Iteration

slug: iteration

one_liner: Repeats steps to refine outcomes.

core_idea:
Iteration is the repeated application of a process or
step, with each application building on the results of the
previous one, in order to converge toward a result, refine
a candidate, or explore a space. The essential commitment
is not merely repetition but the *use* of what each
iteration produces: the output of step n becomes part of
the input to step n+1, and progress is measured across
iterations by a stated notion of improvement, convergence,
or coverage. Every iterative process specifies (1) a
single iteration step (what happens in one round), (2) the
state carried between iterations (what persists and is
updated), (3) the stopping condition (when to halt), and
(4) the notion of progress by which iterations are judged.

structural_signature:
- *The bounded-or-unbounded loop construct* <!-- FACT-D30-031 -->[^backus-1957]
- *The loop-invariant for correctness reasoning* <!-- FACT-D30-032 -->[^hoare-1969]
- *The variant function for termination proof* <!-- FACT-D30-033 -->[^floyd-1967-iter]
- *The index-or-condition-driven control flow* <!-- FACT-D30-034 -->[^knuth-1968]
- *The imperative-versus-functional iteration duality* <!-- FACT-D30-035 -->[^backus-1978-iter]
- *The tail-recursion as iteration-equivalent* <!-- FACT-D30-036 -->[^backus-1978-iter]

what_it_is_not:
- **Not recursion.** Recursion defines a solution in
  terms of itself applied to a smaller instance;
  iteration applies a step repeatedly while carrying
  state across applications. Many recursive
  definitions can be iteratively implemented and vice
  versa, but the conceptual commitment is different.
  See `recursion` for the paired distinction.
- **Not pure repetition.** Repeating a step with
  independent inputs each time (e.g., running a test
  suite on identical inputs) is not iteration unless
  later runs build on earlier ones.
- **Not feedback.** Feedback routes a measured output
  back to the input in a continuous or cyclic
  dynamic; iteration is a discrete sequence of
  applications with explicit state handoff. Feedback
  mechanisms often underlie iterative processes, but
  iteration as a concept is about the sequential
  structure. See `feedback` for the paired
  distinction.
- **Not convergence.** Convergence is a possible
  property of an iterative process, not a defining
  one. Divergent iteration is still iteration, and
  non-convergent methods (search, enumeration) can be
  designed deliberately.
- **Not algorithm.** An algorithm is a procedure for
  computing a result; iteration is one structural
  pattern an algorithm can use. Many algorithms are
  iterative; many iterative processes (e.g.,
  experimental cycles) are not algorithms in the
  formal sense. See `algorithm`.
- **Common misclassification.** Calling something
  iterative because it is repeated without the
  state-carrying, progress-measuring structure — a
  sequence of unrelated attempts is not iteration.
  The "use the last result" is the defining feature.

broad_use:
- **Mathematics**
    - Iterative methods for equations (Newton,
      fixed-point iteration, power iteration);
      successive approximation; iterated maps and
      dynamical systems.
- **Computer science**
    - Loops; iterative algorithms (gradient descent,
      EM, expectation propagation); iterative
      deepening search; iterative refinement in
      numerical linear algebra.
- **Engineering and product design**
    - Prototype-test-refine cycles; agile development;
      lean startup build-measure-learn loops;
      design sprints.
- **Science**
    - Experimental cycles (hypothesize-test-revise);
      model refinement; iterative instrument
      calibration.
- **Education and skill acquisition**
    - Practice loops; deliberate practice with feedback;
      formative assessment cycles; spaced repetition.
- **Organizational change**
    - Continuous improvement (PDCA); retrospectives
      and adjustments; policy pilots with iteration.

clarity:
Iteration clarifies by making the use-of-prior-result
explicit. Claims like "we improved the design" resolve
into "we ran N iterations; the progress measure moved
from X to Y; we stopped at this criterion." This forces
three commitments often left implicit: what counts as
progress, what is carried between iterations, and when
to stop. The clarifying force is to convert "refine
until good" into a disciplined loop whose design can
be examined, optimized, and audited.

manages_complexity:
- Replaces solving a problem in one shot with a
  sequence of small improvements, each of which is
  analytically or practically easier than the whole.
- Enables convergence analysis: rate of progress,
  stability, and termination behavior can be reasoned
  about before execution in many mathematical
  settings.
- Supports anytime behavior: the current iterate is a
  usable (if imperfect) result, and the process can
  be stopped early with a known-quality output.
- Licenses incremental learning under budget:
  iterations allow matching effort to required
  quality, investing more iterations where the
  progress measure indicates continued return.
- Surfaces structure: how quickly iteration
  converges, and where it stalls, reveals the
  landscape of the problem (conditioning, local
  structure, dimensionality).

abstract_reasoning:
Iteration trains a reasoner to ask:

- What is one iteration, concretely — inputs, step,
  outputs, state update?
- What state persists between iterations, and how is
  it updated? Is any useful information being
  discarded that should be carried forward?
- What is the progress measure, and does the process
  actually move along it at every step (or on
  average)?
- What is the stopping condition, and is it tied to
  progress (convergence tolerance) or external
  (iteration count, time budget)?
- What happens when progress stalls — is there a
  detection mechanism, and an alternative strategy
  (restart, step-size adjustment, change of method)?
- Is convergence expected, and if so to what — a
  fixed point, a distribution, an equilibrium,
  something else?

knowledge_transfer:
Role mappings across domains:

- **Iteration step** ↔ loop body / update rule /
  prototype pass / experimental trial / learning
  episode
- **Carried state** ↔ current estimate / working
  prototype / current model / current best
  candidate / learning-to-date
- **Progress measure** ↔ residual / loss / fit metric
  / user-feedback score / KPI / skill level
- **Stopping condition** ↔ convergence tolerance /
  iteration count / quality threshold / budget
  exhausted / "ship it"
- **Convergence** ↔ stabilization / plateau /
  fixed-point reached / learning curve flattening
- **Step size / learning rate** ↔ aggressiveness of
  change / prototype fidelity / experimental
  increment
- **Restart / perturbation** ↔ fresh prototype / model
  reset / strategy change / mindset reset
- **Anytime result** ↔ current best version /
  ship-quality current artifact / preliminary answer

A numerical analyst running Newton's method, a product
team running agile sprints, and a student practicing a
musical passage are all doing the same structural work:
apply a step that uses the current state, update the
state, measure progress toward a stated goal, and stop
when progress has met a criterion. The same diagnostic
— "what is the state update, what is the progress
measure, and what is the stopping rule?" — applies
across all three, with the same failure modes (no
measurable progress, bad step size, wrong stopping
rule) in each.

formal_abstract:
Backus 1957 introduced FORTRAN with the DO loop as a canonical iteration construct. Newton's method for finding roots of f(x) = 0 exemplifies mathematical iteration: the step is `x_{n+1} = x_n - f(x_n)/f'(x_n)`, the carried state is the current estimate x_n, the progress measure is `|f(x_n)|` or `|x_{n+1} - x_n|`, the stopping condition is when progress measure falls below tolerance, and under favorable conditions (near a simple root, well-behaved derivative) convergence is quadratic. Hoare 1969 developed axiomatic semantics to reason formally about loop correctness and termination using loop invariants and variant functions<!-- FACT-D30-037 -->[^hoare-1969]. Every element of the structural signature is present in textbook form— bounded iterations with explicit stopping, carried state, and measurable progress<!-- FACT-D30-038 -->[^knuth-1968].

**Mapped back:** Mathematical iteration codifies the structure: repeatable step, carried state, progress measure, stopping condition, and convergence behavior. Loop invariants and variant functions become the formal language for reasoning about correctness and termination.

applied_industry:
A startup iterating on product-market fit. Iteration step: build a version, release to a cohort, measure response, decide the next change. Carried state: the current product hypothesis, the team's learning, the customer cohort. Progress measure: activation rate, retention curve, or a composite PMF proxy. Stopping condition: metric exceeds threshold, runway exhausts, or strategic pivot. Convergence behavior: typically not quadratic; often sluggish, sometimes punctuated by step-change learnings. Dijkstra 1976 and Manna-Pnueli 1992 showed how the same formal machinery applies to iterative processes in diverse domains. The structural kinship with Newton's method is precise— same diagnostic questions (what is the state update, what is the progress measure, what is the stopping rule), same failure modes (no measurable progress, bad step size, wrong stopping rule)— even though the substrate is product development rather than function-zero finding<!-- FACT-D30-039 -->[^dijkstra-1976-iter].

**Mapped back:** Iteration as control structure abstracts away from computation into any domain where a state is improved across repeated applications, revealing the universal structure of refinement cycles.

title: Progress vs Plateau

body:
Iteration depends on a progress measure actually moving. Iterations that plateau (stuck at local optimum, product at local maximum, skill at plateau) continue expending effort without return. Detection of the plateau is itself a design problem. A common failure is continuing to iterate past the point of useful progress because the measure is miscalibrated or no stopping criterion was ever defined<!-- FACT-D30-040 -->[^liskov-guttag-2000].

