Substitution Effect

one_liner: Isolate the part of a consumer's demand response to a price change that comes purely from shifted relative prices, holding real purchasing power constant, by hypothetically compensating income and observing how she reallocates toward the now-cheaper goods.

origin_subdomain: microeconomics

core_idea:
The substitution effect is the component of a consumer's demand response to a price change that arises purely from the shift in relative prices, holding real purchasing power constant. When the price of one good rises, that good becomes more expensive relative to all other goods; a utility-maximizing consumer will substitute toward cheaper alternatives even if her real wealth were hypothetically restored to its original level. That substitution component is the substitution effect; the remaining adjustment that results from her being genuinely poorer in real terms is the income effect. The Slutsky decomposition — formalized by Eugen Slutsky and interpreted in compensating-variation terms by John Hicks — separates the total observed demand response into exactly these two parts.

The mechanism works through the budget constraint and indifference curve geometry. A price increase rotates the budget constraint inward around the quantity axis of the good that has not changed in price. The Slutsky decomposition performs a hypothetical compensation: give the consumer just enough income to purchase her original bundle at the new prices, then observe how she reallocates. The reallocation under this compensation — away from the now-relatively-more-expensive good and toward the now-relatively-cheaper alternatives — is the substitution effect alone. By construction it is always non-positive for the good whose price rose (the own-price substitution effect cannot be positive, a result known as the Slutsky symmetry and negativity conditions). The income effect then adjusts for the fact that real-world consumers receive no such compensation and are left poorer by the price rise; for normal goods the income effect reinforces the substitution effect, while for inferior goods it partially offsets it, and in the extreme case of Giffen goods the income effect reverses the sign of the total demand response. The substitution effect is the workhorse of tax incidence and deadweight-loss analysis, because the efficiency cost of a tax depends on compensated elasticities — the substitution-only demand response — not on gross (Marshallian) elasticities that conflate substitution with income effects.

scope_of_application:
The substitution effect lives across microeconomic consumer theory and the applied fields that build on it; its reach is bounded to the price-and-budget substrate — a chooser, prices, and a budget constraint — where the identifying compensation can isolate the substitution term, but within that substrate the Slutsky/Hicks decomposition is indifferent to what the "good" is, so it ports across many choice domains. (The bare "shift toward whatever got cheaper" lesson belongs to the parents `substitutability` / `trade_offs` / `incentive_compatibility`, not here.)

- **Consumer demand theory** — the home turf; the Slutsky equation, compensated (Hicksian) demand curves, and the negativity-and-symmetry restrictions that test whether observed behaviour is consistent with utility maximization.
- **Public finance and tax incidence** — its most consequential use; a tax's deadweight loss rides on the *compensated* (substitution-only) elasticity, separating a price wedge's efficiency cost from its redistributive cost.
- **Welfare measurement** — compensating-variation and equivalent-variation welfare measures rely on the same hold-utility-constant decomposition.
- **Labor-supply analysis** — a wage change decomposes into substitution (work versus leisure) and income components, the same two-term split on the time-allocation margin.
- **Intertemporal choice (savings and consumption)** — an interest-rate change decomposes into substitution between present and future consumption and an income effect.
- **Spatial-voting and other discrete-choice models** — party support or option choice shifts with relative "price" along the same compensated-response logic where a budget-like constraint is defined.
- **Giffen/inferior-good diagnostics** — the framework classifies anomalous upward-sloping demand as an inferior good whose income term overpowers the ever-negative substitution term, rather than as irrationality.

structural_signature:
**Sig role-phrases:**

- **the chooser with preferences** — a utility-maximizing consumer over bundles, the agent whose demand response is being decomposed
- **the relative-price change** — a price move on one good that alters its price relative to all others, rotating the budget constraint
- **the hypothetical compensation** — the identifying device: just enough income to restore the original utility (Hicks) or buy the original bundle (Slutsky) at new prices, giving "real purchasing power held constant" an exact meaning
- **the substitution effect** — the reallocation under that compensation toward the now-relatively-cheaper goods, the construct proper
- **the income effect** — the residual response to being genuinely poorer, with sign fixed by the good's normal-vs-inferior classification
- **the Slutsky equation** — the identity that the two terms sum exactly to the total observed (Marshallian) demand response
- **the negativity-and-symmetry guarantees** — the engineered restrictions: own-price substitution never positive, cross-effects symmetric, testable consistency checks on utility maximization
- **the compensated-elasticity payoff** — what the decomposition buys for public finance: deadweight loss rides on the substitution-only (compensated) response, separating a wedge's efficiency cost from its redistributive cost (Giffen = inferior good whose income term overpowers substitution)
- **the price-and-budget substrate limit** — the decomposition's identifying compensation requires a utility function, budget constraint, and prices; absent these the substitution term cannot be cleanly isolated

what_it_is_not:
- **Not the whole demand response to a price change.** The substitution effect is *one component* — the reallocation due purely to changed relative prices, holding real purchasing power constant. The remaining adjustment, from the consumer being genuinely poorer, is the income effect; the Slutsky equation says the two sum to the total observed (Marshallian) response. Treating the observed quantity change as "the substitution effect" conflates the two mechanisms the decomposition exists to separate.
- **Not capable of the wrong sign.** The own-price substitution effect is *never positive* (Slutsky negativity): compensated, a consumer always shifts away from the good that became relatively dearer. A demand curve that slopes the "wrong" way (Giffen) does *not* come from a positive substitution effect — it comes from an inferior good's income effect overpowering the ever-negative substitution term. Attributing Giffen behavior to substitution misplaces the cause.
- **Not the elasticity that governs deadweight loss... when read off gross demand.** A tax's efficiency cost rides on the *compensated* (substitution-only) elasticity, not the gross Marshallian response, which bundles substitution with the consumer's general impoverishment. Reading deadweight loss off the market-observed demand fall double-counts redistribution as waste; only the compensated portion is allocative loss.
- **Not a measure of distribution or fairness.** The substitution effect isolates the *efficiency* consequence of a price wedge (the mutually beneficial trades it suppresses); the income effect captures the *redistributive* consequence (the real income lost). Reading a substitution-based efficiency result as a distributional verdict confuses the two halves of the decomposition.
- **Not the general "shift toward whatever got cheaper" pattern.** That directional lesson is the parent family — `substitutability`, `trade_offs`, `incentive_compatibility` — and it travels anywhere. But the substitution effect's defining content is the *two-term decomposition* with its compensated response and negativity/symmetry guarantees, which requires a utility function, budget constraint, and prices. "Habit substitution" or any "X got harder so people switched" story dressed in substitution-effect language borrows the shape, not the testable apparatus.

clarity:
The Slutsky decomposition makes legible that a single observed fact — "demand fell when the price rose" — is the sum of two mechanisms that can pull in opposite directions, and that conflating them is the source of a string of apparent paradoxes. Before the split, an economist confronting a good whose demand *rises* with its price has only an anomaly; after it, the same observation is fully intelligible — a Giffen good is one whose income effect, being inferior, outweighs an ever-negative substitution effect. The decomposition converts "is this consumer behaving rationally?" into the sharper, structural questions it actually depends on: what is the sign of the income effect (is the good normal or inferior), and does it reinforce or fight the substitution component? The guarantee that the own-price substitution effect is never positive — and that the cross-effects are symmetric — turns loose intuitions about demand into testable restrictions on the data.

