Temporal Orchestration Design

batch: Batch 19

slug: time

slug: sequencing

slug: foresight

slug: recurrence

slug: rhythm

slug: kairos

slug: latency

slug: scheduling

slug: coordination

slug: dependency

slug: feedback

slug: adaptation

slug: constraint

slug: prioritization

slug: concurrency

components: Precedence, Dependency, and Reversibility Map

components: Duration, Effort, and Elapsed-Time Model

components: Latency, Delay, and Feedback-Lag Register

components: Readiness, Kairos, and Opportunity Window

components: Phase, Synchronization, and Clock Contract

components: Buffer, Slack, and Variability Absorption

components: Handoff, Transition, and State-Acceptance Gate

components: Deadline, Milestone, and Commitment Structure

components: Parallelism, Concurrency, and Contention Policy

components: Temporal Observability and Progress Signal

components: Adaptive Rescheduling and Exception Policy

components: Closure, Stop, and Temporal Learning Loop

mechanisms: Dependency Network and Critical-Path Map

essence_one_line: Treat time as a design variable: order activities, fit durations and pace to the system, act within readiness windows, coordinate phases and recurrence, absorb uncertainty, and adapt when temporal assumptions change.

compression_statement: Temporal Orchestration Design makes timing, sequencing, and duration explicit structural responsibilities. It defines the outcome horizon, events and states, precedence and reversibility, effort and elapsed duration, latency and feedback lag, readiness windows, tempo, phase and clocks, cadence, buffers, handoffs, commitments, concurrency, temporal observability, adaptive rescheduling, and closure. It converts a static list of actions into a governed temporal system whose order and timing fit the environment and remain revisable under uncertainty.

core_intuition: The same action can succeed, fail, or cause harm depending on when it occurs, what preceded it, how long it lasts, what else overlaps, and whether the receiving system is ready. Good orchestration aligns these relations instead of optimizing each activity's speed in isolation.

canonical_formula: temporal_fit = sequence_validity + window_alignment + pace_fit + phase_coherence + protected_slack + timely_feedback + adaptive_revision - lag_staleness - contention - premature_commitment - closure_debt

structural_problem: A system treats activities as an unordered or statically scheduled set even though outcomes depend on precedence, timing windows, duration, lag, pace, phase, overlap, recovery, and adaptation. Local optimization makes tasks faster while the whole system waits, misses readiness, amplifies stale feedback, overloads shared resources, or commits at the wrong time.

trigger_conditions: the same activities yield different outcomes in different orders

trigger_conditions: readiness or opportunity windows open and close

trigger_conditions: subsystems operate at different tempos or phases

trigger_conditions: feedback arrives after the state has changed

trigger_conditions: duration uncertainty propagates across dependencies

trigger_conditions: recurring cycles have become ritualized or misaligned

trigger_conditions: plans require frequent rescheduling as evidence changes

symptoms: chronic date slippage despite adequate effort

symptoms: synchronized peaks and resource contention

symptoms: sampling signals that do not change timing decisions

root_tension: Commitment and synchronization enable coordination, while uncertainty and changing states require slack, reversibility, phase diversity, and adaptation.

anti_signatures: the task is only to choose observation resolution

anti_signatures: one isolated event has no dependencies or receiver window

anti_signatures: the primary problem is signal persistence rather than orchestration

anti_signatures: a fixed physical law determines timing and no intervention is possible

anti_signatures: the issue is only prioritizing items without temporal coupling

structural_intervention: Replace static dates and local speed targets with an explicit temporal model of events, states, dependencies, durations, lags, windows, pace, phase, cadence, buffers, handoffs, commitments, concurrency, observation, adaptation, and closure.

decision_rules: Choose temporal structure from system outcome, not calendar convenience.

decision_rules: Respect prerequisites and irreversible ordering before compressing elapsed time.

decision_rules: Act inside evidence-backed readiness windows with full lead-time accounting.

decision_rules: Match pace to environment and receiver absorption capacity.

decision_rules: Synchronize only what requires alignment; stagger to reduce contention and correlated failure.

decision_rules: Protect slack where variability threatens critical dependencies.

decision_rules: Distinguish forecasts, milestones, promises, and hard constraints.

decision_rules: Reschedule only when evidence is material and propagate downstream effects.

decision_rules: Retire cadences and commitments that no longer produce value.

target_invariants:
valid precedence and state transitions
explicit lag and elapsed duration
readiness and window fit
sustainable tempo
bounded phase drift
protected critical slack
honest commitment semantics
traceable rescheduling
fair and safe exception handling
verified closure and learning

expected_outcomes: retired temporal debt and stronger estimates

prose_essence:
Temporal Orchestration Design treats timing, sequencing, and duration as structural parts of an intervention rather than administrative details. It asks not only *what* must happen, but what must precede what, how long states need to mature or recover, when a receiver is ready, how fast the environment moves, which activities must align or stagger, where uncertainty needs slack, and how timing should change when evidence changes.

A mature temporal system coordinates commitment with adaptation. It gives collaborators enough stable sequence, cadence, clocks, and milestones to act together while preserving reversibility, buffers, exception rules, and rescheduling paths. It also closes temporal obligations so outdated cadences, zombie work, and stale commitments do not persist by inertia.

prose_when_to_use:
Use this archetype when outcome depends materially on order, timing windows, duration, pace, phase, recurrence, overlap, waiting, or feedback delay. It is appropriate for supply chains, migrations, clinical pathways, ecological interventions, organizational change, distributed services, learning sequences, and incident response—any setting where the same actions can succeed or fail because their temporal relations differ.

Do not invoke it merely because work has dates. A calendar, timer, deadline, Gantt chart, sprint, or recurring meeting is a mechanism. The archetype is warranted when temporal structure must be reasoned about, tested, governed, adapted, and retired.

prose_structural_problem:
Static plans often model activities but not the system states that make them effective. They count effort while ignoring waiting and recovery; compress tasks while leaving feedback lag untouched; synchronize everything and create contention; overlap work before interfaces stabilize; or honor an internal deadline after the external opportunity has closed. Local punctuality can coexist with global temporal failure.

The core tension is commitment versus adaptation. Coordination needs promises, recurrence, phase relations, and bounded windows. Dynamic environments need uncertainty, slack, reversible steps, and new timing as evidence arrives. Too much rigidity makes the plan stale; too much motion makes the plan impossible to coordinate.

