Agent–Environment Co-Shaping

canonical_family: Environment Shaping & Hazard Relocation

aliases:
Niche Construction Design
Reciprocal Environment Shaping
Endogenous Environment Design
Agent–Environment Feedback Design
Constructed-Niche Stewardship

source_primes: niche_construction feedback agency coevolution reinforcement

slug: feedback

slug: coevolution

slug: reinforcement

slug: affordance

slug: externality

slug: observability

components:
Constructor and Affected-Population Map
Environment Boundary and State Model
Modification Channel
Reciprocal Feedback Pathway
Selection and Reinforcement Field
Persistence and Inheritance Register
Target Niche and Viability Envelope
Coadaptation Observatory
Externality and Displacement Review
Stewardship and Update Rule
Reversal and Escape Path
Constructor–Beneficiary Alignment
Multi-Scale Environment Map
Diversity and Option Reserve
Succession and Transition Plan
Adversarial Adaptation Monitor
Intergenerational Handoff Record

mechanisms: Institutional Rule and Incentive Redesign

compression_statement: When the operating environment is partly produced by its participants, stop treating it as an external constant. Map the constructor–environment–selection loop, modify durable environmental conditions, measure how the changed conditions alter later behavior or population composition, preserve viability and alternatives, and revise the environment as coadaptation, inheritance, and externalities appear.

core_intuition: People, organisms, organizations, markets, and software communities do not merely respond to a world they find. They leave paths, tools, rules, habitats, data, standards, institutions, and expectations behind. Those changes become the environment for the next round of action. A well-designed niche can make desired practices self-supporting; a poorly designed one can lock in fragile behavior, create traps, or export risk. The solution pattern is therefore not a one-time environmental redesign but stewardship of a recursive environment–agent relationship.

canonical_formula: constructor action A_t → environment change ΔE_t → altered affordance/incentive/selection field S(E_{t+1}) → changed future behavior or population P_{t+1} → further environment change ΔE_{t+1}, governed within viability, legitimacy, diversity, observability, and reversibility bounds.

structural_problem: A system is managed as though the environment were fixed and behavior were produced only inside agents, even though participants continuously modify the conditions that shape later participation, strategy, capability, survival, and retention. Agent-side training, incentives, or correction then decay because the surrounding environment keeps reinstating the old pattern—or an environmental intervention succeeds initially but creates ungoverned coadaptation, inherited constraints, and spillovers.

trigger_conditions: Repeated attempts to change behavior fail because the physical, social, institutional, digital, or ecological environment still rewards the old pattern.

trigger_conditions: Participants already create durable workarounds, paths, tools, norms, code, infrastructure, or resource changes that shape what later participants can do.

trigger_conditions: An intervention will persist long enough to affect people, organisms, organizations, or generations that did not make the original change.

trigger_conditions: The intervention may create new roles, specializations, competitors, collaborators, or strategies rather than only changing a single action.

trigger_conditions: Environmental buffering, infrastructure, shared services, or habitat modification could reduce recurring adaptation burden.

trigger_conditions: A platform, market, institution, neighborhood, ecosystem, or organization changes in response to its members, and those changes alter who joins, survives, succeeds, or remains compatible.

trigger_conditions: A beneficial local niche may displace risk or cost to adjacent populations, locations, times, or governance layers.

symptoms: Training and reminders must be repeated because everyday conditions cue and reward contrary behavior.

symptoms: A workaround becomes infrastructure, and the infrastructure makes the workaround the new default.

symptoms: Success attracts a different participant population whose strategies change the system's future operating environment.

symptoms: Rules, standards, layouts, code, or land-use choices outlive their authors and become difficult for successors to question or reverse.

symptoms: A local environment becomes highly optimized for one type, reducing diversity and making alternatives progressively less viable.

symptoms: Metrics show adoption while hidden externalities, maintenance liabilities, or displaced hazards accumulate outside the measured boundary.

symptoms: Users, competitors, pathogens, or other adaptive actors discover a protected zone or exploit created by the redesigned environment.

root_tension: A constructed environment can create durable, low-effort reinforcement and cumulative capability, but the same persistence creates path dependence, power, externalities, delayed effects, and selection pressures that no designer can fully predict in advance.

anti_signatures: The environment is effectively fixed and only the agent's response rule can change; use Adaptive Response Recalibration or another agent-side adaptation pattern.

anti_signatures: The issue is an immediate action possibility, visibility, reachability, or friction mismatch with no persistent reciprocal selection loop; use Affordance Shaping.

anti_signatures: The intervention only changes a cue or trigger for an automatic response; use Associative Cue Redesign.

anti_signatures: The task is to map a feedback loop without constructing or governing environmental conditions; use Circular Causality Mapping.

anti_signatures: The main pattern is a hazard moved across a boundary without its generative pressure being absorbed; preserve Risk Migration as a separate target direction.

anti_signatures: The main pattern is persistence inside a low-contestation refuge that suppression cannot reach; preserve Sanctuary Effect as a separate target direction.

anti_signatures: The proposed environmental shaping lacks legitimacy, meaningful consent, affected-party voice, or a defensible public-interest basis.

structural_intervention: Construct and govern a durable environment-mediated feedback loop: make selected environmental conditions endogenous to participant action, shape those conditions toward a viable target, observe how they alter later behavior and selection, and iteratively adjust the environment while managing persistence, externalities, diversity, and exit.

action_logic: Define the constructor, exposed populations, beneficiaries, maintainers, adjacent systems, and future inheritors before drawing the intervention boundary.

action_logic: Model the current environment as state variables, resource flows, access conditions, infrastructure, rules, norms, interfaces, and ecological relations rather than as undifferentiated context.

action_logic: Trace how participants currently modify that environment, including informal workarounds and cumulative side effects.

action_logic: Close the return path by specifying how each environmental change alters later behavior, capability, participation, compatibility, survival, retention, or strategy.

action_logic: Identify what persists, what decays, what requires maintenance, and what successors will inherit without choosing.

action_logic: Define the desired niche and viability envelope, including diversity, capacity, fairness, safety, ecological limits, and acceptable uncertainty.

action_logic: Choose environmental levers that change repeated conditions rather than relying only on repeated persuasion or individual correction.

action_logic: Pilot reversibly where uncertainty is high, and measure both environmental state and adaptive response over an appropriate feedback horizon.

action_logic: Interpret workarounds, specialization, gaming, migration, and emergent roles as evidence about the new selection field rather than as noise.

action_logic: Revise, damp, expand, or reverse the intervention as coadaptation and cross-boundary effects become visible.

action_logic: Transfer stewardship and rationale so the constructed niche remains governable after the original designers leave.

decision_rules: Use this archetype when changing repeated environmental conditions has more leverage than repeatedly correcting individual behavior.

decision_rules: Require an explicit environment-to-future-behavior return path; otherwise treat the change as a mechanism, example, or neighboring archetype.

decision_rules: Prefer staged and reversible environmental changes when feedback is delayed, nonlinear, or likely to create path dependence.

decision_rules: Preserve alternative strategies, suppliers, habitats, workflows, or participation routes when strong selection could create monoculture or brittle lock-in.

decision_rules: Expand the analysis boundary whenever benefits are local but costs, hazards, congestion, maintenance, or exclusion can move elsewhere.

decision_rules: Treat persistent modifications as inherited governance commitments, not merely completed projects.

decision_rules: Distinguish adaptation that improves fit from gaming, coercive conformity, exploitative specialization, and evolutionary or behavioral traps.

decision_rules: Do not optimize a niche for the constructor alone when different populations experience the resulting selection pressure.