The decomposition's most consequential clarification is that it tells the public-finance analyst *which* elasticity governs the efficiency cost of a tax. Without the split, one is tempted to read deadweight loss off the gross, market-observed (Marshallian) demand response — but that response bundles together the consumer's substitution away from the taxed good and her general impoverishment, and only the former is true allocative waste. By isolating the compensated response, the framework separates the *efficiency* consequence of a price wedge (the substitution-driven, mutually-beneficial trades it kills) from its *redistributive* consequence (the income the consumer loses). The practitioner's question moves from "how much did quantity fall?" to "how much of that fall is compensated substitution?" — and only that second number defensibly enters a deadweight-loss calculation. The clarity lies in giving "real purchasing power held constant" an exact operational meaning, so that the part of behavior caused by relative prices can be measured apart from the part caused by being poorer.

manages_complexity: The sprawl the substitution effect tames is the menagerie of demand responses a price change can produce: demand that falls a little, falls a lot, barely moves, or — in the Giffen case — perversely rises, with no surface rule telling which good will do which. Treated as raw observed responses, each good seems to need its own empirical story. The Slutsky decomposition collapses every one of those responses into the sum of two terms with fixed properties, so the analyst stops cataloguing behaviours and instead tracks just two things: a substitution term whose sign is guaranteed — own-price substitution is never positive, always pushing away from the good that became relatively dearer — and an income term whose sign is read off a single binary classification of the good as normal or inferior. From those, the qualitative shape of the total response follows by a small branch structure rather than case-by-case derivation: for a normal good the income term reinforces substitution and demand unambiguously falls; for an inferior good it offsets, leaving the net response a contest of magnitudes; and only when an inferior good's income term is large enough to overpower the ever-negative substitution term does the total response flip sign into the Giffen anomaly. What looked like a paradox needing special pleading becomes the predictable far end of one parameter, the income term, growing large. The compression is sharpest in tax analysis, where it isolates the single number that matters: deadweight loss rides on the compensated, substitution-only elasticity, so the analyst need not model the full welfare consequence of a price wedge but only the size of the trades the wedge suppresses, with the consumer's general impoverishment set aside as redistribution rather than waste. The high-dimensional question "how will demand respond, and how much of that response is efficiency loss" reduces to "what is the always-negative substitution term, is the good normal or inferior, and which dominates" — a two-term split with signed components in place of an open-ended catalogue of demand behaviours.

abstract_reasoning:
The substitution effect licenses a set of demand inferences built on one decomposition — total response equals a sign-guaranteed substitution term plus an income term whose sign is fixed by a binary classification — and on the operational meaning it gives to "real purchasing power held constant."

*Diagnostic (decompose an observed response, infer the good's type and rationality).* The signature move is to read a single observed demand response as the sum of two mechanisms and infer which is doing the work. The analyst reasons FROM "demand fell when price rose" TO "an always-negative substitution term plus an income term," and FROM the *sign and size* of the income term TO a classification of the good: a fall reinforced beyond the compensated response signals a normal good; a fall smaller than the compensated response signals an inferior good; and demand that *rises* with price is diagnosed not as irrationality but as a Giffen good — an inferior good whose income term overpowers the ever-negative substitution term. The decomposition converts "is this consumer rational?" into "what is the sign of the income effect, and does it reinforce or fight substitution?"

*Predictive (from the good's type, predict the shape of the response).* Running the inference forward, the framework predicts the qualitative response from the always-negative substitution term plus one binary fact. The analyst reasons FROM "this good is normal" TO "the income term reinforces substitution, so demand unambiguously falls"; FROM "inferior" TO "the net response is a contest of magnitudes"; and FROM "inferior with a large income term" TO "the total response flips sign (Giffen)." The Giffen anomaly is predicted as the far end of one parameter — the income term — growing large, rather than as a special case needing its own story.

*Interventionist / boundary-drawing (isolate the compensated response for efficiency cost).* The decomposition's most consequential inference tells the analyst *which* elasticity governs a tax's deadweight loss. The move is to hypothetically compensate — give the consumer just enough income to buy her original bundle at the new prices — and reason FROM that compensated reallocation TO the efficiency cost, because only the substitution-driven, mutually-beneficial trades the wedge kills are allocative waste. Reasoning runs FROM "the gross quantity fell by this much" TO "but only the compensated portion is efficiency loss; the rest is the consumer's impoverishment, which is redistribution, not waste" — so the deadweight-loss calculation defensibly takes the compensated elasticity and sets the income-driven part aside. This is the boundary between the *efficiency* and the *redistributive* consequence of a price wedge.

*Boundary-drawing (structural restrictions, and the substrate edge).* The framework imposes testable restrictions that bound what the data may show: the own-price substitution effect is never positive (Slutsky negativity), and the cross-price substitution effects are symmetric (Slutsky symmetry), so the analyst reasons FROM observed compensated responses TO a check on whether the consumer's behavior is consistent with utility maximization at all. The same identifying compensation marks the concept's edge: isolating the substitution term in a non-arbitrary way requires a utility function, a budget constraint, and prices, so reasoning runs FROM the absence of that price-and-budget structure TO the conclusion that the substitution effect cannot be cleanly separated from the total response. The bare cross-domain lesson — behavior shifts toward whatever became relatively cheaper — travels, but the specific two-term decomposition and its signed guarantees do not leave the price-and-budget substrate.

knowledge_transfer:
Within the home domain — microeconomic consumer theory and the fields that build on it — the substitution effect transfers *as full mechanism*, and its within-economics reach is unusually wide because the Slutsky/Hicks decomposition is indifferent to what the "good" is. The compensated-response construction, the always-negative own-price substitution guarantee, the normal-versus-inferior branch that fixes the income term's sign, the Giffen-as-far-end-of-one-parameter result, and the Slutsky symmetry and negativity restrictions all port intact wherever there is a chooser, prices, and a budget constraint. The same two-term split applies whether the good is ordinary consumption (the Slutsky equation, compensated demand curves), leisure against work in labor-supply analysis (wage changes decompose into substitution and income components), savings and intertemporal allocation, risk-bearing, or even political-party support in spatial-voting models. And it carries its single most consequential use — public finance — across every tax: deadweight loss rides on the *compensated* (substitution-only) elasticity, not the gross Marshallian one, and compensating- and equivalent-variation welfare measurement rely on the same decomposition. This is genuine mechanism transfer because the load-bearing apparatus (the hold-utility-constant compensation, the signed components, the testable restrictions) travels with the vocabulary; only the interpretation of the axis changes.

Beyond the price-and-budget substrate the honest report is a **shared abstract mechanism / metaphor** split, and the seam is sharp because the decomposition's identifying move does not survive the substrate change. The identifying compensation — "give the consumer just enough income to restore the original utility" — only makes sense when there is a utility function, a budget constraint, and prices; remove any of these and the substitution term cannot be isolated from the total response in any non-arbitrary way. So the named two-term decomposition, with its signed guarantees, stays home. What genuinely travels cross-domain is the *general* lesson the effect instantiates — "when one option becomes relatively cheaper or harder, behavior shifts toward the cheaper one" — and that lesson is already carried by the catalogue primes `substitutability` (one component standing in for another), `trade_offs` (giving up one thing to get another as the trade-off ratio shifts), and `incentive_compatibility`. A reader equipped with those primes already has the cross-domain handle; the substitution effect adds nothing portable beyond them except the specific microeconomic decomposition, which is exactly the part that does not leave the substrate. So the loose analogues sometimes invoked — "habit substitution" in behavior, or any "X became harder so people shifted to Y" story — are metaphor when dressed in substitution-effect language: they borrow the directional shape while lacking the compensated response, the income/substitution split, and the negativity-and-symmetry restrictions that give the original its force and testability. The correct cross-domain lesson therefore carries `substitutability`/`trade_offs`/`incentive_compatibility` — not "the substitution effect," whose defining content is the price-and-budget decomposition. Within economics the mechanism transfers in full across every kind of "good"; past the price-and-budget substrate only the general shift-toward-cheaper pattern travels, as the parent primes, and substitution-effect language applied there is analogy (see *Structural Core vs. Domain Accent*).


Supplier Concentration Risk

one_liner: Exposure that arises when a buyer's dependency for a critical input rests on so few suppliers that one node's disruption propagates downstream faster than alternatives can be qualified — a shape property of the dependency distribution, not of any supplier's performance.

status: draft

core_idea:
Supplier concentration risk is the operations and supply-chain pattern in which a buying organisation's dependence for a critical input is distributed across so few upstream suppliers that a disruption, failure, capture, or adverse price action affecting any single supplier propagates to the downstream organisation at a scale it cannot absorb within the timeframe required to find and qualify alternatives.

