WHOLE-CABIN BOARDING SIMULATION
DETERMINISTIC STRESS DATASETS FOR MULTI-REGION DEPENDENCY EVALUATION
Version: Stress Dataset Research Log v1

Purpose
-------
This document begins the final controlled validation programme for the completed
Reactive Dependency-Region Management Architecture.

The preceding architecture report is now frozen. Its final implementation
provides reactive congestion detection, persistent dependency-region lifecycle
tracking, three-tick confirmation, state-change-aware evaluation and hierarchical
dependency-region prioritisation.

The present programme does not redesign that architecture. It investigates
whether deterministic input datasets can be constructed to produce simultaneous
confirmed dependency regions and thereby exercise architectural capabilities that
rarely appeared in the conventional benchmark scenarios.

The architecture is therefore the fixed reference implementation. Dataset
construction becomes the independent variable.


======================================================================
RESEARCH BOUNDARY
======================================================================

This research does not reopen or replace the conclusions of the completed
Reactive Dependency-Region Management Architecture.

It introduces one new independent research variable:

    DETERMINISTIC STRESS DATASET CONSTRUCTION

The programme may alter deterministic passenger manifests, seat-distribution
patterns and queue ordering only when those changes are explicitly documented as
properties of the dataset under test.

It does not introduce:

- probabilistic human behaviour;
- passenger hesitation or personal judgement;
- asynchronous tick execution;
- overtaking;
- reverse aisle movement;
- aisle switching;
- movement beyond an assigned row;
- seat reassignment;
- predictive congestion management;
- advance aisle-space reservation;
- airline policy unless later isolated as a separate deterministic dataset
  variable.

The completed architecture must process each dataset without being changed to
accommodate the expected result.


======================================================================
FIXED REFERENCE ARCHITECTURE
======================================================================

The following architectural components remain enabled and unchanged throughout
this research programme:

- synchronous global movement;
- maximum one forward aisle tile per passenger per tick;
- one unique assigned seat per passenger;
- fixed serving aisle for every passenger;
- no movement beyond the assigned row;
- deterministic stand-yield-sit-reseat transactions;
- middle-bank reservation;
- dependency-aware precedence;
- adaptive rear-boundary forward yield;
- guarded forward-yield checks;
- row-bounded congestion clustering;
- persistent dependency-region lifecycle tracking;
- three-tick region confirmation;
- state-change-gated evaluation;
- deterministic hierarchical region prioritisation;
- 1,500-tick no-seat-progress stall detector;
- 30,000 maximum ticks.

Any later change to one of these components would constitute a new architecture
programme and must not be presented as a stress-dataset experiment.


======================================================================
CONTROLLED CONDITIONS
======================================================================

Within every paired experiment, the following must remain identical unless the
experiment explicitly identifies one of them as the dataset variable:

- cabin configuration;
- total seat capacity;
- occupancy rate;
- scenario seed;
- passenger count;
- entry-headway range;
- synchronous execution;
- passenger movement rules;
- seat-event prerequisites;
- blocker movement;
- yield-space rules;
- middle-bank reservation;
- dependency-region detection;
- lifecycle matching, updating and dissolution;
- confirmation threshold;
- state-change signature;
- region-priority hierarchy;
- stall and maximum-tick limits;
- output instrumentation.

Experiment 1 changes the deterministic dataset family while preserving the
entire architecture and the scenario-level cabin, occupancy, headway and seed.


======================================================================
DATASET CONSTRUCTION CONTRACT
======================================================================

A deterministic stress dataset is a reproducible passenger manifest and entry
ordering designed to increase the opportunity for demanding dependency-region
structures without modifying passenger movement after admission.

All stress datasets must obey the following rules:

1. Every passenger retains one unique assigned seat.
2. Every passenger retains one fixed serving aisle.
3. Dataset construction completes before simulation movement begins.
4. Dataset generation is reproducible from the recorded seed.
5. The boarding engine receives a completed manifest and queue; it does not know
   why a passenger was selected or ordered.
6. No stress generator may create two passengers for the same seat.
7. No generator may place a passenger directly into the aisle or a seat-event
   state.
8. No generator may alter movement speed, event duration or blocker rules.
9. No generator may guarantee or fabricate a dependency region.
10. Failure to create simultaneous regions is a valid dataset result and must be
    documented honestly.


======================================================================
NEW TERMINOLOGY
======================================================================

Deterministic Stress Dataset
----------------------------
A reproducible passenger manifest and entry-order dataset deliberately designed
to expose demanding but valid congestion structures to the frozen architecture.

Standard Deterministic Dataset
------------------------------
The established reference generator using seeded random seat selection and
independently shuffled aisle queues.

Stress Dataset Family
---------------------
A named group of deterministic datasets sharing the same construction principle,
such as row-zone concentration, blocker-prone seating or alternating aisle
admission.

Dataset Characteristic
----------------------
A measurable property of an input dataset, such as row-zone concentration,
blocker-prone seat share or left/right aisle balance.

Confirmed Region Concurrency
----------------------------
The number of dependency regions that have simultaneously survived the
three-tick confirmation threshold.

Region Competition
------------------
A tick on which more than one confirmed, state-change-eligible dependency region
requires hierarchical evaluation.

Competition Window
------------------
A continuous tick interval during which two or more confirmed dependency regions
coexist.

Priority Utilisation
--------------------
Observed use of the completed hierarchical prioritisation layer to rank genuine
simultaneous confirmed regions.

Negative Dataset Finding
------------------------
A valid result in which a stress dataset does not produce the intended region
competition. Such a result informs later dataset refinement without justifying
an architecture change.


======================================================================
RESEARCH OBJECTIVES
======================================================================

1. Construct reproducible deterministic datasets capable of producing multiple
   dependency regions within the same simulation.
2. Progress from detected-region concurrency to simultaneous confirmed-region
   concurrency.
3. Produce genuine priority competitions without modifying the completed
   architecture.
4. Identify which input characteristics increase or reduce multi-region
   dependency formation.
5. Separate dataset effects from entry headway and all movement rules.
6. Retain negative findings when apparently stressful datasets simplify rather
   than intensify congestion.
7. Build a reusable catalogue of deterministic benchmark datasets for future
   validation.
8. Complete a final controlled test of the frozen architecture.


======================================================================
EXPERIMENT 1 - BASELINE DETERMINISTIC STRESS DATASET CONSTRUCTION
======================================================================

Connection to Amit Amlani's Original People-Movement Algorithm
--------------------------------------------------------------
Amit Amlani's original movement algorithm operates on an already constructed
one-dimensional occupancy dataset. The completed boarding architecture similarly
processes the manifest and aisle queues that it receives.

Experiment 1 returns to this foundational distinction. It does not change the
movement algorithm. It changes only the deterministic data supplied to that
algorithm and observes whether the same fixed architecture exposes different
congestion structures.

Purpose
-------
Establish a reusable deterministic stress-dataset generator and compare its
first conservative stress family against the standard deterministic dataset.

The first experiment is primarily a foundation and calibration experiment. It
is not required to produce simultaneous confirmed dependency regions. Its role
is to verify that:

- dataset construction is reproducible;
- the frozen architecture remains unchanged;
- paired datasets retain the same scenario-level cabin, occupancy, headway and
  seed;
- dataset characteristics are recorded explicitly;
- region-concurrency and priority evidence can be compared cleanly.

Paired Modes
------------
Each of the 30 scenarios is executed twice.

1. STANDARD DETERMINISTIC DATASET

   The reference generator selects occupied seats from a seeded shuffled seat
   catalogue and independently shuffles the left- and right-aisle queues.

