Section 371 of 440
Complete canonical tutorial. This reader section contains the same teaching body as PWR-189 · Swarm coordination. Open the Power dossier.
PWR-189 · RESEARCH full tutorial
Evaluate a swarm-coordination claim by reconstructing its local rules, density and collision model
Reconstruct the local rules, density assumptions and collision model in a pedestrian study, then compare them with a human-crowd consensus study. Stress-test the safety interpretation outside the reported density and write the evidence gate for any field proposal. This is a research audit and provides no method for controlling crowds.
1 · Permission and limits
Know exactly what you may do
2 · Get ready
Gather what you need and check the starting conditions
What you need
- Declared Swarm coordination fixture: Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density.
- Setup aid for Extract agents and environment: Keep the task to published data or a harmless computer model; do not recruit or move a crowd.
- Swarm coordination log: Trajectory coordination, time, error and collision risk in an authorized declared crowd model; retain Swarm coordination errors, assistance, stop and fallback.
- Swarm-research extraction sheet: from “Consensus decision making in human crowds”, record participant task, local information and group outcome; from “How simple rules determine pedestrian behavior and crowd disasters”, record the movement rules, density and collision or disaster boundary. Test whether the low-density interpretation survives the supplied extreme-density row. Neither paper is an operational crowd-control guide.
Before you start
- Obtain full methods and any model supplement or code description.
- Keep the task to published data or a harmless computer model; do not recruit or move a crowd.
- Start check for Swarm coordination: The question does not use “collective intelligence” as an outcome.
- Top-of-sheet stop for Swarm coordination: Stop if rule details, density or collision definitions are missing.
3 · The method
Follow these steps in order
- Frame the claim narrowly
Ask whether the named local rule changes trajectory error or collision risk in the declared crowd size, density and environment.
Why: The question does not use “collective intelligence” as an outcome.
Check: The question does not use “collective intelligence” as an outcome.
- Extract agents and environment
Record number of people or simulated agents, area, obstacles, visibility, start points and goal.
Why: Density can be calculated and reproduced.
Check: Density can be calculated and reproduced.
- Rebuild each local rule
Write what information each agent senses, update order, speed limit, separation rule and stopping condition.
Why: Another analyst could implement the rule without guessing.
Check: Another analyst could implement the rule without guessing.
- Map informed members
Record who knows the destination, how information spreads and whether consent or authority is modelled.
Why: Informed status and influence are distinct from simple proximity.
Check: Informed status and influence are distinct from simple proximity.
- Measure trajectories and collisions
Extract path length, time, spacing, contacts, near misses and any severity rule.
Why: Coordination gains cannot hide rising collision risk.
Check: Coordination gains cannot hide rising collision risk.
- Stress the density boundary
Compare the studied density with a documented extreme-density condition and note turbulence or model failure.
Why: The safe-looking claim weakens when density moves outside the tested range.
Check: The safe-looking claim weakens when density moves outside the tested range.
- Test alternative mechanisms
Consider shared visual cues, leader following, boundary effects and simulation assumptions instead of emergence alone.
Why: At least one alternative remains viable if controls are absent.
Check: At least one alternative remains viable if controls are absent.
- Write the ethical gate
Require specialist crowd-safety review, validated models, venue data, consent and emergency procedures before any real-world test.
Why: The output is a research decision, not instructions for moving a crowd.
Check: The output is a research decision, not instructions for moving a crowd.
4 · Worked example
See the whole method used once
Scenario
Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density.
Walkthrough
- Write the question for 40 simulated walkers in a stated corridor: path error and contacts under one alignment rule versus no alignment.
- Extract corridor dimensions, agent radius, speed cap, neighbour radius and update interval from the methods.
- Calculate the reported density and list which walkers know the target exit.
- Copy path-time and collision outcomes, then contrast them with the cited extreme-density turbulence finding.
- List wall-following and visible leader cues as alternative explanations for apparent coordination.
- Conclude that the rule is research-only and require validated venue modelling plus professional crowd-safety review before field use.
Result
The audit defines what the local rule did and where density breaks the safety claim. It does not teach people to control crowds or prove a trainable swarm capability.