decision_rules: Use stronger monitoring and escape paths when the environment is difficult to reverse or when affected actors cannot readily exit.

target_invariants: The reciprocal causal loop from constructor to environment and back to future behavior remains explicit and testable.

target_invariants: The affected-population boundary includes those who bear spillovers, not only those counted as users or beneficiaries.

target_invariants: The environment remains within ecological, social, operational, and capacity limits rather than consuming the substrate that sustains it.

target_invariants: Legitimate agency, voice, appeal, and exit are preserved where human or institutional populations are shaped by the environment.

target_invariants: Diversity and option value are not eliminated faster than uncertainty is resolved.

target_invariants: Persistence, maintenance, ownership, and decommissioning obligations remain visible to successors.

target_invariants: Monitoring covers both environmental state and adaptive response, including delayed and cross-scale effects.

target_invariants: Risk reduction at one site is not counted as success if the hazard is merely displaced beyond the measurement boundary.

expected_outcomes: Desired behavior and capability become supported by ordinary conditions rather than requiring constant exceptional effort or command.

expected_outcomes: Constructive practices leave behind infrastructure, habitat, knowledge, rules, or resources that make later constructive practice easier.

expected_outcomes: The system detects and governs coadaptation before it hardens into harmful lock-in, gaming, ecological traps, or sanctuary effects.

expected_outcomes: Maintenance and legacy responsibilities become explicit, reducing opaque inherited constraints.

expected_outcomes: Environmental design, actor adaptation, and long-horizon outcomes are evaluated as one coupled system.

expected_outcomes: Externalities and displaced risks are surfaced earlier because the intervention boundary includes adjacent and future populations.

prose_essence:
Shape the environment an agent or population inhabits so the resulting conditions improve future behavior and adaptation—and keep governing the feedback as both sides change.

The archetype begins from a simple but consequential correction: the environment is often not an independent backdrop. Agents and populations alter it through accumulated action, and the altered environment changes what later agents can perceive, afford, sustain, enter, learn, reproduce, retain, or profitably choose. The practical unit is therefore a recursive relationship, not a one-way intervention.

A compact representation is:

```text
constructor action
    → environmental state change
    → altered affordance / incentive / selection field
    → changed future behavior or population
    → further environmental state change
```

The loop becomes a solution archetype when it is deliberately shaped and governed inside explicit viability, legitimacy, diversity, observability, and reversibility limits.

prose_tradeoffs:
Durability competes with reversibility. Fit competes with diversity. Local viability can conflict with system-level externalities. Buffering can conceal fragility. Self-reinforcement can reduce the need for constant management while also shifting power into inherited structure. Specialization can create capability while consuming carrying capacity or creating dependence.

These are not side concerns. They are the central governance cost of making an environment endogenous.

Perturbational Niche Construction

variant_type: subtype

short_definition: Change the state of an inhabited environment in place so the altered conditions feed back on the constructor and other exposed populations.

distinctive_feature: The constructor perturbs the environment it already occupies rather than merely adapting internally to an external condition.

distinct_from_parent: The parent includes physical, social, institutional, and digital forms. This variant foregrounds direct in-place change to the environment's state.

still_same_parent_because: The defining logic remains a reciprocal loop from constructor action to environment change and back to future action or selection.

use_when: The decisive intervention is a material, spatial, ecological, infrastructural, or informational change to the current environment.

use_when: The change persists long enough to alter later behavior, viability, compatibility, or selection rather than only the immediate encounter.

domain: ecological restoration

example: Restore water-retaining structures that alter hydrology, species access, and the future maintenance regime of a wetland.

domain: workplace design

example: Reconfigure shared space and tooling so collaboration produces reusable artifacts that make later collaboration easier.

failure_modes: A local improvement exports harm outside the mapped environment.

failure_modes: The modified substrate becomes costly to reverse after assumptions change.

Buffering Niche Construction

short_definition: Modify the environment to reduce harmful variability and keep key conditions within a viable range for the constructor or community.

distinctive_feature: The constructed environment counteracts external variation instead of opening a new opportunity field.

distinct_from_parent: The parent includes both stabilizing and directional forms. This variant specifically aims to hold variables inside a viability envelope.

still_same_parent_because: The buffer changes later selection and behavior by making some environmental states more persistent and others less likely.

use_when: Environmental volatility repeatedly pushes actors outside a safe or productive operating range.

use_when: A durable buffer, shelter, reserve, stabilizing institution, or shared service can regulate the experienced environment.

domain: urban planning

example: Tree canopy, shade, and water-retention infrastructure reduce heat stress and change later mobility and land-use behavior.

domain: organization

example: A shared support function absorbs routine volatility so teams can sustain specialized practices rather than constantly reverting to crisis mode.

failure_modes: Buffering hides accumulating stress until capacity is exceeded.

failure_modes: A protected niche becomes a sanctuary for harmful or unaccountable behavior.

Inceptive Niche Construction

short_definition: Create a new environmental opportunity, constraint, or resource pattern that elicits new behaviors, specializations, and adaptive trajectories.

distinctive_feature: The constructed niche generates a new selection field and opens paths that were previously unavailable or uneconomic.

distinct_from_parent: The parent covers all reciprocal environment shaping. This variant emphasizes novelty and directional change rather than maintenance or buffering.

still_same_parent_because: New opportunities matter through the same environment-mediated feedback loop and persistent selection effects.

use_when: The goal is not only to stabilize an existing practice but to make new strategies or roles viable.

use_when: The intervention may create a burst of experimentation, entry, specialization, or complementary innovation.

domain: software platform

example: A stable extension interface makes new complementor roles viable; those extensions increase platform value and attract further contributors.

domain: regional development

example: Shared infrastructure and procurement standards create a viable market for specialized suppliers that then deepen the local capability base.

failure_modes: The opportunity field produces premature monoculture or winner-take-all lock-in.

failure_modes: Entry expands faster than the environment's carrying capacity or governance.

Legacy-Bearing Niche Construction

short_definition: Construct environmental conditions whose effects persist beyond the original actor or period and shape the options and selection pressures inherited by successors.

distinctive_feature: Persistence and handoff are first-class design concerns rather than incidental side effects.

distinct_from_parent: The parent can operate over short feedback cycles. This variant centers long-lived environmental inheritance and intergenerational governance.

still_same_parent_because: The inherited environment continues to alter later behavior and selection, completing the reciprocal loop across time.

use_when: Infrastructure, code, institutions, land use, data, standards, or ecological change will outlast the original decision-makers.

use_when: Successors will enter a prestructured environment and may not know why its constraints or opportunities exist.

domain: software infrastructure

example: A default data schema shapes what later teams can represent, making compatible workflows cheap and alternatives progressively costly.

example: Street geometry and transit investment influence future travel, development, and political demand long after the original project.

failure_modes: The legacy becomes opaque lock-in after the original rationale expires.

failure_modes: Maintenance obligations are transferred to people who did not consent or benefit.

Institutional Niche Construction

short_definition: Build rules, roles, standards, infrastructure, and shared meanings that become the environment selecting which social and organizational behaviors remain viable.

distinctive_feature: The constructed niche is institutional: actors inhabit a durable field of rules, expectations, tools, and legitimized possibilities.

distinct_from_parent: The parent is substrate-neutral. This variant translates the loop into social and institutional environments.

still_same_parent_because: Institutions are treated as endogenous environments that both result from and select participant behavior.

use_when: Formal and informal institutions repeatedly shape participation, strategy, status, or resource access.

use_when: Participants' adaptations then reinforce, reinterpret, or transform those institutions over time.

example: Contribution rules and shared repositories make transparent reuse easy; reuse then rewards contributors and strengthens the repository as the default environment.

domain: public policy

example: A durable rights and service framework changes household and provider strategies, which in turn alters demand for future institutional development.

failure_modes: Institutional design becomes coercive because affected groups lack voice or exit.

failure_modes: Informal adaptation defeats formal rules while metrics show only nominal compliance.