2. BASELINE DETERMINISTIC STRESS DATASET

   The frozen architecture receives a reproducible composite stress manifest.
   Passenger seats are preferentially selected from two separated row zones,
   blocker-prone seats receive greater selection weight and each serving-aisle
   queue is ordered deterministically around the same two zones.

The second mode changes input data only. The architecture, movement contract,
confirmation threshold, state-change gate and priority hierarchy are identical.

Independent Variable
--------------------

    DATASET GENERATION FAMILY

- standard seeded manifest and shuffled queues; versus
- baseline deterministic two-zone stress manifest and stress-ordered queues.

Baseline Stress Family Design
-----------------------------
The first stress family uses three documented dataset characteristics:

1. TWO SEPARATED ROW ZONES

   Seats near approximately one-third and two-thirds of cabin depth receive
   greater deterministic selection weight. The separation is intended to create
   more than one local area of potential congestion rather than one continuous
   cabin-wide concentration.

2. BLOCKER-PRONE SEAT EMPHASIS

   Window and deeper seats receive greater weight than aisle-adjacent seats. This
   increases the potential for valid seated blockers without changing any seat
   event after the manifest is created.

3. DETERMINISTIC ZONE-ORIENTED QUEUE ORDER

   Each aisle queue is ordered reproducibly so passengers associated with the
   two stress zones enter under the existing headway mechanism. The generator
   does not bypass headway and does not insert passengers directly into the
   aisle.

This is intentionally a composite baseline family. Later experiments may isolate
or ablate individual characteristics after the generator and evidence model have
been validated.

Controlled Conditions
---------------------

- identical cabin configuration within each pair;
- identical occupancy rate;
- identical passenger count;
- identical entry-headway range;
- identical scenario seed;
- identical frozen architecture;
- identical synchronous tick execution;
- identical movement constraints;
- identical stall detector and maximum ticks;
- no human behaviour;
- no asynchronous execution;
- no reverse movement, overtaking, aisle switching or assigned-row overshoot.

Dataset Evidence Collected
--------------------------

- dataset family;
- row-zone concentration percentage;
- blocker-prone seat share;
- left/right aisle-assignment balance;
- passenger count and occupancy;
- entry-headway range;
- dependency regions detected;
- regions created, updated, confirmed and dissolved;
- maximum simultaneous active regions;
- regions active at termination;
- state-change evaluations suppressed and reopened;
- priority competitions;
- lower-priority deferrals;
- cluster selections and cluster-started events;
- complete cabins and passengers seated;
- simulation ticks;
- passengers remaining outside;
- residual stall family.

Experimental Hypothesis
-----------------------
A deterministic two-zone, blocker-emphasised dataset may increase spatially
separated dependency activity relative to the standard dataset. However, the
same ordering may also simplify flow by making local seat events complete more
coherently. Either result is scientifically valid.

The primary Experiment 1 question is therefore not whether the stress dataset
improves or worsens boarding. It is whether the dataset layer is reproducible,
measurable and capable of changing dependency-region evidence without changing
the architecture.

Interpretation Safeguards
-------------------------

- A faster stress run is not automatically a failed experiment.
- A slower stress run is not automatically evidence of better stress.
- More detected regions do not necessarily mean more confirmed concurrent
  regions.
- Multiple regions created at different times do not constitute simultaneous
  dependency competition.
- Priority utilisation requires genuine simultaneous confirmed eligible regions.
- The architecture must not be changed merely because the first stress family
  fails to create competition.
- Any improvement or regression in boarding outcome must be reported as a
  dataset effect.
- Composite stress characteristics must be separated in later experiments before
  causal claims are made about a single characteristic.

Primary Evaluation Order
------------------------

1. Correct experiment identity and deterministic replay.
2. Dataset-characteristic differences between paired modes.
3. Maximum simultaneous active regions.
4. Confirmed-region concurrency.
5. Genuine priority competitions and deferrals.
6. Regions created, confirmed and dissolved.
7. Cluster selections and cluster-started events.
8. Complete cabins and passengers seated.
9. Residual stall classifications.
10. Simulation ticks as a secondary measure.

Expected Outcomes
-----------------

Outcome A - Simultaneous confirmed regions appear

The baseline stress family immediately exercises hierarchical prioritisation and
provides a foundation for controlled refinement.

Outcome B - More regions appear, but not simultaneously

The generator increases congestion activity but requires stronger temporal
alignment in a later dataset.

Outcome C - Fewer regions appear

The deterministic zone ordering simplifies cabin flow. This becomes a useful
negative finding and suggests later experiments should separate seat selection
from queue ordering.

Outcome D - No meaningful difference appears

The baseline generator is too conservative. Later experiments may increase one
dataset characteristic at a time while retaining the same frozen architecture.

Diagram Plan
------------
A diagram should be considered only after results are available. A valid diagram
may compare:

- the standard dispersed manifest;
- the two separated stress zones;
- blocker-prone seat emphasis;
- left/right deterministic queues;
- observed region timelines.

The diagram must distinguish input construction from passenger movement and must
not imply that dependency regions were inserted directly into the simulation.


======================================================================
RUNNING CONCLUSIONS
======================================================================

1. The Reactive Dependency-Region Management Architecture is frozen.
2. Dataset construction is now the independent research variable.
3. Experiment 1 establishes the reusable paired stress-dataset framework.
4. The first stress family combines two separated row zones, blocker-prone seat
   emphasis and deterministic zone-oriented queue ordering.
5. Simultaneous dependency competition must arise from valid deterministic
   simulation state; it must not be manufactured by changing the architecture.
6. Human behaviour, asynchronous execution, backward movement, overtaking,
   aisle switching and assigned-row overshoot remain excluded.
7. Negative dataset findings are retained as evidence for later refinement.
8. Future experiments should isolate individual stress characteristics after the
   baseline generator has been validated.



======================================================================
EXPERIMENT 1 FINDINGS - BASELINE DETERMINISTIC STRESS DATASET CONSTRUCTION
======================================================================

Execution Summary
-----------------
Experiment 1 completed 30 deterministic paired scenarios (60 executions) using
the same fixed architecture, cabin characteristics, occupancy, headway and
scenario seeds in each pair.

The standard dataset and baseline composite stress dataset each completed 27 of
30 cabins. However, the internal evidence differed substantially.

Overall Paired Result
---------------------

- Standard-dataset average completion: 99.71%.
- Composite stress-dataset average completion: 96.82%.
- Standard complete cabins: 27 of 30.
- Composite stress complete cabins: 27 of 30.
- Improved stress pairs: 3.
- Unchanged pairs: 24.
- Worse stress pairs: 3.
- Standard total passengers seated: 7,649.
- Composite stress total passengers seated: 7,391.
- Net seated-passenger difference: -258.
- Standard dependency regions detected: 37.
- Composite stress dependency regions detected: 13.
- Standard regions reaching confirmation: 22.
- Composite stress regions reaching confirmation: 9.
- Standard cluster selections: 25.
- Composite stress cluster selections: 6.
- Standard cluster-started events: 31.
- Composite stress cluster-started events: 15.
- Genuine priority competitions: 0 in both modes.

Principal Findings
------------------

1. The dataset-generation layer was successfully separated from the architecture.

Both paired modes used the frozen Reactive Dependency-Region Management
Architecture. The experiment therefore confirmed that manifests and aisle queues
can be varied reproducibly without changing movement, clustering, lifecycle,
confirmation, state-change gating or prioritisation.

2. The composite stress family did not create multi-region competition.

No simultaneous confirmed-region priority competition occurred. The dataset
therefore did not yet exercise the hierarchical prioritisation layer.

3. The composite stress family reduced dependency-region activity.

The stress mode produced 13 detected regions compared with 37 under the standard
dataset, and only 9 confirmed regions compared with 22. Cluster selections and
cluster-started events were also lower.