Temporal orchestration resolves the tension by making temporal assumptions explicit and connecting them to observable state, authority, consequence, and closure.

prose_intervention_logic:
1. **Temporal Objective and Outcome Horizon.** Defines which outcome depends on temporal structure, the relevant horizon, and the consequence of being early, late, too fast, too slow, or out of order. Timing quality is relative to an outcome and environment. A plan can be internally punctual yet temporally wrong for the system it serves.
2. **Event, State, and Transition Inventory.** Names the events, activities, states, decisions, arrivals, releases, pauses, and transitions whose temporal relations must be governed. The inventory separates real state changes from administrative milestones and prevents invisible waiting or recovery periods from disappearing.
3. **Precedence, Dependency, and Reversibility Map.** Specifies which events must precede others, which may overlap, which depend on outputs, and which ordering choices are reversible. Order often changes outcome even when the same activities occur. Reversibility determines whether experimentation or speculative execution is safe.
4. **Duration, Effort, and Elapsed-Time Model.** Estimates active work, waiting, setup, transfer, maturation, recovery, and elapsed time separately, with uncertainty and context ranges. Conflating effort with elapsed duration creates chronic optimism and hides queues, external dependencies, biological limits, and cooling or consolidation periods.
5. **Latency, Delay, and Feedback-Lag Register.** Records sensing, communication, decision, action, transport, effect, and feedback delays that can make control stale or unstable. A response may be correct but arrive against a state that no longer exists. Lag must be included in every timing decision and escalation window.
6. **Readiness, Kairos, and Opportunity Window.** Identifies transient states in which action can succeed, including opening conditions, closing conditions, lead time, and evidence of readiness. An action's quality does not compensate for a closed window. The component guards against both premature forcing and late excellence.
7. **Pace, Tempo, and Environment Alignment.** Matches the rate of action and decision to the environment's change rate, absorption capacity, recovery needs, and competitive or ecological tempo. Tempo mismatch can make individually correct steps harmful. Faster is useful only when the receiving system can absorb and respond.
8. **Phase, Synchronization, and Clock Contract.** Defines shared clocks, phase relations, tolerances, drift limits, and coordination rules for activities that must align or deliberately stagger. Synchronization can enable coherence but can also create correlated peaks and fragility. The contract includes when desynchronization is safer.
9. **Cadence, Recurrence, and Review Cycle.** Sets recurring action, inspection, communication, replenishment, or learning intervals and the conditions for adapting them. Cadence reduces repeated scheduling decisions, but it must remain connected to process dynamics rather than becoming ritualized activity.
10. **Buffer, Slack, and Variability Absorption.** Places time, capacity, inventory, and decision slack where uncertainty and dependency make exact timing brittle. Buffers should protect the governing objective and critical dependencies, not simply hide poor flow or invite work expansion.
11. **Handoff, Transition, and State-Acceptance Gate.** Defines when responsibility and state may move between stages, including entry criteria, transfer latency, acknowledgement, and rollback. Temporal failures often occur between nominally complete steps. A handoff is not finished until the receiving state is ready and accepted.
12. **Deadline, Milestone, and Commitment Structure.** Distinguishes hard constraints, coordination promises, forecasts, review points, and motivational targets, with consequences and renegotiation rules. Treating all dates as equally hard destroys trust; treating all as flexible destroys coordination. The structure makes commitment type explicit.
13. **Parallelism, Concurrency, and Contention Policy.** Determines which activities may proceed simultaneously, where shared resources or decisions conflict, and how results are reconciled. Overlap can shorten elapsed time but increases coordination, rework, and race risk. Concurrency is warranted only when dependencies and interfaces are explicit.
14. **Temporal Observability and Progress Signal.** Measures state, flow, aging, lateness, cycle time, window status, phase, and forecast confidence at the resolution required for intervention. This uses sampling as evidence inside orchestration; it is broader than selecting observation cadence because it links signals to temporal decisions.
15. **Adaptive Rescheduling and Exception Policy.** Replans timing when duration, readiness, priority, capacity, environment, or dependency assumptions change, while preserving traceability and fairness. Adaptation should revise the temporal model, not merely move dates. Exceptions require explicit authority and downstream impact analysis.
16. **Closure, Stop, and Temporal Learning Loop.** Defines completion, pause, abandonment, timeout, recovery, retrospective evidence, and how observed timing changes future estimates and rules. Without closure, temporal systems accumulate zombie work, recurring meetings, stale milestones, and lessons that never recalibrate the next cycle.

The logic is recursive. A readiness gate can change duration forecasts; new latency can close a window; concurrency can consume a buffer; a changed environmental tempo can require phase or cadence revision. Every change returns to the temporal objective and propagates to affected commitments.

prose_key_components:
### Temporal Objective and Outcome Horizon (`temporal_objective_and_outcome_horizon`)

Defines which outcome depends on temporal structure, the relevant horizon, and the consequence of being early, late, too fast, too slow, or out of order.

Semantic canonical mapping retained the complete legacy component record: {"slug":"temporal_objective_and_outcome_horizon","name":"Temporal Objective and Outcome Horizon","role":"Defines which outcome depends on temporal structure, the relevant horizon, and the consequence of being early, late, too fast, too slow, or out of order.","notes":"Timing quality is relative to an outcome and environment. A plan can be internally punctual yet temporally wrong for the system it serves.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Event, State, and Transition Inventory (`event_state_and_transition_inventory`)

Names the events, activities, states, decisions, arrivals, releases, pauses, and transitions whose temporal relations must be governed.

Semantic canonical mapping retained the complete legacy component record: {"slug":"event_state_and_transition_inventory","name":"Event, State, and Transition Inventory","role":"Names the events, activities, states, decisions, arrivals, releases, pauses, and transitions whose temporal relations must be governed.","notes":"The inventory separates real state changes from administrative milestones and prevents invisible waiting or recovery periods from disappearing.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Precedence, Dependency, and Reversibility Map (`precedence_dependency_and_reversibility_map`)

Specifies which events must precede others, which may overlap, which depend on outputs, and which ordering choices are reversible.

Semantic canonical mapping retained the complete legacy component record: {"slug":"precedence_dependency_and_reversibility_map","name":"Precedence, Dependency, and Reversibility Map","role":"Specifies which events must precede others, which may overlap, which depend on outputs, and which ordering choices are reversible.","notes":"Order often changes outcome even when the same activities occur. Reversibility determines whether experimentation or speculative execution is safe.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Duration, Effort, and Elapsed-Time Model (`duration_effort_and_elapsed_time_model`)

Estimates active work, waiting, setup, transfer, maturation, recovery, and elapsed time separately, with uncertainty and context ranges.

Semantic canonical mapping retained the complete legacy component record: {"slug":"duration_effort_and_elapsed_time_model","name":"Duration, Effort, and Elapsed-Time Model","role":"Estimates active work, waiting, setup, transfer, maturation, recovery, and elapsed time separately, with uncertainty and context ranges.","notes":"Conflating effort with elapsed duration creates chronic optimism and hides queues, external dependencies, biological limits, and cooling or consolidation periods.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Latency, Delay, and Feedback-Lag Register (`latency_delay_and_feedback_lag_register`)

Records sensing, communication, decision, action, transport, effect, and feedback delays that can make control stale or unstable.