The structural mechanism is a skewed dependency-weight distribution. Each upstream supplier holds some share of the buying organisation's sourced volume for the input in question; when that distribution is heavily right-tailed — one or two suppliers holding the majority of share — the downstream organisation's resilience is bounded by the reliability of those top nodes rather than by any buffer or process improvement it can install internally. The failure mode is specifically temporal: even if alternative suppliers exist in principle, qualifying them, ramping their capacity, and adjusting contract terms all take longer than the disruption window, so the buying organisation is exposed to downstream production stoppage, cost spikes, or input rationing during the interval between disruption and substitution. An excellent sole-source supplier can produce this condition — the risk is not a function of supplier performance under normal conditions but of the distribution shape itself, which is why it is invisible to standard supplier-performance review and appears only under concentrated-dependency analysis.

The pattern compounds with lean supply-chain optimisation: each individual procurement decision that concentrates volume on the lowest-cost qualified supplier is locally rational, but the aggregate of many such decisions progressively narrows the dependency-weight distribution and raises tail risk, a dynamic that becomes visible only at disruption events. COVID-era PPE shortages, the 2021–2022 semiconductor supply crisis, and rare-earth supply dependence on single-country refining capacity are canonical instances. The remediation vocabulary is operations-specific: dual-sourcing and multi-sourcing redistribute dependency weight across additional qualified suppliers; geographic dispersion reduces correlated-failure risk from regional events; strategic inventory buffers the substitution latency; long-term supply agreements with capacity commitments reduce exit risk; and standards-based interchangeability makes the input substitutable between suppliers rather than supplier-proprietary. Each lever is measurable against the dependency distribution — HHI of supplier share, top-1 share, top-3 share — which converts the risk from a qualitative concern into a quantifiable property of the procurement portfolio that can be monitored, targeted, and traded off against cost.

scope_of_application:
Supplier concentration risk lives across the industries of supply-chain and operations management; its reach is within that domain, wherever a buyer sources a critical input and the shape of the dependency-weight distribution — not any supplier's performance — governs failure-sensitivity. The cross-substrate cousins (single-vendor IT lock-in, agricultural monoculture, keystone-species dependence) belong to the broader concentration / single-point-of-failure pattern, not here.

- **Industrial manufacturing supply chains** — automotive wire-harness sourcing, aerospace titanium forging, and semiconductor capital equipment (single EUV-lithography vendor), where lean cost-optimisation has quietly narrowed the supplier base.
- **Critical-minerals and energy supply** — rare-earth refining concentrated in a single country, natural-gas dependence on one pipeline or supplier nation, refining capacity clustered in one region; tracked on national strategic-input lists.
- **Pharmaceutical and medical supply** — active-pharmaceutical-ingredient sourcing from one or two factories, specialty reagents, and PPE manufacturing concentrated geographically.
- **Software supply chains** — single-maintainer open-source dependencies (log4j, xz, left-pad) and single cloud, CDN, or identity-provider dependence, the same diagnosis applied to a dependency graph.
- **Financial market infrastructure** — clearing, custody, and payment-rail concentration in a small set of institutions, tracked as systemic risk under regulatory frameworks.
- **Emergency logistics, construction, and utilities** — disaster-response capability on a few contractors or one transport corridor, specialty trades drawn from a small national pool, and utility inputs from a single source.

structural_signature:
**Sig role-phrases:**

- **the downstream buying organisation** — the firm or sector that consumes a critical input and whose continued operation depends on its supply
- **the upstream supplier network** — the set of nodes qualified to provide that input
- **the dependency-weight distribution** — the share of sourced volume each supplier holds, the shape (not the per-supplier performance) that governs exposure
- **the concentration condition** — a heavily right-tailed distribution in which top-1 or top-3 share is high enough that one node's loss is consequential at the buyer's scale (measured by HHI of supplier share, top-k share)
- **the substitution latency** — the time to qualify alternates, ramp their capacity, and re-contract, which turns a static share into a binding exposure only when it exceeds the disruption window
- **the upstream disruption** — node-level failure, capture, geographic event, price action, or political restriction at a concentrated supplier
- **the downstream-loss propagation** — production stoppage, cost spikes, or input rationing during the gap between disruption and substitution, undamped because resilience was bounded by the tail node
- **the concentration drift** — each locally rational award of volume to the lowest-cost qualified supplier narrows the distribution over time, raising tail risk invisibly until a disruption
- **the redistribution remediation** — dual-/multi-sourcing, geographic dispersion, strategic inventory, capacity-committed agreements, and standards-based interchangeability, each a measurable move on the distribution or the latency

what_it_is_not:
- **Not a supplier-performance problem.** The risk is a property of the dependency-weight *distribution*, not of how any supplier performs: an excellent, best-in-portfolio sole-source vendor produces the condition, and a sole-source relationship and a fragile one are indistinguishable on a scorecard. It is invisible to ordinary supplier-performance review and surfaces only under concentration analysis — so "find a better dominant supplier" does not address it; redistributing share does.
- **Not a bottleneck.** A bottleneck limits throughput under normal operation — a capacity-in-flow problem; concentration risk is a loss-on-failure problem, about what happens when a dependency node fails, not about how fast goods move when it works. The dominant supplier may have ample capacity and still be the exposure, because the question is failure-sensitivity, not flow.
- **Not made safe by the mere existence of alternatives.** Alternate suppliers existing on paper does not discharge the risk; what binds is the *substitution latency* — qualification, capacity ramp, re-contracting — measured against the disruption window. If switching is slower than the outage, the buyer is exposed to stoppage, cost spikes, or rationing during the gap regardless of how many suppliers exist in principle.
- **Not eliminated by holding more inventory.** Strategic inventory buffers the substitution window but does not change the shape of the dependency distribution; once the buffer is exhausted, the same tail node still bounds resilience. Inventory shortens the *effective* latency for a bounded time; it is one lever among several, not a substitute for redistributing dependency weight.
- **Not curable by diversification when exposures are correlated.** Risk-pooling reduces variance only across *independent* exposures; when many dependents share the same upstream node — one region, one refiner, one maintainer — the exposures move together and spreading orders across them buys nothing. Apparent diversification that does not break the shared dependency leaves the concentration intact.

clarity:
Naming supplier concentration risk relocates a buyer's exposure from a property of any individual supplier to a *shape property of the dependency distribution* — how much of the sourced volume rests on the top one or two nodes — and that relocation is its central clarifying force. It makes visible that downstream resilience is bounded by that shape, independent of how much the buyer invests downstream in process, quality, or its own inventory: no amount of internal excellence buys out a tail concentrated upstream. The most consequential thing the label exposes is that the failure mode is *not* detectable by ordinary supplier-performance review — the dominant supplier may be the best performer in the portfolio — and surfaces only under concentration analysis that looks at the distribution of dependency rather than at each supplier in isolation. A sole-source relationship with an excellent vendor and a fragile one are, on a scorecard, indistinguishable; on a concentration metric they are not.