4. Deterministic concentration can simplify flow rather than intensify it.

The combination of two row zones, blocker-prone seat weighting and zone-oriented
queue ordering appears to have created more coherent local seating sequences in
many scenarios. This reduced the fragmented dependency activity that the stress
family was intended to increase.

5. Boarding outcome alone is not a sufficient stress measure.

Both modes completed 27 cabins, yet the stress mode seated 258 fewer passengers
across the three incomplete cases. Conversely, three standard stalls converted
to complete stress runs. The mixed result confirms that completion must be read
alongside region creation, confirmation and competition evidence.

6. The negative result provides a clear experimental refinement.

Because Experiment 1 changed row concentration, blocker-prone seat share and
queue ordering together, no single characteristic can be identified as the
cause of reduced region activity. The next experiment should isolate row-zone
concentration while returning seat-depth weighting and queue ordering to the
standard deterministic method.

Experiment 1 Conclusion
-----------------------
The reusable deterministic stress-dataset framework is validated. The first
composite family did not produce simultaneous dependency competition and often
simplified congestion structure. This is retained as a valid negative dataset
finding. Experiment 2 will isolate row-zone concentration as the sole dataset
variable.


======================================================================
EXPERIMENT 2 - CONTROLLED ROW-ZONE CONCENTRATION
======================================================================

Connection to Amit Amlani's Original People-Movement Algorithm
--------------------------------------------------------------
Amit Amlani's original movement algorithm processes the occupancy dataset it is
given without changing the rules of movement in response to how that dataset was
constructed. Experiment 2 follows the same separation by changing only where
assigned seats are concentrated before movement begins.

Purpose
-------
Determine whether two separated row zones, by themselves, increase spatially
separated dependency activity when blocker-prone seat weighting and specialised
queue ordering are removed.

Paired Modes
------------
Each of the 30 scenarios is executed twice using identical cabin configuration,
occupancy, passenger count, headway, scenario seed and frozen architecture.

1. STANDARD DETERMINISTIC DATASET

   Occupied seats are selected from a seeded shuffled seat catalogue. Left- and
   right-aisle queues are independently shuffled.

2. CONTROLLED ROW-ZONE CONCENTRATION DATASET

   Occupied seats are selected preferentially from two separated row zones near
   one-third and two-thirds of cabin depth. All seat positions inside those zones
   receive equal treatment; window and deep seats receive no additional weight.
   Left- and right-aisle queues are independently shuffled exactly as in the
   standard mode.

Independent Variable
--------------------

    ROW-ZONE CONCENTRATION

- dispersed seeded seat selection; versus
- deterministic concentration around two separated row zones.

Removed Experiment 1 Composite Variables
----------------------------------------

The following Experiment 1 characteristics are deliberately removed:

- blocker-prone seat emphasis;
- zone-oriented queue ordering;
- blocker-first queue tie-breaking.

This permits a narrower causal interpretation of any change in region evidence.

Controlled Conditions
---------------------

- frozen Experiment 23 architecture;
- identical cabin, occupancy and passenger count within each pair;
- identical entry-headway range;
- identical scenario seed;
- independently shuffled aisle queues in both modes;
- identical seat-event and blocker rules;
- synchronous execution;
- no backward movement, overtaking, aisle switching or assigned-row overshoot;
- no human behaviour or asynchronous execution.

Evidence Collected
------------------

- row-zone concentration percentage;
- blocker-prone seat share as a diagnostic control;
- aisle-assignment balance;
- dependency regions detected;
- regions created, confirmed and dissolved;
- maximum simultaneous active regions;
- confirmed-region concurrency;
- priority competitions and lower-priority deferrals;
- cluster selections and starts;
- complete cabins and passengers seated;
- residual stall family;
- simulation ticks as a secondary measure.

Experimental Hypothesis
-----------------------
If separated spatial concentration is sufficient to create independent local
congestion, the row-concentrated dataset should increase concurrent region
activity despite retaining standard shuffled queue order. If region activity
remains lower, row concentration itself may promote coherent completion rather
than competition.

Interpretation Safeguards
-------------------------

- Two row zones do not constitute two dependency regions.
- Regions created at different times do not constitute concurrency.
- A priority competition requires simultaneous confirmed and eligible regions.
- Changes in blocker-prone seat share must be reported, even though no explicit
  blocker weighting is used.
- The architecture must not be changed to force the expected result.
- A negative result will motivate a later isolated seat-depth or admission-order
  experiment rather than reopening the architecture.

Primary Evaluation Order
------------------------

1. Dataset identity and deterministic execution.
2. Row-zone concentration difference.
3. Maximum simultaneous active and confirmed regions.
4. Genuine priority competitions.
5. Regions detected and confirmed.
6. Cluster selections and starts.
7. Complete cabins and seated totals.
8. Residual stall families.
9. Simulation ticks as a secondary measure.

Diagram Plan
------------
A later diagram may compare dispersed seat selection with two separated row
concentration zones while showing that both modes retain independently shuffled
left- and right-aisle queues. It must not depict dependency regions as inserted
or guaranteed by the dataset generator.


======================================================================
RUNNING CONCLUSIONS AFTER EXPERIMENT 1
======================================================================

1. The frozen architecture remains the fixed reference implementation.
2. Experiment 1 validated the reusable deterministic dataset layer.
3. The first composite stress family reduced rather than increased region
   activity and produced no priority competition.
4. Composite dataset variables must now be separated before causal claims are
   made.
5. Experiment 2 isolates row-zone concentration and restores standard shuffled
   queue ordering and neutral seat-depth selection.
6. Simultaneous dependencies must emerge from valid deterministic state.
7. Human behaviour, asynchronous execution and movement-rule changes remain
   excluded.

======================================================================
END OF DETERMINISTIC STRESS DATASET RESEARCH LOG v2 - EXPERIMENT 2 DESIGN
======================================================================

======================================================================
EXPERIMENT 2 FINDINGS - CONTROLLED ROW-ZONE CONCENTRATION
======================================================================

Purpose Reviewed
----------------
Experiment 2 isolated deterministic row-zone concentration from the composite
stress variables introduced in Experiment 1. Both paired modes retained the
frozen Reactive Dependency-Region Management Architecture, independently
shuffled aisle queues, neutral seat-depth selection and identical movement
rules. Only the spatial distribution of occupied rows changed.

Overall Paired Result
---------------------

- Scenario pairs: 30.
- Scenario executions: 60.
- Standard average completion: 99.71%.
- Row-concentration average completion: 98.92%.
- Improved row-concentration pairs: 2.
- Unchanged pairs: 22.
- Worse row-concentration pairs: 6.
- Net seated-passenger difference: -75.
- Standard complete cabins: 27 of 30.
- Row-concentration complete cabins: 24 of 30.
- Standard stalls converted to complete cabins: 2.
- Standard dependency regions: 37.
- Row-concentration dependency regions: 36.
- Standard confirmed regions: 22.
- Row-concentration confirmed regions: 13.
- Standard priority competitions: 0.
- Row-concentration priority competitions: 7.
- Maximum simultaneous active regions: 2 in both modes.

Principal Findings
------------------

1. Row-zone concentration changed congestion geometry without consistently
   increasing overall dependency-region volume.

The row-concentrated datasets produced almost the same total number of detected
regions as the standard datasets, but fewer regions reached confirmation. This
shows that spatial concentration can change the persistence and timing of
congestion even when total region counts remain similar.

2. The isolated variable produced both beneficial and harmful outcomes.

Two standard stalls converted to complete cabins, while six paired scenarios
seated fewer passengers under row concentration. The result therefore does not
support row concentration as a generally beneficial or generally harmful
boarding arrangement.