Semantic canonical mapping retained the complete legacy component record: {"slug":"latency_delay_and_feedback_lag_register","name":"Latency, Delay, and Feedback-Lag Register","role":"Records sensing, communication, decision, action, transport, effect, and feedback delays that can make control stale or unstable.","notes":"A response may be correct but arrive against a state that no longer exists. Lag must be included in every timing decision and escalation window.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Readiness, Kairos, and Opportunity Window (`readiness_kairos_and_opportunity_window`)

Identifies transient states in which action can succeed, including opening conditions, closing conditions, lead time, and evidence of readiness.

Semantic canonical mapping retained the complete legacy component record: {"slug":"readiness_kairos_and_opportunity_window","name":"Readiness, Kairos, and Opportunity Window","role":"Identifies transient states in which action can succeed, including opening conditions, closing conditions, lead time, and evidence of readiness.","notes":"An action's quality does not compensate for a closed window. The component guards against both premature forcing and late excellence.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Pace, Tempo, and Environment Alignment (`pace_tempo_and_environment_alignment`)

Matches the rate of action and decision to the environment's change rate, absorption capacity, recovery needs, and competitive or ecological tempo.

Semantic canonical mapping retained the complete legacy component record: {"slug":"pace_tempo_and_environment_alignment","name":"Pace, Tempo, and Environment Alignment","role":"Matches the rate of action and decision to the environment's change rate, absorption capacity, recovery needs, and competitive or ecological tempo.","notes":"Tempo mismatch can make individually correct steps harmful. Faster is useful only when the receiving system can absorb and respond.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Phase, Synchronization, and Clock Contract (`phase_synchronization_and_clock_contract`)

Defines shared clocks, phase relations, tolerances, drift limits, and coordination rules for activities that must align or deliberately stagger.

Semantic canonical mapping retained the complete legacy component record: {"slug":"phase_synchronization_and_clock_contract","name":"Phase, Synchronization, and Clock Contract","role":"Defines shared clocks, phase relations, tolerances, drift limits, and coordination rules for activities that must align or deliberately stagger.","notes":"Synchronization can enable coherence but can also create correlated peaks and fragility. The contract includes when desynchronization is safer.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Cadence, Recurrence, and Review Cycle (`cadence_recurrence_and_review_cycle`)

Sets recurring action, inspection, communication, replenishment, or learning intervals and the conditions for adapting them.

Semantic canonical mapping retained the complete legacy component record: {"slug":"cadence_recurrence_and_review_cycle","name":"Cadence, Recurrence, and Review Cycle","role":"Sets recurring action, inspection, communication, replenishment, or learning intervals and the conditions for adapting them.","notes":"Cadence reduces repeated scheduling decisions, but it must remain connected to process dynamics rather than becoming ritualized activity.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Buffer, Slack, and Variability Absorption (`buffer_slack_and_variability_absorption`)

Places time, capacity, inventory, and decision slack where uncertainty and dependency make exact timing brittle.

Semantic canonical mapping retained the complete legacy component record: {"slug":"buffer_slack_and_variability_absorption","name":"Buffer, Slack, and Variability Absorption","role":"Places time, capacity, inventory, and decision slack where uncertainty and dependency make exact timing brittle.","notes":"Buffers should protect the governing objective and critical dependencies, not simply hide poor flow or invite work expansion.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Handoff, Transition, and State-Acceptance Gate (`handoff_transition_and_state_acceptance_gate`)

Defines when responsibility and state may move between stages, including entry criteria, transfer latency, acknowledgement, and rollback.

Semantic canonical mapping retained the complete legacy component record: {"slug":"handoff_transition_and_state_acceptance_gate","name":"Handoff, Transition, and State-Acceptance Gate","role":"Defines when responsibility and state may move between stages, including entry criteria, transfer latency, acknowledgement, and rollback.","notes":"Temporal failures often occur between nominally complete steps. A handoff is not finished until the receiving state is ready and accepted.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Deadline, Milestone, and Commitment Structure (`deadline_milestone_and_commitment_structure`)

Distinguishes hard constraints, coordination promises, forecasts, review points, and motivational targets, with consequences and renegotiation rules.

Semantic canonical mapping retained the complete legacy component record: {"slug":"deadline_milestone_and_commitment_structure","name":"Deadline, Milestone, and Commitment Structure","role":"Distinguishes hard constraints, coordination promises, forecasts, review points, and motivational targets, with consequences and renegotiation rules.","notes":"Treating all dates as equally hard destroys trust; treating all as flexible destroys coordination. The structure makes commitment type explicit.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Parallelism, Concurrency, and Contention Policy (`parallelism_concurrency_and_contention_policy`)

Determines which activities may proceed simultaneously, where shared resources or decisions conflict, and how results are reconciled.

Semantic canonical mapping retained the complete legacy component record: {"slug":"parallelism_concurrency_and_contention_policy","name":"Parallelism, Concurrency, and Contention Policy","role":"Determines which activities may proceed simultaneously, where shared resources or decisions conflict, and how results are reconciled.","notes":"Overlap can shorten elapsed time but increases coordination, rework, and race risk. Concurrency is warranted only when dependencies and interfaces are explicit.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Temporal Observability and Progress Signal (`temporal_observability_and_progress_signal`)

Measures state, flow, aging, lateness, cycle time, window status, phase, and forecast confidence at the resolution required for intervention.

Semantic canonical mapping retained the complete legacy component record: {"slug":"temporal_observability_and_progress_signal","name":"Temporal Observability and Progress Signal","role":"Measures state, flow, aging, lateness, cycle time, window status, phase, and forecast confidence at the resolution required for intervention.","notes":"This uses sampling as evidence inside orchestration; it is broader than selecting observation cadence because it links signals to temporal decisions.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Adaptive Rescheduling and Exception Policy (`adaptive_rescheduling_and_exception_policy`)

Replans timing when duration, readiness, priority, capacity, environment, or dependency assumptions change, while preserving traceability and fairness.

Semantic canonical mapping retained the complete legacy component record: {"slug":"adaptive_rescheduling_and_exception_policy","name":"Adaptive Rescheduling and Exception Policy","role":"Replans timing when duration, readiness, priority, capacity, environment, or dependency assumptions change, while preserving traceability and fairness.","notes":"Adaptation should revise the temporal model, not merely move dates. Exceptions require explicit authority and downstream impact analysis.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

### Closure, Stop, and Temporal Learning Loop (`closure_stop_and_temporal_learning_loop`)

Defines completion, pause, abandonment, timeout, recovery, retrospective evidence, and how observed timing changes future estimates and rules.