- **T2: Step Size and Stability.**
  Small steps give stable, slow progress; large steps are fast but risk overshoot, oscillation, or divergence. The right step size depends on local curvature, problem scale, and noise tolerance. The trade-off appears as "aggressive vs conservative changes" in product and learning contexts. A common failure is taking steps too large (redesigning the whole product each sprint) and never stabilizing, or too small (micro-tweaks) and running out of budget<!-- FACT-D30-041 -->[^cormen-2009-iter].

- **T3: Iterating the Wrong Thing.**
  Iteration improves the carried state along the progress measure. If the state does not capture relevant dimensions (missing variables, wrong representation) or the progress measure is a proxy for something else, successful iteration produces the wrong outcome efficiently. A common failure is polishing a feature while the underlying value proposition is wrong, or optimizing a method on a badly-posed problem formulation<!-- FACT-D30-042 -->[^liskov-guttag-2000].

- **T4: Stopping Discipline.**
  Iteration naturally runs as long as budget allows. Without a disciplined stopping rule tied to progress or external deadline, it consumes more than warranted. But stopping too early forfeits realized value another iteration would deliver. A common failure is perfectionist iteration beyond point where additional cycles are worth more than alternative uses of effort, or premature stopping driven by fatigue rather than calibrated criterion<!-- FACT-D30-043 -->[^dijkstra-1976-iter].

- **T5: State Representation and Observability.**
  The carried state must capture all information relevant to progress toward the goal. If important state is discarded between iterations or never observed, progress becomes invisible and the stopping criterion becomes arbitrary. A common failure is designing iteration around a state variable that is convenient to compute but not the one that actually matters for success<!-- FACT-D30-044 -->[^wirth-1976-iter].

- **T6: Tail Recursion vs Explicit Loop.**
  Any tail-recursive definition can be iteratively implemented with explicit state management. Conversely, most loops can be expressed as tail recursion. The choice between them affects readability, stack usage, and compiler optimization. A common failure is choosing the syntactically convenient form while ignoring the computational costs (unbounded call stacks, memory overhead)<!-- FACT-D30-045 -->[^aho-hopcroft-ullman-1974-iter].

prose:
_(Pass B — deferred)_

### Catalog cross-references (v1, batches 02–29)

Solution archetypes that reference this prime (per `solution_archetypes/_catalog/catalog_v1_batches_02_29.yaml`):

**As source prime (5):** `cadence_design`, `convergence_guidance`, `iterative_refinement_loop`, `progressive_fidelity_increase`, `termination_condition_design`

**As related prime (5):** `chaos_exposure_testing`, `cycle_breaking`, `layered_model_validation`, `resonance_tuning`, `titrated_intervention`

notes: Iteration is the most concrete and widely-used control structure in programming and practical problem-solving. It bridges formal computation (loop invariants, variant functions) and everyday refinement (product cycles, learning spirals). The relationship between recursion and iteration is deep: every recursion can be implemented iteratively with explicit stacks, and most loops can be written as tail recursion. The interplay between the two defines much of algorithm design and programming language semantics.


Jevons Paradox

type: prime

one_liner: Improving the efficiency with which a resource is used lowers the effective price of its output, expands demand, and can raise total resource consumption rather than lower it.

status: draft

core_idea:
Jevons paradox is the structural pattern in which improving the *efficiency* with which a resource is used — more output per unit input — *lowers the effective price* of obtaining the output, *raises demand* for that output and for the input it requires, and so causes *total resource consumption to increase rather than decrease*. The pattern sits at the intersection of three structural facts: any efficiency gain that lowers the per-unit cost of using a resource is *price-equivalent* to a fall in that resource's price; price drops expand demand both by substitution toward the now-cheaper output and by opening new demand niches previously priced out; and when the demand expansion is *more than proportional* to the efficiency gain, total input use rises, the opposite of the naive prediction.<!-- FACT-D65-626 -->[^jevons-1865]

The structural commitments are precise. There is an *efficiency improvement* on a specific resource-using process. There is a *price channel* by which the improvement reduces the effective cost of the output produced from that resource. There is a *demand elasticity* — end-users respond to the lower effective price by consuming more output, substituting toward it, and inventing new uses. And there is an *aggregate recombination* — across all users and all new uses, total resource demand is summed and compared to the efficiency gain. When the rebound exceeds 100% the case is the Jevons paradox proper (super-rebound); sub-100% rebound is the more general Efficiency Rebound, the same mechanism operating at lower intensity.<!-- FACT-D65-627 -->[^khazzoom-1980]

The pattern's whole force is in the gap between the per-unit and the aggregate. The naive expectation — improve efficiency, save resources — is an inference from the micro level to the macro level that the structure shows to be invalid without an elasticity model. Efficiency does not merely reduce consumption per use; it changes the price landscape in which use is decided, and the aggregate response can swamp the per-unit saving.

structural_signature:
*the efficiency improvement on a resource-using process* — *the price channel translating the gain into a lower effective cost of output* — *the demand elasticity responding to that lower price* — *the latent-demand niches the price drop unlocks* — *the aggregate recombination summing use across all users and new uses* — *the per-unit-versus-aggregate gap that flips the sign*

The pattern holds when each of the following is present:

1. **An efficiency improvement.** A specific process is made to yield more output per unit of input — a genuine, measurable per-unit gain.

2. **A price channel.** The efficiency gain is price-equivalent: it lowers the effective cost of obtaining the output, whether that cost is money, time, attention, risk, or effort.

3. **A demand elasticity.** Users respond to the lower effective price by consuming more output and substituting toward it; the magnitude of this response is the load-bearing parameter.

4. **Latent-demand unlocking.** The price drop opens uses previously priced out — new users, new applications, new niches — a qualitative expansion, not merely a quantitative one.

5. **An aggregate recombination.** Total input use is summed across all existing and newly-feasible uses and compared to the efficiency gain.

6. **A per-unit-versus-aggregate sign condition.** When the demand expansion is more than proportional to the gain (rebound above 100%), total input use *rises* — the Jevons paradox proper; sub-100% rebound is the same mechanism at lower intensity.

These compose so that the inference from a micro per-unit saving to a macro reduction is invalid without an elasticity model: efficiency reshapes the price landscape in which use is decided, and the aggregate response can swamp the per-unit saving.

what_it_is_not:
- **Not the general `efficiency_rebound`.** Efficiency Rebound is the same mechanism at any intensity — efficiency lowers effective price, demand partly responds, and some saving can survive. Jevons paradox is the *super-rebound* special case where the response *exceeds* the gain (rebound above 100%) and total use rises; it is the sign-flip, not the whole rebound family.
- **Not `increasing_returns`.** Increasing returns is a supply-side dynamic where output per input rises with scale or adoption; Jevons is a demand-side dynamic where an efficiency gain lowers effective price and *induces* consumption. The two can co-occur but name different couplings.
- **Not `diminishing_returns`.** Diminishing returns describes falling marginal output as an input is added; Jevons describes total input use *rising* because efficiency unlocked demand — a different quantity moving in a different direction.
- **Not `price_elasticity` alone.** Elasticity is the demand-response parameter the prime depends on, but Jevons is the *full generator* — efficiency, effective-price drop, demand response, latent-niche unlock, aggregate recombination — not the elasticity in isolation.
- **Not `peltzman_effect` (risk compensation) per se.** Risk compensation is a behavioural cousin where a safety gain elicits offsetting risk-taking; it shares the rebound skeleton but is a narrower, behaviour-specific instance, whereas Jevons spans any "price" currency (money, time, attention).<!-- FACT-D65-628 -->[^peltzman-1975]
- **Common misclassification.** Inferring macro reduction from a micro efficiency gain by multiplying the per-unit saving by current usage as if quantity were fixed. The tell: has the demand response been modelled, or has the rebound simply been assumed away?

broad_use:
- **Energy and resources** — the origin: efficiency improvements in resource-driven power expand the applications and lower the price, raising total resource consumption rather than sparing reserves.<!-- FACT-D65-629 -->[^jevons-1865]
- **Transport and induced demand** — a wider road lowers the time-cost of travelling a corridor; demand expands until congestion returns; the substrate is travel-time rather than fuel, but the structure is identical.<!-- FACT-D65-630 -->[^downs-1962]
- **Computing** — each generation's fall in cost-per-operation expands use into previously infeasible applications, and total energy use rises despite enormous per-operation efficiency gains.<!-- FACT-D65-631 -->[^iea-data-centres]
- **Attention and information** — better filters reduce the cost per relevant item; users consume more total information; algorithmic efficiency in selecting content increases rather than decreases time spent.<!-- FACT-D65-632 -->[^simon-1971]
- **Water and food** — more efficient irrigation per unit yield is offset by expanded cropped area and thirstier crops; cheaper food expands total caloric consumption.<!-- FACT-D65-633 -->[^grafton-etal-2018]
- **Risk and safety** — safer equipment or routes can elicit riskier behaviour that consumes the safety gain, a behavioural cousin of the same rebound dynamic.

clarity:
Naming a phenomenon as Jevons paradox, rather than as "we expected savings and got increases," disciplines the analyst to specify four things: which *efficiency* — per what input, per what unit of output; which *price channel* — does the gain show up as a lower money price, time-cost, attention-cost, or risk-cost; which *elasticity* — how does demand respond to the effective price drop; and which *aggregation level* — micro, where this user consumes less input, versus macro, where the system consumes more. Each of these is a question the naive savings narrative never asks, and each can change the sign of the predicted result.