3. Genuine simultaneous-region competition was observed.

Unlike the previous benchmark and Experiment 1, the row-concentration datasets
produced seven priority competitions. This is the first direct evidence within
the stress-dataset programme that deterministic manifest construction alone can
exercise the hierarchical prioritisation layer of the frozen architecture.

4. Concurrency did not require human behaviour or asynchronous ticks.

The priority competitions emerged under synchronous deterministic movement,
fixed headway rules and unchanged architecture. Dataset geometry alone was
sufficient to create overlapping confirmed-region eligibility.

5. Row concentration is therefore a useful stress component, but not a complete
   causal explanation.

Because row concentration also changed which seats happened to be selected, the
next experiment must isolate seat-depth distribution directly while restoring
dispersed row selection. This will determine whether blocker-prone seats increase
confirmed-region activity independently of row concentration.

Experiment 2 Conclusion
-----------------------
Controlled row-zone concentration is supported as a valid deterministic stress
variable. It generated the first seven genuine priority competitions without
changing the frozen architecture, human behaviour or synchronous execution.
However, its mixed completion effects and reduced confirmation count show that
it should be treated as one stress-building component rather than a complete
solution for repeatable multi-region generation.


======================================================================
EXPERIMENT 3 - CONTROLLED BLOCKER-PRONE SEAT CONCENTRATION
======================================================================

Connection to the Completed Architecture
----------------------------------------
The Reactive Dependency-Region Management Architecture remains frozen. This
experiment changes only the manifest's seat-depth distribution so that the
existing blocker, yield-space and seat-event rules are exercised more often.
The architecture is not informed that a stress dataset is in use and receives
only the completed deterministic passenger manifest.

Purpose
-------
Determine whether deterministic concentration of window and deep inner seats
increases persistent blocker-dependent seat events, confirmed dependency regions
and simultaneous priority competitions when row selection and aisle queues
remain dispersed.

Controlled-Variable Clarification
---------------------------------
Increasing the probability of blocker-prone assigned seats does not alter a
controlled movement variable. The following remain controlled and unchanged:

- the definition of a blocker;
- the stand-yield-sit-reseat transaction;
- the number of yield tiles required by a given seat event;
- blocker movement direction;
- synchronous tick execution;
- passenger movement speed;
- entry-headway range;
- clustering, lifecycle, confirmation, gating and prioritisation rules.

The assigned-seat distribution is intentionally the independent dataset
variable in Experiment 3. Therefore individual seat assignments cannot remain
identical between the two paired modes; instead, cabin type, occupancy,
passenger count, headway, scenario seed and all architecture and movement rules
remain controlled. This distinction is essential to the new research programme,
where dataset construction is the subject under investigation.

Paired Modes
------------

1. STANDARD DETERMINISTIC DATASET

   Occupied seats are selected from a seeded shuffled catalogue. Left- and
   right-aisle queues are independently shuffled.

2. CONTROLLED BLOCKER-PRONE SEAT CONCENTRATION DATASET

   Occupied seats are selected preferentially by deterministic blocker potential.
   Window seats and deep inner seats are selected before easier aisle-side seats.
   Rows remain dispersed through seeded tie-breaking, and both aisle queues are
   independently shuffled exactly as in the standard mode.

Independent Variable
--------------------

    ASSIGNED-SEAT DEPTH DISTRIBUTION

- seeded neutral seat selection; versus
- deterministic concentration of blocker-prone window and deep inner seats.

Controlled Conditions
---------------------

- frozen Experiment 23 architecture;
- identical cabin configuration within each pair;
- identical occupancy rate and passenger count;
- identical entry-headway range;
- identical scenario seed;
- dispersed row selection in both modes;
- independently shuffled aisle queues in both modes;
- unchanged seat-event, blocker and yield-space rules;
- synchronous execution;
- no backward movement, overtaking, aisle switching or assigned-row overshoot;
- no human behaviour or asynchronous execution.

Evidence Collected
------------------

- blocker-prone seat share;
- row-zone concentration as a diagnostic control;
- aisle-assignment balance;
- single- and multiple-blocker seat events;
- temporary aisle reoccupations;
- dependency regions detected;
- regions created, confirmed and dissolved;
- maximum simultaneous active regions;
- priority competitions and lower-priority deferrals;
- cluster selections and starts;
- complete cabins and passengers seated;
- residual stall family;
- simulation ticks as a secondary measure.

Experimental Hypothesis
-----------------------
If blocker-prone seat depth is an important contributor to independent congestion
zones, the concentrated dataset should increase multiple-blocker events,
confirmed dependency regions or priority competitions while row concentration
remains near the standard distribution. A null result would indicate that seat
depth alone is insufficient and that later experiments should combine it with
spatial or admission-pattern stress.

Interpretation Safeguards
-------------------------

- More window seats do not automatically constitute more dependency regions.
- More blocker events do not automatically constitute simultaneous regions.
- A priority competition requires simultaneous confirmed and eligible regions.
- Row-zone concentration must remain a diagnostic control and be reported.
- Different seat assignments are the intended independent-variable change, not
  a breach of the movement contract.
- The architecture must not be modified to force the expected outcome.
- Any worse completion outcome must be reported honestly as a dataset effect.

Primary Evaluation Order
------------------------

1. Dataset identity and deterministic execution.
2. Blocker-prone seat-share difference.
3. Row-zone concentration control.
4. Multiple-blocker events and temporary aisle reoccupations.
5. Confirmed regions and maximum simultaneous regions.
6. Genuine priority competitions.
7. Cluster selections and starts.
8. Complete cabins and seated totals.
9. Residual stall families.
10. Simulation ticks as a secondary measure.

Diagram Plan
------------
A later diagram may compare neutral seat selection with deterministic
window/deep-seat concentration across dispersed rows. It must show that seat-event
rules remain unchanged and must not depict blockers, dependency regions or
priority competitions as inserted directly by the dataset generator.


======================================================================
RUNNING CONCLUSIONS AFTER EXPERIMENT 2
======================================================================

1. The frozen architecture remains the fixed reference implementation.
2. Experiment 1 established the reusable deterministic dataset layer.
3. Experiment 2 generated seven genuine priority competitions through row-zone
   concentration alone.
4. Simultaneous dependencies therefore do not require human behaviour or
   asynchronous execution.
5. Row concentration produced mixed boarding outcomes and fewer confirmed
   regions overall, so it remains a stress component rather than a complete
   benchmark solution.
6. Experiment 3 isolates assigned-seat depth while restoring dispersed row
   selection and standard shuffled queues.
7. Seat-event rules remain controlled; only the deterministic seat-assignment
   distribution changes.
8. No passenger may pass the assigned row or move backwards.

======================================================================
END OF DETERMINISTIC STRESS DATASET RESEARCH LOG v3 - EXPERIMENT 3 DESIGN
======================================================================


======================================================================
EXPERIMENT 3 FINDINGS - CONTROLLED BLOCKER-PRONE SEAT CONCENTRATION
======================================================================

Purpose Reviewed
----------------
Experiment 3 isolated deterministic assigned-seat depth while restoring dispersed
row selection and independently shuffled aisle queues. The completed Reactive
Dependency-Region Management Architecture remained frozen. The independent
variable was the proportion of window and deep inner seats selected into the
passenger manifest.

Execution Summary
-----------------
Thirty paired scenarios were executed, producing sixty deterministic runs.

Across the complete batch:

- Standard average completion: 99.71%.
- Blocker-prone dataset average completion: 98.96%.
- Improved blocker-prone pairs: 3 of 30.
- Unchanged pairs: 22 of 30.
- Worse blocker-prone pairs: 5 of 30.
- Net seated-passenger difference: -55.
- Standard complete cabins: 27 of 30.
- Blocker-prone complete cabins: 25 of 30.
- Standard dependency regions: 37.
- Blocker-prone dependency regions: 45.
- Standard confirmed regions: 22.
- Blocker-prone confirmed regions: 34.
- Standard cluster selections: 25.
- Blocker-prone cluster selections: 36.
- Standard cluster-started events: 31.
- Blocker-prone cluster-started events: 41.
- Blocker-prone priority competitions: 6.
- Standard priority competitions: 0.

