!-- FACT-D22-180 -->; (3) context disambiguates — hearers/readers select the intended sense from local cues (syntactic slot, discourse topic, world knowledge); (4) polysemy is distinct from both **homonymy** (unrelated senses sharing a form by accident) and **vagueness** (a single sense with fuzzy boundaries)[^cruse-1986]<!-- FACT-D22-185 -->.

Signifier–Signified Duality

Polysemy presupposes signifier-signified duality because multiple related senses sharing one form requires the two-faced sign structure to host the one-to-many mapping.

Polysemy presupposes signifier-signified duality because the condition of a single form carrying multiple related senses requires the two-faced sign structure in which a signifier is conventionally linked to a signified. Without the duality, there is no signifier to bear multiple sense-attachments and no signified concepts to which it can point. The duality supplies the foundational sign architecture; polysemy is the specific deviation from the simplest one-signifier-one-signified case to one-signifier-many-related-signifieds, where context disambiguates among motivated senses sharing a conceptual core.


Potentiation

Potentiation is a dynamic response phenomenon originating in pharmacology but recurrent across neuroscience, immunology, physiology, and behavioral science: a condition in which exposure to a stimulus, dose, or agent causes the system to become *sensitized*, such that a subsequent identical, related, or even lower dose produces a *disproportionately larger response* than the prior exposure did, as Brunton and colleagues (2018) develop in their canonical pharmacological treatment. The response increase is history-dependent, not inherent to the stimulus alone; the system's responsiveness changes—not the stimulus—meaning potentiation fundamentally describes how organisms become more reactive, not less, under repeated or primed conditions. It stands as the direct opposite of tolerance, with which it shares structural logic but inverted direction: both are time-dependent response changes under repeated exposure, but potentiation amplifies while tolerance dampens.<!-- FACT-D47-061 -->[^goodman-gilman-2018]

Adaptation

Potentiation is a specific kind of adaptation where prior exposure modifies the system to respond more strongly to subsequent stimuli.

Potentiation is a specialization of adaptation. The general pattern is a process by which a system changes its internal structure or parameters in response to sustained environmental input, preserving or improving fit to new conditions; the modification alters the system itself rather than being a moment-in-time response. Potentiation instantiates this with the modification being increased responsiveness: prior stimulus exposure sensitizes the system so that a subsequent identical or smaller dose produces a disproportionately larger response. It is adaptation directed toward amplified rather than dampened reactivity to the recurring stimulus class.

State and State Transition

Potentiation is a specific kind of state transition where prior exposure shifts the system into a sensitized state with different response dynamics.

Potentiation is a specialization of state and state transition. The general pattern specifies a state space, a transition relation, and the Markov-style commitment that future behaviour depends on current state plus inputs. Potentiation instantiates this with prior stimulus exposure as the trigger that transitions the system from a baseline state into a sensitized state; in the new state, the same input produces a disproportionately larger output. The history-dependence of the response is compressed into the system's current state variable (sensitization level), exactly the state-as-history-compression structure the parent pattern names.

Feedback

Potentiation presupposes feedback because history-dependent sensitization requires past output to route back and modify the system's response gain.

Potentiation is the structural pattern in which prior exposure causes a system to respond disproportionately to a subsequent stimulus — the system's responsiveness, not the stimulus, has changed. This history-dependence requires that the past response be routed back to alter the parameters governing the next response, closing a loop between output and gain. The feedback structure supplies exactly that: a portion of the output influences subsequent input handling. Without that loop closure, no history-dependent sensitization could persist beyond the initial exposure.


Pragmatic Politeness Strategies

Pragmatic Politeness Strategies are the systematic means by which speakers manage relational risk in communication. At foundation, the framework operates on two interdependent concepts:[^brown-levinson-1987]<!-- FACT-D23-120 --> the positive-and-negative face (public self-image decomposed into desire for approval vs. desire for autonomy), and the face-threatening act (FTA)—any utterance that intrinsically endangers the hearer's or speaker's relational standing. Speakers navigate face-threat through five super-strategies arranged on a directness continuum:[^goffman-1967]<!-- FACT-D23-121 --> bald-on-record (direct, no mitigation), positive politeness (in-group solidarity markers), negative politeness (deference, hedges, apologies), off-record (deniable hints, indirect), and non-utterance (silence). The selection among these strategies is governed by a weight calculation—the function $W = D + P + R$ summing social distance, power asymmetry, and rank of imposition—such that heavier weights trigger more pronounced mitigation.

Social Norms

Pragmatic Politeness Strategies are a kind of social norm: face-management conventions group members expect, enforce, and internalize.

Pragmatic Politeness Strategies are the patterned, group-shared expectations about how speakers should manage face threats in interaction, with deviations meeting disapproval and compliance sustained jointly by internalization and external sanction (raised eyebrows, social withdrawal, reputational cost). That is the defining structure of a social norm; politeness strategies specialize it to the domain of conversational face-work, supplying the specific repertoire (bald-on-record, positive politeness, negative politeness, off-record, withhold) that the norm prescribes.

Speech Act Theory (Illocution, Perlocution)

Pragmatic politeness strategies presuppose speech act theory because face management operates on the illocutionary force of utterances threatening or supporting hearer-face.

Pragmatic politeness strategies presuppose speech act theory because the face-threatening act framework operates on illocutions -- requests, refusals, criticisms, apologies, declarations -- whose performative force imposes the relational risk that politeness mitigates. Without speech act theory's separation of locution, illocution, and perlocution, there is no FTA to classify and no continuum of indirectness (bald-on-record through off-record) to navigate. The politeness vehicle works by adjusting how illocutionary force is delivered while preserving its perlocutionary aim.


Precedent (Stare Decisis)

Precedent (Stare Decisis — "to stand by things decided") is the decision-making principle that (1) the outcome of a prior similar case carries presumptive weight for the present case — a current decision-maker should decide like cases alike, treating prior decisions as binding or strongly persuasive unless there is principled reason to distinguish or overrule, an idea Hart (1961) develops as one of the rule-of-recognition criteria characterizing mature legal systems. <!-- FACT-D49-031 -->[^hart-1961] Precedent operates through analogical reasoning — identifying which features of prior cases are materially similar to the present case, and which distinctions justify different treatment. The craft of precedent is the craft of distinguishing cases, as Levi (1949) lays out in his canonical account of legal reasoning by example. <!-- FACT-D49-032 -->[^levi-1949] Precedent produces three concurrent goods: predictability (parties can anticipate outcomes), consistency (like cases treated alike across time and decision-makers), and decisional efficiency (past reasoning is reused rather than redeveloped) — values Schauer (1987) systematically defends as the core justifications for precedential constraint. <!-- FACT-D49-033 -->[^schauer-1987] And precedent must be balanced against change — stare decisis is not absolute; wrong prior decisions can be distinguished, narrowed, or overruled, but the threshold for overruling is intentionally high to preserve the system's predictability value, a balance Lee (1999) examines through the lens of the Supreme Court's overruling jurisprudence. <!-- FACT-D49-034 -->[^lee-1999]

Analogy

Precedent presupposes analogy because deciding like cases alike requires mapping the structural features of a prior case onto the present one.

Stare decisis treats a prior case's outcome as presumptively binding on the present case when the cases are materially similar, and the work of identifying material similarity is analogical reasoning. Analogy provides the structural-mapping machinery — relational role-alignment between source and target — that lets the court project the rule from the precedent case onto the new fact pattern, and that lets the craft of distinguishing operate. Without analogical mapping, there is no way to determine which prior cases govern, so precedent cannot function without analogy as its inferential engine.

Path Dependence

Precedent is the specific shape path dependence takes when prior decisions in a legal system bind or strongly guide present analogous cases.

Precedent is the legal particularization of path dependence: the trajectory of prior decisions constrains present rulings through the binding or strongly-persuasive weight of analogous earlier cases. Where path dependence names the constitutive role of historical sequence in current options generally, precedent fixes the substrate as legal decision-making, the carrier of history as the published body of prior rulings, and the lock-in mechanism as analogical reasoning under stare decisis — a particular shape history takes when accumulated decisions structure subsequent ones.