The clarity cost is sharp. A great many efficiency policies assume a *linear* relationship between efficiency and total use; the Jevons frame forces the analyst to *predict the rebound* before claiming the savings. This converts a confident assertion ("the efficient option will reduce consumption") into a conditional one ("it will reduce consumption only if the rebound is below 100%, which depends on the demand elasticity and on whether the price drop opens new niches"). The reframing is uncomfortable precisely because it withdraws a reassuring inference, but it is the inference the structure shows to be unwarranted, and the discipline of naming the four parameters is what prevents the error.

manages_complexity:
The pattern compresses a long list of "the efficiency improvement did not deliver the savings we expected" stories into a small structural schema and a small intervention catalogue. The schema is the three-step generator: efficiency raises output per input, which lowers the effective price of the output, which raises demand. The interventions all act on the same point — the price channel — and form a transferable family. *Couple the efficiency gain to a price floor or cap* so the price drop does not translate into demand growth. *Quota the input directly* with hard caps on the resource itself. *Set standards on the output* rather than on per-unit efficiency. *Model the new demand niches* rather than only the per-unit saving. *Time the policy* so the efficiency gain arrives together with the price-floor mechanism rather than before it.

This compression has real predictive economy. Rather than studying energy, transport, computing, water, and attention as separate domains each with its own surprising rebound, the analyst recognises one structure parameterised by domain-specific elasticities, and reaches the intervention catalogue directly. The catalogue's unifying insight — that the leverage point is the price channel, not the per-unit efficiency — tells the designer where to act in any instance: capping or pricing the input breaks the translation from efficiency to demand, while improving efficiency alone, however well-intentioned, leaves the rebound mechanism untouched and can make total consumption worse.

abstract_reasoning:
The pattern licenses inferences the linear-efficiency frame cannot reach. *Per-unit savings are not total savings*: the leap from a microeconomic efficiency gain to a macro reduction is invalid without an elasticity model, and the structure makes the missing premise explicit. *Efficiency unlocks latent demand*: the demand that was price-rationed before the gain becomes feasible after, which is a qualitative expansion — new uses, new users, new niches — not merely a quantitative one, so modelling only the existing uses understates the rebound. *The substrate need not be money*: time-cost, attention-cost, risk-cost, and effort-cost are all "prices" subject to the same rebound dynamic, which is why the pattern reaches far beyond energy economics into traffic, information, and behaviour.

Two further inferences follow. *Behavioural offsets are partial rebound*: risk compensation in safety, moral licensing in ethics, and similar effects share the structural skeleton — a gain in one currency is partly spent on more of the behaviour that the gain was meant to reduce. And *the right policy is composite*: efficiency plus a price-floor, not efficiency alone, because only the composite acts on the channel through which efficiency becomes demand. The reasoner who holds the structure in mind anticipates rebound as a default expectation rather than discovering it as a surprise, and asks for the elasticity before crediting any efficiency intervention with savings — which is the characteristic discipline a prime is meant to install.

knowledge_transfer:
The intervention catalogue and the analytic frame travel cleanly, and the transfers are structural predictions rather than analogies. *Energy economics into transport planning*: the prediction that road expansion induces demand rather than relieving congestion is a direct Jevons application, and the policy response — congestion pricing — is the price-floor remedy ported intact, since a toll restores the price the efficiency gain removed.<!-- FACT-D65-634 -->[^downs-1962] *Transport into computing*: scaling capacity for ambitious workloads induces more ambitious workloads that consume the capacity, the same rebound dynamic, so capacity expansion alone does not bound total resource use. *Resource economics into behavioural design*: a feature that makes a desired behaviour easier may cause offsetting expansion of that behaviour, requiring a structural cap rather than a per-instance efficiency. *Engineering efficiency into health behaviour*: a safer product encourages riskier use, requiring a usage cap rather than only safer hardware.

The transferable form is a single recipe: *predict the elasticity, model the rebound, add the structural cap, then measure total use rather than per-unit use.* That recipe carries the same intervention — cap the input directly, do not merely improve per-unit consumption — across at least eight substrates that share the generator (efficiency, effective-price drop, demand response, total use) while differing only in their parameters. The cross-domain reach is the load-bearing argument for the pattern's status: the same structural moves predict and explain rebound in energy, transport, computing, attention, water, food, and behaviour, with the *same* corrective lever in each. The pattern's economic-historical origin gives it a price-and-elasticity vocabulary that is core to the mechanism rather than incidental, which is why it reads as framed: the structure is genuinely portable, but stating it without the language of effective price and demand elasticity is awkward, so the vocabulary travels with it even as the structure recurs across substrates that are not markets.

formal_abstract:
Induced travel demand on a congested road is the cleanest case where the substrate is not energy but time, and it can be worked quantitatively. The *efficiency improvement* is widening a highway from two lanes to three on a congested corridor — a genuine per-trip gain in throughput.<!-- FACT-D65-635 -->[^duranton-turner-2011] The *price channel* is travel time: more lanes lower the time-cost of using the corridor at the margin. The *demand elasticity* is the load-bearing parameter: lower time-cost induces drivers who previously avoided the corridor (taking transit, travelling off-peak, or not travelling) to return to it. The *latent-demand niches* are qualitative — trips that were simply not worth making at the old congestion level become worth making at the new lower cost, a new-use expansion, not merely more of the existing trips. The *aggregate recombination* sums all trips, old and newly-induced; the *per-unit-versus-aggregate sign condition* is the paradox: when the induced demand is more than proportional to the capacity gain, total vehicle-miles rise and congestion returns to its prior level, the widened road now carrying more cars at the same speed.<!-- FACT-D65-636 -->[^duranton-turner-2011] The structure makes the missing premise explicit — the inference "more lanes, less congestion" is invalid without an elasticity model — and prescribes the corrective lever precisely: act on the price channel that the efficiency gain removed, by congestion pricing, which restores the time-or-money cost the extra lane eliminated.<!-- FACT-D65-637 -->[^vickrey-1963] Widening alone, however well-intentioned, leaves the rebound mechanism untouched.

**Mapped back:** The extra lane is the efficiency improvement, travel-time is the price channel, returning and induced drivers are the demand response, and congestion pricing is the structural cap on the channel — the Jevons paradox operating on time rather than fuel.

applied_industry:
Computing efficiency is the modern industrial instance, where the substrate is energy and the rebound has run for decades. The *efficiency improvement* is each generation's fall in energy-per-operation — chips delivering more computation per joule. The *price channel* is the effective cost of computation: cheaper operations lower the cost of any compute-driven product. The *demand elasticity* is enormous: as cost-per-operation falls, applications that were economically infeasible — large-scale model training, ubiquitous video streaming, always-on services — become viable and proliferate. The *latent-demand unlocking* is the qualitative engine: entire product categories that could not have existed at the old cost-per-operation appear, a new-use expansion that dwarfs the per-operation saving. The *aggregate recombination* sums energy across all uses old and new; the *sign condition* flips — despite per-operation efficiency gains of many orders of magnitude, total energy consumed by computing has *risen*, because the demand expansion outpaced the per-unit gain.<!-- FACT-D65-638 -->[^iea-data-centres] The intervention catalogue ports intact: capping the input directly (a hard energy budget or carbon price on data-centre power) acts on the channel, whereas merely improving chip efficiency leaves the rebound untouched and can worsen total use. The same structure governs efficient irrigation (per-unit water savings offset by expanded cropped area and thirstier crops, requiring a water quota rather than only better drip lines) and safety equipment (safer gear eliciting riskier behaviour that consumes the safety margin, requiring a usage cap rather than only better hardware).

**Mapped back:** The efficiency gain is more computation per joule, the price channel is cheaper compute, newly-feasible applications are the unlocked latent demand, and the energy budget or carbon price is the structural cap — total resource use rising despite per-unit efficiency, the canonical Jevons sign-flip, corrected only at the input channel.


Joint Attention

one_liner: Two or more agents share an attentional target and each knows the other attends to it.

core_idea:
Joint attention is the structural configuration in which two or more agents share an attentional pointer to *the same object or event at the same time*, and each agent knows that the other is also attending to it. The defining commitment is not merely that both look at the same thing — which could be coincidence — but that the *shared targeting* is itself mutually known: each agent's behavior signals "I see what you see; I see that you see; I see that you see that I see." This second-order awareness is what licenses everything downstream — efficient reference ("look there!"), pedagogy (teacher and student both attending to the same diagram), coordinated action (surgeon and assistant attending to the same vessel), and the bootstrapping of language acquisition.