The concept also separates two operational worlds that lean, just-in-time, cost-optimised practice routinely fuses: a supply base that is *operationally efficient* (thin, cheap, well-performing under normal conditions) and one that is *structurally robust* (dependency weight spread across qualified, dispersed, substitutable sources). Holding those apart lets a practitioner see a dynamic otherwise invisible — that each locally rational decision to award more volume to the lowest-cost qualified supplier narrows the distribution and quietly raises tail risk, so an optimisation that looks like prudent cost discipline is simultaneously a slow accumulation of fragility that only reveals itself at a disruption. The sharper question the buyer can now ask is no longer "are my suppliers performing?" but "what is my top-1 and top-3 share for this critical input, what is the substitution latency if the dominant node fails, and is that exposure priced against the cost I saved by concentrating?" — converting a vague qualitative worry into a measurable, monitorable, tradeable property of the procurement portfolio.

manages_complexity: A buyer's supply base for a critical input can run to dozens of suppliers, each with its own performance history, contract terms, geography, and failure modes — an unwieldy field that ordinary supplier-by-supplier review never resolves into a single read on exposure. Supplier concentration risk compresses that field to a property of the dependency-weight *distribution*, summarised by a handful of scalars — top-1 share, top-3 share, the HHI of supplier share — plus the substitution latency for the dominant node. Instead of forecasting the joint failure behaviour of the whole supply base, the practitioner tracks how skewed the share distribution is and how long substitution takes, and reads the qualitative outcome off them: a heavily right-tailed distribution with long substitution latency is bound to upstream events regardless of downstream investment, a dispersed one is not. That same small parameter set then orders the otherwise-disparate remediation catalogue — dual-sourcing, geographic dispersion, strategic inventory, capacity commitments, standards-based interchangeability — because each lever is just a different way of moving the distribution or shortening the latency, so the buyer compares interventions on their measured effect on one metric rather than reasoning about each in isolation. The concept thereby turns a high-dimensional, qualitative "are my suppliers risky" problem into a low-dimensional, monitorable, tradeable one: track the shape of the dependency distribution and the substitution window, and both the fragility and the right corrective follow.

abstract_reasoning:
The concept licenses a set of inferences that all run through one relocation — from supplier performance to the *shape of the dependency-weight distribution* — and through the substitution latency that turns a static share into a dynamic exposure.

The core *diagnostic* move infers a hidden fragility from a distribution statistic that ordinary review cannot see. The reasoning runs FROM "top-1 (or top-3) share for this critical input is high" TO "downstream resilience is bounded by the reliability of those nodes, regardless of any downstream investment in process, quality, or inventory" — and the discriminating power is precisely that this exposure is *invisible on a supplier scorecard*. A sole-source relationship with the best-performing vendor in the portfolio and a fragile one are indistinguishable on performance, so the analyst reasons FROM "concentration metric is high" TO "this input is at risk" *even when every individual supplier is excellent*. The move deliberately bypasses per-supplier reasoning because the failure mode is a property of the distribution, not of any node in it; the correct read-out is HHI of supplier share, top-1 share, and top-3 share, not a performance history.

A quantitative *risk-decomposition* inference underlies that diagnosis and makes it predictive. Expected disruption loss in a period is read as the sum, across upstream nodes, of (probability that node is disrupted) × (downstream loss given that node's disruption), where the second factor is itself a rising function of the node's share of input. The analyst reasons FROM the share distribution TO where total risk concentrates: when share piles onto a few nodes, the per-node downstream-loss conditional is large and the system's total exposure is *dominated by tail events at the concentrated nodes*. This converts a vague worry into a structured estimate and tells the practitioner that mitigating the dominant node moves far more risk than improving a minor one — the inference runs FROM the skew of the distribution TO the identity of the exposures worth pricing.

The *temporal* refinement is what separates a benign concentration from a binding one, and it is a distinct move. A high share is only consequential relative to the *substitution latency* — the time to qualify alternates, ramp their capacity, and adjust contracts — measured against the disruption window. The reasoning runs FROM "alternatives exist in principle" NOT to "the buyer is safe" but to "compare qualification-plus-ramp time to the expected outage duration": if substitution is slower than the disruption, the buyer is exposed to stoppage, cost spikes, or rationing during the gap, regardless of how many suppliers exist on paper. The earthquake-stops-the-dominant-fab case is read this way — the minor suppliers' qualification cycles and capacity assumptions made them unable to absorb the volume *in time*, so the relevant quantity was latency, not the mere existence of alternates.

The *interventionist* move treats every remediation as an operation on the same two quantities, which lets otherwise-disparate levers be compared on one axis. The reasoning runs FROM a candidate action TO its measured effect on the distribution or the latency: dual- and multi-sourcing redistribute dependency weight (lowering top-k share); geographic dispersion reduces correlated-failure risk from regional events (so that one shock does not take down several nodes at once); strategic inventory buffers the substitution window (shortening the *effective* latency the buyer is exposed to); capacity-committed long-term agreements reduce exit risk; standards-based interchangeability makes the input substitutable between suppliers rather than proprietary (collapsing qualification time). Because each lever maps to a movement of one metric, the analyst reasons FROM "I need to reduce this exposure by this much" TO "these are the interventions that move the number, compared on measured effect and traded off against the cost of concentrating" — converting an open-ended search for resilience into a portfolio-optimisation over a small parameter set.

A characteristic *historical / drift* inference predicts how the risk arises in the first place, and it is the concept's subtlest move. Each individual procurement decision that awards more volume to the lowest-cost qualified supplier is locally rational, but the analyst reasons FROM "many such locally rational decisions accumulate" TO "the dependency-weight distribution narrows and tail risk rises" — a globally fragility-increasing drift that is invisible at the level of any single award and surfaces only at a disruption event. This licenses a forward warning: a lean, cost-optimised supply base trends toward concentration over time even with no single bad decision, so the practitioner should monitor the *trajectory* of the concentration metric, not just its current value. The move reasons FROM the aggregation of cost-minimising choices TO an emergent structural property, and it is why the risk is associated with exactly the disciplined cost management that looks most prudent.

Finally, a *boundary-drawing* move fixes when the concept is the right tool and distinguishes it from neighbours it is confused with. It applies where exposure is a *distribution-shape and failure-sensitivity* problem — reasoning FROM "the question is what happens when a dependency node fails" TO "this is concentration risk." It is *not* a throughput problem: a bottleneck limits flow under normal operation, whereas concentration risk concerns failure-sensitivity, so the analyst separates the two by asking whether the issue is capacity in flow or loss on failure. And it marks where risk-pooling logic *fails* rather than applies — pooling reduces variance only across *independent* exposures, so when many dependents share the same upstream node the exposures are correlated and pooling buys nothing, an inference that tells the practitioner when diversification will and will not help.

knowledge_transfer:
Within supply-chain and operations management the diagnostic transfers *as mechanism* across every industry that sources a critical input, because the structure being measured — the shape of the dependency-weight distribution and the substitution latency of the dominant node — is identical regardless of what the input is. The same metrics (HHI of supplier share, top-1 share, top-3 share), the same diagnostics (concentration analysis rather than per-supplier scorecard; compare qualification-plus-ramp time to the disruption window), and the same remediation catalogue (dual- and multi-sourcing, geographic dispersion, strategic inventory, capacity-committed agreements, standards-based interchangeability, reshoring/nearshoring, Tier-N mapping) carry intact from **automotive** wire-harness sourcing to **aerospace** titanium forging to **semiconductor** EUV-lithography dependence to **pharmaceutical** active-ingredient and reagent supply to **software supply chains** (single-maintainer open-source dependencies like log4j or xz; single cloud, CDN, or identity provider) to **energy and utilities** (one pipeline or supplier nation), **emergency logistics** (one transport corridor), **construction** (a small national pool of a specialty trade), and **financial market infrastructure** (clearing, custody, and payment-rail concentration tracked as systemic risk). These are not analogies between separate problems; they are the same operations diagnosis with the input swapped, which is exactly why a method developed for industrial procurement applies without modification to a software dependency graph. The boundary *within* the domain is the regime condition the concept draws itself: it is a failure-sensitivity problem, not a throughput problem (a bottleneck limits flow under normal operation; concentration risk concerns loss-on-failure), and risk-pooling logic helps only across *independent* exposures, so where many dependents share one upstream node, diversification buys nothing.

Beyond operations the situation is the third case: the *named* diagnostic does not travel, but a *more general structural pattern that it instantiates* does. Supplier concentration risk is the operations instantiation of a substrate-portable phenomenon — *concentrated dependency weight on few nodes binds downstream fragility*, the graded form of single-point-of-failure — and that parent genuinely recurs as co-instances across distinct substrates: single-vendor IT lock-in, agricultural and ecological monoculture, keystone-species dependence in an ecosystem, single-archive information storage, single-sourced biological pathways. What carries the cross-domain lesson is *that general pattern*, not "supplier concentration risk" with its operations clothing. The home-bound cargo is precisely the layer that makes the entry domain-specific: the *supplier* vocabulary (sole-source, dual-source, qualified alternates, Tier-N), the *measurement instruments* (HHI of supplier share, top-k share — procurement metrics, not generic ones), and the *intervention catalogue* (qualification programs, strategic stockpile, reshoring). None of that imports directly into ecology or information storage — a monoculture has no "supplier qualification cycle," an archive no "dual-sourcing contract" — even though the underlying distribution-shape-governs-fragility logic is the same.