Digital-Platform Niche Construction

short_definition: Shape platform rules, interfaces, APIs, data, and contribution pathways so participant adaptations recursively change the platform ecosystem.

distinctive_feature: The environment is programmable and rapidly co-constructed by platform operators, users, complementors, and automated agents.

distinct_from_parent: The parent covers all environments. This variant centers high-speed digital feedback, rule changes, data accumulation, and ecosystem governance.

still_same_parent_because: Platform participation changes the environment that then selects and channels subsequent participation.

use_when: A platform's architecture and governance determine which participants and complements can thrive.

use_when: Participant behavior, content, extensions, or strategic adaptation materially changes the environment for later participants.

domain: open-source ecosystem

example: Contribution tooling and modular interfaces attract extensions; the extensions change what maintainers prioritize and what new contributors learn first.

domain: online marketplace

example: Ranking and seller tools shape listing strategies, and accumulated seller adaptation changes buyer expectations and future ranking policy.

failure_modes: Fast adaptation produces gaming and policy whiplash.

failure_modes: Network effects convert a useful niche into exclusionary lock-in.

near_names:
Niche Construction Design
Reciprocal Environment Shaping
Endogenous Environment Design
Agent–Environment Feedback Design
Ecosystem Engineering
Ecological-Inheritance Design


Agentic Control Loop Design

type: archetype

source: generated

batch: Batch 14

aliases:
agency loop design
goal-model-action coupling
agentic capacity design
intentional action loop design
belief-sensitive action design
agentic operating loop

slug: agency

slug: controllability

slug: decision

slug: sensemaking

slug: accountability

slug: governance

slug: autonomy

slug: scaffolding

slug: constraint

slug: boundary

components:
Represented Goal
World Model
Observation Channel
Action Repertoire
Selection Policy
Legitimate Action Boundary
Execution Capability
Effect Feedback Loop
Model Update Rule
Proportional Accountability Frame
Support Scaffold
Override and Escalation Path
Goal Conflict Resolver
Agency Health Signal

mechanisms:
Agency Loop Map
Decision Rights Matrix
Briefback or Intent Confirmation
Action-Effect Feedback Review
Controllability Mapping Checklist
Graduated Autonomy Ramp
Model Assumption Register
Safe Action Menu
After-Action Learning Cycle
Agency Health Dashboard

essence_one_line: Agency becomes real when goals, situation models, available actions, authority, execution, feedback, and learning are coupled into a loop that can intentionally change outcomes.

compression_statement: Agentic Control Loop Design applies when a person, team, institution, software agent, or other system is expected to pursue goals but lacks a coherent coupling among goal representation, world model, action-option generation, action selection, permission to act, execution resources, feedback, and model update. The archetype builds or repairs that loop so action is not random motion, compliance, helpless reaction, or delegated execution without understanding, but belief-sensitive pursuit of represented goals under constraints.

core_intuition: Agency is not just freedom, motivation, or authority. An agent needs something it is trying to make true, a model of the situation, possible actions, a way to choose among them, enough authority and resources to execute, and feedback that updates the next choice. Remove any link and agency collapses into drift, obedience, wishful thinking, or paralysis.

canonical_formula: agency = represented_goal + world_model + action_options + selection_policy + execution_authority + feedback_update; design = couple_and_calibrate_these_links_under_constraints.

structural_problem: A system is nominally responsible for outcomes but cannot reliably pursue them because its goal representation, situation understanding, action repertoire, authority, execution pathway, and feedback are disconnected or distorted.

trigger_conditions: People or teams are told to own outcomes but lack authority, information, resources, or feedback to influence them.

trigger_conditions: An autonomous or semi-autonomous system acts without a stable goal model, update loop, or boundary of legitimate action.

trigger_conditions: Decision-makers have options but cannot connect them to a current model of the situation.

trigger_conditions: A process has goals and metrics but no live action-selection coupling that turns those goals into adaptive behavior.

trigger_conditions: Repeated failures produce passivity, learned helplessness, or ritual compliance instead of corrective action.

trigger_conditions: A delegation arrangement gives discretion but not enough context, feedback, accountability, or escalation rules.

trigger_conditions: The environment changes quickly enough that fixed instructions no longer substitute for situated agency.

trigger_conditions: A model, dashboard, or plan exists but is not connected to actual decision rights or action paths.

symptoms: Actors wait for permission even when local information is available.

symptoms: Teams optimize local tasks without understanding how actions affect the represented goal.

symptoms: Feedback arrives too late, too noisily, or too abstractly to update behavior.

symptoms: A person or system has motivation but no feasible action path.

symptoms: A tool recommends actions without explaining the world-model assumptions behind them.

symptoms: Escalations substitute for local judgment because authority and model confidence are unclear.

symptoms: Outcomes are attributed to “lack of ownership” even though the agency loop is structurally broken.

symptoms: Agentic language masks coercion: actors are blamed for outcomes they could not observe or control.

root_tension: The system needs intentional adaptive action, but agency requires coupling several fragile links. Too little coupling creates helplessness or drift; too much external control removes the local model and choice that make agency meaningful.

anti_signatures: The main issue is only protecting an already-legitimate autonomous zone from interference; use Autonomous Action Zone Protection.

anti_signatures: The main issue is only preserving choice while imposing constraints; use Autonomy-Supportive Constraint Design.

anti_signatures: The main issue is only building confidence in capability through practice and feedback; use Self-Efficacy Scaffolding.

anti_signatures: The main issue is principal-agent incentive conflict in delegation; use Principal-Agent Alignment.

anti_signatures: The main issue is clarifying formal decision authority; use Decision Rights Clarification.

anti_signatures: The actor has no legitimate goal or should not be empowered to act in the domain; do not use agency-building as a bypass for governance.

anti_signatures: The task requires standardized execution with minimal discretion because variance is unsafe; use procedural control and clear escalation instead.

structural_intervention: Map the agency loop, identify broken couplings among goals, models, options, authority, execution, feedback, and learning, then repair those couplings with bounded decision rights, information flows, viable action paths, feedback cadence, and accountability.

action_logic: State the represented goal or purpose that the agent is meant to pursue.

action_logic: Map the agent’s current world model, including what it can observe, infer, ignore, or misunderstand.

action_logic: Inventory feasible action options and the resources, permissions, tools, and constraints attached to them.

action_logic: Define the selection policy: how the agent chooses among actions given goals, beliefs, risks, and constraints.

action_logic: Create or clarify the legitimate action boundary, including autonomy, escalation, override, and accountability conditions.

action_logic: Connect execution to feedback so the agent can see effects of action soon enough to learn.

action_logic: Update the world model and goal interpretation when feedback contradicts assumptions.

action_logic: Add safeguards for overreach, coercion, misaligned goals, learned helplessness, and false responsibility.

decision_rules: Do not assign outcome responsibility unless the actor has at least partial observability, controllability, action options, and feedback.

decision_rules: Increase autonomy only inside a clear boundary of legitimate goals, constraints, and accountability.

decision_rules: If the actor lacks capability belief but has real controllability, pair this archetype with Self-Efficacy Scaffolding.

decision_rules: If authority exists without model quality, improve situation awareness and mental-model alignment before expanding discretion.

decision_rules: If action options exist but are unsafe or misaligned, constrain the action repertoire rather than destroying all agency.