The pattern is real across domains: any time coordinated action requires that agents not just be aimed at the same thing but *register* that they are co-aimed. The structural ingredients — multiple agents, a shared target, mutually visible orienting cues (gaze, gesture, deictic markers, cursor position), and the resulting common reference frame — recur whether the agents are toddler and parent, surgeon and nurse, designer and client, predator and pack, or human and AI. The load-bearing element is the mutual registration, which converts mere co-attention into a shared frame that both parties can build on.

The prime is, by its nature, bound to attending agents. Unlike substrate-neutral structural primes, joint attention presupposes parties that *have* attentional states and *can* register one another's — so its reach is confined to cognitive, social, and animal-cognition substrates. Within that range, however, the configuration recurs with the same structure and the same downstream consequences, which is what gives it cross-domain force despite its narrower substrate base.

structural_signature:
*the two or more attending agents* — *the shared attentional target* — *the orienting cues (gaze, gesture, deixis, cursor)* — *the mutual registration of co-attention* — *the common reference frame* — *the precondition-not-agreement boundary*

A configuration is joint attention when each of the following holds:

- **Two or more attending agents.** There are parties that *have* attentional states — the configuration presupposes attenders, which is what confines it to cognitive, social, and animal-cognition substrates.
- **A shared target.** The agents are oriented toward the same object or event at the same time. Mere parallel aiming is necessary but not sufficient.
- **Orienting cues.** Each agent's attention is made legible to the other through observable signals — gaze direction, pointing, deictic markers, cursor position, callouts — that broadcast where attention is aimed.
- **Mutual registration.** The load-bearing invariant: each agent knows the other is attending to the target, and knows that the other knows — second-order awareness ("I see that you see that I see"). This is what converts coincidental co-attention into joint attention; without it the configuration is absent however well the targets align.
- **A common reference frame.** As a consequence, a shared frame is established against which a minimal deictic signal ("this one," "look there") suffices in place of full absolute specification.
- **The precondition boundary.** Joint attention secures a shared *referent*, not a shared interpretation, valuation, or decision. It sits upstream of agreement — the precondition for productive disagreement, never a substitute for resolution.

Composed: attending agents made mutually aware of their co-orientation toward a shared target establish a common frame that collapses the cost of reference — a frame that is established or repaired by managing the orienting cues, and whose presence is distinct from any agreement built on top of it.

what_it_is_not:
- **Not `attention`.** Plain attention is one agent selectively orienting to a target; joint attention requires *two or more* agents plus the *mutual registration* that each knows the other attends.<!-- FACT-D65-651 -->[^tomasello-1995] Joint attention is not "more attention" but a second-order, multi-agent relation built on top of it.
- **Not `coordination`.** Coordination is agents aligning their *actions* toward a joint outcome; joint attention aligns their *attentional referents* and is upstream of action. A team can be jointly attending to a target and still fail to coordinate on what to do about it.
- **Not `common_knowledge`.** Common knowledge is the unbounded epistemic tower ("I know that you know that I know…" ad infinitum) about a proposition; joint attention is the concrete perceptual-behavioral achievement of mutually registered co-orientation, typically only second-order, and grounded in observable cues like gaze.<!-- FACT-D65-652 -->[^lewis-1969]
- **Not `common_ground`.** Common ground is the accumulated shared *background* of beliefs and prior references a conversation rests on; joint attention is the live, in-the-moment co-orientation to a *present* target. One is a standing store, the other a momentary frame.
- **Not `sensemaking`.** Sensemaking is the interpretive work of constructing *meaning* from a situation; joint attention only secures a shared *referent*, not a shared interpretation. Two agents can jointly attend to the same sentence and read it oppositely.
- **Not agreement.** Establishing a shared focus does not commit anyone to the same valuation or decision; joint attention is the *precondition* for productive disagreement, never a substitute for resolution.
- **Common misclassification.** Treating "both can see the shared display" as joint attention. Catch it by checking for the mutual-registration loop (gaze-checking, confirmation) — if there is no evidence each party knows the other is attending, the configuration is mere co-attention, not joint attention.

broad_use:
- **Developmental psychology and language acquisition**: the classic Tomasello finding that infants begin establishing joint attention with caregivers around nine months, which scaffolds word learning by letting the toddler map a new word onto the co-attended object.<!-- FACT-D65-653 -->[^tomasello-1995]
- **Teaching and pedagogy**: deixis ("look at this part of the diagram"), shared whiteboards, and classroom gaze management; instructors check that students' attention is on the target before explaining.
- **Teamwork and operating-room coordination**: surgical teams establish joint attention on the target tissue before acting; aviation crews use verify-and-confirm protocols.
- **Animal cognition**: gaze-following in primates, dogs, and corvids; pointing in domestic dogs; coordinated hunting in cetaceans and wolves.<!-- FACT-D65-654 -->[^call-tomasello-2008]
- **Human-computer interaction**: shared cursors, co-editing tools, presence indicators, and multiplayer ping systems — interfaces engineered to manufacture joint attention.<!-- FACT-D65-655 -->[^gutwin-greenberg-2002]
- **Design collaboration**: critique sessions that direct attention to the same artifact, where without joint attention the critique misfires.
- **Military and tactical coordination**: target designation, laser-painting, and deictic radio calls, where the target must enter the shared attention frame before action.<!-- FACT-D65-656 -->[^jp-3-09-1]
- **Conversation analysis**: gaze coordination, head movements, and back-channel signals that maintain joint attention on a topic, whose breakdown precedes misunderstanding.

clarity:
The prime clarifies that "we are both looking at it" is not the same as "we are attending together." The latter requires mutual awareness of co-attention, and that mutual awareness is what supports efficient reference, shared learning, and coordinated action. Confusion between the two is the source of many design failures — a screen everyone can see but no one's eye is tracked to, or a meeting where each person is in the same room but mentally elsewhere.

A second clarification, equally load-bearing, is that *shared focus is not agreement*. Joint attention establishes a common referent; it does not commit anyone to the same interpretation, valuation, or course of action. A teacher and student can be jointly attending to a sentence while reading it oppositely; a surgical team can be jointly attending to a vessel and disagree on whether to clip it. Joint attention is the *precondition* for productive disagreement, not a substitute for resolution. Keeping this distinction sharp prevents a characteristic error — treating the achievement of a shared referent as if it had already secured consensus — and locates joint attention precisely where it belongs, upstream of interpretation rather than in place of it.

manages_complexity:
Joint attention reduces the combinatorial complexity of multi-agent communication. Without a shared attentional frame, every reference must be specified absolutely — "the third object from the left, the small red one" — but with joint attention established, deixis works and "this one" suffices. Joint attention is the foundation on which efficient deictic language, gesture, and shared-cursor interfaces all economize, because it lets a tiny signal stand in for an elaborate description.

It also lets agents work with much smaller communication channels. A glance, a point, or a click carries the same content as paragraphs of description, *provided* the shared frame is in place. The complexity joint attention manages is the cost of reference in a multi-agent setting: in its absence, reference is expensive and error-prone, requiring full specification and risking mismatch; in its presence, reference collapses to a deictic gesture against the shared frame. The prime thus identifies the precondition that makes cheap reference possible and, by naming it, makes its presence or absence something a designer can check and engineer rather than assume.

abstract_reasoning:
Joint attention supports several characteristic reasoning moves. One can *diagnose communication failure* by checking the joint-attention precondition — were both parties attending to the same referent at the time? — because if not, the misunderstanding is upstream of message content and re-explaining will not fix it. One can *design interfaces and protocols* that manufacture joint attention where it is not natural, through shared cursors, gaze tracking, deictic radio calls, and pointer flashes. One can *predict learning outcomes* by measuring joint-attention frequency in infant-caregiver dyads, classrooms, and mentor-apprentice pairs.

One can *repair breakdowns* by re-establishing the shared frame ("wait, what are we looking at?") rather than restating content, which is a structurally different and often more effective intervention. And one can *distinguish coordination failures* by separating failure to *establish* joint attention from failure to *act* once it is established — two failures that present similarly but call for different remedies. The transferable move underlying all of these is a single question: in this multi-agent setting, what mechanism establishes mutual awareness of co-attention, and what does it cost when it is absent? That question travels across every substrate where attending agents must coordinate.

knowledge_transfer:
The transfers run across the cognitive, social, and engineered-interface substrates the prime inhabits. Developmental-psychology findings on shared-attention indicators imported directly into HCI design — cursors, presence dots, ping markers are the engineering of joint attention into collaborative software. Animal-cognition gaze-following experiments informed the design of social robots whose direction of attention is legible to humans, using head orientation to signal where the system is attending.<!-- FACT-D65-657 -->[^scassellati-2002] Pedagogy's attention-monitoring techniques transferred into operating-room training.<!-- FACT-D65-658 -->[^kantor-etal-2024] Conversation analysis transferred into tele-meeting design, where gallery view, speaker pinning, and hand-raise signals are explicit attempts to reconstruct the joint-attention affordances of in-person meetings that video fragments by design.<!-- FACT-D65-659 -->[^heath-luff-1991] And joint-attention thinking transferred into architecture and exhibit design, where sightlines, seating, and control-room layouts are engineered so designated targets enter the shared attention frame.