So the honest report has two layers. Across the *industries* of the home domain, the operations diagnostic transfers literally and is the right tool to reach for, swapping only the input. *Beyond* the domain, the move that overclaims is invoking "supplier concentration risk" by name for a vendor-lock-in or monoculture case; the move that claims exactly what holds is to recognize the case as a co-instance of the general *concentration / single-point-of-failure* pattern — the parent the entry sits under — and to carry *that*, along with its neighbours `dependency`, `systemic_risk`, `redundancy`, and `risk_pooling`, while leaving the procurement-specific metrics and remediations at home. The distribution-shape skeleton lifts; the supplier vocabulary does not. (See *Structural Core vs. Domain Accent*.)


Supply

slug: supply

one_liner: Model producer behavior as a whole price-to-quantity schedule rather than a single quantity, upward-sloping because expanding output raises marginal cost, so any disturbance either moves output along the curve (only the good's own price) or shifts the whole curve (everything else).

core_idea:
Supply is the relationship between the price of a good or service and the quantity that producers are willing and able to bring to market at that price, all else equal. The supply curve — upward-sloping in standard cases — encodes that higher prices make production profitable across a wider range of production units and at higher levels of output: as price rises above a producer's marginal cost, additional units become worth producing, drawing in more output from existing producers and attracting entry from higher-cost producers who were previously unprofitable. The supply schedule is not a fixed quantity but a conditional mapping from each price to a quantity, and the relevant object of analysis is the full schedule rather than any single point on it.

The mechanism that generates an upward-sloping supply curve is the rising marginal cost of production. In the short run, expanding output requires applying more variable inputs (labor, materials, energy) to a fixed capital stock; diminishing marginal productivity means each additional unit costs more to produce than the last, so higher prices are required to justify higher output. In the long run, both inputs are variable, and the slope of the supply curve is shaped by returns to scale and by the behavior of input prices as the industry expands. A shift in the supply curve — as opposed to a movement along it — occurs when something other than price changes: input costs, technology, the prices of related goods in production, taxes or subsidies, or the number of producers in the market. Market-clearing requires that the quantity demanded at a price equal the quantity supplied at that price; supply's role in that equilibration is to set the schedule from which the market-clearing price is found jointly with demand.

scope_of_application:
Supply lives within microeconomics and the genuine price-mediated markets economists model; its reach is bounded to that substrate — a price signal, a distinct producer side, and a cost structure that fixes profitable output at each price — and "supply of X" applied to non-markets requires redescription before the schedule-and-slope machinery applies. (The bare "output scales with the incentive offered" lesson belongs to the parents `incentive_compatibility` / `gradient` / `scarcity` / `reserve`, not here.)

- **Microeconomic price theory** — the home turf; the Marshallian and Hicksian supply curve, the movement-along-versus-shift-of channel test, and the comparative-statics of perturbing position and slope to find the new market-clearing intersection with demand.
- **Production and cost theory** — the upward slope is read back to rising marginal cost against fixed capacity in the short run, and to returns to scale and input-price behaviour in the long run.
- **Labor markets** — workers' supply of hours rises with the wage, decomposable into the same channels (the labor-supply curve).
- **Wholesale energy markets** — generators bid quantities at each price, and high-cost gas peakers enter exactly when the spiked price exceeds their marginal cost.
- **Platform / gig markets with surge pricing** — more rideshare drivers come online as the offered rate climbs above their reservation cost, a genuine price-mediated producer response.
- **Spectrum and commons-access markets** — rights-holders supply access at varying lease rates, the same schedule of quantity offered against price.
- **Market-equilibrium and policy analysis** — pairing supply with demand reduces allocation to a single market-clearing intersection, from which the incidence of taxes, subsidies, and cost shocks is read.

structural_signature:
**Sig role-phrases:**

- **the producer side** — agents with control over a quantity decision, willing and able to bring output to market
- **the price signal** — the coordinating variable observable to producers, the curve's axis
- **the cost structure** — the marginal-cost schedule that fixes how much output is profitable at each price (rising against fixed capacity in the short run)
- **the supply schedule** — the conditional mapping from each price to a quantity offered, the curve that *is* the object of analysis (not any single point)
- **the upward slope** — higher prices drawing in more output and higher-cost producers as price exceeds their marginal cost, the entry/activation logic
- **the movement-along-vs-shift-of distinction** — the load-bearing channel test: only the good's own price walks output along the curve; everything else (input costs, technology, related-good prices, taxes, producer count) shifts the whole schedule
- **the comparative-statics perturbation** — position and slope perturbed independently of price, the new demand-intersection read off the diagram to predict equilibrium price and quantity
- **the horizon dependence** — short-run slope from fixed capital and diminishing marginal productivity, long-run shape from returns to scale and input-price behavior
- **the price-and-producer substrate limit** — the schedule-and-slope machinery requires a price-mediated market with a producer side and a cost structure; non-market "supply of X" needs redescription before it applies

what_it_is_not:
- **Not a quantity or a stock.** Economic supply is a *schedule* — a conditional mapping from each price to a quantity producers would bring forth — not the number of units on hand. "The supply of oil" as a barrel count is the lay sense; the analyzable object is the whole curve. The same producer offering one unit at $5 and ten at $50 is not inconsistent — both points lie on one schedule.
- **Not "more was supplied because the price rose" being the same as "supply increased."** A movement *along* the curve (the good's own price changed) and a *shift of* the curve (costs, technology, producer count, taxes changed) are different events with different market consequences, even when the immediate quantity change looks identical. Only a shift changes quantity at *every* price; collapsing the two is the ambiguity the schedule view exists to forbid.
- **Not upward-sloping because of scarcity.** The slope comes from *rising marginal cost* of expanding output — in the short run, more variable inputs against fixed capital under diminishing marginal productivity. It is a checkable cost cause, not a vague "scarcity pushes prices up"; a flat curve reflects ample capacity or constant costs, not an absence of scarcity.
- **Not applicable to any "supply of X" without redescription.** The schedule-and-slope machinery presupposes a price as coordinating signal, a distinct producer side, and a cost structure fixing profitable output at each price. The "supply" of attention, ideas, or volunteer effort lacks one or more of these, so applying the curve requires re-describing the domain *as if* it had a price and a marginal cost — and that redescription, not the machinery, does the work. Where the stand-in is forced, the usage is metaphor.
- **Not a standalone prime or the general incentive pattern.** What travels cross-domain is the parent family — `incentive_compatibility` (output responds to the incentive offered), `gradient` + `trade_offs` (optimizing along a cost gradient), `scarcity`, `reserve` — not "supply," whose apparatus is specific to price-mediated allocation with a producer side. By the same logic the catalogue ships no standalone "demand" either; if price-mediated allocation needs a representative, it is something like `market_clearing`, not supply as a free-standing piece.

clarity:
Treating supply as a *schedule* rather than a *quantity* is the move that makes producer behavior analyzable at all. In ordinary speech "the supply of oil" names a number of barrels; the economist's supply is instead a conditional mapping — how much producers would bring forth at each possible price — and insisting on the whole curve dissolves a confusion that would otherwise make every price change look like a contradiction. The same producer offering one unit at $5 and ten at $50 is not behaving inconsistently; both points lie on one schedule. This reframing turns the practitioner's question from "how much is supplied?" into "what is the shape of the price-to-quantity relation, and what is its slope?" — and it locates the upward slope in a specific, checkable cause, the rising marginal cost of expanding output against fixed capacity, rather than in any vague notion that scarcity pushes prices up.