decision_rules: If feedback is delayed, noisy, or punitive, redesign feedback before blaming motivation.

decision_rules: If the agent’s goals diverge from the principal’s goals, hand off to Principal-Agent Alignment or goal-governance patterns.

decision_rules: If a human actor is being held responsible without actual control, treat that as an ethical failure of the design.

target_invariants: The actor’s represented goal remains visible and contestable.

target_invariants: World-model assumptions are linked to available observations and feedback.

target_invariants: Action authority stays within a legitimate boundary.

target_invariants: Feedback is connected to action effects rather than only retrospective judgment.

target_invariants: Accountability is proportional to actual control and information.

target_invariants: Model updates can change future action selection.

target_invariants: Safeguards prevent agency enablement from becoming unbounded discretion or blame shifting.

expected_outcomes: Actors can intentionally select actions rather than merely comply, freeze, or escalate.

expected_outcomes: Goals, models, options, and feedback become mutually intelligible.

expected_outcomes: Local adaptation improves without abandoning governance.

expected_outcomes: Responsibility becomes fairer because authority, observability, and controllability are explicit.

expected_outcomes: Systems detect when agency is missing because a loop component is absent.

expected_outcomes: Learning accelerates because action effects update the next decision.

prose_essence:
Agentic Control Loop Design makes agency structurally possible. It does not equate agency with autonomy, motivation, confidence, or authority alone. An agent needs a represented goal, a model of its situation, feasible actions, a way to select among them, permission and capability to execute, feedback about effects, and a rule for updating the next action.

When those links are coupled, action can become intentional and adaptive. When they are broken, “agency” becomes a slogan: people are blamed for outcomes they cannot control, teams wait for permission, software agents act without boundaries, or organizations mistake dashboards for empowerment.

Human Agency Restoration

short_definition: A variant focused on restoring real and perceived ability to act after passivity, learned helplessness, or disempowerment.

distinctive_feature: Emphasizes recovery of controllability and confidence.

distinct_from_parent: Narrower and psychologically sensitive.

still_same_parent_because: The intervention still repairs the goal-model-action-feedback loop.

use_when: The actor has experienced repeated uncontrollability.

use_when: Small real choices and visible effects can rebuild action-effect coupling.

Delegated Agentic Mandate

short_definition: A governance variant where a principal gives intent, boundaries, resources, and feedback so an agent can adapt locally.

distinctive_feature: Centers legitimate delegated discretion under intent.

distinct_from_parent: Narrower and more governance-oriented.

still_same_parent_because: Delegation is meaningful only when the agency loop is structurally coupled.

use_when: Central instruction cannot keep up with local conditions.

use_when: Accountability and escalation can be made explicit.

Software Agent Control Loop

short_definition: A software or AI-system variant where objective, state model, tool-use permissions, action policy, monitoring, and escalation are explicitly coupled.

distinctive_feature: Operationalizes agency in nonhuman systems with strict action boundaries.

distinct_from_parent: More technical and safety-sensitive than the general parent.

still_same_parent_because: The same goal-model-action-feedback structure defines the agentic loop.

source_primes: agency mental_model feedback controllability

use_when: A software agent or automation can act in an environment.

use_when: Tool access, objective, model confidence, and escalation need boundaries.

use_when: Feedback must update future action selection.

Collective Agency Coordination

short_definition: A group-level variant where shared goals, shared models, distributed action rights, and feedback allow a collective to act as a coherent agent.

distinctive_feature: The agentic loop is distributed across people or units.

distinct_from_parent: Adds coordination and shared-model requirements.

still_same_parent_because: Collective agency still requires goal-model-action-feedback coupling.

use_when: Multiple actors need to pursue a shared goal.

use_when: No single actor controls the whole system.

use_when: Coordination, feedback, and role clarity are necessary for collective action.

near_names:
Goal-Model-Action Coupling
Agency Loop Design
Agentic Capacity Design
Belief-Sensitive Action Design


name: Aggregation Bias Detection and Correction

batch: Second Iteration/Batch 01

slug: paradox

slug: confounding

slug: stratification

slug: abstraction

slug: compression

slug: uncertainty

slug: representation

slug: scale

slug: causality

slug: equity

components: Interpretation and Correction Decision Rule

mechanisms: Representativeness and Nonresponse Review

essence_one_line: Protect decisions from misleading aggregate summaries by disaggregating the data, comparing subgroup and overall patterns, correcting composition effects, and restating only the claims the evidence can support.

compression_statement: This archetype treats aggregation as a useful but lossy transformation. It asks whether the combined statistic preserves the relationships that matter for a decision, or whether subgroup composition, confounding, weighting, or level-of-analysis errors have produced a false aggregate story.

core_intuition: An aggregate is not wrong merely because it is simplified. It becomes dangerous when the simplification hides the structure that determines interpretation. The correction is to make the lost structure visible, decide which level of analysis is appropriate, and change the claim or estimate accordingly.

canonical_formula: valid_aggregate_claim ≈ aggregate_metric + explicit_unit + subgroup_structure + composition_or_confounder_controls + weighting_rule + interpretation_boundary

structural_problem: A summarized metric, pooled estimate, average, rate, or group-level pattern appears actionable, but the act of aggregation may have hidden subgroup differences, reversed relationships, misweighted populations, or encouraged claims at the wrong level of analysis.

trigger_conditions: A decision depends on an average, rate, benchmark, pooled outcome, composite indicator, or regional/group-level metric.

trigger_conditions: The aggregate finding conflicts with stakeholder experience, subgroup evidence, domain theory, or visible case-level variation.

trigger_conditions: Groups differ in baseline rates, exposure, access, timing, sample size, missingness, or selection into the observed data.

trigger_conditions: The analysis moves between individual-level, group-level, regional-level, or time-window claims.

trigger_conditions: A high-stakes conclusion is being drawn from a dashboard, survey, public-health rate, educational metric, economic indicator, or product experiment rollup.

symptoms: Averages look acceptable while some groups experience severe underperformance, harm, exclusion, or benefit concentration.

symptoms: An aggregate trend points one way while subgroup trends point the other way or vary materially by context.

symptoms: Decision-makers argue from group-level statistics to individual-level causes without intermediate evidence.

symptoms: Changing weights, time windows, or grouping definitions changes the conclusion more than expected.

symptoms: The metric is treated as neutral even though its construction privileges large groups, frequent users, dominant regions, or easier-to-measure outcomes.

root_tension: Aggregation makes information tractable by compressing many observations into a small number of claims, but the same compression can erase heterogeneity that is essential for valid explanation, fair decision-making, and causal interpretation.

anti_signatures: The problem is only to simplify a complex set for easier overview, with no concern that the summary may mislead interpretation or action.

anti_signatures: The central need is to design a representative sample before data collection rather than to evaluate distortion introduced by aggregation and interpretation.

anti_signatures: The issue is a generic cognitive bias in judgment rather than a distortion created by pooled data, group-level inference, subgroup composition, or weighting.

anti_signatures: The aggregate claim is purely descriptive, low-stakes, explicitly bounded, and not being used to infer subgroup or individual-level conclusions.

anti_signatures: The data are too sparse or sensitive for responsible disaggregation, and no alternative evidence can be collected or disclosed.

structural_intervention: Convert an aggregate claim into an auditable multi-level claim by defining the unit of aggregation, mapping relevant subgroups, disaggregating where valid, testing for composition and confounding effects, correcting weights or models when justified, and restating the conclusion with explicit scope.

action_logic: Name the aggregate claim and the decision it is being used to support.

action_logic: Define the aggregation unit, population boundary, time window, numerator, denominator, grouping keys, and transformations.