Predictive Coding

A system predicts its input and propagates only the prediction error.

Predictive coding is the structural pattern in which a system maintains an internal generative model that continuously predicts its incoming signal, compares that prediction against the actual input, and then transmits, stores, or acts upon only the *residual* — the prediction error. The essential commitment is that the expected part of the signal is suppressed and only the *surprising* part propagates; the model is then updated by the error so that future predictions improve. <!-- FACT-D54-766 -->[^rao-ballard-1999] It is a predict–compare–correct loop, not merely a smaller encoding: the residual is both the message and the teaching signal. The concept crystallized in computational neuroscience through Rao and Ballard's (1999) account of the visual cortex as a hierarchy of predictors, in which higher areas send predictions downward and lower areas return only the unexplained error upward. <!-- FACT-D54-767 -->[^rao-ballard-1999] What makes the pattern a prime rather than a single algorithm is that the same four-part shape — generative model, prediction, comparison, residual-driven correction — recurs wherever a system must track a changing source under bandwidth, energy, or attention constraints. It answers a recurring question: when reality mostly conforms to expectation, why pay the full cost of representing it again, instead of paying only for where it departs?

Compression

Predictive coding presupposes compression because transmitting only the prediction error exploits the predictable signal's redundancy to shorten its representation.

Predictive coding presupposes compression because its central move is to suppress the expected component of an incoming signal and transmit only the residual prediction error: this is exactly the compression strategy of exploiting predictable structure to shorten what must be encoded. Compression supplies the general principle that redundant statistical regularity can be removed without loss; predictive coding instantiates it by making a generative model the source of the predicted regularity, so only the surprising remainder propagates as both message and learning signal.

Feedback

Predictive coding presupposes feedback because the predict-compare-correct loop routes prediction-error output back to update the generative model.

Predictive coding maintains an internal generative model that predicts incoming signal, compares prediction to actual input, and transmits or acts upon only the residual error — which then updates the model. The construct is constitutively a closed loop: the error output feeds back to update the predictor that generated the prediction. Feedback supplies that loop closure between output and subsequent input. Without a feedback structure routing the residual back to revise the model, there would be no improvement of predictions over time and no closed predict-compare-correct cycle.


Preference

Agent's ordering over a choice set on some evaluative dimension.

Preference is an agent's ordering over a choice set on some evaluative dimension — a disposition that, when consulted, says which alternatives are favored, disfavored, indifferent, or incomparable, the framing Mas-Colell, Whinston, and Green (1995) take as the canonical primitive of modern microeconomic theory. <!-- FACT-D56-226 -->[^mas-colell-whinston-green-1995] The four roles travel together everywhere the prime is invoked: a **choice set** (the alternatives being ordered), an **evaluator** (the agent, criterion, system, or model whose preference this is), an **ordering relation** over the set (which may be complete or partial, transitive or inconsistent, strict or weak), and a **context** in which the ordering does work — guiding selection, predicting behavior, or supplying the objective that optimization will then maximize, the decomposition Mas-Colell, Whinston, and Green (1995) develop in their chapter contrasting the preference-based and choice-based approaches. <!-- FACT-D56-227 -->[^mas-colell-whinston-green-1995]


Preference Heterogeneity and Conflict

Incompatible agent preferences create impasses and partial dissatisfaction.

Preference heterogeneity and conflict is the structural condition where different agents in a system hold substantively incompatible preferences, goals, or values that cannot simultaneously be fully satisfied, creating decision impasses or outcomes in which all agents experience partial dissatisfaction, a condition Arrow (1951) established as foundational to social choice theory. <!-- FACT-D53-346 -->[^arrow-1951] Unlike logistical coordination problems (which have technical solutions), preference conflict is fundamentally about irreconcilable wants. The condition arises because agents prioritize different ends: one person's gain is another's loss; one stakeholder's ideal outcome precludes another's, as Schelling (1960) developed in his analysis of mixed-motive interaction. <!-- FACT-D53-347 -->[^schelling-1960] This prime concerns the structural irreducibility of these collisions—situations where no allocation, policy, or mechanism can leave all parties fully satisfied.

Diversity

Preference heterogeneity and conflict presupposes diversity because the irreconcilable wants it names require substantively varied agents whose ends differ.

Preference heterogeneity and conflict presupposes diversity because the impasse it diagnoses — agents holding incompatible goals that cannot simultaneously be satisfied — requires the prior presence of meaningful, functional variation across the agents in their values, priorities, or stakes. Without diversity's general apparatus of distinct types whose differences have functional consequences, all agents would converge on a single preference and conflict would dissolve. Preference heterogeneity inherits diversity's structure of variation-that-matters and specializes it to the case where the dimension of variation is final ends, and the consequences are decision impasses rather than productive complementarity.

Preference

Preference heterogeneity and conflict presupposes preference because incompatible wants across agents requires each agent to have a definable preference ordering.

Preference heterogeneity and conflict presupposes preference because the condition of substantively incompatible wants across agents only arises if each agent has a definable ordering over the choice set in the first place. Without preference as the per-agent ordering primitive, there is no individual ranking whose differences across agents could be incompatible, and no Arrow-style social-choice problem of aggregating those rankings. Preference supplies the per-agent ordering; heterogeneity and conflict name the structural condition where those orderings cannot be simultaneously satisfied, creating decision impasses requiring negotiation or aggregation.


Presentism

Presentism is an interpretive error pattern in which (1) the values, knowledge, concepts, and expectations of the interpreter's present are imported uncritically into the interpretation of past actors, events, or cultures, (2) past actors are evaluated against present norms they had no access to and could not reasonably have held, (3) past events are explained using conceptual frameworks that did not exist at the time, and (4) the resulting interpretation produces a distorted account that systematically over-credits past actors who happened to anticipate present views and under-credits or condemns those whose views reflected the norms of their own time. <!-- FACT-D40-046 -->[^butterfield-1931]

Anachronism

Presentism is a specialization of anachronism in which the temporally misplaced element is the interpreter's own present-day values and concepts.

Presentism is a specialization of anachronism in which the element placed in a time where it does not belong is specifically the interpreter's present-day values, concepts, and expectations, imported uncritically into the interpretation of past actors and events. It inherits anachronism's general structure of period-violation, where an item bound to one period appears in another, and specializes by fixing the direction (present-into-past), the carrier (the interpreter rather than a depicted object), and the consequence (systematic over-crediting of those who anticipated present views and under-crediting of those whose views reflected their own time's norms).


Price Discrimination

*Price Discrimination* names the abstraction that (1) when a seller has meaningful market power and can *distinguish* among buyer segments with different willingnesses to pay, (2) charging *different prices* to different buyers for the same or effectively identical product (3) captures more *consumer surplus* as producer revenue than a single uniform price would, (4) provided the seller can both identify the relevant segments and *prevent arbitrage* between low-priced and high-priced customers who would otherwise resell to each other.

Price Mechanism

Price discrimination presupposes the price mechanism because it depends on the prior existence of prices as the medium through which value is captured.

Price discrimination presupposes the price mechanism because its structure — charging different prices to different buyer segments for the same good — operates on top of the prior apparatus of prices as the medium through which exchanges occur and surplus is allocated between buyer and seller. Without the price mechanism's prior structure of buyer willingness-to-pay meeting seller offer, there is no scalar dimension along which to discriminate. Price discrimination inherits this machinery and adds the further commitments that the seller has market power, can identify segments by willingness-to-pay, and can prevent arbitrage between low- and high-priced buyers.