What makes these transfers genuine is the interchangeability of structural roles. *The agents* as two or more attention-bearing parties, *the shared target* toward which their attention is co-aimed, *the orienting cues* (gaze, point, deixis, cursor, callout) that make each agent's attention legible to the other, *the mutual registration* of each agent's knowledge that the other is co-attending, *the common reference frame* that licenses deictic communication, *the breakdown signature* of a misunderstanding traceable to misaligned attention rather than misaligned content, and *the repair move* of re-establishing the shared frame rather than restating the message — these map one-to-one across infant-caregiver dyads, surgical teams, collaborative software, and human-AI handoffs. The substrate base is narrower than that of a fully substrate-neutral prime, since every instance requires agents that can attend and register one another's attention; but within that base the structure and its consequences are stable, and the portable design question — what manufactures mutual awareness of co-attention here, and what does its absence cost? — recurs unchanged.

formal_abstract:
The infant word-learning dyad is the prime's foundational case, and it can be laid out with structural precision. Take a caregiver and a nine-month-old in a room with several toys. The *two attending agents* are the dyad. The caregiver *orients* — turns her head and gaze toward a ball and points — making her attention legible (the *orienting cue*). The crucial developmental step the infant has just acquired is *gaze-following*: rather than fixating on the pointing hand, the infant follows the vector of the gaze to its target, the ball, establishing the *shared target*.<!-- FACT-D65-660 -->[^tomasello-1995] But shared target alone is not yet joint attention; the *mutual registration* is the load-bearing move, and it is observable as *gaze-checking* — the infant looks back to the caregiver's face and back to the ball, confirming "I see that you see it." That second-order loop converts coincidental co-orientation into a *common reference frame*. Only now does the caregiver say "ball" — and the word maps unambiguously onto the co-attended object, solving the otherwise crippling reference problem (which of the many objects, properties, or actions does the new word denote?). The diagnostic this affords is sharp and is exactly how developmental researchers and clinicians use it: measure the *frequency and duration of joint-attention episodes* in a dyad. Reduced gaze-following and gaze-checking is one of the earliest behavioral markers screened for in autism assessment, precisely because the missing element is the mutual-registration invariant, not the capacity to look at objects.<!-- FACT-D65-661 -->[^mundy-etal-1990] The intervention follows: therapies that scaffold gaze-following and back-and-forth checking target the *establishment* of the shared frame rather than vocabulary directly, because word learning is downstream of the frame.

**Mapped back:** The caregiver-infant dyad instantiates the full signature — two attenders, gaze-and-point orienting cues, a shared target via gaze-following, and the load-bearing mutual registration via gaze-checking — showing that language bootstraps on the common reference frame, and that its measurable absence (not mere inattention) is a diagnostic marker.

applied_industry:
A surgical team and a remote collaborative-editing tool show the same configuration engineered in two very different settings — and a glance at animal cognition shows it arising without engineering at all. In the operating room, before a critical step the surgeon establishes joint attention on the target tissue: a deictic call ("clamp here, this vessel") plus a physical point is the *orienting cue*, and the scrub nurse's verbal confirmation ("this one, yes") supplies the *mutual registration* — the verify-and-confirm protocol exists precisely to guarantee second-order awareness before anyone acts, because a coordination failure here is catastrophic. Distinguishing the two failure modes the prime names is operationally vital: failing to *establish* joint attention (nurse hands the wrong instrument because she never registered which vessel) is a different error, with a different fix, than failing to *act* once it is established. Collaborative software manufactures the same frame for distributed users who lack a shared physical room: shared cursors, presence dots, and ping markers are direct engineering of orienting cues, and a "X is looking at this cell" indicator is an explicit attempt to supply the mutual-registration signal that co-presence would otherwise provide for free — repairing the joint-attention affordances that screens fragment. The transferable intervention is identical across both: when communication breaks down, *re-establish the shared frame* ("wait — which vessel?" / "which row are you editing?") rather than restating content, because the fault is upstream of the message. The same structure, unengineered, appears in coordinated wolf hunting and in dogs following a human point — animals registering a co-attended target — which is why the configuration is studied as a genuine cross-substrate pattern rather than a human artifact.<!-- FACT-D65-662 -->[^call-tomasello-2008]

**Mapped back:** Operating-room target designation and shared-cursor co-editing both deliberately manufacture mutual registration of co-attention (verify-and-confirm; presence indicators) to enable cheap deictic reference, and the breakdown-repair move — rebuild the frame, do not restate the message — transfers across the surgical, software, and animal-coordination substrates.


Joint vs. Separate Evaluation

one_liner: The same option is ranked differently when evaluated alongside alternatives than when evaluated in isolation against an internal reference, because each evaluation mode makes a different subset of attributes evaluable and thus carries the verdict.

core_idea:
The *same option* is ranked differently when evaluated alongside alternatives (joint mode) than when evaluated in isolation against an internal category reference (separate mode), because different attributes become *evaluable* in each mode. Attributes whose value is hard to read from a single instance — precise quantities, technical specifications, comparative quality — become readable when two options sit side by side; attributes that read easily against an internal standard — overall impression, fit with a category prototype, valence — dominate when one option sits alone. Switching modes shifts which attributes carry the evaluation, sometimes *reversing the ranking* of the same options. The structural commitment is a *mode-attribute coupling*: each evaluation mode activates a distinct subset of attributes as the basis of judgment. The mode is not a measurement artefact to be neutralised; it is a structural toggle on the cognitive procedure that produces the verdict, so designing the mode is designing the verdict.

What changes when one names this pattern is the second-order question. Before any object is evaluated, the analyst asks: *in which mode will this evaluation happen, and which attributes does that mode make evaluable?* Pre-registering the mode determines what counts as the basis of judgment as much as choosing the attributes. The structural relation is between three objects — the option set being evaluated, the attribute pool available to base the judgment on, and the evaluation procedure (joint or separate) — and each procedure projects the attribute pool onto a subset of *evaluable* attributes: separate procedures elicit attributes expressible against an internal reference, joint procedures elicit attributes expressible against a comparison-set distribution, and the verdict is a function of the evaluable subset, not the full pool. The pattern is inherently about human evaluation modes; its vocabulary travels somewhat, but the phenomenon is bound to cognitive practice, which is why it reads as framed.

structural_signature:
*the option set being evaluated* — *the attribute pool from which a verdict could draw* — *the evaluation procedure (joint or separate)* — *the mode-attribute coupling projecting the pool onto an evaluable subset* — *the verdict as a function of the evaluable subset rather than the full pool* — *the mode-relativity invariant: switching procedures can reverse the ranking of the same options*

An evaluation exhibits the joint-versus-separate structure when each of the following holds:

- **An option set.** One or more options are candidates for evaluation — products, candidates, papers, programs, patients — each carrying multiple attributes.
- **An attribute pool.** A set of attributes could in principle base the judgment — some readable only by comparison (precise quantities, comparative quality), some readable against an internal reference (overall impression, category fit, valence).
- **An evaluation procedure.** The options are assessed either jointly (side by side against the comparison set) or separately (one at a time against an internal category reference).
- **A mode-attribute coupling.** Each procedure projects the attribute pool onto a distinct evaluable subset: separate mode elicits internal-reference attributes, joint mode elicits comparison-set attributes.
- **A mode-determined verdict.** The verdict is a function of the evaluable subset that the chosen mode activates, not of the full attribute pool.
- **The mode-relativity invariant.** Because different modes carry the verdict on different attributes, switching modes can reverse the ranking of the same options; a verdict is incomplete until the mode that produced it is named.