The schedule view buys its sharpest distinction: *movement along* the supply curve versus a *shift of* the whole curve. Without it, a rise in the quantity supplied is ambiguous — it could mean producers responded to a higher price, or that costs fell, or technology improved, and these have opposite implications for where the market is heading. Naming the difference forces the analyst to ask which price-irrelevant variable moved (input costs, technology, the number of producers, a tax) before reading any consequence off the diagram, and it is exactly this discipline that makes comparative statics possible: one can predict how equilibrium price and quantity respond to a cost shock only because supply is held as a function whose position and slope can be perturbed independently of price. The clarity is in refusing to collapse "the price went up so more was supplied" and "more was supplied at every price" into one statement, since the market consequences of the two are not the same.

manages_complexity: The sprawl supply tames is the indefinite list of forces that change how much producers bring to market — a wage rise, a fuel-cost spike, a new technology, a poor harvest, a tax, a competitor's entry or exit, and the price itself — each of which, taken on its own terms, seems to demand a separate account of where output is heading. The supply curve collapses that list into a single object with two summary parameters: the *position* of the schedule and its *slope*. Every force is then sorted into one of two channels — it either moves the producer along a fixed curve (only the good's own price does this) or shifts the whole curve sideways (everything else: input costs, technology, related-good prices, taxes, the number of producers) — and once sorted, its consequence for equilibrium is read off the diagram rather than re-argued. The analyst therefore tracks not the heterogeneous causes but where the curve sits and how steeply it rises, plus which of the two channels a given shock acts through; from those, the qualitative outcome of any disturbance follows by the comparative-statics branch structure. A rise in the good's price walks output up an unmoved schedule; a cost shock or technology gain slides the schedule and so changes quantity at *every* price, which is a different prediction with different market consequences even when the immediate change in quantity looks identical. The slope itself carries a checkable cause — rising marginal cost against fixed capacity in the short run — so the analyst need not posit a fresh behavioural story for why output resists expansion; the schedule's steepness already encodes it, and pairing the curve with demand reduces the entire allocation problem to one intersection where price clears the market. The high-dimensional question "what will all these forces do to how much gets produced and at what price" compresses to "did the good's own price move (slide along) or did something else (shift the curve), and given the schedule's position and slope, where does the new intersection with demand fall" — a two-parameter object plus a binary channel test in place of a case-by-case ledger of every supply disturbance.

abstract_reasoning:
Supply licenses a family of producer-side inferences, all turning on treating supply as a *schedule* with a position and a slope, and on the binary test of whether a disturbance moves the producer *along* the curve or *shifts* it.

*Boundary-drawing (the channel test: movement-along versus shift-of).* The foundational move, prior to any prediction, is to classify a disturbance by its channel: only the good's own price walks output along a fixed schedule; everything else — input costs, technology, related-good prices, taxes, the number of producers — shifts the whole curve, changing quantity at *every* price. The analyst reasons FROM "what changed?" TO which channel is operating, and that classification is load-bearing because the two have different market consequences even when the immediate change in quantity looks identical. Reasoning runs FROM "a cost shock, not a price move" TO "the schedule shifted, so the new quantity holds across all prices," forbidding the conflation of "price rose so more was supplied" with "more was supplied at every price."

*Predictive / comparative-static (perturb the schedule, read the new intersection).* Treating position and slope as the two parameters that summarize all producer behavior, the framework predicts the equilibrium response to any shock by perturbing the curve and locating its new intersection with demand. The analyst reasons FROM "input costs rose" TO "the supply schedule shifts leftward" TO "the demand intersection moves to a higher price and lower quantity"; FROM "technology improved" TO "the schedule shifts rightward, lowering price and raising quantity." This is possible only because supply is held as a function whose position and slope can be perturbed independently of price — so the prediction of how equilibrium price and quantity respond to a cost or technology shock is read off the diagram rather than re-argued from each producer's problem.

*Diagnostic (read the slope back to its cost cause; read entry/exit off price-versus-cost).* The upward slope carries a checkable cause, so the framework licenses inferring producer cost structure from observed responsiveness: a steep supply curve is diagnosed as rising marginal cost against fixed capacity in the short run, a flat one as ample capacity or constant costs. At the level of individual production units, the analyst reasons FROM "price has risen above this producer's marginal cost" TO "this unit now enters / this output level now becomes worth producing," predicting which higher-cost producers are drawn in and which marginal units come online as price climbs — the inference that explains why uneconomic capacity activates exactly when price exceeds its marginal cost.

*Boundary-drawing (horizon and the substrate edge).* The slope's interpretation depends on horizon: in the short run the analyst reasons FROM fixed capital and diminishing marginal productivity TO a rising marginal-cost slope; in the long run, with all inputs variable, FROM returns to scale and the behavior of input prices as the industry expands TO the curve's shape. The same price-and-cost machinery marks the concept's edge: the schedule-and-slope reasoning requires a price-mediated market with a distinct producer side and a cost structure that fixes profitable output at each price, so domains where "supply" is invoked without those — attention, ideas, volunteer effort — require redescription before the machinery applies, and the bare cross-domain lesson "how much of X is brought forth depends on the incentive offered" travels while the cost-of-production slope and the comparative-statics apparatus do not.

knowledge_transfer:
Within the home domain — microeconomics and the markets economists model — supply transfers *as full mechanism*. The schedule-not-quantity reframing, the movement-along-versus-shift-of channel test, the comparative-statics perturbation of position and slope, the rising-marginal-cost reading of the slope, and the entry/exit-at-price-exceeds-marginal-cost logic all port intact across the Marshallian and Hicksian treatments, the short-run/long-run distinction, and Le Chatelier-style adjustment analysis. The transfer extends across genuine markets that have a price signal, a producer side, and a cost structure: labor markets (workers' hours rise with the wage, decomposable into the same channels), wholesale energy markets (generators bidding quantities at each price; gas peakers entering when the spiked price exceeds their marginal cost), rideshare surge pricing (more drivers online as the offered rate climbs above their reservation cost), and spectrum or commons access supplied by rights-holders at varying lease rates. These are not analogies — they are real price-mediated markets, so the cost-of-production slope and the comparative-statics apparatus apply in full. The transfer is mechanistic because the load-bearing content (the price-to-quantity schedule, the cost structure fixing profitable output, the position-and-slope summary) travels with the vocabulary.

Beyond price-mediated markets the honest report turns on a subtle but decisive point: the supply construct *presupposes its own substrate*, and where that substrate is absent the apparent transfer is doing its work by quiet *redescription*, not by the machinery traveling. The schedule-and-slope reasoning requires a price as the coordinating signal, a distinct producer side, and a cost structure that fixes profitable output at each price; many domains the word "supply" reaches for — the "supply" of attention, of ideas, of volunteer effort — lack one or more of these, so to apply the supply curve one must first re-describe the domain *as if* it had a price, a producer, and a marginal cost, and that re-description is what carries the analysis. Where the re-description is forced (symbolic or social "compensation" standing in for price, with no real cost-of-production slope), the usage is metaphor: it borrows the upward-sloping-schedule picture while lacking the cost mechanism that gives the slope its meaning and the comparative statics their predictive force.

What genuinely travels cross-domain is not supply but the more general primes it instantiates, each of which already covers one of supply's structural moves without presupposing a market: `incentive_compatibility` ("how much of X is brought forth depends on the incentive offered" — behavior responds to incentives); `gradient` together with `trade_offs` (the producer optimizing along a marginal-cost gradient); `scarcity` (limited inputs capping the quantity that can be brought forth); and `reserve` (a buffer of capacity beyond current use). So the correct cross-domain lesson carries those parents — "effort/output scales with the incentive, optimized along a cost gradient against scarce inputs" — not "supply," whose schedule-and-slope apparatus is specific to price-mediated allocation with a producer side. (The same logic, the seed notes, argues against shipping a separate "demand" abstraction; if price-mediated allocation needs a structural representative at all, it would be something like `price_as_coordinating_signal` or `market_clearing`, not supply and demand as standalone pieces.) Within economics the mechanism transfers in full across every genuine market; past the price-and-producer substrate only the general incentive/gradient/scarcity parents travel, and "supply of X" applied to non-markets is redescription-driven analogy (see *Structural Core vs. Domain Accent*).