Price Elasticity

Price elasticity is the dimensionless ratio that quantifies how responsive the quantity demanded (or supplied) of a good is to a proportional change in its price — formally E = (dQ/Q)/(dP/P) = (dQ/dP) × (P/Q) — providing a scale-free measure that allows comparison of responsiveness across goods of different units, price levels, and quantities. The essential commitment is that responsiveness to price changes varies across goods, markets, and contexts; that the economically meaningful measure is proportional rather than absolute responsiveness (so that a $1 price change on a $2 good and on a $200 good affect different numbers of buyers in different proportions); and that the elasticity magnitude determines important consequences for revenue, incidence of taxes, and welfare effects of price changes.

Marginal Analysis

Price elasticity is the specific shape marginal analysis takes when applied to the responsiveness of quantity to price changes.

Marginal analysis is the systematic deployment of incremental reasoning — evaluating decisions by comparing the marginal change in costs and benefits along one axis. Price elasticity is the particular shape this technique takes when the small change is in price and the marginal response is in quantity demanded or supplied. The dimensionless ratio (dQ/Q)/(dP/P) is the proportional marginal-response measure that scales the marginal-analysis derivative into a unit-free elasticity. It is a structurally-particularized instance of incremental reasoning specialized to price-quantity sensitivity.


Price Mechanism

The *Price Mechanism* names the abstraction that (1) a market price, emerging from the aggregate interaction of buyers' demand and sellers' supply, (2) compresses vast amounts of decentralized information about relative scarcities, preferences, and production costs into a single scalar signal, (3) which each buyer and seller can act on locally by comparing to their own opportunity cost or willingness to pay, so that (4) countless independent decisions self-coordinate into a coherent allocation of resources without any central planner possessing or transmitting the underlying information — the foundational claim of Smith (1776). <!-- FACT-D45-031 -->[^smith-1776]

Exchange

The price mechanism presupposes exchange because emergent prices arise only from the aggregate interaction of conditional transfers between buyers and sellers.

The price mechanism presupposes exchange because the scalar price signal emerges from the aggregate interaction of buyers' and sellers' willingness to make conditional transfers. Without exchange's bilateral-transfer structure, there is no demand-supply interaction to compress into a price, and no opportunity-cost comparison for participants to make. Exchange supplies the substrate of conditional movements that, in aggregate across many participants, produces the decentralized scalar that then self-coordinates allocation. The invisible hand operates on exchange relations; price is what those relations summarize.

Allocation

The price mechanism is the specific shape allocation takes when assignments to claimants are coordinated by market prices summarizing decentralized information.

The price mechanism is the specific shape allocation takes when the assignment of scarce resources to competing uses is coordinated by market prices that emerge from buyer demand and seller supply. It is a structurally-particularized instance of dividing limited supply among claimants, with the added commitment that the assignment is not directed by a central planner but produced by countless local decisions responding to a scalar signal — the price — that compresses dispersed information about scarcity, preferences, and costs. Allocation emerges as the aggregate of self-interested choices coordinated through that signal.


Primary vs. Secondary Sources

Primary vs. Secondary Sources is an epistemic classification in which (1) evidence is sorted by its causal and temporal proximity to the phenomenon being studied, (2) materials produced by or directly in contact with the phenomenon at the time of its occurrence are classified as primary (diaries, original recordings, experimental logs, direct testimony, raw sensor output), (3) materials that analyze, synthesize, or interpret primary materials after the fact are classified as secondary (scholarly articles, review papers, edited volumes, summary reports), and (4) the classification governs how each class is used in argument — primary materials supply evidence whose reliability is evaluated at the point of production, secondary materials supply interpretation whose reliability is evaluated against the primary record they claim to interpret, as Howell and Prevenier (2001) systematize for working historians. <!-- FACT-D40-031 -->[^howell-prevenier-2001]

Classification

Primary-vs-secondary sources is the specific shape classification takes when evidence is sorted by causal and temporal proximity to the phenomenon studied.

Primary-vs-secondary sources is the specific shape classification takes when the entities being sorted are evidentiary materials and the criterion is causal-temporal proximity to the phenomenon under study: primary materials are produced by or in direct contact with the phenomenon at its time of occurrence, secondary materials interpret or synthesize primary materials afterward. It is a structurally-particularized instance of assigning items to discrete categories by explicit criteria, with the added commitment that the classification governs argumentative use — primary materials supply evidence, secondary materials supply interpretation — and that boundary cases require source-criticism.


Priming

Prior exposure to a stimulus transiently activates related representations in memory, biasing or speeding subsequent processing, often without awareness.

Priming is a short-term cognitive phenomenon in which (1) **prior exposure to a stimulus** — a word, image, concept, or context — (2) **transiently activates related representations** in memory, such that (3) **subsequent processing of related stimuli is facilitated or biased** — faster recognition, biased interpretation, altered choice probability — (4) **often without conscious awareness** that the prime is influencing the response. The phenomenon has tight, well-replicated forms (semantic priming in lexical-decision tasks; perceptual priming in identification tasks) and broader, more contested forms (behavioral priming, goal priming, social priming) whose replicability has been the subject of substantial recent scrutiny. The core mechanism — spreading activation in associative networks — is robust; the range of downstream behaviors it can causally modulate is what is contested.

Attention

Priming presupposes attention because the prime's facilitating effect runs through which representations attention transiently activates and gates downstream.

Priming presupposes attention because the spreading-activation mechanism that makes a prior stimulus facilitate processing of related ones operates on the selective allocation of cognitive resource. The prime transiently raises activation of related representations, biasing what attention selects and processes deeply downstream. Without attention's gating function -- limited resource directed at some items at the expense of others -- there is no foreground for the prime's activation residue to bias. Priming exploits and depends on the same selection bottleneck attention names.


Principle of Least Action

The principle of least action (more precisely, the principle of stationary action) is the foundational variational principle of classical and quantum physics, which states that the actual trajectory of a physical system between specified initial and final configurations is the one that makes a particular integral functional — the action S = ∫L dt (where L is the Lagrangian, the difference between kinetic and potential energy in the simplest case) — stationary with respect to small variations of the trajectory. <!-- FACT-D10-060 -->[^lagrange-1788] The essential commitment is that the dynamical laws of physics can be reformulated from local equations of motion (Newton's second law, Maxwell's equations) into a global variational statement: of all conceivable paths, the one the system actually takes is the one whose action is stationary (usually a minimum, sometimes a saddle point) [^maupertuis-1744] <!-- FACT-D10-060 -->. Every principle-of-least-action articulation specifies (1) the configuration space and coordinates parameterizing system trajectories; (2) the Lagrangian — a function of coordinates, velocities, and possibly time, chosen so that Euler-Lagrange equations yield the correct equations of motion; (3) the boundary conditions — typically fixed endpoints in configuration space and time; and (4) the consequences — Euler-Lagrange equations of motion, conservation laws via Noether's theorem [^noether-1918], extensions to field theory (action densities, Lagrangian densities), and the quantum-mechanical generalization (Feynman's path integral [^feynman-1948], where all paths contribute with weights exp(iS/ℏ) and the classical path is the stationary-phase contribution).


Prioritization

Ordering competing claims on finite resources by a value or urgency metric to produce a ranked sequence of action under constraint, making explicit what gets done first and what does not get done at all.

Prioritization is the active process of ordering competing claims on finite resources—attention, time, capital, personnel, decision slots—by some criterion of value, urgency, dependency, or feasibility, producing a sequence of action that maximizes a chosen objective under constraint, as Pinedo (2016) develops formally in his canonical scheduling textbook. <!-- FACT-D52-376 -->[^pinedo-2016] It separates the act of *ranking* (applying a value metric to items) from the *execution order* (which items receive treatment first), and commits to honoring the ranking when multiple items compete for a single resource. The concept spans operations research (scheduling, knapsack problems, weighted shortest job first), product management (ICE/RICE scoring, MoSCoW classification), engineering (incident severity tiers P0–P3), military doctrine (commander's intent, lines of effort), medicine (triage protocols), and personal productivity (Eisenhower matrix, OKRs).

Optimization

Prioritization is a kind of optimization: it selects an execution sequence that maximizes value under resource constraints.