action_logic: Identify subgroup structures that could plausibly change interpretation, including strata, contexts, cohorts, exposure groups, and equity-relevant categories.

action_logic: Disaggregate according to a predeclared plan and compare subgroup patterns with the aggregate pattern.

action_logic: Check for composition shifts, confounders, base-rate differences, missingness, nonresponse, and level-of-analysis errors.

action_logic: Choose a correction: retain the aggregate with caveats, report subgroup results, reweight or standardize, use multilevel modeling, collect better data, or refuse the unsupported claim.

action_logic: Document the interpretation boundary so future users know what the corrected claim does and does not say.

decision_rules: Use the archetype when aggregation is necessary but the validity of the aggregate interpretation is uncertain or consequential.

decision_rules: Prioritize disaggregation dimensions that are theoretically relevant, decision-relevant, equity-relevant, or known to affect base rates and exposure.

decision_rules: Do not treat every subgroup difference as meaningful; inspect uncertainty, cell size, stability, multiplicity, and privacy constraints.

decision_rules: Prefer subgroup-specific conclusions when the aggregate hides materially different patterns that change action.

decision_rules: Prefer a multi-level or weighted correction when both aggregate and subgroup views contain valid but incomplete information.

decision_rules: Stop and collect better evidence when neither the aggregate nor the available subgroup slices can support the proposed decision.

target_invariants: The aggregation unit and population boundary remain explicit.

target_invariants: Aggregate claims are not used to assert subgroup or individual-level conclusions without bridging evidence.

target_invariants: Subgroup inspection is planned, justified, and constrained by uncertainty and privacy.

target_invariants: Correction choices are tied to a target population, decision context, and valid level of analysis.

target_invariants: Weighting, normalization, filtering, and exclusion rules remain visible and reviewable.

target_invariants: The final claim preserves ambiguity when the evidence does not justify a single clean conclusion.

expected_outcomes: Misleading aggregate conclusions are detected before they drive policy, product, organizational, or scientific decisions.

expected_outcomes: Decision-makers can see whether an overall pattern is robust, subgroup-specific, reversed, exaggerated, or unsupported.

expected_outcomes: Aggregate metrics become more trustworthy because their limits and construction are transparent.

expected_outcomes: Hidden harms or uneven benefits are surfaced when average performance masks subgroup outcomes.

expected_outcomes: Analysts can defend whether they retained an aggregate, reported subgroups, reweighted estimates, modeled levels, or refused a claim.

prose_essence:
Aggregation Bias Detection and Correction is the pattern of making aggregate evidence safe to use. It recognizes that averages, pooled rates, composite scores, and group-level summaries are powerful because they compress many observations into one interpretable signal. The same compression can also erase the very structure needed for a valid conclusion.

The archetype does not say “never aggregate.” It says that aggregation must remain auditable. A useful aggregate should preserve the claim it is being used to support, or it should be corrected, narrowed, split, reweighted, or rejected.

prose_when_to_use:
Use this archetype when a decision, explanation, policy, dashboard, experiment, or public claim depends on an aggregate metric and there is a plausible risk that subgroup structure, composition, weighting, missingness, or level of analysis changes the interpretation.

It is especially useful when an average looks acceptable but lived experience or subgroup evidence suggests hidden harm; when an overall trend conflicts with within-group patterns; when group-level data are being used to make individual-level claims; or when a survey, experiment, or observational dataset has differential participation.

Do not use it as a generic instruction to slice every metric endlessly. Disaggregation should be relevant, valid, and bounded by uncertainty, privacy, and decision consequences.

prose_structural_problem:
The structural problem is that aggregation is a lossy transformation. It collapses many observations into a smaller representation. That representation may be appropriate for capacity planning, high-level monitoring, or communication, but it can become misleading when the decision requires subgroup, causal, contextual, or individual-level interpretation.

Aggregation bias appears when the pooled statistic tells a story that is not true for important subgroups, when group composition creates an apparent relationship, when an aggregate rate is mistaken for individual behavior, or when weighting choices silently privilege one population over another.

The result is a decision system that feels evidence-based while acting on a distorted view of the evidence.

prose_intervention_logic:
The intervention begins by naming the aggregate claim and the decision it is meant to support. Then it defines the unit of aggregation, the population boundary, the numerator and denominator, the time window, the grouping keys, and any transformations.

Next, it maps subgroup structure. Which strata, contexts, cohorts, exposure groups, or equity-relevant categories could change interpretation? The analyst then disaggregates according to a justified plan, compares aggregate and subgroup patterns, checks for confounders and composition shifts, and decides whether to retain, qualify, split, reweight, model, or reject the aggregate claim.

The key move is not the use of any one statistical method. The archetype is the full correction loop: aggregate claim → unit and subgroup map → comparison → correction → bounded interpretation.

prose_key_components:
### Aggregate Claim Boundary

The aggregate claim boundary states what the summary statistic is allowed to mean. It prevents an overall average from becoming a universal explanation, a subgroup claim, or a causal statement without support.

### Aggregation Unit Definition

The aggregation unit definition clarifies what was combined. A person-level average, household-level rate, transaction-level metric, school-level score, and region-level indicator can all imply different claims.

### Subgroup Structure Map

The subgroup structure map names the partitions that may matter: strata, contexts, cohorts, regions, exposure groups, access categories, or equity-relevant groups. It makes hidden heterogeneity inspectable.

### Disaggregation Plan

The disaggregation plan specifies how the aggregate will be decomposed. It keeps subgroup review from becoming either absent or opportunistic, and it records minimum cell-size, privacy, relevance, and uncertainty constraints.

### Confounder and Composition Marker Set

This component tracks variables that may explain why the aggregate differs from subgroup patterns. It is central for Simpson-like reversals, composition shifts, exposure differences, and base-rate mismatches.

### Aggregate–Subgroup Comparison Rule

The comparison rule asks whether the aggregate and subgroup patterns agree, diverge, reverse, attenuate, exaggerate, or support claims at different levels. It turns disaggregation into diagnosis.

### Weighting and Normalization Rule

The weighting rule decides which population the corrected estimate should represent. It forces explicit choices about whether observations, groups, target populations, or exposure bases should drive the estimate.

### Interpretation and Correction Decision Rule

The correction decision rule closes the loop. It decides whether to retain the aggregate, report subgroups, reweight, model hierarchically, collect more evidence, or refuse the unsupported claim.

prose_common_mechanisms:
### Stratified Analysis Protocol

A stratified analysis protocol implements the archetype by comparing patterns within predefined strata. It is useful when the relevant subgroup dimensions are known and the data can support within-group comparison.

### Simpson's Paradox Check

A Simpson's paradox check implements the archetype by looking for aggregate relationships that reverse or materially change after conditioning on a subgroup or confounder. It is a diagnostic mechanism, not the parent archetype itself.

### Ecological Fallacy Guardrail

An ecological fallacy guardrail implements the archetype by preventing group-level statistics from being used as unsupported individual-level claims. It protects the level of analysis.

### Poststratification or Reweighting

Poststratification or reweighting implements the archetype when the observed composition differs from a target population. It corrects the estimate only when the weighting basis is explicit and defensible.

### Multilevel Modeling Review

A multilevel modeling review implements the archetype when neither a pure aggregate nor isolated subgroup estimates are adequate. It is useful for nested contexts and partial pooling.

### Sensitivity Analysis by Group

Sensitivity analysis by group implements the archetype by testing whether conclusions survive alternative grouping rules, weights, time windows, or exclusions.

### Subgroup Dashboard with Warning Flags

A subgroup dashboard implements the archetype operationally by showing aggregate and subgroup patterns together. It is only effective when the warning rules are tied to valid subgroup definitions and decision thresholds.