Supporting Effort

one_liner: Tag a concurrent military activity as subordinate to the main effort so that, under scarcity, support and command attention flow to the decisive operation first and the supporting activity yields by design rather than neglect.

origin_subdomain: doctrine

core_idea:
A supporting effort is any military activity whose purpose is to create conditions, fix or distract the enemy, secure flanks, or sustain logistics so that the *main effort* — the operation whose success is decisive for the commander's intent — can succeed. The doctrinal distinction is not merely descriptive; it carries an explicit resource-flow rule: when scarcity forces a choice, priority of support, risk acceptance, and command attention flow to the main effort first, with supporting efforts deliberately under-resourced relative to their nominal importance because their value is realized only through the main effort's success.

US and NATO doctrine (JP 3-0, FM 3-0) formalise this as a tripartite classification — decisive, shaping, and sustaining operations — but the *supporting effort* label applies broadly to any concurrent activity whose decisive value is indirect and mediated. The structural commitment is asymmetric: the main effort is the load-bearing element; supporting efforts are expendable if the main effort's needs demand their resources or their re-tasking. This produces the characteristic failure mode: supporting efforts that continue on plan after their main effort has been cancelled, redirected, or succeeded — work that persists without any decisive outcome it still enables.

scope_of_application:
Supporting effort lives within military doctrine and operational planning, enumerated here by the kind of supporting activity and the echelon at which the main/supporting ordering is drawn; its reach is within that domain. The genuinely portable decision-rule it encodes (name the decisive outcome and feed it first) travels to other organizations through the parent primes `prioritisation` + `dependency`, not through the doctrinal label, whose enemy-facing and risk-acceptance content stays home.

- **Operational-art campaign planning** — the home: designating a main effort against the commander's intent and arraying concurrent operations as supporting efforts, the move that makes the priority-of-support ration decidable.
- **Decisive-shaping-sustaining doctrine** — the formal JP 3-0 / FM 3-0 tripartite classification of operations, the codified form of the main-versus-supporting ordering.
- **Deception and economy-of-force operations** — feints, demonstrations, and fixing or distracting actions whose decisive value is realized only through the main effort (Fortitude relative to the Normandy main effort).
- **Fire support and flank security** — naval and artillery suppression and flank-guard missions tasked to create the conditions the decisive operation requires.
- **Combat-service-support and sustainment planning** — logistics, obstacle reduction, and rear-area sustainment as supporting efforts deliberately under-resourced relative to nominal importance, with priority of support flowing to the main effort.
- **Reserve and re-tasking employment** — the doctrinal habitat where supporting efforts are redirected toward a faltering main effort rather than allowed to compete with it (the canonical Omaha re-tasking).

structural_signature:
**Sig role-phrases:**

- **the decisive outcome** — the commander's intent that defines what counts as winning, the single anchor against which everything is ranked
- **the main effort** — the operation whose success *directly* produces the decisive outcome; the load-bearing element
- **the supporting efforts** — concurrent activities (deception, fixing/distracting the enemy, flank security, fire support, logistics) whose value reaches the outcome only *through* the main effort
- **the relation-to-intent test** — the classifier tagging each activity main or supporting by whether its success directly or only indirectly produces the decisive outcome
- **the priority-flow rule** — under scarcity, sustainment, fires, reserves, lift, and command attention flow to the main effort *first*; supporting efforts yield by design, not neglect
- **the importance/claim split** — an activity's nominal importance held distinct from its claim on resources, so two essential operations still have a settled yield order
- **the re-tasking option** — supporting efforts redirected toward a main effort whose needs have grown, rather than allowed to compete with it
- **the loss-of-purpose failure** — a supporting effort running on its original timeline after its main effort was cancelled, redirected, or already succeeded — work enabling nothing
- **the per-effort state** — each supporting effort decidable as keep / redirect / accept-as-risk against the single test "does this still terminate in some decisive outcome?"

what_it_is_not:
- **Not an unimportant or optional task.** A supporting effort can be wholly essential to the plan; what defines it is that its value reaches the decisive outcome only *through* the main effort, so under scarcity it yields by design, not because it is dispensable. The label settles who gives way, not whether the work matters.
- **Not a fixed attribute of an activity.** Whether something is "main" or "supporting" is keyed to which outcome the commander has named decisive. Rename the decisive outcome and the labels flip — the same operation can be a main effort under one intent and a supporting effort under another. It is a labelling overlay tied to a value judgment, not an intrinsic property of the activity.
- **Not merely a dependency.** A dependency states "B requires A" symmetrically as a fact about ordering; a supporting effort adds a *priority direction* — A serves B — so when resources are rationed, A yields to B rather than the relationship being neutral. The enabling relation alone is not the concept; the resource-flow asymmetry is.
- **Not scaffolding.** Scaffolding is temporary support during a build-up that is removed once the structure stands; a supporting effort runs *concurrent* with the main effort and persists through it. The two differ in timing and in whether the support is meant to fall away on completion.
- **Not a parallel objective.** An activity that does not create conditions for, fix or distract the enemy on behalf of, secure flanks for, or sustain the main effort is not a supporting effort at all — it is a separate objective, deferred work, or a sunk commitment. The category requires an enabling relation to the decisive operation, not mere concurrency with it.
- **Not entitled to resources by its importance.** The framework deliberately holds an activity's nominal importance distinct from its claim on resources. Two operations may both be essential, yet the main/supporting tags settle the yield order in advance — so reasoning that "this is important, therefore it should be resourced first" is exactly the entitlement the distinction is built to override.

clarity:
Naming an activity a *supporting effort* makes one thing legible that "important task" or "necessary operation" hides: priority is non-fungible under scarcity. Two operations can both be essential to the plan, yet the label settles in advance who yields when sustainment, fires, reserves, or command attention must be rationed — the supporting effort gives way to the main effort, by design, not by neglect. This dissolves a recurring planning confusion in which every line of operation argues for resources on the grounds that it is needed, leaving the commander with no principled cut. Once efforts are tagged main and supporting, "is this needed?" stops being the operative question; the sharper one is "does this create the conditions the *decisive* operation requires, and if the main effort's needs grow, can this be re-tasked or accepted as risk?"

The label also makes a specific failure mode visible and nameable: the supporting effort that has lost its main effort — a deception, a fix, a flank guard, or a logistics push that runs on the original timeline after the operation it was meant to enable has been cancelled, has shifted its decisive point, or has already succeeded. Without the main-versus-supporting distinction, such activity reads as ongoing work in good order; with it, the practitioner can ask whether each supporting effort still terminates in some decisive outcome, and prune or redirect the ones whose purpose has expired. The distinction thus separates an operation's nominal importance from its claim on resources, and ties every concurrent activity back to the commander's intent as the single test of what the whole effort is for.

manages_complexity:
A campaign in motion is a sprawl of concurrent activity — landings, deception schemes, flank guards, fire-support missions, logistics pushes, reserves, civil-affairs tasks, each with its own commander arguing for sustainment, fires, lift, and command attention, each able to claim it is *needed*. Read that sprawl on its own terms and the planner faces a flat list of essential tasks with no principled way to ration scarce support among them; every line of operation looks load-bearing, so any cut looks like negligence and the contest for resources is settled by advocacy or proximity rather than design. The main-versus-supporting distinction collapses that high-dimensional allocation problem onto a single ordering keyed to one parameter: each activity's relation to the commander's intent — does its success *directly* produce the decisive outcome (main), or does its value reach the outcome only *through* the main effort (supporting)? Once every concurrent activity carries that tag, the planner no longer re-derives priority task by task. The flow rule reads straight off the labels — support, risk acceptance, and attention go to the main effort first; supporting efforts yield, in order of their marginal contribution to the main effort, by design rather than neglect — so the whole ration-under-scarcity question reduces to a contest the labels have already decided.