Prioritization searches for an ordering of items that, when executed against finite resources, maximizes a chosen objective such as value delivered, urgency satisfied, or dependencies cleared. That is the optimization triplet: decision variable (the ordering), objective (the chosen value criterion), and constraints (resource and dependency limits). Prioritization specializes optimization to the case where the decision variable is an execution sequence and the constraints are scarce attention, time, or capital.

Preference

Prioritization is a kind of preference: it expresses an evaluator's ordering over competing claims and acts on that ordering.

Prioritization actively orders competing claims on finite resources by some criterion of value, urgency, dependency, or feasibility and commits to honoring that ranking. The ranking step is exactly an instance of preference: an evaluator imposes an ordering relation over a choice set on some evaluative dimension. Prioritization specializes preference by binding the ranking to scarce resources and to a downstream commitment about which item is served first, turning a disposition into an enforceable sequence.

Allocation

Prioritization presupposes allocation because ordering competing claims is only meaningful when a finite resource must be assigned among them.

Prioritization actively orders competing claims on finite resources by some criterion of value, urgency, or dependency, producing a sequence that maximizes a chosen objective. The ordering is meaningful only when the resource is scarce and multiple demands compete for it — exactly the allocation problem of assigning limited supply across competing claimants. Allocation supplies the bare assignment structure; prioritization is allocation refined by an explicit ranking rule. Without the underlying scarcity-driven assignment problem, prioritization has nothing to rank and no resource to route by its ordering.


Probability

**Probability is the calibrated quantification of uncertainty: a numerical assignment to events or propositions that obeys a stated set of coherence rules and supports consistent reasoning and decision-making under incomplete information**, the formal commitment articulated by Kolmogorov (1933) in the measure-theoretic axiomatization of probability. <!-- FACT-D38-001 -->[^kolmogorov-1933] Its essential commitment is that degrees of belief, frequencies of occurrence, or physical propensities can be represented by numbers in [0, 1] whose combination is governed by fixed laws — additivity, normalization, conditioning — laws that turn uncertainty into an object that can be combined, compared, conditioned, and tested rather than left as verbal hedging. Every probability claim names (1) a sample space of possible outcomes, (2) an event or proposition whose probability is being asserted, (3) a probability measure assigning numbers to events, and (4) an interpretation — frequentist, Bayesian, or propensity — that fixes what the number means and how it can be tested. **Without all four parts a probability claim is incoherent; with them, the apparatus of expected value, conditioning, dependence structure, and tail behavior becomes available, and the resulting numbers can be aggregated by anyone obeying the same axioms.**


Problem Space

A problem space is the formal representation a cognitive agent (human, team, or computational system) constructs to impose structure on a problem-solving task—comprising an initial state, goal state(s), a set of operators that transform states, and the lattice of intermediate states reachable by operator application. The foundational insight, articulated by Newell and Simon (1972), is that problems do not come with intrinsic structure; structure is imposed through representation. <!-- FACT-D51-376 -->[^newell-simon-1972] Different representations of the same underlying problem produce different search dynamics, solution trajectories, and difficulty profiles. A problem-space claim specifies: (1) the initial and goal states as represented at a chosen level of abstraction; (2) the operators available and their costs, preconditions, or constraints; (3) the search method navigating the space (blind, heuristic, or domain-specific); and (4) how the representation shapes tractability and the solution paths visible to the reasoner.

Representation

A problem space is a specialization of representation in which the represented target is a problem-solving task and the medium is a state-and-operator structure.

A problem space is a kind of representation specialized to problem-solving tasks: the agent imposes a structured mapping from the task onto a formal medium comprising initial state, goal state, operators, and intermediate-state lattice. It inherits representation's commitment to structured mapping from a target onto a medium under a stated convention preserving selected target features, and supplies the specific case where the target is a problem-solving task whose structure does not come intrinsically given but is imposed through the agent's chosen representation, with different problem-space encodings producing different search dynamics.

State and State Transition

Problem space is a constituent piece of state and state transition; it specifies the initial state, goal states, and operators that transform states in a problem-solving task.

A problem space is built from state-transition machinery: an initial state, one or more goal states, a set of operators that transform states, and the reachable lattice of intermediate states. The problem-solving task is then framed as searching this state space under operator application. The state-and-transition prime supplies the substrate — state space, transition relation, initial state — that the problem-space construct organizes for cognitive and computational use; the problem-space is a particular configuration of the more general state-transition framework.


Procedural Fairness (Due Process)

Procedural fairness and due process comprise the legal and administrative principles that governmental, quasi-governmental, and increasingly private actors must follow when rendering decisions affecting individual rights, liberties, or legitimate interests. Central elements are notice (affected parties know a decision is pending), opportunity to be heard (they can present evidence and argument), impartial decision-making (absence of bias and conflict of interest), and reasoned justification (explicit explanation of findings and legal conclusions), as Tyler (1990) documents in his foundational empirical study showing that perceived fairness of process drives compliance and acceptance independently of outcome favorability. <!-- FACT-D49-016 -->[^tyler-1990] The doctrine emerges from common-law traditions and constitutional frameworks, establishing that *how* decisions are made matters as much as their substantive correctness. Central to procedural justice is the recognition that affected persons deserve voice, transparency, and rational accountability in processes that alter their legal or social status. Procedural fairness operates as a mechanism for converting disputes over *substance* into disputes over *process*—parties who cannot agree on the right outcome can nonetheless accept a process as legitimate when it incorporates notice, hearing, impartiality, and reasoned decision-making. This substitution proves essential in pluralistic societies where substantive consensus is impossible but procedural consensus is achievable.

Fairness

Procedural fairness (due process) is a specialization of fairness that locates the impartiality standard in the decision-making procedure rather than its outcomes.

Procedural fairness is a specialization of fairness. Specifically, it instantiates the impartiality-or-principled-differentiation standard fairness names by attaching it to the procedure -- notice, opportunity to be heard, impartial decision-maker, reasoned justification -- rather than to outcome distributions. Like other fairness criteria, it satisfies a defensible standard of equal regard; due process is the subclass where consistency, voice, and bias-absence in process drive legitimacy and compliance independent of outcome favorability, distinguishing it from substantive fairness criteria over allocations.


Processing Fluency

Cognitive ease with stimulus influences judgment independent of content.

Processing fluency is the subjective ease with which cognitive operations proceed on a stimulus, directly influencing evaluative judgments, preference formation, and decision-making independently of stimulus content or objective quality, as Reber, Schwarz, and Winkielman (2004) established in their integrative review of fluency and aesthetic pleasure. <!-- FACT-D53-361 -->[^reber-schwarz-winkielman-2004] When a stimulus is easy to process (fluent), agents attribute the ease to positive properties of the stimulus (familiarity, quality, truthfulness); when a stimulus is difficult to process (disfluent), agents attribute the difficulty to negative properties, producing lower evaluations and reduced preference, as Schwarz (2004) developed in his theory of metacognitive experiences in consumer judgment. <!-- FACT-D53-362 -->[^schwarz-2004] The mechanism operates through misattribution: agents mistake the *experience of processing ease* for evidence of stimulus quality, creating systematic biases that persist even when the agent is aware of the bias.

Bias

Processing fluency is a specific kind of bias where the ease of cognitive processing systematically displaces evaluative judgments.

Processing fluency is a specialization of bias. The general pattern is a systematic, sign-bearing displacement of a process's outputs away from a true or intended value that survives the infinite-sample limit. Processing fluency instantiates this with the process being evaluative judgment and the displacement being misattribution: stimuli that are easy to process are systematically rated higher on familiarity, quality, and truthfulness independently of content. The bias has a consistent sign (fluency raises evaluations, disfluency lowers them) and persists across repeated judgments, satisfying the structural definition of a bias source.

Heuristic

Processing fluency presupposes the heuristic pattern because ease-of-processing is itself a fast cue substituted for slower analytical judgment.