The components compose into a logically-prior design move: the mode is a structural toggle on the cognitive procedure, so pre-registering the mode determines the basis of judgment as much as choosing the attributes does.

what_it_is_not:
- **Not `comparison`.** Comparison is the cognitive operation of relating two items on an attribute; joint-versus-separate evaluation is the *structural toggle that determines whether comparison happens at all* — separate mode suppresses cross-item comparison and lets an internal reference carry the verdict.
- **Not `framing`.** Framing alters how a *single* option is described (gain versus loss); the mode toggle changes which *attributes become evaluable* across a procedure, and its signature is a ranking *reversal* on identical options, not a description-induced shift in one.
- **Not `anchoring`.** Anchoring is contamination of a judgment by an arbitrary reference value; the separate-mode internal reference is a *category prototype*, not an arbitrary anchor, and the mode effect is a structural property of the evaluation procedure, not a bias seeded by a number.
- **Not `contrast`.** Contrast (perceptual or judgmental) is the heightening of a difference when items sit together; it is *one mechanism* by which joint mode makes comparative attributes evaluable, not the whole mode-attribute coupling, which also includes what separate mode elicits.
- **Not `preference` instability.** A ranking reversal across modes is not an unstable preference; it is a *mode-conditional* preference, evidence of a mode shift that activated a different evaluable attribute, which redirects remediation from fixing the evaluator to fixing the procedure.
- **Common misclassification.** Attributing every reversal to mode. The pattern requires a *human evaluator running a procedure*; a fixed scoring rule that scores every attribute identically regardless of presentation has no mode toggle, and a reversal that persists under fixed mode is an evaluator problem, not a mode effect.

broad_use:
- **Consumer behaviour**: an intact 24-piece dinnerware set is preferred to a 31-piece set with some broken items when evaluated separately, while the 31-piece set is preferred jointly, because the comparable piece-count becomes evaluable in comparison.<!-- FACT-D65-676 -->[^hsee-1998]
- **Hiring**: sequential evaluation of candidates produces stereotype-laden rankings on performance proxies, while side-by-side evaluation reduces stereotype bias because the performance attribute becomes more evaluable than the demographic prior.<!-- FACT-D65-677 -->[^bohnet-etal-2016]
- **Peer review and policy evaluation**: rating papers one at a time against an absolute scale yields different orderings than ranking them as a slate, and a program's cost-effectiveness ratio evaluated alone against a threshold differs from the same ratio evaluated head-to-head against competing programs.
- **Negotiation**: issue-by-issue (sequential separate) versus package (joint) negotiation activates different reference points, and integrative gains usually require joint mode because cross-issue trade-offs are only evaluable when issues sit together.<!-- FACT-D65-678 -->[^bazerman-etal-1992]
- **Software procurement**: feature matrices that put products in columns produce different rankings than separate demo walkthroughs, because matrices activate "is this more of attribute A?" while demos activate "is this good?".<!-- FACT-D65-679 -->[^hsee-1996]
- **Wine, art, and medical triage**: critics rating in flight versus singly produce diverging hierarchies, and patients evaluated one-at-a-time against guideline thresholds versus as a queue against each other produce different prioritisations.<!-- FACT-D65-680 -->[^hodgson-2008]

clarity:
Naming the pattern separates two readings of "good": *good against an internal reference* (does this clear my category prototype?) and *good against the explicit comparison set* (is this better than the alternatives in front of me?). Both are coherent, they yield different rankings, and neither is uniformly correct; the mode-attribute coupling makes the distinction visible. The clarifying force is to expose that "good" is mode-relative, so a verdict is incomplete until the mode that produced it is named.

The frame also dissolves a class of apparent inconsistencies. When the same evaluator ranks A above B in one setting and B above A in another, the default explanation reaches for bias, mood, or unstable preferences; the mode-attribute frame offers a structural alternative — different modes elicited different attributes, so the preference is mode-conditional, not inconsistent. This reframing matters because it redirects remediation from "fix the evaluator" to "fix the procedure." A further clarity benefit is that the frame supplies a structural answer to the mode-selection question: align the mode with the decision's actual structure — if the decision is *which of these?*, evaluate jointly; if the decision is *is this one acceptable?* against a standard, evaluate separately — so that mode-decision mismatch is identifiable as a known and avoidable source of error rather than an inevitable feature of judgment.

manages_complexity:
The frame manages complexity by collapsing many seemingly disparate cognitive effects into one mechanism. Less-is-better effects, sequence effects in hiring, preference reversals in willingness-to-pay studies, debate-effect amplifications in deliberation, and the dinnerware-and-chocolate families of consumer studies are all instances of one structure: attribute evaluability is mode-dependent.<!-- FACT-D65-681 -->[^hsee-etal-1999] The complexity absorbed is the proliferation of separately named effects that turn out to share a single generating mechanism, which lets a practitioner who understands the coupling predict new instances rather than memorising a catalogue.

The frame also compresses the design space for evaluation procedures. Instead of the open-ended question "how should we structure this evaluation?", the analyst has a structured dichotomy (joint or separate) and a derived question (which attributes do we want to carry the verdict, and which mode makes those attributes evaluable?). This yields portable interventions: match the mode to the decision (choice-among-alternatives goes joint, gatekeeping-against-a-standard goes separate); force-shift the mode when the bias lives in the prototype (shift to joint to dilute the prototype's grip and elevate comparative attributes); audit the elicited attributes before trusting a verdict; beware mode-asymmetric incentives (sellers want separate mode when side-by-side comparison is unfavourable, buyers want joint mode when it reveals weak attributes, so the choice of mode is itself adversarial); and pre-register the mode for high-stakes evaluation so that post-hoc mode-shifts cannot rationalise a desired verdict. Each intervention is a move on the mode rather than on the evaluator, which is where the structural leverage lies.

abstract_reasoning:
The prime trains a reasoner to ask, before any object is evaluated, in which mode the evaluation will happen and which attributes that mode makes evaluable. The structural relation is between three objects — the option set, the attribute pool from which the judgment could draw, and the evaluation procedure (joint or separate) — and each procedure projects the attribute pool onto a subset of *evaluable* attributes: separate procedures elicit attributes expressible against an internal reference (category prototype, absolute scale), joint procedures elicit attributes expressible against a comparison-set distribution (relative quantities, rankings, trade-offs), and the verdict is a function of the evaluable subset rather than the full pool.

From this relation the prime licenses several substrate-neutral inferences. The mode-selection question has a structural answer: align the mode with the decision's actual structure — if the decision is *which of these?*, evaluate jointly; if it is *is this one acceptable?* against a standard, evaluate separately — so that mode-decision mismatch is a recognisable, avoidable source of error rather than an inevitable feature of judgment. The reasoner learns to *force-shift the mode* when the bias lives in the prototype, moving to joint mode to dilute the prototype's grip and elevate comparative attributes, and to *audit the elicited attributes* before trusting a verdict, since a verdict based on invisible attributes is downstream of an upstream mode choice. The reasoner is also alerted that mode choice is *adversarial*: sellers want separate mode when side-by-side comparison is unfavourable, buyers want joint mode when comparison reveals weak attributes, so the choice of mode is itself a contested move, and *pre-registering the mode* before the options are known reduces post-hoc mode-shifts that rationalise a desired verdict. The deepest inference is that a ranking reversal on the same options is evidence of a mode shift rather than of evaluator instability, which redirects remediation from fixing the evaluator to fixing the procedure.

knowledge_transfer:
The transferable content is the *option-set / attribute-pool / evaluation-procedure* relation together with the mode-attribute coupling and the portable interventions (match mode to decision, force-shift to debias the prototype, audit elicited attributes, beware mode-asymmetric incentives, pre-register the mode). The role mappings are regular across human-evaluation substrates: the *option set* maps to dinnerware sets, candidates, papers, programs, products, wines, patients; the *separate-mode attributes* map to overall impression, category fit, prototype valence, gut acceptability; the *joint-mode attributes* map to piece-count, publication-relative novelty, opportunity-cost comparison, feature-by-feature specification, relative severity; the *mode toggle* maps to one-at-a-time-against-a-rubric versus side-by-side-as-a-slate.

The transfers are reuses of one toggle. The hiring debiasing move — shifting from sequential to side-by-side evaluation so the performance attribute outweighs the demographic prior — is structurally the same as the dinnerware reversal, the peer-review batch ranking, and the feature-matrix procurement: in each case the mode choice upstream determines which attribute downstream becomes the basis of judgment.<!-- FACT-D65-682 -->[^bohnet-etal-2016] The diagnostic question — which attributes does each mode make evaluable? — carries unchanged from consumer choice to peer review to procurement to triage. The load-bearing recognition that transfers is that mode selection is a substrate-neutral move available in any evaluation procedure, logically prior to and substantively shaping the evaluation, and that ranking reversals on the same options are evidence of a mode shift rather than of evaluator instability. Because the pattern is inherently about human evaluation modes and is bound to cognitive practice — there is no evaluation without an evaluator running one procedure or the other — the transfer runs across human evaluative-judgment substrates (consumer choice, hiring, peer review, policy, negotiation, procurement, connoisseurship, triage), and within that family the structural toggle and its intervention catalogue carry intact even where the attributes and the stakes differ entirely.

formal_abstract:
The dinnerware-set reversal is the cleanest worked instance because it produces an actual ranking reversal on the same options. The *option set* is two crockery sets: Set A, twenty-four intact pieces; Set B, forty pieces of which thirty-one are intact and nine are broken.<!-- FACT-D65-683 -->[^hsee-1998] The *attribute pool* contains an internal-reference attribute (overall impression — "is this a good set?") and a comparison-set attribute (intact piece-count — twenty-four versus thirty-one). Under *separate evaluation*, where each set is assessed alone against a category prototype, the *mode-attribute coupling* makes overall impression the evaluable basis: Set B reads as "a set with broken pieces in it," and respondents pay more for Set A.<!-- FACT-D65-684 -->[^hsee-1998] Under *joint evaluation*, where the two sit side by side, the comparable piece-count becomes evaluable — thirty-one intact pieces visibly exceeds twenty-four — and respondents pay more for Set B.<!-- FACT-D65-685 -->[^hsee-1998] Same options, opposite ranking, with the *mode-relativity invariant* on full display. The structural diagnosis is precise: the reversal is not evaluator instability, mood, or bias but evidence of a mode shift that activated a different evaluable attribute. The *mode-selection* prescription follows from aligning mode to decision structure: if the question is "is this set acceptable?" evaluate separately; if it is "which set?" evaluate jointly. Pre-registering the mode before the options are known forecloses the post-hoc mode-shift that would rationalise a preferred verdict.