Principal Findings
------------------

1. Seat-depth concentration produced stronger dependency evidence than row
   concentration alone.

The blocker-prone dataset increased detected regions from 37 to 45, confirmed
regions from 22 to 34, cluster selections from 25 to 36 and cluster-started
events from 31 to 41. This supports assigned-seat depth as a meaningful
component of deterministic congestion construction.

2. Genuine hierarchical priority competition was exercised.

Six priority competitions occurred in the blocker-prone dataset. Scenario 19
provided the clearest evidence: two simultaneously active regions reached
confirmation, twelve region rankings were recorded across six competitions, and
the hierarchy selected between left- and right-aisle regions using the completed
deterministic priority score.

3. More blocker-prone seats did not produce a uniform outcome.

Three scenarios improved, twenty-two remained unchanged and five became worse.
Scenario 4 converted a standard rear-boundary stall at 200/204 into a complete
204/204 cabin, while Scenario 7 changed from complete boarding to 209/216 with a
mixed dependency residual. The result confirms that dependency topology, rather
than blocker count alone, controls the final congestion pattern.

4. The dataset changed the geometry of dependency formation.

The blocker-prone manifest produced linked row-event chains, yield-tile
occupancy dependencies, deeper critical-blocker trees and longer-lived confirmed
regions in selected scenarios. In other scenarios, the changed seat geometry
removed a previous bottleneck. Seat-depth concentration therefore changes the
structure and timing of dependencies rather than merely increasing their number.

5. Simultaneous dependencies require spatial separation as well as blocker
   density.

Although six priority competitions were observed, most blocker-prone scenarios
still contained only one active confirmed region at a time. The next experiment
should therefore retain blocker-prone seat depth but deliberately distribute it
across two separated aisle-specific row zones.

6. The frozen architecture remained controlled.

No passenger moved backwards, passed the assigned row, overtook another
passenger, changed aisle or changed assigned seat. Entry headway, synchronous
execution, seat-event prerequisites, blocker movement, lifecycle confirmation,
state-change gating and hierarchical prioritisation were unchanged.

Scientific Interpretation
-------------------------
Experiment 3 establishes Dependency Topology as a central concept for the new
research programme.

Dependency Topology is the spatial arrangement of blocker-dependent seat events
and the resulting dependency chains within a deterministic boarding dataset.
The findings demonstrate that topology cannot be inferred from blocker density
alone. A larger blocker-prone share increases the opportunity for dependencies,
but their independence, coexistence and final effect depend upon where those
seat events occur and how the two aisle flows encounter them.

Experiment 3 Conclusion
-----------------------
Controlled blocker-prone seat concentration is supported as a stronger stress
component than row-zone concentration alone. It increased region and
intervention evidence and produced six genuine hierarchy competitions. However,
it did not reliably create two independent congestion zones. The next experiment
therefore constructs an explicit dual-aisle dependency topology while preserving
the frozen architecture and ordinary deterministic movement rules.


======================================================================
NEW TERMINOLOGY FOR EXPERIMENT 4
======================================================================

Dependency Topology
-------------------
The spatial arrangement of blocker-dependent seat events and their resulting
dependency chains within a deterministic boarding dataset.

Independent Congestion Zone
---------------------------
A locally formed dependency region that remains structurally separate from
another active dependency region, allowing both to coexist without being one
continuous aisle chain.

Dual-Aisle Dependency Topology
------------------------------
A deterministic manifest design that concentrates blocker-prone assigned seats
around two separated row bands, one primarily served by each aisle, while
leaving movement and congestion-management rules unchanged.

Topology Competition
--------------------
A genuine hierarchical priority competition arising because two independently
formed confirmed regions are simultaneously eligible for evaluation.


======================================================================
EXPERIMENT 4 - DUAL-AISLE DEPENDENCY TOPOLOGY CONSTRUCTION
======================================================================

Connection to Amit Amlani's Original People-Movement Algorithm
--------------------------------------------------------------
Amit Amlani's original algorithm investigates how the arrangement of occupied
positions changes movement opportunities within a one-dimensional dataset.
Experiment 4 retains that principle but varies the deterministic input geometry
across two aisle datasets. The completed movement and congestion-management
architecture remains fixed; only the assigned-seat topology supplied to it
changes.

Planned Purpose
---------------
Determine whether a deliberately separated dual-aisle seat topology can produce
more repeatable simultaneous confirmed dependency regions than blocker-prone
seat concentration alone.

Paired Modes
------------
Each scenario is executed twice using the same cabin, occupancy, entry headway,
scenario seed and frozen architecture.

1. STANDARD DETERMINISTIC DATASET

   Seats are selected using the established seeded shuffle and both aisle queues
   are independently shuffled.

2. DUAL-AISLE DEPENDENCY TOPOLOGY DATASET

   Blocker-prone assigned seats are preferentially selected around two separated
   row bands. The earlier band is primarily associated with the left aisle and
   the later band with the right aisle. Middle-bank seats are assigned
   deterministically toward the nearer side of the topology. Both entry queues
   remain independently shuffled.

Only manifest topology changes. The generator does not insert congestion,
create a region directly or determine which region the hierarchy must select.

Independent Variable
--------------------

    DUAL-AISLE SPATIAL DEPENDENCY TOPOLOGY

- standard dispersed deterministic seat selection; versus
- blocker-prone seats distributed across two separated aisle-specific row bands.

Controlled Conditions
---------------------

- completed Reactive Dependency-Region Management Architecture;
- reactive congestion detection;
- persistent lifecycle tracking;
- three-tick lifecycle confirmation;
- state-change-gated evaluation;
- hierarchical region prioritisation;
- cabin configuration and occupancy within each pair;
- scenario seed and entry-headway range;
- synchronous global movement;
- one forward aisle tile per passenger per tick;
- assigned-seat integrity;
- no movement beyond the assigned row;
- no backward movement, overtaking or aisle switching;
- unchanged seat-event, blocker, yield-space and reservation rules;
- independently shuffled entry queues.

Dataset Construction Contract
-----------------------------

1. Two separated target row bands are derived deterministically from cabin size.
2. Left-served blocker-prone seats receive preference around the earlier band.
3. Right-served blocker-prone seats receive preference around the later band.
4. Middle-bank seats use deterministic side assignment only in the topology
   dataset so that the two intended zones remain spatially distinct.
5. Passenger IDs, assigned seats and queue order remain deterministic under the
   scenario seed.
6. The generator does not pre-create region objects or alter runtime movement.
7. A priority competition is counted only when the frozen architecture observes
   two confirmed and eligible regions naturally during execution.

New Evidence Collected
----------------------

- blocker-prone seat share;
- row-zone concentration as a diagnostic control;
- aisle-assignment balance;
- dependency regions created and confirmed;
- maximum simultaneous active regions;
- scenarios containing two active regions;
- priority competitions;
- hierarchical selections;
- left- and right-aisle priority wins;
- lower-priority deferrals;
- cluster selections and starts;
- complete cabins and passengers seated;
- residual stall families;
- simulation ticks as a secondary measure.

Experimental Hypothesis
-----------------------
If simultaneous region competition depends upon spatial separation as well as
blocker density, the dual-aisle topology dataset should produce more scenarios
with two active confirmed regions and more priority competitions than either the
standard dataset or blocker-prone concentration alone.

A null result would indicate that admission timing or another deterministic
interaction is required in addition to seat topology.