Processing fluency presupposes the heuristic pattern because it functions as a metacognitive shortcut: subjective ease is misattributed to stimulus properties (familiarity, truth, quality), substituting a fast subjective signal for slower content-evaluation. Without the heuristic framing -- a simplified rule that trades accuracy for speed and yields good-enough judgments under ecological fit -- fluency effects would be mere measurement artifacts rather than a systematic decision principle. The ease-as-cue mechanism IS the heuristic rule whose costs and benefits fluency research charts.


Progressive Refinement from Core Model

Progressive Refinement from Core Model is the generalized pattern in which (1) a complex phenomenon is modeled by **first identifying a simpler, solvable baseline** that captures the dominant structure, (2) the full phenomenon is written as **baseline + correction**, with the correction controlled by a small parameter ε representing distance from the baseline regime, (3) refinements are added **systematically and in increasing orders of ε**, stopping at the order that meets accuracy requirements, and (4) the pattern's domain of validity is **self-diagnostic**: as long as each correction is small compared to what it corrects, the refinement is working; once higher-order terms grow comparably to lower-order ones, the baseline is the wrong baseline and no amount of refinement will help. <!-- FACT-D39-137 -->[^boehm-1976] <!-- FACT-D39-138 -->[^boehm-1988]

Abstraction

Progressive refinement from core model presupposes abstraction because identifying a simpler solvable baseline requires purpose-relative retention of the dominant structure.

Progressive refinement from a core model starts by identifying a simpler, solvable baseline that captures the dominant structure of a complex phenomenon. This presupposes abstraction: the purpose-relative retention of load-bearing structure, selecting what matters for the reasoning at hand while discarding what does not. The baseline is precisely such an abstraction, judged to retain the structure that controls leading-order behaviour while the corrections account for what was dropped. Without abstraction's judged-retention move, there is no principled baseline against which the small-parameter expansion can be organized.

Approximation

Progressive refinement from a core model presupposes approximation because each successive correction is a controlled error term added to a tractable baseline.

Progressive refinement from a core model presupposes approximation because its baseline-plus-correction structure requires that the baseline serves as a tractable surrogate for the full phenomenon with a named small-parameter error, and that each higher-order correction is controlled in size relative to what it corrects. Without approximation's discipline -- a controlled and bounded error in known units -- the refinement series has no convergence diagnostic and no stopping rule. The self-diagnostic ('higher-order terms must stay small') IS the approximation tolerance check applied recursively.

Iteration

Progressive refinement from core model presupposes iteration because successive correction terms are added round by round until accuracy targets are met.

Progressive refinement from a core model writes the full phenomenon as baseline plus correction, with refinements added systematically in increasing orders of a small parameter until the required accuracy is reached. This presupposes iteration: the repeated application of a step with state carried between rounds, a progress measure, and a stopping condition. Each correction order is an iteration step that consumes the prior approximation and produces the next; the self-diagnostic check that higher-order terms remain small is the progress measure that licenses continued iteration or signals that the baseline must be reconsidered.


Propagation

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.

The systematic spreading of a signal, effect, change, state, or condition through a medium, network, or population from a source or region of disturbance. Propagation encompasses both deterministic, directed, and stochastic forms of spread, moving beyond purely random diffusion. The propagation rules and the structure of the medium determine how fast the influence spreads, how it is attenuated, and what paths it follows, as Newman (2003) systematizes in his review of structure and dynamics of complex networks. <!-- FACT-D52-391 -->[^newman-2003] Unlike diffusion, which often implies random-walk dynamics, propagation names the mechanism by which an influence moves systematically—whether via wave fronts, network pathways, contact patterns, or causal chains—making visible both the speed and the medium-dependence of the spread.


Property Rights

An enforceable bundle of exclusive entitlements over a resource.

Property rights are an enforceable assignment of a bundle of exclusive entitlements over a resource — typically the rights to use it, to capture the value it produces, to exclude others, and to transfer it — to a defined holder. The defining commitment is *excludability backed by enforcement*: a holder may exclude non-holders from a resource and internalize the consequences of its use, which makes the resource a locus of accountable decision-making rather than open access. The bundle can be split, attenuated, or recombined across holders, which is why the regime is best understood not as a single all-or-nothing fact but as a portfolio of separable entitlements held in some configuration by some set of parties. <!-- FACT-D54-046 -->[^honore-1961]

Boundary

Property rights are the specific shape boundary takes when the demarcated entity is a resource and the criterion is enforceable excludability with bundled entitlements.

Property rights are the specific shape boundary takes when what is demarcated is a resource and the demarcation criterion is enforceable exclusion of non-holders by a defined holder. The four boundary components map directly: the bounded entity is the resource, the demarcation criterion is the legal-enforcement rule, permeability is the bundle of entitlements (use, capture, exclude, transfer) governing crossings, and accountability follows from holder identification. Property rights are jurisdictional boundaries around resources, structured as separable entitlement bundles backed by enforcement.


Proportion and Scale

Proportion and scale deal with the relative sizes, ratios, and dimensional relationships among elements within a work—how parts relate to each other and to the whole, and how those relationships affect perception, aesthetic impact, and functional performance. The essential commitment is to *relational sizing*: not the absolute dimensions of individual elements but the **ratios** and relationships that govern how elements are perceived relative to each other and to the viewer's own scale and experience. Every application of proportion and scale specifies (1) the ratio between the size of one element and another (whether mathematical like the golden ratio or intuitive like "slightly larger"), (2) the consistency or deliberate variation in proportional relationships across a composition, (3) the reference scale—whether elements are scaled to human body (anthropomorphic scale), to the viewer's distance (far/near), or to each other, (4) the correspondence between visual perceived size and functional use (a button must be large enough to press, a headline must be visibly larger than body text), and (5) the expression of meaning through scale (monumental scale conveying power, miniature scale conveying delicacy or intimacy). The classical insights from classical architecture (Vitruvius's proportional canons, the golden ratio in Renaissance design) and modern design (Le Corbusier's Modulor, Hambidge's dynamic symmetry) establish that proportional relationships are not arbitrary but that certain ratios (1:1.618, 1:1.5, 2:3) recur as aesthetically harmonious across cultures and over time, likely because they relate to patterns in nature and the human body. The practice originated in sculpture and architecture (the proportions of the human figure, classical orders with fixed proportional ratios) and has evolved into a foundational principle across visual design (graphic design, photography, film), spatial design (architecture, landscape, interior design), product design (ergonomics and use-scaled sizing), and mathematics (understanding scaling laws and fractal relationships)<!-- FACT-D36-031 -->[^vitruvius-da].

Scale

Proportion and scale is the specific shape scale takes when relative sizes among elements within a work are the operative concern.

Proportion and scale is the specific shape scale takes when the band of interest is the relative sizing among elements within a single composition or work, rather than the level of aggregation at which a system is described. It is a structurally-particularized instance of treating scale as a first-class object, with the added commitment that the relevant quantity is ratio — golden ratio, perceptual relative size, dimensional relationship to the viewer — and that the aesthetic and functional payoff is governed by how parts relate to each other and to the whole rather than by absolute dimensions.


Proportionality

Normativity

Proportionality presupposes normativity because it specifies a standard of correctness — fit between response and triggering cause — for evaluating actions.

Proportionality presupposes normativity because it is constituted as an ought-side standard: actions, sanctions, or restrictions are required to be calibrated in scale to the wrong, threat, or interest they address, and deviations from that fit are subject to criticism. Without the prior structure of evaluation against a standard, the claim that a response is too harsh or too lenient has no purchase. Proportionality inherits normativity's general machinery of correctness-claims and specializes it by making the standard a comparative one: the response must match the triggering cause in magnitude or weight.

Commensurability

Proportionality is the specific shape commensurability takes when comparing the magnitude of a response to the magnitude of its triggering cause.