**Mapped back:** The dinnerware reversal instantiates every role — two options, an attribute pool split into prototype-readable and comparison-readable attributes, the joint/separate toggle — and demonstrates the mode-relativity invariant directly, with the ranking reversal serving as evidence of a mode shift rather than evaluator inconsistency.

applied_industry:
Hiring debiasing is the applied case where the toggle is deployed deliberately as an intervention. The *option set* is a slate of candidates; the *attribute pool* contains a comparison-readable attribute (a performance proxy — a work-sample score, a structured-task result) and an internal-reference attribute (a demographic prototype that triggers a stereotype). Under *separate evaluation* — candidates assessed sequentially, one at a time against a hiring manager's internal prototype — the *mode-attribute coupling* lets the prototype carry the verdict, and stereotype-laden rankings result because the demographic prior is more readily evaluable against an internal standard than the performance attribute is. The documented intervention is a *force-shift to joint mode*: evaluate candidates side by side on the same task, which makes the comparative performance attribute evaluable and dilutes the prototype's grip, reducing stereotype bias.<!-- FACT-D65-686 -->[^bohnet-etal-2016] A parallel applied instance is software procurement: a feature matrix (joint mode, products in columns) activates "which has more of attribute A?" and produces one ranking, while separate demo walkthroughs (each product alone against a gut sense of "is this good?") produce another — and the mode choice is *adversarial*, since a vendor whose product loses on side-by-side comparison prefers separate demos, while a buyer who wants weak attributes exposed prefers the joint matrix. The remediation in both cases is structural — fix the procedure, not the evaluator — by matching the mode to the actual decision and pre-registering it.

**Mapped back:** Sequential-versus-side-by-side hiring and matrix-versus-demo procurement are the same mode toggle as the dinnerware reversal, with the demographic prototype and the gut "is this good?" as separate-mode attributes and the performance proxy and feature comparison as joint-mode attributes — debiasing achieved by force-shifting the mode rather than correcting the evaluator.


Journalistic Objectivity

one_liner: A claim is licensed by a procedure whose authority depends on holding the verifier structurally outside the interests at stake, so preferred-outcome leakage is made expensive.

origin_domain: philosophy

core_idea:
Journalistic objectivity, read structurally, is the pattern by which a claim or judgment is licensed by a *procedure* whose authority depends on the judge being held *outside* the interests at stake. The pattern has three load-bearing elements: an *interested party* who would benefit from the claim coming out a particular way; a *verifier* positioned and constrained so that the interested party cannot reach through them; and a *procedure* — evidence rules, declaration of interests, recusal, blinding, second readers, adversarial check — that turns the verifier's structural distance into an actual constraint on the claim. The output is not "the truth" but a *warrantedly held claim* whose force comes from the structure that produced it.

The candidate enters the catalogue under the journalism label, but that names one institutional instantiation of a pattern recurring wherever a system must license public-facing claims under pressure from interested parties. The structural unit is the *outsider-verifier configuration*: independence is not a virtue of an individual but a property of the role's structural position and the procedure attached to it. The defining elements form a recognizable signature — the interested party whose stake would bias the claim, the verifier whose authority depends on structural distance from that party, the procedure that converts distance into constraint, the *path-cutting* that severs the routes (payment, employment, social tie, agenda control) by which the interested party could reach the verifier, the *capture* failure mode in which the distance collapses while the formal architecture persists, and the *warrant* on the output: not "true" but "produced under conditions that make preferred-outcome leakage expensive."

structural_signature:
*the interested party who would benefit from a particular outcome* — *the verifier held structurally outside that interest* — *the procedure that converts distance into a binding constraint* — *the path-cutting that severs routes from interest to verifier* — *the warranted-claim output* — *the capture failure mode (distance collapses while the architecture persists)*

The pattern is present when the following components are jointly in play:

- **The interested party (the stakeholder).** An actor who would benefit from the claim coming out a particular way — the sponsor, management, author, campaign, firm. Its stake is the threat the structure must neutralize.
- **The verifier (the outsider).** A judge positioned and constrained so the interested party cannot reach through them. Independence is a property of the role's structural position, not a virtue of the individual.
- **The procedure (the distance-to-constraint converter).** Evidence rules, declaration of interests, recusal, blinding, second readers, adversarial check — the mechanism that turns the verifier's structural distance into an actual constraint on the claim.
- **The path-cutting (the severing of routes).** The deliberate cutting of the channels — payment, employment, social tie, agenda control — by which the interested party could reach the verifier.
- **The warranted-claim output (the licensed product).** Not "the truth" but a claim produced under conditions that make preferred-outcome leakage expensive; its credibility ceiling is set by the architecture, not the content.
- **The capture failure mode.** The verifier's distance collapses while the formal architecture persists, so the system fails *toward* the interested party's preferred outcome and continues to *look* independent — the gap between formal and effective independence.

Composed, these license a claim by holding the verifier structurally outside the interest and cutting the paths by which interest could reach them, so the warrant rests on position and procedure rather than on the verifier's character.

what_it_is_not:
- **Not impartiality.** `impartiality` is a *disposition* of the judge — even-handedness, lack of favoritism; journalistic objectivity (detached verification) is a *structural arrangement* — the verifier held outside the interest by procedure and path-cutting. One is a virtue of character; the other is a property of position robust to bad actors.
- **Not procedural fairness.** `procedural_fairness_due_process` is the broad family of "decisions warrant their force by the procedure that produced them"; this prime is the *subclass* aimed specifically at neutralizing an *interested party's path to the verifier*. Procedural fairness is the genus; detached verification is the conflict-of-interest-cutting species.
- **Not information asymmetry.** `information_asymmetry` is the *condition* that motivates verification (one party knows more); detached verification is the *response architecture*. One is the problem; the other is the structural answer.
- **Not trust.** `trust` is the downstream *state* this architecture produces at scale without personal acquaintance; the prime is the *mechanism* that manufactures warranted claims. Trust is the output; the verification structure is the producer.
- **Not blinding.** `blinding` is *one mechanism* in the toolkit (hiding identity to cut a path); the prime is the whole pattern — interested party, outsider verifier, procedure, path-cutting. Blinding is a single lever, not the architecture.
- **Not a conflict of interest.** `conflict_of_interest` names the *threat* (the verifier's stake) that the architecture neutralizes; detached verification is the structural neutralization of exactly that threat.
- **Common misclassification.** Defending verification by pointing to the verifier's honesty ("we hired good people"). That is a character-based defence, defeasible by any bad actor; the prime's warrant rests on *structural position* — a verifier the interested party cannot reach through. Catch it by asking whether removing the procedure would leave only the verifier's good faith between interest and outcome; if so, the independence is character-based, not structural.

broad_use:
- **Journalism and editorial practice.** Reporters separated from advertisers and owners, multiple sourcing, fact-checking, corrections policies, recusal on conflicts — the verifier's distance from the story licenses the claim.<!-- FACT-D65-701 -->[^schudson-2001]
- **Scientific peer review and replication.** Blinded review, editor-selected reviewers without financial stake, pre-registration binding authors before outcomes are known, and independent replication as external verification.<!-- FACT-D65-702 -->[^nosek-etal-2018]
- **Audit and assurance.** Auditor independence from the audited firm, rotation requirements, prohibition on non-audit services to audit clients, and oversight inspection of the auditors themselves.<!-- FACT-D65-703 -->[^sox-2002]
- **Judicial process.** Judges recused where they have a stake, juries drawn from outside the parties, appellate review by judges who did not try the case, and adversarial procedure pitting claimant against contrarian.<!-- FACT-D65-704 -->[^usc-28-455]
- **Clinical trials and regulatory science.** Blinded outcome assessors, independent data-monitoring committees, sponsor-separated advisory panels, and pre-specified analysis plans that bind the sponsor before unblinding.<!-- FACT-D65-705 -->[^ich-e6-r2]
- **Institutional review and ombuds work.** Review boards, ethics committees, ombudspersons, and inspectors general designed to investigate within institutions while insulated from the line authority that would benefit from a favorable finding.
- **Standards, certification, and elections.** Third-party certifiers deriving licence from independence of producers and consumers; election commissions and outside observers brought in precisely because they are external to the contest.<!-- FACT-D65-706 -->[^iso-iec-17000]

clarity:
Naming the pattern makes visible a distinction the lay discussion conflates: *bias* (the verifier's preferences) versus *structural exposure to interest* (the verifier's position). A verifier with strong opinions but no exposure to the outcome is in a different epistemic situation from one with no opinion but a financial stake in the result; the second is more dangerous, and the procedure is built to handle it. Recognizing this separates "we hired good people" — a character-based defence, easily defeated — from "we built a structure they cannot reach through" — a position-based defence robust to bad actors.