Semantic canonical mapping retained the complete legacy component record: {"slug":"closure_stop_and_temporal_learning_loop","name":"Closure, Stop, and Temporal Learning Loop","role":"Defines completion, pause, abandonment, timeout, recovery, retrospective evidence, and how observed timing changes future estimates and rules.","notes":"Without closure, temporal systems accumulate zombie work, recurring meetings, stale milestones, and lessons that never recalibrate the next cycle.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["temporal_orchestration_design"],"not_a_mechanism_because":"This is a persistent responsibility in temporal orchestration, not a particular calendar, chart, timer, meeting, or software tool."}

prose_common_mechanisms:
### Dependency Network and Critical-Path Map (`dependency_network_and_critical_path_map`)

**Type:** `analysis`

Maps precedence, float, critical dependencies, alternative paths, and the effect of delay.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"dependency_network_and_critical_path_map","name":"Dependency Network and Critical-Path Map","mechanism_type":"analysis","role":"Maps precedence, float, critical dependencies, alternative paths, and the effect of delay.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Time-Window and Readiness Gate (`time_window_and_readiness_gate`)

**Type:** `decision_gate`

Opens action only when evidence of readiness and remaining opportunity justify commitment.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"time_window_and_readiness_gate","name":"Time-Window and Readiness Gate","mechanism_type":"decision_gate","role":"Opens action only when evidence of readiness and remaining opportunity justify commitment.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Rolling-Wave Schedule (`rolling_wave_schedule`)

**Type:** `planning`

Commits near-term detail while retaining bounded flexibility for later work as uncertainty resolves.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"rolling_wave_schedule","name":"Rolling-Wave Schedule","mechanism_type":"planning","role":"Commits near-term detail while retaining bounded flexibility for later work as uncertainty resolves.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Timebox and Timeout Rule (`timebox_and_timeout_rule`)

**Type:** `control`

Limits duration, triggers review, and defines what happens when work or waiting exceeds the bound.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"timebox_and_timeout_rule","name":"Timebox and Timeout Rule","mechanism_type":"control","role":"Limits duration, triggers review, and defines what happens when work or waiting exceeds the bound.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Phase-Alignment and Staggering Plan (`phase_alignment_and_staggering_plan`)

**Type:** `coordination`

Synchronizes dependencies that need coherence and offsets activities where peaks or correlated failure would be harmful.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"phase_alignment_and_staggering_plan","name":"Phase-Alignment and Staggering Plan","mechanism_type":"coordination","role":"Synchronizes dependencies that need coherence and offsets activities where peaks or correlated failure would be harmful.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Cadence Calendar with Adaptation Rule (`cadence_calendar_with_adaptation_rule`)

**Type:** `schedule`

Makes recurrence explicit and links interval changes to observed dynamics rather than habit.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"cadence_calendar_with_adaptation_rule","name":"Cadence Calendar with Adaptation Rule","mechanism_type":"schedule","role":"Makes recurrence explicit and links interval changes to observed dynamics rather than habit.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Buffer and Float Allocation Model (`buffer_and_float_allocation_model`)

**Type:** `optimization`

Places protective slack at project, stage, queue, or dependency boundaries according to variability and consequence.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"buffer_and_float_allocation_model","name":"Buffer and Float Allocation Model","mechanism_type":"optimization","role":"Places protective slack at project, stage, queue, or dependency boundaries according to variability and consequence.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Event-Triggered Rescheduling Controller (`event_triggered_rescheduling_controller`)

**Type:** `control`

Recomputes timing when readiness, state, delay, capacity, or environmental triggers materially change.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"event_triggered_rescheduling_controller","name":"Event-Triggered Rescheduling Controller","mechanism_type":"control","role":"Recomputes timing when readiness, state, delay, capacity, or environmental triggers materially change.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Temporal Scenario and Stress Test (`temporal_scenario_and_stress_test`)

**Type:** `simulation`

Tests early, late, bursty, stalled, reordered, desynchronized, and overlapping conditions before deployment.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"temporal_scenario_and_stress_test","name":"Temporal Scenario and Stress Test","mechanism_type":"simulation","role":"Tests early, late, bursty, stalled, reordered, desynchronized, and overlapping conditions before deployment.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

### Cycle-Time and Lag Retrospective (`cycle_time_and_lag_retrospective`)

**Type:** `review`

Compares predicted and actual durations, waits, handoffs, windows, and effects to recalibrate future temporal rules.

Selection constraints and retained implementation evidence: fit the temporal objective and dependency structure; expose uncertainty and downstream effects; preserve authority, fairness, and recovery rules; Complete legacy mechanism record retained: {"slug":"cycle_time_and_lag_retrospective","name":"Cycle-Time and Lag Retrospective","mechanism_type":"review","role":"Compares predicted and actual durations, waits, handoffs, windows, and effects to recalibrate future temporal rules.","maturity":"provisional","instantiates_archetypes":["temporal_orchestration_design"],"selection_constraints":["fit the temporal objective and dependency structure","expose uncertainty and downstream effects","preserve authority, fairness, and recovery rules"],"not_an_archetype_because":"This is a concrete analysis, schedule, gate, controller, simulation, or review used to implement the broader temporal lifecycle."}

prose_failure_modes:
### Correct Action Wrong Time

**Cause.** An otherwise sound action arrives before readiness or after the state has changed.

**Mitigation.** Use window, lead-time, and lag evidence tied to the receiving system. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Hidden Waiting Time

**Cause.** Plans count active effort but omit queues, approvals, transport, maturation, recovery, and setup.

**Mitigation.** Model elapsed-time components separately and measure them. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Tempo Mismatch

**Cause.** The action rate exceeds absorption or lags environmental change.

**Mitigation.** Match pace to both environment and receiving capacity; use buffers or parallelism selectively. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Stale Feedback Control

**Cause.** Delays cause decisions to respond to an obsolete state.

**Mitigation.** Register end-to-end lag, forecast forward, reduce delay, or slow control gain. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Over Synchronization

**Cause.** All activities peak together, creating contention or correlated failure.

**Mitigation.** Stagger where simultaneity is not required and preserve phase diversity. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Under Synchronization

**Cause.** Dependent activities arrive out of phase and wait, spoil, conflict, or miss windows.

**Mitigation.** Define clock contract, tolerance, handoff readiness, and drift correction. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Deadline Semantic Confusion

**Cause.** Forecasts, aspirations, review points, and hard constraints are treated as identical commitments.

**Mitigation.** Label date type, authority, consequence, and renegotiation rule. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Buffer Consumption And Hiding

**Cause.** Slack is consumed by added work or used to conceal poor flow.

**Mitigation.** Protect buffer purpose, expose drawdown, and escalate before exhaustion. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Parallelism Rework Trap

**Cause.** Activities overlap despite unstable interfaces or unresolved prerequisites.

**Mitigation.** Map dependencies, bound speculation, and define reconciliation and rollback. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Schedule Thrashing

**Cause.** Frequent changes create more coordination cost than adaptation value.

**Mitigation.** Use materiality thresholds, freeze windows, and downstream impact checks. Re-test downstream dependencies, commitment semantics, and closure after the change.