Interpretation Safeguards
-------------------------

- The dataset creates favourable input geometry, not congestion objects.
- Two target zones do not prove that two dependency regions will form.
- More active regions do not automatically mean both are confirmed or eligible.
- Priority competitions must be recorded only by the frozen runtime hierarchy.
- Deterministic middle-bank side assignment is part of dataset construction and
  does not permit aisle switching during simulation.
- Any improved or worse boarding result must be reported as a topology effect.
- The architecture must not be modified to force simultaneous dependencies.

Primary Evaluation Order
------------------------

1. Dataset identity and deterministic replay.
2. Maximum simultaneous active regions.
3. Number of genuine priority competitions.
4. Regions ranked and hierarchical selections.
5. Left/right priority wins and lower-ranked deferrals.
6. Confirmed regions and region lifetimes.
7. Cluster selections and starts.
8. Complete cabins and seated totals.
9. Residual stall families.
10. Simulation ticks as a secondary measure.

Diagram Plan
------------
A later diagram may show two separated row bands, one associated primarily with
left-aisle blocker-prone seating and one with right-aisle blocker-prone seating.
It must clearly distinguish dataset geometry from runtime dependency regions and
must not imply that congestion, confirmation or priority is inserted in advance.


======================================================================
RUNNING CONCLUSIONS AFTER EXPERIMENT 3
======================================================================

1. The frozen architecture remains the fixed reference implementation.
2. Experiment 1 established the reusable deterministic dataset layer.
3. Experiment 2 demonstrated that row concentration can create genuine priority
   competition but does not consistently increase confirmed-region evidence.
4. Experiment 3 increased dependency regions, confirmations, cluster activity
   and produced six genuine priority competitions.
5. Blocker density alone does not determine the resulting dependency topology.
6. Experiment 4 retains blocker-prone depth but separates it across two
   aisle-specific row bands.
7. Simultaneous dependencies remain runtime observations, not objects inserted by
   the dataset generator.
8. No passenger may pass the assigned row or move backwards.

======================================================================
END OF DETERMINISTIC STRESS DATASET RESEARCH LOG v4 - EXPERIMENT 4 DESIGN
======================================================================


======================================================================
EXPERIMENT 4 FINDINGS - DUAL-AISLE DEPENDENCY TOPOLOGY CONSTRUCTION
======================================================================

Purpose Reviewed
----------------
Experiment 4 tested whether two spatially separated, aisle-specific bands of
blocker-prone assigned seats could cause independent dependency regions to coexist
under the completed and frozen architecture.

The experiment changed deterministic manifest topology only. Passenger movement,
entry-headway ranges, synchronous ticks, seat-event rules, clustering, lifecycle
tracking, confirmation, state-change gating and hierarchical prioritisation were
unchanged.

Overall Paired Result
---------------------

- Scenario pairs: 30.
- Standard average completion: 99.71%.
- Dual-aisle topology average completion: 99.81%.
- Improved topology pairs: 3.
- Unchanged pairs: 26.
- Worse topology pairs: 1.
- Net additional passengers seated: +3.
- Standard complete cabins: 27.
- Dual-aisle topology complete cabins: 29.
- Standard dependency regions: 37.
- Dual-aisle topology dependency regions: 7.
- Standard confirmed regions: 22.
- Dual-aisle topology confirmed regions: 6.
- Standard cluster selections: 25.
- Dual-aisle topology cluster selections: 6.
- Priority competitions in either mode: 0.

Principal Findings
------------------

1. Spatial separation alone did not create sustained multi-region competition.

The two deterministic row bands did not produce a genuine priority competition.
Although the intended zones existed in the manifest, the frozen architecture did
not observe two confirmed and simultaneously eligible regions during execution.

2. The topology frequently simplified rather than intensified congestion.

Region creation fell from 37 in the standard datasets to 7 in the topology
datasets. Confirmed regions fell from 22 to 6. This indicates that organising
blocker-prone seats into coherent aisle-specific bands can cause seat events to
resolve in orderly local sequences instead of producing independent sustained
congestion.

3. Boarding outcomes changed in both directions.

Three topology pairs improved and one became worse. Scenario 4 converted a
rear-boundary stall to complete boarding, while Scenario 26 changed from complete
boarding to a 323/342 rear-boundary stall. Scenario 29 converted a 258/274 mixed
residual stall to complete boarding. These contrasting outcomes confirm that
manifest topology changes dependency geometry rather than applying a uniformly
positive or negative pressure.

4. High blocker-prone seat share is not sufficient evidence of congestion.

Several topology datasets recorded very high blocker-prone seat shares while
producing no dependency region at all. Blocker potential becomes consequential
only when admission order, aisle occupancy and seat-event timing combine to make
temporary yield space unavailable.

5. The null priority result identifies the next missing component.

Experiment 4 created spatially separated zones but did not align their active
lifetimes. One zone could form and resolve before the second became confirmed.
The next controlled variable should therefore be temporal overlap between the
left- and right-aisle stress zones, while preserving the existing headway ranges
and frozen movement architecture.

Experiment 4 Conclusion
-----------------------
Dual-aisle dependency topology is not supported as a sufficient standalone method
for producing simultaneous confirmed dependency regions. It remains a useful
spatial component, but the evidence indicates that spatial independence must be
combined with deterministic temporal alignment before the hierarchy can be
reliably exercised.


======================================================================
DISCUSSION - WHY CONGESTION IS BEING DELIBERATELY CREATED
======================================================================

The present programme may initially appear to contradict the preceding
architecture research. The earlier work introduced row-bounded clustering and
reactive dependency-region management to alleviate bottlenecks, whereas the
current work deliberately constructs datasets more likely to produce them.

The objectives are complementary. A completed congestion-management architecture
cannot be fully evaluated unless the benchmark environment produces sufficiently
rich and independent congestion structures. The stress datasets do not create
congestion for operational benefit and do not insert congestion objects into the
simulation. They create controlled deterministic input conditions under which
congestion may arise naturally through the unchanged movement and seat-event
rules.

Once simultaneous confirmed regions can be reproduced reliably, the frozen
hierarchy can be tested on the larger question: which independent congestion
region should receive evaluation first, and whether that prioritisation releases
more of the cabin or avoids a deeper residual stall.


======================================================================
RESEARCH POSITION - FIRST-PRINCIPLES BOARDING EVIDENCE
======================================================================

This research deliberately examines aircraft boarding before introducing airline
boarding policies or probabilistic human-behaviour assumptions. It begins with
seat ownership, aisle occupancy, blocker movement and temporary yield-space
requirements, and develops congestion from those deterministic first principles.

This does not claim that policy or behavioural research is unnecessary. It
establishes a controlled mechanical foundation beneath those later layers. By
first identifying how seat-event dependencies form, evolve and interact, future
policy or behavioural studies can be interpreted against known congestion
mechanisms rather than treating boarding time as an unexplained final outcome.

The overall programme therefore contains three connected levels:

1. Volumes 1 and 2 explain how deterministic cabin congestion forms.
2. The Reactive Dependency-Region Management Architecture explains how that
   congestion is detected, retained, confirmed and prioritised.
3. The Deterministic Stress Dataset programme constructs controlled evidence
   capable of challenging the completed architecture.


======================================================================
EXPERIMENT 5 - DUAL-AISLE DEPENDENCY TIMING ALIGNMENT
======================================================================

Connection to Amit Amlani's Original People-Movement Algorithm
--------------------------------------------------------------
Amit's original people-movement algorithm acts upon the occupancy state present
at each deterministic execution step. Experiment 5 preserves that movement
contract while changing only when two deterministic passenger subsets are
presented to the frozen cabin architecture.

The dataset does not delay a passenger in response to observed congestion and
does not predict a future bottleneck. Queue order is constructed before the run
from the scenario seed. Runtime movement then proceeds under the unchanged
synchronous rules.