The vocabulary also sharpens the failure modes by unifying them. Capture, revolving doors, sponsor-paid reviewers, advertiser-influenced coverage, and self-dealing audits are not isolated scandals; they are the same structural failure — the verifier's distance to the interested party has collapsed — and they respond to the same class of remedy: restore distance, change procedure. Clarity comes from seeing a sprawling literature of distinct scandals as instances of one collapsed configuration.

manages_complexity:
The pattern compresses a sprawling literature of governance and institutional design — audit independence, judicial recusal, peer-review reform, regulatory capture, press ethics — into a single diagnostic frame with four named variables: who is interested, who verifies, what procedure binds them, and what structural distance separates them. Across substrates the same diagnostic questions apply: where do the interested party's incentives flow, what paths do they take to reach the verifier, what procedural or positional cuts in those paths exist or could exist, and what failure mode does the current configuration leak. A heterogeneous body of institutional knowledge becomes four reusable handles.

The compression is genuine because the same intervention library ports across substrates: rotation, blinding, declaration of interest, recusal, separation of function, third-party check, adversarial review, cooling-off periods, structural firewalls. None of these is specific to journalism, science, or law; each is a move in a shared procedural grammar. Managing complexity well means reading any verification problem as a path-cutting problem — locate the routes from interest to verifier, and apply the procedural cut that severs them.

abstract_reasoning:
The pattern supports inference about *when claims should be trusted* without inspecting their content. If a claim is produced by an interested party with no detached verifier, its credibility ceiling is structurally bounded — not because the claimant is dishonest but because nothing in the procedure prevents preferred-outcome leakage. The corollary is that strengthening claims requires modifying the procedure — add a verifier, increase their distance — rather than asking the claimant to try harder. It also supports inference about *failure direction*: when the verifier-of-the-verifier is captured, the system does not fail randomly but *toward* the interested party's preferred outcome, and the failure persists because the system continues to *look* like detached verification after the distance has collapsed. This explains the persistent gap between formal independence (the verifier is nominally separate) and effective independence (the procedure actually constrains them).

Reasoning at this level asks, of any claim-licensing system: *who is interested, who verifies, what paths run from the first to the second, which procedural cuts are in place, and would this claim's status change under a different verification architecture?* That last is a near-universal move in institutional design. These questions distinguish the pattern from *procedural fairness* broadly (the wider family of "decisions warrant their force by the procedure that produced them," of which this is the subclass aimed specifically at neutralizing an interested party's path to the verifier), from *information asymmetry* (the condition that motivates verification, not the response architecture), from *trust* (the downstream state this produces at scale without personal acquaintance), and from *blinding* (one mechanism in the toolkit, not the whole pattern).

knowledge_transfer:
The pattern transfers as a procedural grammar, carried by stable role mappings: the *interested party* maps to the sponsor, the management, the author, the campaign, the firm; the *verifier* maps to the auditor, the referee, the judge, the observer, the data-monitoring committee; the *procedure* maps to blinding, recusal, pre-registration, rotation, sourcing rules; and the *path-cutting* maps to payment firewalls, employment separation, agenda-control limits, cooling-off periods. With these fixed, an audit-independence regime and a peer-review reform recognize one another as the same move on different substrates.

Documented transfers run in several directions. Pre-registration, blinded review, and conflict-of-interest disclosure carry from clinical trials into investigative reporting — declare relationships before reporting, bind methodology before results, route verification through someone without exposure to the subject. Independence requirements developed for financial audit — no consulting relationships, no performance-tied compensation, rotation — carry into model evaluation, where third-party evaluators cannot be paid on whether the model passes and cannot also be optimizing it.<!-- FACT-D65-707 -->[^raji-etal-2022] The judicial recusal logic — the verifier must be structurally outside the case — carries into algorithmic-fairness review, where the team that built the model cannot audit it and the procedural cut must be specified before the audit, not invented afterward.<!-- FACT-D65-708 -->[^raji-etal-2022] Reviewer-conflict procedures carry from peer review into grant-making, and the outside-witness model carries from election observation into whistleblower protection and independent-director requirements. A pharmaceutical company running a trial of its own drug is held at arm's length at every decision point — a pre-blessed protocol fixing the endpoint, an independent monitoring committee holding data the company cannot see, blinded assessors, external statisticians, independent peer review — and the structural move is identical to cutting the path between a beverage company and the reporters covering nutrition science. The transfer is robust because the strip-the-jargon residue — *license a claim by holding the verifier structurally outside the interest, and cut the paths by which the interest could reach them* — survives into audit, judiciary, peer review, certification, and elections alike. The pattern is strongly framed under its current journalism slug, which conflates a professional ideology with the broader structure and imports heavy normative context; the cross-domain transferability lives in the structure (which would be better named *detached verification* or *independence of verification*), not in the ideology, and the contested ideological status of the journalism slogan does not undermine the structural recurrence elsewhere.

formal_abstract:
Financial statement audit is the cleanest worked instance of the outsider-verifier configuration, because the structure is codified in regulation. The *interested party* is the audited firm's management, which benefits if the financials report favorably. The *verifier* is the external audit firm, whose authority depends entirely on being held *structurally outside* management's reach — independence here is a property of the role, not the auditor's personal honesty. The *procedure* converts that distance into a binding constraint: prescribed audit standards, sampling and evidence rules, a second-partner review, and a sign-off that exposes the auditor to liability.<!-- FACT-D65-709 -->[^sox-2002] The *path-cutting* is explicit and legally mandated — prohibition on the audit firm selling lucrative consulting services to the same client, mandatory partner rotation, and bars on performance-tied compensation — each severing a route (fees, employment, ongoing relationship) by which management could reach the verifier.<!-- FACT-D65-710 -->[^sox-2002] The *warranted-claim output* is precisely characterized: the audit opinion does not assert the financials are *true*, only that they were *produced and checked under conditions that make preferred-outcome leakage expensive* — its credibility ceiling is set by the architecture, not by inspecting every transaction. The *capture failure mode* is the structurally predicted danger and the reason the path-cutting rules exist: when an audit firm earns most of its revenue from consulting for the client it audits, the distance collapses while the formal architecture persists, so the system fails *toward* management's preferred outcome and continues to *look* independent — the gap between formal and effective independence that the prime makes diagnosable. The remedy the prime prescribes is structural, not exhortatory: restore distance (rotate, firewall, ban the side-fees), do not ask the auditor to "try harder."

**Mapped back:** Management is the interested party, the external audit firm is the verifier, audit standards and second-partner review are the procedure, the consulting-services ban and rotation are the path-cutting, the audit opinion is the warranted claim, and a consulting-captured auditor is the capture failure mode.

applied_industry:
Clinical-trial design and independent model evaluation instantiate the same path-cutting grammar in pharmaceutical and AI substrates. When a pharmaceutical company tests its own drug, the *interested party* is the sponsor (favorable results mean approval and revenue), and the structure holds it at arm's length at every decision point: a *pre-registered protocol* fixes the endpoint *before* results are known (binding the sponsor before unblinding), an *independent data-monitoring committee* holds interim data the company cannot see, *blinded outcome assessors* judge effects without knowing arm assignment, and external statisticians run the pre-specified analysis.<!-- FACT-D65-711 -->[^ich-e6-r2] Each is a *procedure* converting the *verifier's* structural distance into a constraint, and each *cuts a path* by which the sponsor's stake could leak into the result — the *warranted claim* being "produced under conditions that make preferred-outcome leakage expensive," not "true." The prime's *capture* warning names the failure when these cuts erode: a sponsor that controls the analysis plan and unblinds early can steer the outcome while the trial still *looks* rigorous. Independent model evaluation runs the structurally identical move in AI: the team that *built* a model has an interest in it passing, so the prime prescribes routing evaluation through a *third-party evaluator* who is not paid on whether the model passes and is not also optimizing it — independence requirements lifted almost verbatim from audit (no performance-tied compensation, no conflicting relationship) and from judicial recusal (the builder cannot audit their own system; the procedural cut must be specified *before* the audit, not invented afterward). The transfer is not metaphor: the same procedural library — pre-registration, blinding, rotation, separation of function, adversarial check — ports across journalism, trials, audit, and model evaluation because each reads a verification problem as a *path-cutting* problem.

**Mapped back:** The sponsor and the model-building team are interested parties; the data-monitoring committee and the third-party evaluator are verifiers; pre-registration and blinded assessment are procedures; the early-unblinding and pay-to-pass routes are the paths cut; and a steered trial or a conflicted evaluation is the capture failure mode the prime predicts.