### Ritualized Cadence

**Cause.** Recurring events continue after their decision or learning value disappears.

**Mitigation.** Require outputs, interval review, and retirement criteria. Re-test downstream dependencies, commitment semantics, and closure after the change.

### No Temporal Closure

**Cause.** Zombie work, stale milestones, and recurring obligations survive without outcome or stop rule.

**Mitigation.** Define timeout, abandonment, closure evidence, and owner release. Re-test downstream dependencies, commitment semantics, and closure after the change.

prose_neighbor_distinctions:
### `temporal_resolution_sampling_rate_design`

Chooses when and how finely to observe a process; temporal orchestration uses observation to govern the process's order, windows, pace, phase, commitments, and adaptation.

### `cadence_design`

Establishes recurring operational rhythm; cadence is one component of the broader temporal lifecycle.

### `cycle_phase_alignment`

Aligns cyclic phases; orchestration also governs noncyclic precedence, duration, windows, buffers, and closure.

### `synchrony_induction_and_rhythm_alignment`

Induces synchrony or shared rhythm; orchestration can deliberately stagger and coordinate different rates.

### `cognitive_workflow_sequencing`

Orders reasoning steps; orchestration is cross-domain and includes tempo, lags, phase, buffers, commitments, and adaptation.

### `order_sensitive_configuration`

Specializes configurations where order changes result; orchestration includes many temporal relations beyond order.

### `concurrent_cross_functional_integration`

Overlaps specialized work to surface conflict early; concurrency is one bounded temporal policy.

### `staged_commitment`

Delays irreversible commitment through stages; orchestration also manages routine timing and coordination after commitment.

### `adaptive_scheduling`

If present, specializes schedule adjustment; orchestration supplies the full event, dependency, window, pace, phase, and closure model.

### `signal_persistence_and_refresh_design`

Maintains signal strength across time or space; orchestration governs the timing structure of actions and transitions.

### `periodic_review_and_reset`

Runs recurring review and reset; temporal orchestration governs when recurrence is appropriate and when it should change or end.

### `priority_based_admission`

Chooses which work enters under constraint; orchestration governs subsequent temporal relations and commitments.

The key boundary is between observing time and designing time. Temporal Resolution and Sampling Rate Design determines what temporal changes become visible. Temporal Orchestration Design uses that evidence to alter order, pace, windows, phase, commitments, and adaptation. Both can coexist in one system without being duplicates.

prose_cross_domain_examples:
### Supply Chain

A cold-chain operation sequences preparation, transport, handoff, and use around maturation, travel, temperature recovery, and expiry windows. Precedence, elapsed duration, windows, handoffs, buffers, and closure interact. The record exposes dependencies, duration and lag, window evidence, commitments, buffers, adaptation, and closure.

### Organizational Change

A change program times training, authority transfer, system release, support, and old-process retirement to local readiness. Kairos, pace, commitments, concurrency, and adaptive rescheduling determine adoption. The record exposes dependencies, duration and lag, window evidence, commitments, buffers, adaptation, and closure.

### Distributed Systems

Services coordinate clocks, leases, retries, timeouts, and staggered maintenance across variable latency. Lag, phase, concurrency, expiry, and correlated peaks matter. The record exposes dependencies, duration and lag, window evidence, commitments, buffers, adaptation, and closure.

### Ecology

Habitat intervention aligns with breeding, migration, recovery, and seasonal resource cycles rather than administrative dates. Environmental tempo and transient windows govern effectiveness. The record exposes dependencies, duration and lag, window evidence, commitments, buffers, adaptation, and closure.

### Clinical Workflow

A pathway sequences tests, stabilization, intervention, observation, and discharge while protecting recovery and escalation windows. State gates, duration uncertainty, latency, handoffs, and safety closure are load-bearing. The record exposes dependencies, duration and lag, window evidence, commitments, buffers, adaptation, and closure.

### Extended service-migration example

A multi-site service migration begins by defining the outcome horizon and the states each site must cross. A dependency map separates reversible preparation from irreversible cutover. Duration estimates include approval, data transfer, validation, user learning, and recovery rather than engineering effort alone. Sites enter a readiness window only after local evidence passes; the program staggers cutovers to protect support capacity while keeping shared interfaces phase-compatible. Buffers protect rollback and escalation, commitments are labeled as forecasts or hard external windows, and event-triggered replanning propagates every change to downstream sites. Closure requires verified old-system retirement, released temporary staffing, and a cycle-time retrospective that changes the next site's model.

Across domains, the artifact changes but the lifecycle remains: model temporal relations, fit them to the receiving system, preserve honest commitments, adapt on material evidence, and close temporal obligations.

prose_non_examples:
- Choosing only a sampling interval
- Sorting independent tasks by preference
- Refreshing a fading message
- Running a standing meeting with no outcome or retirement rule
- Setting an arbitrary deadline without dependency or readiness evidence
- Making every subsystem run at the same pace despite different natural rates

A plan with dates is not automatically temporally orchestrated. Nor is a fast process necessarily well timed. The archetype requires explicit state, dependency, window, lag, pace, commitment, adaptation, and closure reasoning.

prose_tradeoffs:
- **Speed versus readiness:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Commitment versus adaptability:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Synchronization versus phase diversity:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Parallelism versus rework:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Precision versus uncertainty:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Buffering versus delay:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Cadence stability versus responsiveness:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Deadline pressure versus quality:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Local utilization versus end-to-end flow:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.
- **Frequent replanning versus coordination stability:** the balance depends on reversibility, uncertainty, receiver readiness, dependency consequence, and the cost of being early or late.

Temporal tradeoffs distribute burden. Faster completion may mean overtime, reduced recovery, higher inventory, rushed consent, or delayed work elsewhere. Mature orchestration makes those transfers visible rather than treating speed as free.

prose_invariants_to_preserve:
- Precedence and state-transition constraints remain valid.
- Elapsed duration includes waits, handoffs, recovery, and feedback lag.
- Actions occur only inside legitimate readiness or opportunity conditions.
- Pace remains compatible with environment and receiver capacity.
- Synchronization and staggering decisions are explicit and monitored for drift.
- Critical slack is protected and visible.
- Commitment types and consequences remain honest.
- Concurrency has bounded interfaces and reconciliation paths.
- Rescheduling is traceable and propagates downstream effects.
- Cadences, timeouts, and temporary obligations have working closure rules.