Proportionality is the specific shape commensurability takes when the dimensions being placed on a common scale are a triggering cause and a response — a wrong and its sanction, a threat and a restriction, a contribution and a reward. It is a structurally-particularized instance of bringing heterogeneous quantities into a common metric, with the added commitment that the comparison yields a fitness verdict: the response is appropriate when its measured magnitude tracks the cause's measured magnitude, excessive when it overshoots, insufficient when it undershoots. Without commensurability's prior reduction to comparable units, the proportionality judgment has nothing to weigh.


Provenance

A documented, traceable record of an entity's origin and successive custody transfers that establishes authenticity and assigns accountability by linking present state back to first known state.

Provenance is the traceable, documented record of an entity's origin, custody transfers, and transformations over time, as Moreau and Missier (2013) formalize in the W3C PROV-DM data model. It establishes authenticity, enables verification of claims, and creates accountability by making visible the chain through which something came to exist and passed through successive hands, contexts, or states. <!-- FACT-D52-406 -->[^moreau-missier-2013] The concept emerged from art-historical authentication and archival science but now extends across software supply chains, scientific data management, food safety, cryptocurrency, legal evidence, and organizational decision trails. Provenance answers a foundational epistemic problem: how do we verify that something is what it claims to be, and how do we assign responsibility or credit for subsequent transformations?

Traceability

Provenance presupposes traceability because the documented chain of origin and custody requires the underlying infrastructure that links elements to their history.

Provenance presupposes traceability because the documented record of an entity's origin, custody transfers, and transformations is the content claim that traceability's infrastructure makes verifiable. Without traceability's backward-and-forward linkage capability — the navigable chain from any element to its derivation history — provenance assertions would be unsupported claims about origin with no way to audit them. Traceability supplies the infrastructure; provenance supplies the specific content of authentic-origin claims that traceability's chain makes inspectable and assignable for credit, blame, or verification.


Psychological Safety

Psychological Safety is the shared belief in a team or group that interpersonal risk-taking is safe—that members can speak up with questions, concerns, ideas, or admissions of error without fear of ridicule, rejection, punishment, or retaliatory consequences<!-- FACT-D32-136 -->[^edmondson-1999]. The essential commitment is that the absence of interpersonal threat does not mean agreement or harmony, but rather that disagreement, challenge, and critical feedback can occur within a climate of mutual respect and good intent; that psychological safety is established through leadership, norms, practices, and routines that signal receptiveness to voice and error as organizational resources; that low psychological safety suppresses information flow, conceals problems and failures, and reduces the diversity of thinking available to the group; and that groups with high psychological safety achieve faster error detection, more innovation, better collaboration, and greater resilience to disruption and change.

Trust

Psychological safety presupposes trust because it is the team-level condition in which members can be vulnerable in interpersonal risk-taking without fear.

Psychological safety presupposes trust because its core claim — that team members can speak up, admit error, and disagree without fear of ridicule or retaliation — requires trust's prior structure of confident reliance under vulnerability. Without the willingness to be vulnerable to others based on positive expectations of their ability, benevolence, and integrity, the interpersonal risk-taking psychological safety names is unavailable. Psychological safety inherits trust's vulnerability-under-incomplete-monitoring structure and specializes it to the team setting, where the shared belief that good-faith voice will be received without reprisal scales individual trust into a climate-level resource.


Public Goods

(1) **Public Goods are resources or services characterized by two joint properties: *non-excludability* (once provided, users cannot be practically prevented from consuming them) and *non-rivalry* (one person's consumption does not diminish the quantity or quality available to others).** [^definition-public-goods] <!-- FACT-D09-040 --> The combination produces a distinctive market-failure dynamic — because no potential user can be excluded from benefit, each user has an incentive to under-contribute toward provision (the free-rider problem), so markets systematically under-provide public goods relative to the socially optimal level, even when the aggregate willingness-to-pay substantially exceeds the cost of provision. The modern formal treatment originates with Paul Samuelson's "The Pure Theory of Public Expenditure" (*Review of Economics and Statistics*, 1954) <!-- FACT-D09-041 -->[^samuelson-1954] and "Diagrammatic Exposition of a Theory of Public Expenditure" (1955) <!-- FACT-D09-042 -->[^samuelson-1955], which derived the Samuelson condition for optimal provision (sum of marginal rates of substitution equals marginal cost), distinguishing public goods sharply from private goods (rivalrous and excludable) and foreshadowing later refinements (Buchanan's club goods 1965 <!-- FACT-D09-045 -->[^buchanan-1965], Ostrom and Ostrom's four-way goods taxonomy 1977). Antecedent work by Harl Bowen, "The Interpretation of Voting in the Allocation of Economic Resources" (*Quarterly Journal of Economics*, 1948) <!-- FACT-D09-043 -->[^bowen-1948], provided pre-Samuelson foundational treatment of public-expenditure problems, though Samuelson's formalization became canonical.

Property Rights

Public goods presuppose property rights because their defining trait — non-excludability — is a failure of the exclusion entitlement that property rights normally confer.

Public goods presuppose property rights because their defining trait — non-excludability — is constituted by the absence of an enforceable right to exclude non-payers from consumption, the very entitlement property rights normally supply. Without the prior framework of property rights as a bundle of separable entitlements anchored in enforceable exclusion, there is no baseline against which non-excludability can be diagnosed as a market-relevant failure. Public goods inherit the property-rights framework and identify the case where the exclusion entitlement is absent or unenforceable, producing under-provision and the characteristic free-rider problem in lieu of ordinary market provision.

Social Dilemma

Public goods typically presuppose social dilemma because non-excludable non-rival goods generate the free-rider conflict between individual rationality and collective welfare.

Public goods are characterized by non-excludability and non-rivalry, which together generate the free-rider problem: each user has an individually rational incentive to under-contribute, while universal under-contribution leaves all worse off than universal contribution would. This is precisely the social-dilemma structure of dominant defection producing collectively suboptimal outcomes. Social dilemma supplies the canonical conflict-between-individual-rationality-and-collective-welfare; public-good provision is one of its principal instances, typically presupposing it though some institutional arrangements can suppress the dilemma without eliminating publicness.


Public vs. Private Contexts

Audience presence alters motivation and behavior through reputation.

Public vs. private contexts is the structural distinction between decisions and behaviors made in front of an audience (where reputation, face-saving, and social evaluation shape motivation) versus decisions and behaviors made in solitude or anonymity (where authentic preferences and private incentives dominate), as Goffman (1959) developed in his dramaturgical analysis of self-presentation. <!-- FACT-D53-376 -->[^goffman-1959] The context fundamentally alters what actions seem appropriate and what costs and benefits are salient, producing systematic behavioral divergence between public and private settings even with identical underlying preferences, a pattern Kuran (1995) formalizes as preference falsification. <!-- FACT-D53-377 -->[^kuran-1995] This pattern manifests across organizational hierarchies, voting systems, consumer markets, interpersonal relationships, and institutional design: context itself becomes a force that structures motivation.

Observability

Public vs. private contexts is the specific shape observability takes when behavior's audience-visibility status systematically alters action.

Public vs. private contexts is the structurally-particularized form observability takes in the social-behavioral case: when an actor's choices are externally observable, internal preferences are inferable through the action; when private, the same preferences may produce divergent action. It inherits observability's commitment to the inferability of internal state from external outputs, particularized to the case where the actor anticipates the observer's inference and adjusts behavior accordingly, producing the systematic public–private behavioral gap Kuran formalized.


Purity and Pollution

Classifying things as clean or contaminating, with contagion and ritual cleansing.

Purity and pollution is the structural pattern in which a system of meaning sorts things into the *pure* (in its proper place, intact, clean) and the *polluting* (matter or conduct that is out of place, mixed, or defiling), and treats pollution as **contagious, transmissible by contact, and removable only through ritual or remedial cleansing**. The classic formulation belongs to Mary Douglas (1966), who defined dirt as "matter out of place" and showed that pollution rules are not pre-scientific hygiene but a symbolic system that defends the integrity of a culture's classificatory boundaries. <!-- FACT-D54-781 -->[^douglas-1966] The essential commitment is binary-plus-contagion: contamination is not a graded continuum but a categorical taint that spreads on contact and demands an act of purification to undo, independent of any physical harm. A single drop of an impure substance can defile an entire vessel, and no amount of careful measurement of *quantity* dissolves the defilement, because the logic is one of kind, not of dose. <!-- FACT-D54-782 -->[^douglas-1966]

Contagion

Purity and pollution presupposes contagion because pollution is treated as transmissible by contact, spreading the categorical taint through networks.