The same parameter governs the branch structure the analyst then tracks over time. Holding "nominal importance" distinct from "claim on resources" turns a continuously contested portfolio into a small set of decidable states per supporting effort: still creating the conditions the decisive operation requires (keep on plan), re-taskable toward a main effort whose needs have grown (redirect), or acceptable as risk if those resources are demanded elsewhere (cut). It also makes one failure mode that is otherwise invisible jump out — the supporting effort whose main effort has been cancelled, has shifted its decisive point, or has already succeeded, yet which runs on its original timeline looking like work in good order. Tracking each supporting effort against the single test "does this still terminate in some decisive outcome?" lets the practitioner read off, without re-litigating the whole plan, which activities to prune or redirect because their enabling purpose has expired. A field of competing essential tasks becomes a binary ranking plus a flow rule plus a short list of per-effort states, all driven by relation-to-intent — the move from an unrankable activity sprawl to a low-parameter ordering whose allocation and pruning decisions follow.

abstract_reasoning:
The main-versus-supporting distinction licenses a set of inferential moves within military planning, all keyed to one test — each activity's relation to the commander's intent — from which priority, risk, and pruning decisions are read.

*Diagnostic — classify each activity by its path to the decisive outcome, and detect the effort that has lost its purpose.* The signature move is relation-to-intent classification: for every concurrent activity the planner asks whether its success *directly* produces the decisive outcome (main effort) or whether its value reaches that outcome only *through* the main effort (supporting effort), and tags it accordingly. From the tag, the planner infers the activity's standing under scarcity before any resource contest is run, because the label encodes who yields. The crucial diagnostic that the distinction makes possible is detecting a *supporting effort that has lost its main effort* — a deception, a fix, a flank guard, or a logistics push that runs on its original timeline after the operation it was meant to enable has been cancelled, has shifted its decisive point, or has already succeeded. Without the distinction such activity reads as work in good order; with it, each supporting effort is tested against "does this still terminate in some decisive outcome?", and one that does not is diagnosed as expired purpose rather than ongoing progress. The distinction also diagnoses a recurring planning pathology: when every line of operation argues for resources on the grounds that it is *needed*, the flat field of "essential tasks" is read as undecidable until the main/supporting tags impose an ordering — so "is this needed?" is recognised as the wrong question and "does this enable the decisive operation?" substituted.

*Interventionist — flow priority to the main effort, and choose keep, redirect, or accept-as-risk for each supporting effort.* Because the labels encode priority, the concept dictates the allocation lever directly: when sustainment, fires, reserves, lift, or command attention must be rationed, support flows to the main effort *first*, and supporting efforts yield in order of their marginal contribution to the main effort — by design, not by neglect. The predicted consequence is that supporting efforts are deliberately under-resourced relative to their nominal importance, since their value is realised only through the main effort's success. For each supporting effort over time, the concept defines a small set of decidable interventions read off its current state: keep it on plan while it still creates the conditions the decisive operation requires; redirect (re-task) it toward a main effort whose needs have grown; or accept it as risk and cut it if its resources are demanded elsewhere. The re-tasking option is itself a predicted move when the main effort stalls — supporting efforts are reallocated to it rather than allowed to compete with it — so the planner anticipates that a faltering main effort will pull resources from its supporting efforts rather than the reverse.

*Boundary-drawing — what is a supporting effort and what is not, and where the framework applies.* The distinction draws a sharp boundary around the *supporting effort* category itself: an activity qualifies only if it creates conditions for, removes obstacles to, fixes or distracts the enemy on behalf of, secures flanks for, or sustains the main effort. An activity that does none of these is bounded out — it is a parallel objective, deferred work, or a sunk commitment, not a supporting effort — so the framework forces the planner to name what each activity is actually for rather than assume everything concurrent is "support." A second boundary separates an activity's *nominal importance* from its *claim on resources*: two operations may both be essential, yet the label settles in advance who yields, so reasoning that treats importance as entitlement to resources is out of bounds. The framework also bounds its own domain of application: it presupposes a single decisive outcome (the commander's intent) against which everything else is ranked, so in an environment of multiple equally-weighted objectives with no decisive effort, the main/supporting ordering has no anchor and the reasoning does not bind.

*Predictive and order-of-events.* The concept supports forward prediction of how a plan will behave under stress: as the main effort's needs grow, the planner predicts which supporting efforts will be re-tasked or cut, in ascending order of their contribution to the main effort, before the cuts are made. It also predicts the characteristic failure trajectory — once a main effort is cancelled, redirected, or completed, its supporting efforts will, if untracked, continue on the original timeline producing work that enables nothing — so the planner anticipates the need to prune at exactly the moments the decisive point moves, rather than discovering orphaned effort after the fact.

knowledge_transfer:
Within military doctrine the concept transfers *as mechanism*, carrying its relation-to-intent classification, its resource-flow rule, and its loss-of-purpose failure mode intact across echelons and operation types. The same main-versus-supporting tagging, the same priority-to-the-main-effort-first allocation under scarcity, the same keep/redirect/accept-as-risk per-effort states, and the same detection of the orphaned supporting effort carry across the formal decisive-shaping-sustaining taxonomy of JP 3-0 and FM 3-0 and across the full range of supporting activities — deception, fixing or distracting the enemy, flank security, fire support, logistics sustainment, obstacle reduction. It is literal mechanism within this substrate because *commander's intent*, *main effort*, and the enemy-facing purposes (fix, distract, secure flanks) are the actual operative terms; the Normandy landings — Omaha/Utah as main effort, Fortitude deception, flank airborne drops, naval bombardment, and the Mulberry harbour as supporting efforts re-tasked when the main effort faltered — is the canonical worked case.

Beyond the military substrate the honest verdict is **a genuinely portable decision-rule that is carried by a higher prime, with the named military concept itself metaphorical**. What ports cleanly to other organizations is not a distinctive military mechanism but the *management heuristic* the doctrine encodes — *name the single decisive outcome and feed it first; rank every concurrent activity by its marginal contribution to that outcome; let the rest yield by design, not neglect* — and that heuristic is `prioritisation` applied to a `dependency` graph against one named decisive outcome. So it ports usefully to any organization running multiple concurrent activities under resource scarcity with a single decisive outcome: a software release as main effort with platform-team, vendor-coordination, and tooling work as supporting efforts; a surgical act with anaesthesia, sterilisation, and post-op nursing supporting; a shoot day with art-department pre-builds and location scouting supporting; a campaign's swing-state ground game with national fundraising backstopping. The cross-domain lesson should be carried by `prioritisation` + `dependency` (with a labelled priority direction), because that is what actually travels — and it ports *poorly* exactly where the precondition fails: in steady-state operations or portfolio management with multiple equally-weighted objectives and no decisive effort, the main/supporting ordering has no anchor and the reasoning does not bind. The home-bound cargo that does not survive extraction is the military-doctrinal content: the *decisive-shaping-sustaining* operational taxonomy, the *enemy-facing* purposes (fix, distract, secure flanks), and the doctrinal connotation of *risk acceptance* as a command act. When the term is used loosely off-substrate — "platform teams," "backstage labour," "sales-enablement" — its content thins to "some work creates conditions for other work," which is already what `dependency`, `scaffolding`, and `enabling constraint` carry, plus the prioritisation overlay; importing "supporting effort" there renames the enabling work and borrows the main-versus-supporting shape while shedding the doctrinal resource-flow apparatus, which is metaphor. A further structural subtlety reinforces the placement: whether an activity is "main" or "supporting" depends entirely on which outcome the commander has named decisive — rename the decisive outcome and the labels flip — so the pattern is a *labelling overlay on a dependency graph keyed to a value judgment*, not a mechanism with its own mechanics, which is precisely why the substrate-independent content belongs to `prioritisation` + `dependency` rather than to a standalone concept. This is the boundary drawn in *Structural Core vs. Domain Accent*: the prioritise-the-decisive-outcome-on-a-dependency-graph skeleton lifts to `prioritisation` + `dependency` and the *decision rule* genuinely ports across organizations with one decisive outcome; the military-doctrinal accent — decisive/shaping/sustaining, enemy-facing supporting purposes, command risk acceptance — stays home, and the named concept travels only by metaphor.