### Representativeness and Nonresponse Review

This workflow implements the archetype by checking whether participation, missingness, or sampling differences distort the aggregate. It connects to Representative Sampling Design but remains focused on aggregate interpretation.

prose_failure_modes:
A common failure mode is overdisaggregation: analysts search many slices until they find a dramatic difference. Another is aggregate laundering, where a favorable average hides known subgroup harm. A third is arbitrary weighting, where correction becomes a way to produce a desired result.

Other failure modes include ecological inference error, confounder blindness, privacy-violating subgroup exposure, and corrected-but-unusable complexity. The mitigation is to use planned disaggregation, explicit weighting rules, uncertainty displays, privacy guardrails, and a clear correction decision rule.

prose_neighbor_distinctions:
Aggregation to Manage Complexity creates useful summaries; this archetype audits whether those summaries are safe to interpret. Representative Sampling Design improves evidence collection before aggregation; this archetype evaluates and corrects aggregate interpretation after observations are combined. Bias-Specific Decision Audit addresses decision biases broadly; this archetype targets aggregate-data distortion. Uncertainty Explicitness communicates uncertainty; this archetype diagnoses and corrects a specific source of misleading inference.

Selection Bias Correction is a close merge-review neighbor. When selection into the observed data drives the aggregate distortion, selection-bias mechanisms support this archetype, but the parent pattern here remains aggregate-claim correction.

prose_cross_domain_examples: In epidemiology, a pooled treatment outcome may need severity-stratified analysis before a treatment recommendation is valid. In survey research, a population average may need nonresponse review and poststratification. In economic analysis, a regional trend may need sector and household-level interpretation before it can support policy. In education, district averages may need subgroup dashboards so hidden underperformance is not masked. In product analytics, an A/B test rollup may need cohort review before launch.

prose_non_examples: A total request count used only for server capacity is not this archetype. A sampling plan created before data collection is usually Representative Sampling Design. A dashboard drilldown filter without claim-validity logic is only an interface mechanism. A generic fairness audit may use subgroup analysis, but it is broader than aggregation-induced distortion unless the aggregate claim is the central problem.

prose_tradeoffs:
Disaggregation improves validity but can increase complexity, noise, privacy risk, and alert fatigue. Reweighting can better match a target population but introduces assumptions that stakeholders may not understand. A simple aggregate is easier to communicate, while a valid multi-level claim may be harder to summarize.

The archetype therefore needs judgment. It should not turn every summary into an infinite analysis project, and it should not let the desire for simplicity conceal known distortion.

prose_invariants_to_preserve: The aggregation unit and population boundary must stay explicit. Aggregate claims must not be used for subgroup or individual-level conclusions without bridging evidence. Subgroup inspection must be relevant, planned, and constrained by uncertainty and privacy. Weighting, filtering, and normalization choices must remain visible. The final claim must preserve ambiguity when the evidence does not justify a clean conclusion.

prose_target_outcomes:
The desired outcome is not maximal complexity. It is a more valid aggregate interpretation. Decision-makers should know whether the aggregate is robust, subgroup-specific, reversed, exaggerated, composition-driven, or unsupported.

A successful application produces a corrected claim and a defensible decision boundary. It may preserve the aggregate, replace it with subgroup reporting, adjust it with weights, model it across levels, or require better data.

prose_parameter_dimensions:
The first tuning dimension is aggregation level: individual, subgroup, organization, region, time window, population, or system. The wrong level can produce ecological fallacy or individualistic inference errors.

The second is subgroup granularity. Coarse disaggregation hides meaningful heterogeneity; excessive granularity creates noise, privacy risk, and false precision.

The third is weighting basis. A corrected estimate may weight by sample size, target population, exposure, equal-group concern, or policy relevance. The choice is both statistical and normative.

The fourth is confounder-control depth. Undercontrol preserves distortion; overcontrol can hide distributional patterns that matter for action.

The fifth is consequence threshold. High-stakes decisions require stronger validation before aggregate claims are accepted; low-stakes summaries may need lighter review.

The sixth is claim specificity. The final wording should be no broader than the evidence supports.

prose_variants_and_near_names:
Simpson's Paradox Detection and Resolution is a recognized variant for aggregate relationships that reverse or materially change after stratification. Ecological Fallacy Prevention is a variant for invalid level-of-analysis leaps from group data to individual claims. Masked Subgroup Harm Detection focuses on averages that hide uneven harm or benefit. Target-Population Reweighting Correction focuses on explicit weighting and standardization.

Near names include aggregate bias correction, aggregation distortion audit, aggregate metric disaggregation review, ecological fallacy avoidance, and aggregate-subgroup reconciliation.

name: Simpson's Paradox Detection and Resolution

short_definition: Detects when an aggregate association reverses or materially changes after conditioning on a relevant subgroup or confounder.

distinctive_feature: The decisive signal is a direction change or material relationship change between aggregate and stratified views.

distinct_from_parent: The parent covers many aggregation distortions; this variant focuses on reversal or relationship change caused by stratification and confounding.

still_same_parent_because: The intervention still follows the parent logic: inspect subgroup structure, identify composition effects, and correct the claim boundary.

source_primes: paradox confounding stratification

use_when: An overall comparison or trend conflicts with subgroup comparisons.

use_when: Group composition differs across conditions, cohorts, treatments, regions, or time periods.

Subgroup Structure Map

role: Names strata that may reverse the aggregate pattern.

Confounder and Composition Marker Set

role: Identifies variables that change the direction or strength of the relationship.

Aggregate–Subgroup Comparison Rule

role: Flags reversal or material divergence between levels.

Simpson's Paradox Check

role: Compares aggregate and stratified associations.

Stratified Analysis Protocol

mechanism_type: procedure

role: Runs the subgroup comparisons required for the check.

example_domains:
epidemiology
education analytics
product experimentation

domain: public health

example: Treatment effectiveness is reinterpreted after severity strata reveal that the pooled comparison was composition-driven.

domain: product analytics

example: An overall experiment lift is rejected after platform-specific strata reveal opposite effects.

failure_modes: Overcorrecting for a variable that is downstream of the effect of interest.

failure_modes: Treating any subgroup reversal as definitive when subgroup estimates are unstable.

Ecological Fallacy Prevention

short_definition: Prevents group-level aggregate findings from being used as unsupported individual-level explanations or predictions.

distinctive_feature: The central error is not only weighting or hidden subgroups, but an invalid leap across levels of analysis.

distinct_from_parent: The parent covers any aggregation-induced distortion; this variant specifically guards against group-to-individual inference errors.

still_same_parent_because: The intervention still repairs aggregate interpretation by making units, levels, and claim boundaries explicit.

use_when: A group-level rate, region-level measure, school-level average, or organizational metric is being used to infer individual behavior.

use_when: The level of measurement differs from the level of claim or intervention.

Aggregate Claim Boundary

role: States which level the claim can support.

Aggregation Unit Definition

role: Clarifies whether data describe individuals, groups, regions, or time windows.

name: Interpretation and Correction Decision Rule

role: Rewrites invalid individual-level claims as group-level claims or requires new evidence.

Ecological Fallacy Guardrail

mechanism_type: checklist

role: Checks whether the proposed inference crosses levels without support.

Multilevel Modeling Review

mechanism_type: method

role: Represents cross-level structure when both levels matter.

example_domains:
regional economics
public policy
education metrics

domain: regional policy

example: A county-level statistic is not used to infer individual household behavior without individual-level evidence.

failure_modes: Leaving the audience with a corrected but vague warning instead of a clear valid claim.