Planned Purpose
---------------
Determine whether temporal alignment of the two spatial stress zones created in
Experiment 4 can increase the overlap of their dependency-region lifecycles and
thereby produce genuine hierarchical priority competitions.

Paired Modes
------------
Each scenario is executed twice using the same cabin, occupancy, entry-headway
range and scenario seed.

1. STANDARD DETERMINISTIC DATASET

   The established seeded manifest and independently shuffled aisle queues.

2. DUAL-AISLE DEPENDENCY TIMING ALIGNMENT DATASET

   The dual-zone blocker-prone manifest is retained. Each aisle queue is then
   deterministically ordered so passengers associated with its target stress
   band enter during corresponding early waves. The numerical entry-headway range
   remains identical to the paired standard run.

Independent Variable
--------------------

    PRE-RUN TEMPORAL ALIGNMENT OF AISLE-SPECIFIC STRESS ZONES

The experiment changes deterministic queue construction, which is an authorised
dataset variable in this research programme. It does not change admission
headway, movement speed or runtime scheduling.

Controlled Conditions
---------------------

- frozen Experiment 23 congestion-management architecture;
- identical cabin configuration and occupancy;
- identical scenario seed;
- identical entry-headway range;
- one assigned seat per passenger;
- fixed serving aisle after manifest creation;
- synchronous global execution;
- maximum one forward aisle tile per passenger per tick;
- no backward movement, overtaking or aisle switching;
- unchanged blocker, yield-space and middle-bank reservation rules;
- unchanged clustering, lifecycle, confirmation, gating and priority rules;
- unchanged stall detector and maximum tick limit.

Timing-Alignment Contract
-------------------------

1. Two separated target row bands are derived deterministically from cabin size.
2. Blocker-prone seats remain concentrated around their aisle-specific bands.
3. Within each aisle queue, target-band and blocker-prone passengers receive an
   earlier deterministic queue score.
4. Left and right queues are scored independently using corresponding target
   bands so their stress waves can overlap.
5. Entry-headway values are not reduced or bypassed.
6. No passenger order changes after simulation begins.
7. No region is inserted, extended or held alive by the dataset generator.
8. A competition is counted only when the frozen runtime architecture observes
   two confirmed and eligible regions simultaneously.

New Evidence Collected
----------------------

- dependency regions created and confirmed;
- maximum simultaneous active regions;
- priority competitions and lower-priority deferrals;
- hierarchical selections and left/right wins;
- region-lifecycle overlap;
- cluster selections and cluster-started events;
- complete cabins and seated totals;
- residual stall families;
- simulation ticks as a secondary comparison.

Experimental Hypothesis
-----------------------
If Experiment 4 failed primarily because its two spatial zones activated at
different times, deterministic queue alignment should increase lifecycle overlap
and create more genuine priority competitions without changing the architecture
or entry-headway mechanism.

A null result would indicate that spatial and temporal alignment remain
insufficient and that a later dataset may need controlled admission density or a
more explicit multi-wave construction.

Interpretation Safeguards
-------------------------

- Queue alignment is fixed before execution and is not reactive passenger
  behaviour.
- Synchronous ticks remain unchanged.
- Headway remains a controlled scenario characteristic.
- The dataset does not hold passengers to preserve an active region.
- More congestion is not automatically a successful result; the primary target
  is independent simultaneous confirmed regions.
- Any deterioration in boarding completion must be reported as a dataset cost.
- Priority competition must not be inferred from two designed row bands; it must
  be measured by the frozen architecture.

Primary Evaluation Order
------------------------

1. Genuine priority competitions.
2. Maximum simultaneous active and confirmed regions.
3. Hierarchical selections and lower-priority deferrals.
4. Region-lifecycle overlap evidence.
5. Confirmed regions and region lifetimes.
6. Cluster selections and starts.
7. Complete cabins and seated totals.
8. Residual stall classifications.
9. Simulation ticks as a secondary measure.

Diagram Plan
------------
A later diagram may show two pre-run deterministic entry queues feeding separated
left- and right-aisle target bands during corresponding time windows. The diagram
must distinguish planned dataset timing from runtime dependency detection and
must not imply asynchronous execution, passenger holding or inserted congestion.

======================================================================
EXPERIMENT 5 FINDINGS - DUAL-AISLE DEPENDENCY TIMING ALIGNMENT
======================================================================

Execution Summary
-----------------
Experiment 5 completed 30 deterministic paired scenarios, producing 60 scenario
executions. Both paired modes used the frozen Experiment 23 architecture,
identical cabin configuration, occupancy, entry-headway range and scenario seed.
Only the pre-run construction of the dual-aisle entry queues changed.

Overall Paired Result
---------------------

- Standard average completion: 99.71%.
- Timing-alignment average completion: 99.85%.
- Average change: +0.14 percentage points.
- Improved pairs: 3 of 30.
- Unchanged pairs: 24 of 30.
- Worse pairs: 3 of 30.
- Net additional passengers seated: +8.
- Standard complete cabins: 27.
- Timing-alignment complete cabins: 27.
- Standard dependency regions: 37.
- Timing-alignment dependency regions: 6.
- Standard confirmed regions: 22.
- Timing-alignment confirmed regions: 6.
- Standard cluster selections: 25.
- Timing-alignment cluster selections: 5.
- Standard cluster-started events: 31.
- Timing-alignment cluster-started events: 4.
- Priority competitions: 0 in both modes.

Principal Findings
------------------

1. Deterministic timing changed outcomes in both directions.

The timing-alignment dataset converted three standard stalls to complete cabins,
but it also introduced three new incomplete outcomes. This confirms that
temporal dataset construction is a genuine stress variable rather than a
one-directional improvement mechanism.

2. Timing could remove a previously stable congestion structure.

Scenario 4 changed from a 200/204 rear-boundary lock to complete boarding.
Scenario 29 changed from a 258/274 mixed residual dependency to complete
boarding. These cases demonstrate that deterministic queue timing can alter the
dependency topology sufficiently to prevent an otherwise stable stall.

3. Timing could also create new linked row-event chains.

Scenario 5 changed from complete boarding to 233/235 with a linked row-event
chain. Scenario 30 changed from complete boarding to 286/288 with another linked
row-event chain. The timing dataset therefore created distinct deterministic
dependencies even though movement rules and entry-headway ranges were unchanged.

4. Broad timing alignment simplified dependency-region activity overall.

Despite the mixed outcome changes, dependency regions fell from 37 to 6 and
confirmed regions fell from 22 to 6. Cluster selections and cluster-started
events also reduced sharply. The aligned queues frequently produced orderly
local seat-event sequences rather than two sustained independent congestion
zones.

5. No hierarchical competition occurred.

The central objective was not achieved. Priority competitions remained zero,
showing that spatial separation plus broad temporal alignment was still
insufficient to create two simultaneously confirmed and eligible regions.

6. The result supports a more constructive two-wave design.

The next dataset should not merely bring both aisle zones forward at similar
times. It should first seed potential seated blockers in each target zone and
then introduce deeper-seat passengers behind them, creating the prerequisites
for independent blocker-dependent congestion on both aisles.

Scientific Interpretation
-------------------------
Experiment 5 confirms that time is a separate deterministic component of
dependency topology. However, simply aligning two broad stress waves does not
guarantee simultaneous dependency regions. The internal order of passengers
within each wave is equally important because congestion requires a specific
sequence: blockers must become seated before later passengers require access to
deeper seats.

This finding preserves the first-principles nature of the programme. No runtime
behaviour was added and no passenger was held in response to congestion. The
evidence arose solely from deterministic pre-run dataset construction.