The invariant is not adherence to the original date. It is temporal fitness: the system preserves valid dependencies, readiness, safety, coordination, and outcome as evidence changes.

prose_target_outcomes:
- fewer correct actions delivered at the wrong time
- valid sequence with less hidden waiting and rework
- better fit between action tempo and environmental change
- reliable readiness and opportunity-window use
- controlled synchronization without correlated peaks
- visible and protected buffers
- clearer milestones and commitment trust
- safe, bounded concurrency
- adaptive schedules without thrashing
- retired zombie work and stronger future estimates

Success is measured in system outcomes, not calendar compliance or local utilization. A deliberate pause, stagger, buffer, rollback, or deadline change can be the correct temporal intervention.

prose_parameter_dimensions:
- **Outcome horizon:** The future interval over which timing consequences matter.
- **Sequence rigidity:** How much precedence is mandatory versus preferred or reversible.
- **Duration uncertainty:** Ranges and distributions for active work, waiting, recovery, and maturation.
- **End-to-end latency:** Sensing, decision, action, transport, effect, and feedback delays.
- **Window width:** How long readiness or opportunity remains open and how much lead time is required.
- **Tempo:** The rate of action relative to environmental change and receiver absorption.
- **Phase tolerance:** Permitted lead, lag, and drift among coordinated processes.
- **Cadence interval:** The recurrence period and trigger for review or adaptation.
- **Buffer location:** Where slack protects critical dependencies without hiding poor flow.
- **Commitment hardness:** Forecast, target, milestone, promise, regulatory constraint, or physical limit.
- **Concurrency level:** How much overlap is allowed before contention and rework dominate.
- **Freeze window:** How close to execution timing becomes stable absent emergency evidence.
- **Rescheduling threshold:** The materiality of change required to revise commitments.
- **Timeout:** When waiting or work stops, escalates, rolls back, or changes mode.
- **Observation resolution:** The temporal granularity needed to detect state and intervene.
- **Retirement interval:** How often recurring obligations are reviewed for continuation or closure.

Parameters interact. More concurrency can consume buffers; tighter synchronization can increase peak contention; harder commitments can require larger uncertainty margins; faster cadence can outpace absorption; more frequent replanning can destabilize every downstream promise. Tune the system as a coupled temporal model.

prose_variants_and_near_names:
### Sequence-Sensitive Orchestration

Govern work where outcome depends primarily on precedence, state transitions, and irreversible ordering choices.

Makes precedence and reversibility more important than wall-clock synchronization. It retains the same event, duration, lag, handoff, observability, and learning responsibilities.

Use it when the same activities produce different outcomes in different orders; and outputs are prerequisites; and rollback is costly or impossible. Characteristic risks include premature step, irreversible lock-in, hidden prerequisite, rework loop.

### Window-Sensitive Action

Prepare and commit action around a transient readiness or opportunity window whose opening and closure determine effectiveness.

Centers lead time, readiness evidence, commitment timing, and window closure. The parent supplies duration, lag, pace, buffer, observation, exception, and learning controls.

Use it when receiver readiness changes; and the environment creates a short opportunity; and early and late action both fail. Characteristic risks include premature action, late commitment, false readiness, missed lead time.

### Multi-Rate Phase Coordination

Coordinate subsystems operating at different natural rates and phases without forcing harmful uniform tempo.

Makes phase relations, clock contracts, drift, beat frequencies, and cross-rate buffers load-bearing. Temporal objective, delays, observability, buffers, adaptation, and learning remain shared.

Use it when subsystems have different cycle times; and handoffs cross clock domains; and synchronization and staggering both matter. Characteristic risks include forced synchronization, phase drift, resonant congestion, starvation.

### Adaptive Temporal Orchestration

Continuously revise sequence, pace, windows, and commitments as state and duration evidence changes.

Uses event-triggered rescheduling and explicit stability limits rather than periodic manual replanning alone. Adaptation changes temporal parameters but preserves the same objective, dependencies, commitments, and closure contract.

Use it when uncertainty resolves during execution; and environmental tempo changes; and fixed plans become stale quickly. Characteristic risks include schedule thrashing, untraceable priority change, gaming triggers, downstream instability.

`timing_and_sequencing_design`, `temporal_structure_design`, and `time_aware_orchestration` are near names. Critical-path charts, readiness gates, timeboxes, phase plans, cadence calendars, buffers, and rescheduling controllers remain mechanisms.

Sequence-Sensitive Orchestration

distinct_from_parent: The parent also covers pace, windows, phase, recurrence, buffers, and adaptive timing.

source_primes: temporal_dynamics sequencing dependency

use_when: the same activities produce different outcomes in different orders

failure_modes:
premature step
irreversible lock-in
hidden prerequisite
rework loop

Window-Sensitive Action

distinct_from_parent: Many temporal systems have no narrow kairotic window.

source_primes: temporal_dynamics kairos time

use_when: the environment creates a short opportunity

failure_modes:
premature action
late commitment
false readiness
missed lead time

Multi-Rate Phase Coordination

distinct_from_parent: The parent also covers noncyclic sequence, windows, commitments, and closure.

use_when: synchronization and staggering both matter

failure_modes:
forced synchronization
phase drift
resonant congestion
starvation

Adaptive Temporal Orchestration

distinct_from_parent: The parent can use stable timing when dynamics are predictable.

source_primes: temporal_dynamics adaptation feedback

use_when:
uncertainty resolves during execution
environmental tempo changes
fixed plans become stale quickly

failure_modes:
schedule thrashing
untraceable priority change
gaming triggers
downstream instability

near_names:
Timing and Sequencing Design
Temporal Structure Design
Time-Aware Orchestration


type: archetype

source: generated

name: Temporal Resolution and Sampling Rate Design

batch: Second Iteration/Batch 07

aliases:
Temporal Sampling Resolution Design
Sampling Cadence Selection
Observation Granularity Design
Measurement Cadence Framework
Time-Step Selection Protocol
Temporal Discretization Design

slug: periodicity

slug: observability

slug: stationarity

slug: discreteness

slug: continuity

slug: attention

slug: threshold

slug: scale

slug: approximation

slug: intermittency

essence_one_line: Choose the time resolution of observation so important changes are visible without creating aliasing, blind spots, noise, or overload.

compression_statement: This archetype treats temporal resolution as a design decision rather than a default. It identifies the relevant change rates, transition events, delays, and decision latencies; chooses whether the process should be observed continuously, periodically, in event-triggered bursts, or at multiple resolutions; and tests whether the chosen cadence is fine enough to capture meaningful dynamics but coarse enough to avoid needless noise, cost, alert fatigue, and false precision.

core_intuition: The same process can look stable, chaotic, delayed, or causal depending on how often it is observed. A good cadence makes the important temporal structure observable without pretending that more timestamps always mean better understanding.