Masked Subgroup Harm Detection

short_definition: Surfaces cases where an acceptable aggregate metric hides severe underperformance, harm, exclusion, or benefit concentration in a subgroup.

distinctive_feature: The correction is driven by distributional or equity-relevant harm that is invisible in the average.

distinct_from_parent: The parent is evidence-validity focused; this variant emphasizes subgroup harm and distributive consequences.

still_same_parent_because: The structural move remains disaggregation, comparison, and correction of an overbroad aggregate claim.

use_when: The aggregate metric is being used to declare a policy, product, service, or intervention successful.

use_when: Distributional consequences matter and averages may conceal uneven outcomes.

Equity-Sensitive Slice Set

role: Names dimensions where hidden harm must be inspected.

Minimum Cell Size Guardrail

role: Prevents harmful overinterpretation of sparse slices.

role: Revises success claims when subgroup harm is material.

Subgroup Dashboard with Warning Flags

role: Displays overall and subgroup outcomes together.

name: Representativeness and Nonresponse Review

mechanism_type: workflow

role: Checks whether missing or underrepresented groups are distorting the average.

example_domains:
education
workforce analytics
public service delivery

domain: education

example: A strong district average is revised after subgroup graduation rates reveal hidden failure in a small student population.

failure_modes: Using equity categories mechanically without checking relevance, consent, privacy, or risk of stigmatization.

short_definition: Corrects an aggregate estimate by reweighting observations or groups so the result reflects a justified target population rather than the observed composition.

distinctive_feature: The main corrective move is an explicit weighting or standardization rule.

distinct_from_parent: The parent includes non-weighting corrections; this variant is anchored in target-population adjustment.

still_same_parent_because: Reweighting remains one implementation of aggregate-claim correction rather than a standalone intervention logic.

use_when: The observed sample, response pool, or case mix differs from the population the claim is meant to represent.

use_when: Reliable target-population proportions or exposure bases are available.

Weighting and Normalization Rule

role: Defines the target population and contribution weights.

Aggregation Provenance Record

role: Documents filters and transformations before weighting.

Subgroup Uncertainty Indicator

role: Shows uncertainty introduced by weighted estimates.

Poststratification or Reweighting

role: Adjusts group contributions to a target population.

Sensitivity Analysis by Group

role: Tests whether conclusions depend on the selected weighting scheme.

example_domains:
survey research
market research
public opinion measurement

domain: survey research

example: A survey average is poststratified by age, region, and access channel before being reported as population-level sentiment.

failure_modes: Treating weights as a legitimacy seal while hiding target-population assumptions or residual nonresponse bias.

near_names:
Simpson's Paradox Correction
Ecological Fallacy Avoidance
Aggregate Metric Disaggregation Review
Aggregation Distortion Audit
Aggregate–Subgroup Reconciliation


name: Aggregation Function Design and Weighting

batch: Second Iteration/Batch 02

aliases:
Aggregation Rule Design
Weighted Aggregation Function Design
Composite Score Aggregation Design
Summary Rule Selection
Preference Aggregation Rule Design

slug: aggregation

slug: fairness

slug: decision

slug: invariance

slug: proportionality

slug: optimization

components:
Input Item Set
Aggregation Purpose Statement
Scale Normalization Map
Weight Assignment Scheme
Aggregation Rule
Loss Function or Preservation Target
Tail Visibility Guardrail
Sensitivity Check
Audit Trace
Tie-Break or Exception Rule
Stakeholder Legitimacy Constraint
Drill-Down Path

mechanisms:
Weighted Scoring Rubric
Weight-Sweep Sensitivity Table
Median, Trimmed-Mean, or Quantile Rule
Ranked-Choice or Approval Voting Rule
Ensemble Weighting Table
Dashboard Rollup Formula
Aggregation Bias Audit

essence_one_line: Turn many inputs into one usable output by explicitly choosing the aggregation rule, weights, normalization, and information-loss guardrails.

compression_statement: Aggregation Function Design and Weighting is the intervention pattern of constructing a many-to-one rule that combines observations, preferences, judgments, criteria, or local metrics into a summary, score, ranking, or collective decision while preserving the information that matters for the intended use.

core_intuition: An aggregate is never just a neutral summary. It decides what counts, what is comparable, how much each input matters, and which details disappear. A good aggregation function makes those choices explicit, tests their consequences, and protects against misleading compression.

canonical_formula: aggregate_output = R(normalize(inputs), weights, constraints, preservation_targets, exception_rules)

structural_problem: A system has too many inputs, perspectives, measurements, or signals to act on directly, but a naive summary can erase important variation, introduce hidden value judgments, or produce a decision artifact that users mistake for objective truth.

trigger_conditions: Many inputs must be summarized into one score, ranking, allocation, decision, or reportable metric.

trigger_conditions: Inputs differ in scale, reliability, legitimacy, importance, or decision relevance.

trigger_conditions: Different plausible aggregation rules would produce different outputs.

trigger_conditions: The aggregate will guide consequential choices such as funding, ranking, triage, access, reputation, or public accountability.

trigger_conditions: Stakeholders disagree about which inputs should dominate or how tradeoffs should be represented.

symptoms: Averages hide subgroup differences, tail risk, outliers, or minority preferences.

symptoms: Weights are inherited from spreadsheet defaults, historical precedent, data availability, or convenience rather than explicit reasoning.

symptoms: A composite score produces rankings that users cannot explain or contest.

symptoms: Changing normalization or rule family changes the winner, but the system treats the result as stable.

symptoms: Local details disappear when metrics roll up to higher levels.

symptoms: Stakeholders argue about results because they are really arguing about the aggregation function.

root_tension: The system needs compression for action, but compression inevitably discards or transforms information; the design problem is to choose a rule whose information loss, weighting logic, and legitimacy constraints fit the intended decision.

anti_signatures: The decision can be made directly from a single input without combining multiple sources.

anti_signatures: The goal is only to allocate scarce resources under already-fixed objectives and constraints; constrained_resource_allocation may be a better fit.

anti_signatures: The core problem is only whether value weights are legitimate or procedurally fair; objective_weighting_governance may be the better parent.

anti_signatures: The task is only to create a coarse macro category without a decision-oriented aggregation rule; coarse_graining may be a better fit.

anti_signatures: The artifact is a routine formula, dashboard, or scorecard with no reusable rule-design issue.

structural_intervention: Define the purpose of aggregation, select and normalize inputs, assign justified influence weights, choose a rule family, preserve critical invariants, test sensitivity, and expose the resulting information loss and assumptions.

action_logic: Clarify the decision or representation job the aggregate must perform.

action_logic: Define the input set and check whether it is representative, legitimate, complete, and comparable enough for aggregation.

action_logic: Convert inputs into compatible scales only after documenting what the conversion preserves and distorts.

action_logic: Choose the aggregation rule family that matches the preservation target: mean-like smoothing, median-like robustness, max-min protection, weighted scoring, voting, ensemble averaging, or hierarchical rollup.

action_logic: Assign weights according to an explicit rationale such as reliability, priority, exposure, stake, cost, legitimacy, uncertainty, or normative importance.

action_logic: Add guardrails for outliers, tail cases, minority preferences, missing data, subgroup variation, and tie or exception handling.

action_logic: Run sensitivity and robustness checks to identify whether outputs are artifacts of arbitrary weights, scales, or rule choices.

action_logic: Publish an audit trace that lets users interpret, challenge, and revise the aggregate.

decision_rules: Use mean-like aggregation when total tendency matters and outliers are meaningful or controlled.

decision_rules: Use median, trimmed mean, quantile, or max-min rules when robustness, tail behavior, or worst-case protection matters more than smooth averaging.