Purity and pollution presupposes contagion because the structural claim that pollution is removable only through cleansing rests on a prior commitment that pollution propagates contact-to-contact through a social network. It inherits contagion's pattern in which a state spreads from an affected element to a connected one and reproduces in each new host, particularized to symbolic-categorical taint rather than infection or behavior. Without contagion's transmission-by-contact structure, the binary classificatory defilement could not spread to threaten boundary integrity.

Symbolic Boundaries

Purity and pollution presupposes symbolic boundaries because the pure-polluting partition is a particular classificatory operation defending cultural categories.

Purity and pollution sorts things into pure and polluting, treating pollution as contagious matter-out-of-place that defiles classificatory order and requires ritual cleansing. This is a particular case of symbolic boundaries: conceptual distinctions actors deploy to categorize the social field with substantial consequences. Symbolic boundaries supply the underlying classificatory operation that sorts the world into kinds. Purity and pollution specialize this by adding the contagion logic and the ritual-purification mechanism, defending the integrity of the categorial scheme against transgression. Without symbolic boundaries as substrate, there would be no classifications for pollution to threaten.


Quality Control

Checking output against a specification before release and rejecting or reworking non-conforming items, binding process variation to defined tolerances through a measure-compare-act feedback gate.

Quality control is the systematic process of checking output against specification before release, with rejection or rework triggered for non-conforming items, intended to keep observed quality within defined tolerances, as Juran (1951) codifies in his foundational handbook. <!-- FACT-D52-421 -->[^juran-1951] Distinct from quality assurance (which focuses on preventing defects through process design) and quality improvement (which raises the specification itself), quality control operates at the boundary between production and customer, serving as a feedback gate that binds process variation to defined limits, a tripartite distinction Feigenbaum (1961) develops in his framework for total quality control. <!-- FACT-D52-422 -->[^feigenbaum-1961] The concept emerges from statistical process control in manufacturing (Shewhart control charts, Deming, Juran) but generalizes across software testing, publishing editorial review, pharmaceutical batch release, food safety systems, data validation pipelines, academic peer review, and AI model evaluation. It answers a recurring operational problem: how do we efficiently detect defects before they reach customers, and how do we extract actionable signals from sampling and inspection data?

Verification

Quality control is a specialization of verification in which the conformance check operates as a release gate against manufacturing-style tolerance specs.

Quality control is a kind of verification specialized to the production-to-customer interface: the object being checked is each unit of output, the specification is the defined tolerance, and the procedure is the inspection or sampling test that triggers rejection or rework when items deviate. It inherits verification's commitment to checking conformance against a stated criterion via an evidence-producing procedure yielding accept/reject verdicts, and supplies the specific feedback-gate function that binds process variation to defined limits before release, distinct from the prevention and improvement layers around it.


Queueing

Organizes tasks into a waiting line based on arrival and service rates.

Queueing is the structured accumulation of work items (requests, customers, packets, cars, patients, jobs) awaiting service at a resource with finite capacity, together with the rules (queue discipline: FIFO, LIFO, priority, random) and parameters (arrival process, service process, number of servers, buffer size) that determine how items wait, how long, and with what predictability — a ubiquitous phenomenon whose mathematical analysis (queueing theory) gives quantitative tools for predicting wait times, utilization, throughput, and loss under different workloads. The essential commitment is that whenever demand is stochastic and service capacity is finite, waiting occurs; that the shape and size of the queue depend on arrival process, service process, and queue discipline; that Little's law (L = λW — average number in system equals arrival rate times average time in system) provides a universal relationship independent of discipline; and that approaching 100% utilization causes wait times to grow without bound (queue explosion). Every queueing articulation specifies (1) the arrival process — deterministic, Poisson (M), general (G), bursty, with rate λ; (2) the service process — deterministic (D), exponential (M), general (G), with rate μ per server; (3) the number of servers and buffer capacity — M/M/1, M/M/c, M/M/c/K, M/G/1, G/G/1; and (4) the queue discipline — FIFO, LIFO, SJF, priority, processor-sharing, fair queueing. The field has foundations in Erlang's telephone-traffic work (1909), Kendall's notation (A/B/c/K/N/D; 1953), Little's law (1961), Jackson networks (1957), and extensive applications in computing, telecommunications, and service operations.

Allocation

Queueing is a kind of allocation that distributes finite service capacity across arriving demands by determining who waits and for how long.

Queueing is a specialization of allocation: it assigns a limited supply (server time at a finite-capacity resource) across competing claimants (arriving jobs, requests, customers) under a feasibility constraint (one job at a time per server) guided by a discipline (FIFO, priority, LIFO). It inherits allocation's structural commitment — finite stuff flowing to multiple sinks — and particularizes it to the temporal case where the assignment is by wait order rather than instantaneous division, with arrival and service processes setting the dynamics.

Flow

Queueing presupposes flow because waiting only arises when an inflow of work items meets a service capacity that constrains throughput.

Queueing presupposes flow because its structural setting is a directional transfer of items from arrival to service to departure, with rates, conservation, and channel constraints all native to flow. Without the prior availability of flow as a continuous directional transfer through a system with defined source, channel, and sink, there is no inflow-and-outflow geometry against which finite service capacity can create accumulation. Little's law and the rest of queueing theory presuppose the flow conservation that ties arrival rate to throughput and time-in-system.