canonical_formula: Choose observation rule R = {mode, interval Δt, trigger, aggregation window, retention horizon} such that max_relevant_change_rate is observable while cost, noise, latency, and overload remain within bounds.

structural_problem: A changing process is observed, modeled, or acted on at a time resolution that does not match the dynamics that matter, causing missed events, aliased patterns, delayed response, noisy over-measurement, or unnecessary burden.

trigger_conditions: Important system states change between ordinary observation points.

trigger_conditions: The same data stream could be treated as continuous, periodic samples, event records, or aggregated intervals.

trigger_conditions: Decision makers need to detect excursions, trends, regime changes, or short-lived events before they decay or compound.

trigger_conditions: High-frequency data creates noise, alert fatigue, storage burden, privacy exposure, or misleading apparent precision.

trigger_conditions: The action latency, reporting cadence, or control loop depends on how often new observations arrive.

trigger_conditions: Different stakeholders need different temporal views of the same process: raw events, near-real-time dashboards, daily aggregates, or long-run trends.

symptoms: Critical events occur between samples and are reconstructed only after failure or complaint.

symptoms: A dashboard oscillates wildly because it shows data at a finer resolution than the process can meaningfully support.

symptoms: Periodic reports miss intraday, seasonal, burst, or threshold-crossing behavior.

symptoms: The team collects high-frequency telemetry but aggregates it before anyone can use the temporal detail.

symptoms: Alerts arrive so often that users ignore them, mute them, or build unofficial filters.

symptoms: Causal claims depend on arbitrary before/after windows rather than a documented timing model.

symptoms: Sampling choices are copied from tools or vendors without stating what dynamics they are meant to capture.

root_tension: Temporal detail improves observability only when it matches the process and decision. Too little resolution hides dynamics; too much resolution can amplify noise, cost, cognitive load, surveillance burden, and false confidence.

anti_signatures: The phenomenon is effectively static for the decision horizon and temporal cadence is not material.

anti_signatures: The main problem is population representativeness rather than when observations occur; Representative Sampling Design is closer.

anti_signatures: The main problem is inferring hidden state from noisy observations after the cadence is fixed; State Estimation is closer.

anti_signatures: The main problem is choosing whether a conceptual model is discrete or continuous without operational cadence or sampling consequences; Discrete-Continuous Model Selection is closer.

anti_signatures: The main problem is validating whether a baseline remains stable across time; Stationarity Validation is closer.

structural_intervention: Make temporal resolution explicit: characterize the process change rate and decision latency, choose a sampling mode and interval, specify event triggers and aggregation windows, test for aliasing and overload, and document when the cadence must adapt.

action_logic: Define the process, state variables, decision uses, and time-sensitive outcomes that must be observed.

action_logic: Estimate relevant change rates, event durations, delay tolerances, seasonal cycles, burst behavior, and decay dynamics.

action_logic: Choose a representation mode: continuous stream, fixed-interval sampling, event-triggered capture, batch interval, rolling window, or multi-resolution view.

action_logic: Set an initial interval, trigger, and aggregation window that can capture the fastest material dynamics without overwhelming users or systems.

action_logic: Run aliasing, missed-event, alert-fatigue, storage, privacy, and cost checks against realistic scenarios.

action_logic: Preserve enough raw or higher-resolution data for retrospective analysis where delayed reconstruction matters.

action_logic: Define adaptation rules for raising or lowering cadence during suspected incidents, quiet periods, transitions, or known seasonal cycles.

action_logic: Report downstream analyses with the cadence, aggregation, latency, and blind-spot assumptions visible.

decision_rules: Use finer sampling when event duration is short, transition speed is high, or delayed detection has high cost.

decision_rules: Use coarser aggregation when the process changes slowly, users need stable trends, or high-frequency noise would dominate interpretation.

decision_rules: Use event-triggered or burst sampling when rare short-lived events matter more than continuous routine monitoring.

decision_rules: Use multi-resolution views when operational responders need immediacy while planners need stable trend context.

decision_rules: Treat cadence as invalid when plausible dynamics can occur between observations without any capture path.

decision_rules: Do not collect high-frequency data merely because the tool permits it; tie frequency to use, risk, and burden.

target_invariants: The sampling interval or trigger logic is explicitly documented.

target_invariants: The chosen cadence is tied to relevant process dynamics and decision latency.

target_invariants: Aggregation windows do not erase events that the decision requires.

target_invariants: Users are not overloaded with resolution they cannot interpret or act on.

target_invariants: Raw, aggregate, and retrospective views preserve known lineage between time scales.

target_invariants: Cadence changes are governed by declared rules rather than ad hoc panic or convenience.

expected_outcomes: Important transitions, excursions, bursts, and delays become observable at the right time scale.

expected_outcomes: Models and dashboards stop confusing sampling artifacts with real dynamics.

expected_outcomes: Monitoring systems reduce both missed events and alert fatigue.

expected_outcomes: Temporal assumptions become auditable in reports, experiments, and operational decisions.

expected_outcomes: Different time scales can coexist without silently contradicting one another.

prose_essence:
Temporal Resolution and Sampling Rate Design is the pattern of choosing how often, how continuously, and at what aggregation level a changing process should be observed. It treats time scale as part of the solution design rather than as a default inherited from tools, reporting cycles, or convenience.

The archetype asks what temporal structure must remain visible. Does the system change continuously or in events? How fast can important transitions happen? How long can the decision tolerate delay? Which details must be retained raw, and which can be safely summarized? A good answer makes the dynamics observable without drowning users in noise.

prose_key_components:
### Process Dynamics Profile

This profile describes change rates, event durations, cycles, burst behavior, and decay. It is the reason a cadence exists. Without it, sampling intervals are only habits or tool settings.

### Decision Latency Requirement

Observation only matters if it arrives in time for interpretation or action. A high-stakes control loop, a clinical escalation path, and a quarterly governance review can all need different temporal resolutions.

### Sampling Mode Selection

The design may use continuous streams, fixed-interval samples, event-triggered capture, batch intervals, rolling windows, or multi-resolution views. The mode should match both process dynamics and human or system response capacity.

### Sampling Interval or Trigger Rule

The interval or trigger specifies when observations occur. It should be explicit enough that another evaluator can see what events might be captured and what events might be missed.

### Aggregation Window Definition

Aggregation makes data interpretable, but it also hides sequence and short excursions. This component states what raw data become minute, hourly, daily, or quarterly summaries and what lineage is preserved.

### Aliasing and Missed-Event Check

This guardrail asks whether plausible dynamics could occur between samples or be transformed into a misleading slower pattern. It is especially important for periodic, oscillating, bursty, or rapidly changing phenomena.

### Oversampling Burden Check

The opposing guardrail asks whether the chosen resolution creates noise, cost, storage burden, privacy exposure, alert fatigue, or false precision. The best cadence is often not the maximum cadence.