decision_rules: Use weighted scoring when heterogeneous criteria must be commensurated and explicit tradeoffs are acceptable.

decision_rules: Use preference aggregation rules when inputs are stakeholder preferences and legitimacy depends on collective-choice procedure.

decision_rules: Use hierarchical rollups when local information must be summarized for higher levels but drill-down and exception visibility remain necessary.

decision_rules: Require sensitivity review whenever small changes in weights or normalization change the winner, ranking, or allocation materially.

decision_rules: Require subgroup or tail visibility when the aggregate may mask unequal burden, concentrated harm, or rare high-severity cases.

target_invariants: Decision relevance: the aggregate must answer the question it is being used to answer.

target_invariants: Traceability: the path from inputs to aggregate output must be reconstructible.

target_invariants: Scale compatibility: input transformations must not create accidental weights or false comparability.

target_invariants: Justified influence: high-impact inputs must have an explicit reason for their weight.

target_invariants: Robustness: plausible rule, weight, or missing-data changes should not silently reverse consequential outputs.

target_invariants: Tail and subgroup visibility: critical extremes or local differences should remain visible when they matter.

target_invariants: Contestability: users should be able to challenge inputs, weights, assumptions, and rule choice.

expected_outcomes: More transparent summary scores, rankings, rollups, or collective decisions.

expected_outcomes: Reduced accidental bias from incompatible scales, arbitrary weights, or hidden missing-data rules.

expected_outcomes: Better fit between aggregate outputs and the decisions they guide.

expected_outcomes: Improved stakeholder trust because weighting and information-loss choices are visible.

expected_outcomes: Earlier detection of aggregation artifacts such as masked subgroup harm or unstable rankings.

expected_outcomes: Reusable aggregation logic that can be audited, adapted, and improved over time.

prose_tradeoffs: The major tradeoff is simplicity versus fidelity. A single number is easy to communicate, but it may erase the very details that should guide action. Transparency also creates a gaming risk: once actors know the formula, they may optimize the measured inputs rather than the underlying purpose. Robust statistics resist noise, but they can understate extreme cases. Weighted scores allow explicit tradeoffs, but they can make morally difficult choices look like arithmetic.

Weighted Composite Score Aggregation

short_definition: Combine heterogeneous criteria into a single score after normalizing scales and assigning explicit weights.

distinctive_feature: The aggregation rule is dominated by explicit weights over normalized criterion scores.

distinct_from_parent: The parent covers all aggregation rule design; this variant focuses on composite scoring where criterion weights are the primary design lever.

still_same_parent_because: It still performs the same core move: converting many inputs into one decision-usable aggregate under stated information-preservation constraints.

use_when: A decision requires one ranked or thresholded result from multiple criteria.

use_when: The criteria are not naturally commensurate and need scale conversion or normalization.

use_when: Stakeholders need to see how each criterion contributes to the final score.

example_domains:
grant scoring
search ranking
vendor selection
budget prioritization

domain: Public budgeting

example: Weight urgency, affected population, cost, and implementation readiness before ranking projects.

domain: Product search

example: Combine relevance, freshness, trust, and conversion signals into one ranking score.

failure_modes: Hidden value judgments become disguised as neutral arithmetic.

failure_modes: Scale normalization artifacts dominate the intended weight structure.

Preference Aggregation Rule Design

short_definition: Choose a voting, ranking, or consensus rule that converts plural preferences into a collective choice.

distinctive_feature: The inputs are preference orderings, ratings, vetoes, or approvals, so legitimacy and strategic behavior matter as much as numerical accuracy.

distinct_from_parent: The parent includes preference aggregation but also covers statistical summaries, score construction, and signal rollups.

still_same_parent_because: It remains an aggregation function problem: plural inputs are mapped into a collective output by an explicit rule.

use_when: Inputs are stakeholder preferences rather than purely measured quantities.

use_when: The system needs a legitimate collective decision under disagreement.

use_when: Different aggregation rules may change the winner, ranking, or accepted option.

example_domains:
committee voting
survey synthesis
governance decisions

domain: Governance

example: Use approval voting when acceptable breadth matters more than first-choice intensity.

domain: Product prioritization

example: Aggregate team preference rankings into a roadmap order.

failure_modes: The rule creates incentives for strategic voting or suppresses minority intensity.

Robust Statistical Summary Aggregation

short_definition: Select summary statistics such as mean, median, trimmed mean, or quantiles so the aggregate reflects the distribution without being misled by noise or outliers.

distinctive_feature: The decisive design choice is robustness to distributional shape, outliers, missing data, and sample representativeness.

distinct_from_parent: The parent covers any aggregation function; this variant specifically handles statistical summaries under sampling and distributional uncertainty.

still_same_parent_because: It is still a many-observation-to-summary mapping governed by explicit information-loss constraints.

use_when: Inputs are observations drawn from a population or process.

use_when: Outliers, skew, or missingness can distort a simple average.

use_when: The summary must preserve distributional features relevant to action.

example_domains:
public health indicators
service-level dashboards
school performance reports

domain: Compensation analysis

example: Report median pay rather than mean pay when extreme executive salaries would dominate the summary.

domain: Operations

example: Use p95 latency rather than average latency when user pain is concentrated in the tail.

failure_modes: A robust statistic hides the total magnitude or tail burden that the decision actually needs.

Ensemble Judgment Aggregation

short_definition: Combine multiple expert, model, or heuristic judgments into a more stable aggregate estimate or decision.

distinctive_feature: The aggregation unit is a judgment source with reliability and dependence properties, not just a scalar feature.

distinct_from_parent: The parent is broader; this variant focuses on combining partially independent judgments under uncertainty.

still_same_parent_because: It still designs a rule for mapping many inputs into one aggregate output.

source_primes: aggregation uncertainty decision

use_when: No single observer, expert, or model is reliably dominant across all cases.

use_when: Judgments have partially independent errors or biases.

use_when: The aggregate must balance accuracy, diversity, calibration, and accountability.

example_domains:
forecasting
medical review panels
machine-learning ensembles

domain: Forecasting

example: Average calibrated probability forecasts from several forecasters while discounting correlated information sources.

domain: Clinical decision support

example: Combine specialist assessments into a triage recommendation with confidence notes.

failure_modes: Correlated errors are mistaken for independent confirmation.

Hierarchical Rollup Aggregation

short_definition: Aggregate lower-level units into higher-level summaries while preserving drill-down paths and preventing category-level masking.

distinctive_feature: The aggregate is produced across nested levels, so rollup boundaries and drill-down capability are central.

distinct_from_parent: The parent includes one-stage aggregation; this variant is specifically multi-level aggregation across a hierarchy or nested partition.

still_same_parent_because: The fundamental intervention remains explicit design of the many-to-one mapping and its information-loss guardrails.

source_primes: aggregation compression representation

use_when: Information must move from local detail to an organizational, regional, or system-level view.

use_when: Higher levels need summaries but lower-level variation remains decision-relevant.

use_when: Rollups may hide subgroup, location, or tail differences.

example_domains:
executive dashboards
public-health surveillance
educational reporting

domain: Corporate reporting

example: Summarize department metrics at division level while preserving exception flags for troubled teams.

domain: Public health

example: Roll county indicators into state-level figures while retaining subgroup breakdowns.

failure_modes: Averages over nested groups create aggregation bias or mask local harm.

near_names:
Aggregation Rule Design
Weighted Aggregation Function
Composite Score Design
Weighted Scoring Formula
Voting Rule Design
Summary Statistic Selection
Ensemble-Average Aggregation
Rollup Metric Design