Experiment 5 Conclusion
-----------------------
Dual-aisle timing alignment is supported as an influential dataset variable but
not as a sufficient standalone method for exercising hierarchical region
priority. The next experiment should use deterministic blocker-seeding and
dependency-trigger waves on both aisles to construct a stronger opportunity for
simultaneous confirmed regions.


======================================================================
DISCUSSION - FROM TIMING ALIGNMENT TO CONSTRUCTIVE CONVERGENCE
======================================================================

The first five stress experiments demonstrate that congestion does not arise
from a single variable in isolation. Row concentration, blocker-prone seat
distribution, spatial separation and timing can each alter the outcome, but the
desired multi-region state requires these elements to occur in the correct
causal order.

For a blocker-dependent row event to form, an aisle-side or intermediate
passenger must first become seated. A later passenger assigned to a deeper seat
must then reach the same row while the required temporary yield tiles are
occupied or otherwise unavailable. To create two independent regions, this
sequence must occur in two separated aisle-specific zones during overlapping
lifecycle windows.

Experiment 6 therefore remains a dataset experiment rather than an architectural
or behavioural intervention. It constructs the causal prerequisites before the
run begins but allows the frozen movement engine to determine whether genuine
congestion actually forms.


======================================================================
EXPERIMENT 6 - CONSTRUCTIVE DEPENDENCY CONVERGENCE
======================================================================

Connection to Amit Amlani's Original People-Movement Algorithm
--------------------------------------------------------------
Amit's original people-movement algorithm operates on explicit occupancy states
and deterministic scan order. Experiment 6 applies the same principle to dataset
construction by arranging two pre-run occupancy sequences:

1. passengers likely to become seated blockers;
2. passengers whose deeper seats may later require those blockers to yield.

The movement engine remains unchanged. The dataset creates only the initial
ordering conditions from which the original deterministic architecture may
produce congestion naturally.

Planned Purpose
---------------
Determine whether a deterministic two-wave sequence on each aisle can produce
two independent blocker-dependent congestion regions whose confirmed lifecycles
overlap sufficiently to activate the frozen hierarchical prioritisation layer.

Paired Modes
------------
Each scenario is executed twice with the same cabin, occupancy, entry-headway
range and scenario seed.

1. STANDARD DETERMINISTIC DATASET

   The established seeded manifest with independently shuffled aisle queues.

2. CONSTRUCTIVE DEPENDENCY CONVERGENCE DATASET

   The dual-zone manifest is retained. Each aisle queue is divided
   deterministically into:

   - Wave A: target-zone passengers seated close to the serving aisle, intended
     only to establish potential blockers through normal boarding;
   - Wave B: target-zone passengers assigned to deeper seats, arriving after
     Wave A and therefore capable of generating blocker-dependent seat events;
   - remaining passengers, admitted afterwards under the same headway rules.

Left and right aisle waves are constructed independently but use corresponding
target zones, giving both dependency structures an opportunity to converge in
time.

Independent Variable
--------------------

    PRE-RUN BLOCKER-SEEDING AND DEPENDENCY-TRIGGER WAVE ORDER

This is a deterministic dataset variable. It does not modify seat-event rules,
passenger movement, runtime scheduling or architectural priority.

Controlled Conditions
---------------------

- frozen Experiment 23 congestion-management architecture;
- identical cabin configuration and occupancy;
- identical scenario seed;
- identical entry-headway range;
- one unique assigned seat per passenger;
- fixed serving aisle after manifest creation;
- synchronous global execution;
- maximum one forward aisle tile per passenger per tick;
- no backward movement, overtaking or aisle switching;
- no movement beyond the assigned row;
- unchanged blocker, yield-space and middle-bank reservation rules;
- unchanged clustering, lifecycle, confirmation, gating and hierarchy;
- unchanged stall detector and maximum tick limit;
- no behavioural assumptions or asynchronous execution.

Constructive Convergence Contract
---------------------------------

1. Two separated target row bands are derived from cabin size.
2. The manifest retains deterministic blocker-prone occupancy around both bands.
3. Wave A contains target-band passengers closest to their serving aisle.
4. Wave B contains target-band passengers assigned to deeper seats.
5. Wave A is ordered before Wave B independently on each aisle.
6. The same numerical entry-headway range remains active.
7. No passenger is delayed after execution begins to preserve the waves.
8. No seated blocker is artificially inserted.
9. No congestion region is inserted, extended or protected.
10. A priority competition is recorded only when the frozen runtime architecture
    observes multiple confirmed and eligible regions simultaneously.

New Evidence Collected
----------------------

- dependency regions created and confirmed;
- maximum simultaneous active regions;
- simultaneous confirmed-region competitions;
- hierarchical selections and lower-priority deferrals;
- left/right priority wins and priority ties;
- region lifetimes and overlap;
- blocker-dependent seat-event attempts;
- single- and multiple-blocker seat events;
- cluster selections and cluster-started events;
- complete cabins and seated totals;
- residual stall families;
- simulation ticks as a secondary measure.

Experimental Hypothesis
-----------------------
If the absence of priority competition in Experiment 5 resulted from an
incorrect within-wave causal order, seating potential blockers before deeper-seat
passengers should create more persistent independent dependencies and increase
the probability of simultaneous confirmed-region competition.

A null result would indicate that queue-wave construction alone remains
insufficient and that later work may need a more narrowly targeted benchmark
catalogue or controlled variation in admission density.

Interpretation Safeguards
-------------------------

- Wave order is fixed before execution.
- Wave A does not guarantee that a passenger becomes a blocker.
- Wave B does not guarantee that a seat event becomes blocked.
- The runtime architecture remains solely responsible for detecting regions.
- Headway remains unchanged within each paired scenario.
- No passenger is held because a region has or has not formed.
- More incomplete cabins are a dataset cost, not automatically a success.
- The primary success criterion is genuine simultaneous confirmed-region
  competition, not congestion volume alone.
- Zero competitions must be reported honestly.

Primary Evaluation Order
------------------------

1. Genuine priority competitions.
2. Maximum simultaneous active and confirmed regions.
3. Hierarchical selections and lower-priority deferrals.
4. Confirmed-region lifecycle overlap.
5. Regions created, confirmed and dissolved.
6. Blocker-dependent and multiple-blocker seat-event evidence.
7. Cluster selections and starts.
8. Complete cabins and seated totals.
9. Residual stall classifications.
10. Simulation ticks as a secondary measure.

Diagram Plan
------------
A later diagram may show matching left- and right-aisle target zones with Wave A
aisle-side passengers entering first and Wave B deeper-seat passengers following.
The diagram must show that congestion is an observed runtime result rather than
an inserted dataset object and must not imply passenger holding, asynchronous
execution or changed headway.


======================================================================
RUNNING CONCLUSIONS AFTER EXPERIMENT 5
======================================================================

1. The frozen architecture remains the fixed reference implementation.
2. Experiment 1 established the reusable deterministic dataset layer.
3. Experiment 2 showed that row-zone concentration can create occasional
   priority competition but does not reliably sustain multi-region evidence.
4. Experiment 3 showed that blocker-prone concentration increases dependency
   complexity and can create genuine priority competition.
5. Experiment 4 showed that spatial separation alone often simplifies
   congestion.
6. Experiment 5 established timing as an independent deterministic variable but
   produced no priority competition.
7. Broad spatial and temporal alignment is insufficient without the correct
   causal order between seated blockers and deeper-seat arrivals.
8. Experiment 6 tests deterministic blocker-seeding and dependency-trigger waves.
9. The programme continues to construct controlled first-principles evidence
   before introducing boarding policies or human behaviour.
10. No passenger may pass the assigned row or move backwards.

======================================================================
END OF DETERMINISTIC STRESS DATASET RESEARCH LOG v6 - EXPERIMENT 6 DESIGN
======================================================================