Randomization

Randomization is the causal-inference-through-chance-assignment principle that: (1) **randomization is the procedure by which experimental units — patients, plots of land, users, classrooms, firms, animals, or other entities — are assigned to treatment conditions by an explicitly stochastic mechanism (coin flip, random-number draw, random-digit table, or algorithmic pseudorandom generator), such that each unit's probability of receiving any given treatment is specified in advance and independent of the unit's observed or unobserved characteristics; the fundamental consequence of this random assignment is that, across the ensemble of possible random assignments, treatment groups are expected to be statistically equivalent on all pre-treatment variables — both those the investigator has measured and those the investigator cannot observe — making any post-treatment difference between groups attributable to treatment (within stochastic error bounded by statistical theory) rather than to pre-existing differences**; the canonical formulation was established by R.A. Fisher in his 1925 *Statistical Methods for Research Workers* and elaborated in his 1935 *The Design of Experiments*, with the argument that randomization is the "reasoned basis for inference" in experimental science — providing the probabilistic foundation on which inferential statistics rest; (2) the concept has **several identifiable components and distinctions**: *simple randomization* (each unit independently assigned with fixed probabilities) versus *restricted randomization* (permuted-block, stratified, cluster, adaptive) that preserves randomization's inferential properties while imposing practical constraints, *randomization within blocks* (blocking to control for known confounders while randomizing within blocks, see #442 blocking), *cluster randomization* (randomizing groups rather than individuals when contamination or coordination effects make individual randomization infeasible), *adaptive randomization* (assignment probabilities change based on accumulating data — response-adaptive or covariate-adaptive), *stratified randomization* (randomizing within strata defined by prognostic factors to guarantee balance on those factors), *physical randomization* (the act of assigning) versus *randomization-based inference* (the statistical theory that treats the randomization distribution itself as the reference distribution for inference, as in Fisher's exact test), *allocation concealment* (hiding the assignment sequence from enrollers until after enrollment, a distinct protection from randomization itself), *blinding* (hiding assignment from participants, clinicians, outcome assessors; distinct from allocation mechanism but often paired), *fair coins* (equal allocation) versus *unequal allocation* (e.g., 2:1 treatment:control when treatment is costly or ethically-preferred), and *within-subject randomization* (crossover designs randomizing treatment order within subject); (3) the deeper logic is that **randomization is the uniquely powerful defense against confounding because it breaks — in expectation and with calculable probability — the association between treatment assignment and all potential confounders, both observed and unobserved**; alternative approaches to causal inference in observational studies (matching, regression adjustment, instrumental variables, difference-in-differences, regression discontinuity, propensity scores) require untestable assumptions about the relationship between treatment and confounders, whereas randomization, when properly implemented, replaces those assumptions with a known probabilistic mechanism; the strength of evidence from a well-randomized experiment comes precisely from this mechanism — not from the sample size, not from the precision of measurement, but from the inferential warrant provided by the known random-assignment distribution; this is why randomization is central to evidence-based practice in medicine (RCTs as gold standard), A/B testing in technology (causal inference for product decisions), program evaluation in policy (RCTs in development economics, education, criminal justice), and controlled experiments in agriculture, industry, and science; the deeper methodological principle — that causal inference requires either intervention-with-randomization or strong untestable assumptions — structures the entire discipline of causal inference; (4) the concept appears across domains — **agriculture and field experiments** (Fisher's original context at Rothamsted; randomized block designs and Latin squares for field-plot experiments), **clinical trials and biomedicine** (randomized controlled trials as gold standard for therapeutic efficacy since the 1948 streptomycin trial for tuberculosis, the first modern RCT; CONSORT reporting standards for RCTs; ICH-GCP regulatory framework), **development economics and public policy** (Banerjee-Duflo-Kremer 2019 Nobel Prize for RCT use in development; J-PAL and IPA research networks; RCTs across health, education, finance, agriculture in developing economies; criminal-justice RCTs including the Kansas City Preventive Patrol experiment), **education and learning sciences** (randomized educational trials from early reading interventions to school-level reforms; What Works Clearinghouse standards), **technology and A/B testing** (massive-scale randomized experiments at Google, Facebook, Microsoft, Netflix, Amazon; Kohavi-Tang-Xu *Trustworthy Online Controlled Experiments* 2020; continuous experimentation cultures), **social science field experiments** (Gerber-Green 2012 *Field Experiments*; political-science GOTV experiments; labor-market audit studies), **industrial quality and process improvement** (Taguchi methods for randomized industrial experiments; Design of Experiments in manufacturing), **behavioral economics and psychology** (laboratory randomization standard; field-experiment growth in behavioral economics), **operations research and simulation** (random-assignment in Monte Carlo methods; random-sampling in simulation), **animal and ecological experiments** (randomized-block designs in ecology; controlled vs. observational field studies) — across these, the causal-inference-through-chance-assignment principle is shared, with domain-specific implementation details (cluster-randomization in public health; factorial randomization in industry; stratified randomization in clinical trials).

Causality

Randomization presupposes causality because its purpose is to identify causal effects by severing the link between treatment and confounders.

Randomization presupposes causality because its entire warrant is causal inference: random assignment severs the link between treatment and pre-treatment characteristics, so observed differences in outcomes can be attributed to the productive connection from cause to effect. Without causality's four-part structure — cause, effect, productive connection, modal robustness — there would be no target relation for the randomization to identify. Random assignment is precisely the procedure that licenses the counterfactual claim causality requires.

Experimental Design

Randomization is the specific shape experimental design takes when treatment assignment is made stochastic to neutralize observed and unobserved confounders.

Randomization is the particular form experimental design takes for the assignment step: units are allocated to treatment conditions by an explicitly stochastic mechanism with specified probabilities independent of unit characteristics. This achieves the structural property that treatment groups are expected statistically equivalent on all pre-treatment variables — measured and unmeasured. The general architecture of principled comparison for causal inference is specialized here to the stochastic-assignment mechanism, with chance-driven allocation as the lever that neutralizes confounding and supports valid causal inference.

Probability

Randomization is the specific shape probability takes when the chance mechanism is deliberately injected to assign units to treatments.

Randomization is the structurally-particularized form probability takes when a stochastic mechanism is purposefully used to assign experimental units to conditions, ensuring each unit's probability of any treatment is specified in advance and independent of its characteristics. It inherits probability's coherence apparatus — sample space, events, calibrated assignment of numbers in [0,1] — particularized to the assignment-procedure case. The expected statistical equivalence of treatment groups follows directly from the probability calculus applied to the assignment.


Randomness

**Randomness is the property of a process or sequence whose individual outcomes resist prediction within a specified scheme, yet whose ensembles obey lawful statistical regularities.** The essential commitment is to that dual fact: individuality is unpredictable, ensemble is constrained — and the apparent contradiction is resolved by the scheme-relativity of randomness claims, which always specify what counts as "unpredictable" and to whom. Every randomness claim names (1) the process generating the outcomes, (2) the reference scheme against which unpredictability is asserted (no pattern recoverable by this class of methods), (3) the statistical regularities that do hold (a distribution, a stationarity property, an exchangeability condition), and (4) the source of the unpredictability — fundamental indeterminism (aleatoric), deterministic [`chaos`](chaos.md) in the sense of Lorenz (1963)<!-- FACT-D38-037 -->[^lorenz-1963] read through limited information, high-dimensional complexity, or designed-in pseudorandom generation in the computability tradition of Church (1940)<!-- FACT-D38-038 -->[^church-1940]. **Without all four parts a randomness claim is undefined; with them, the spectrum from quantum measurement to a cryptographic PRNG to a clinical-trial assignment can be analyzed within one diagnostic vocabulary, and the source-quality match between application and generator becomes a checkable engineering question rather than a hand-waved assumption.**


Reactance

Psychological reactance is a motivational state aroused as a four-component process.[^brehm-1966] <!-- FACT-D18-070 --> The process unfolds as follows: (1) *the perceived freedom*, an individual perceives themselves to hold a specific behavioral freedom—a choice they believe is available to them; (2) *the threat-to-freedom event*, that freedom is threatened, eliminated, or imminently threatened-with-elimination by an external agent, message, or situational constraint; (3) *the reactance arousal*, the threatened freedom is important to the individual's sense of autonomy and identity, and an aversive motivational state emerges proportional to the magnitude of the threat and the value of the freedom; and (4) *the freedom-restoration motivation*, the resulting state motivates compensatory behavior directed at restoring or reasserting the freedom—often by performing the forbidden action, deprecating the restricting agent, devaluing the restricted option, or asserting independence in adjacent domains—even when this behavior acts against other valued interests. <!-- FACT-D18-071 -->[^brehm-1966] The phenomenon is foundational to Brehm's (1966) theory and has been empirically elaborated across five decades of social-psychological research.[^brehm-brehm-1981] <!-- FACT-D18-072 --> Reactance is not mere disagreement, rational opposition, or trait-level stubbornness, but rather a transient, freedom-specific motivational state triggered by the particular structure of autonomy threat, regardless of the target's intrinsic merit.

Constraint

Reactance presupposes constraint because the motivational state only arises when a perceived freedom is threatened or restricted by an external limit.

Reactance is a motivational state triggered specifically by the perceived restriction of a behavioral freedom: without a constraint imposing itself on the admissible set of actions the individual believes they possess, there is no threat-to-freedom event, no aversive arousal, and no freedom-restoration motivation. The four-component process presupposes that the structural feature being responded to is a binding restriction on what one is allowed to choose — the very situation constraint names — and reactance is the psychological reaction to that situation.

Homeostasis

Reactance is the specific shape homeostasis takes when perceived autonomy is the regulated variable and freedom-restoration is the corrective response.

Reactance is the homeostatic particularization for the variable of perceived behavioral freedom: when external constraint threatens to push the variable below its acceptable band, an aversive motivational state arises and drives corrective behavior aimed at restoring the freedom. Where homeostasis names closed-loop self-regulation that holds a variable within a setpoint range generally, reactance fixes the regulated variable as autonomy and the corrective action as freedom-restoration motivation — a particular sensor-comparator-actuator loop operating on the psychological variable of choice.