### Cadence Adaptation Policy

Processes may need different temporal resolution during incidents, quiet periods, seasonal cycles, or suspected transitions. The adaptation policy defines when to raise, lower, or switch cadence.

prose_common_mechanisms: Common mechanisms include fixed-interval sampling schedules, adaptive sampling-rate controllers, event-triggered capture gates, anti-aliasing filters, rolling-window aggregates, multi-resolution dashboards, alert cooldown rules, and burst-sampling protocols. These are mechanisms, not the archetype itself. The archetype is the reasoning pattern that explains why one of those mechanisms fits the time dynamics.

prose_neighbor_distinctions: Intermittent Sampling is a close neighbor but focuses on partial or non-continuous observation strategies. This archetype is broader: it chooses the temporal resolution, observation mode, trigger logic, and aggregation level. Discrete-Continuous Model Selection chooses the conceptual representation; this archetype operationalizes time-scale choice. State Estimation infers hidden states from observations; this archetype decides when observations should exist. Stationarity Validation tests stability over time; this archetype chooses the time scale needed to see stability or breakage.

prose_invariants_to_preserve: The cadence must be explicit. The chosen resolution must be tied to process dynamics and decision latency. Aggregation must not silently erase events that the decision requires. Higher-frequency data must not overload users or create unjustified surveillance. Adaptive cadence changes must be marked so later analyses do not compare unlike observation regimes.

prose_target_outcomes: A successful application makes short-lived events, transitions, bursts, and delays visible at the right scale. It reduces sampling artifacts, alert fatigue, and arbitrary time-window debates. It lets operational, analytic, and strategic users work at different time scales without confusing those scales.

Fixed-Interval Cadence Design

short_definition: A clock-based variant that sets a regular observation interval matched to expected process change.

distinctive_feature: The time base is a fixed interval rather than an event trigger or adaptive switch.

distinct_from_parent: The parent includes any cadence mode; this variant is regular clock sampling.

still_same_parent_because: It still solves temporal resolution design by matching interval to dynamics and decision latency.

source_primes: time periodicity observability

use_when: The process has stable enough dynamics for periodic observation.

use_when: Users need predictable reports, samples, or control-loop updates.

example_domains:
industrial monitoring
clinical checks
operations reporting

domain: hospital operations

example: Stable patients are checked every four hours while unstable patients exit the fixed cadence.

failure_modes: Choosing the interval because it is convenient rather than because it captures change

Event-Triggered Temporal Capture

short_definition: A trigger-based variant that increases or initiates observation when a threshold, state transition, or event occurs.

distinctive_feature: The capture decision is state-dependent rather than clock-only.

distinct_from_parent: The parent includes all temporal resolution choices; this variant centers on event logic.

still_same_parent_because: It remains a temporal-resolution pattern because the trigger determines when higher-resolution time data exists.

source_primes: time threshold observability

use_when: Events are rare, short, costly, or bursty.

use_when: Continuous high-frequency monitoring would be wasteful or overwhelming.

example_domains:
security logging
fault diagnostics
public health surveillance

domain: equipment monitoring

example: A machine logs coarse summaries normally, then captures high-resolution vibration around a threshold crossing.

failure_modes: Trigger thresholds too insensitive to pre-failure signals

failure_modes: Missing events because only known triggers are instrumented

Adaptive Sampling-Rate Adjustment

short_definition: A feedback variant that changes observation frequency as volatility, risk, uncertainty, or decision urgency changes.

distinctive_feature: Sampling rate is a controlled variable that responds to conditions.

distinct_from_parent: The parent may choose a static cadence; this variant defines rules for cadence change.

still_same_parent_because: It still manages the fit between temporal observation and process dynamics.

source_primes: time feedback adaptive_capacity

use_when: Normal and abnormal periods need different temporal resolution.

use_when: Data cost or attention cost makes permanent high frequency undesirable.

example_domains:
network telemetry
environmental sensing
clinical monitoring

domain: network operations

example: Telemetry increases to second-level samples during incidents and returns to minute summaries when stable.

failure_modes: Rate switching creates gaps or inconsistent comparability

failure_modes: Adaptive rules hide low-frequency precursors

Multi-Resolution Temporal Viewing

short_definition: A scale variant that preserves linked views at multiple time resolutions for different analytic or operational purposes.

distinctive_feature: It explicitly manages multiple temporal scales rather than choosing one cadence.

distinct_from_parent: The parent can choose one or several time scales; this variant centers on coherent cross-scale viewing.

still_same_parent_because: The central design act is still temporal resolution selection and lineage between views.

source_primes: time scale observability

use_when: Operators need immediate signals while planners need stable trends.

use_when: Aggregates and raw events both matter and must not be confused.

example_domains:
service reliability
finance
public health

domain: service reliability

example: An incident dashboard links second-level traces, minute alerts, hourly SLO summaries, and monthly reliability trends.

failure_modes: Aggregates contradict raw event views without explanation

Anti-Aliasing Sampling Design

short_definition: A fidelity-protection variant that prevents sampling cadence from turning fast or periodic dynamics into false slower patterns.

distinctive_feature: It focuses on aliasing artifacts and fidelity loss.

distinct_from_parent: The parent balances many cadence goals; this variant prioritizes under-sampling artifact prevention.

still_same_parent_because: Aliasing prevention is a core reason for temporal-resolution design.

use_when: Periodic or fast-changing signals could be misread at the chosen sampling rate.

use_when: Downsampled data will drive model or operational decisions.

example_domains:
sensor systems
market data
audio or vibration diagnostics

domain: sensor systems

example: A vibration sensor uses filtering and a fast enough cadence before producing lower-frequency maintenance summaries.

failure_modes: Overconfidence in a neat trend that is a sampling artifact

failure_modes: Filters remove the very signal being sought

Alert-Fatigue Cadence Balancing

short_definition: A human-attention variant that sets monitoring frequency and notification rules so high-resolution data does not overwhelm responders.

distinctive_feature: It treats attention capacity as a binding temporal-resolution constraint.

distinct_from_parent: The parent includes technical fidelity; this variant emphasizes human response limits.

still_same_parent_because: It still resolves cadence selection for a changing process.

source_primes: time attention cognitive_load

use_when: Monitoring is actionable only if humans can attend to signals.

use_when: False positives, repeated alerts, or noisy refresh cycles cause ignored warnings.

example_domains:
clinical alarms
security operations
site reliability engineering

domain: clinical monitoring

example: A unit suppresses duplicate alarms within a short window while escalating sustained deterioration.

failure_modes: Cool-down rules hide repeated independent events

failure_modes: Suppressing alerts to reduce annoyance rather than calibrating risk

near_names:
Sampling Cadence Selection
Observation Frequency Design
Monitoring Frequency Design
Time-Step Selection