5 · Right and wrong
Compare correct or safer execution with the common wrong version
| Moment | Right / safer | Wrong / riskier | Why it matters |
|---|---|---|---|
| Local rule | Specify sensed neighbours, update timing and speed. | Describe “swarm instinct” without a rule. | Vague emergence cannot be replicated or audited. |
| Density in crowd-model density audit | Report people per area and obstacle geometry. | Transfer a low-density result to a packed venue. | Extreme density can produce turbulence and dangerous contacts. |
| Outcome in crowd-model density audit | Count path and severity-weighted collision measures. | Use visual orderliness as proof of safety. | Smooth-looking motion can conceal pressure and harm. |
| Authority in crowd-model density audit | Keep work in simulation and research review. | Run an unsupervised real crowd experiment. | Movement research can create immediate public risk and consent problems. |
6 · Common mistakes
Spot the error and apply the correction
| Mistake | Fix |
|---|---|
| Describe “swarm instinct” without a rule. | Translate the claim into agent inputs and outputs. |
| Transfer a low-density result to a packed venue. | Treat density as a boundary, not a footnote. |
| Use visual orderliness as proof of safety. | Extract contacts, near misses and failure states. |
| Run an unsupervised real crowd experiment. | Require accountable crowd-safety and ethics approval. |
7 · Practice
Turn the steps into a usable skill
First session
- Swarm-model evidence audit: Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density.
- Local-rule safety question: Frame the claim narrowly: Ask whether the named local rule changes trajectory error or collision risk in the declared crowd size, density and environment.
- Agent-rule reconstruction: Rebuild each local rule: Write what information each agent senses, update order, speed limit, separation rule and stopping condition.
- Extreme-density stress test: Stress the density boundary: Compare the studied density with a documented extreme-density condition and note turbulence or model failure.
- Crowd-safety governance gate: Write the ethical gate: Require specialist crowd-safety review, validated models, venue data, consent and emergency procedures before any real-world test. File the output as a research audit.
Repeat plan
Review one model and one human study in a 90-minute session. Revisit quarterly or when a replication reports collision data; progress to synthesis only when rule, density and safety outcomes are comparable.
Progress when
- The question does not use “collective intelligence” as an outcome.
- Density can be calculated and reproduced.
- Another analyst could implement the rule without guessing.
- A second analyst can reproduce the local rule and density, every benefit is paired with collision evidence, and the conclusion prohibits unsupervised field testing.
Do not progress when
- Do not continue while this error remains: Describe “swarm instinct” without a rule.
- Pause until this correction works: Treat density as a boundary, not a footnote.
- This Swarm coordination stop ends the block: Stop if rule details, density or collision definitions are missing.
8 · Check the result
Measure what changed
Trajectory coordination, time, error and collision risk in an authorized declared crowd model
How: Evidence fixture: Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density. Audit action — Rebuild each local rule: Write what information each agent senses, update order, speed limit, separation rule and stopping condition. Then enter Trajectory coordination, time, error and collision risk in an authorized declared crowd model, its comparator and uncertainty. Disconfirmation uses “Stress the density boundary”.
Good result: A second analyst can reproduce the local rule and density, every benefit is paired with collision evidence, and the conclusion prohibits unsupervised field testing.
This does not prove: Boundary for Swarm coordination: “Trajectory coordination, time, error and collision risk in an authorized declared crowd model” describes only Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density. It cannot establish “Unlock a human hive mind”.
Self-check
- Without the example, demonstrate: The question does not use “collective intelligence” as an outcome.
- Find the fault in this attempt: “Describe “swarm instinct” without a rule.” Apply “Translate the claim into agent inputs and outputs.”; what changes?
- What evidence in the completed record shows that this is wrong: “Transfer a low-density result to a packed venue.”?
- Swarm coordination stop decision: Stop if rule details, density or collision definitions are missing.
9 · Stop, adapt or get help
Keep the safety boundary practical
Stop and get help
- Stop if rule details, density or collision definitions are missing.
- Do not test crowd manipulation, evacuation or consensus methods in public.
- Route venue or event safety decisions to qualified crowd-safety professionals and local authorities.
Accessibility and adaptations
- Use a static trajectory table or verbal route description if animated crowd displays are inaccessible.
- Provide formulas for density and collision rate in a calculator-ready sheet.
10 · Evidence and limits
Why these instructions are here
- primary research
Registered support for Swarm coordination: “Consensus decision making in human crowds”. It bears on Trajectory coordination, time, error and collision risk in an authorized declared crowd model inside the Swarm coordination fixture. It does not validate “Unlock a human hive mind”.
Consensus decision making in human crowds - primary research
Constraint for Swarm coordination, drawn from “How simple rules determine pedestrian behavior and crowd disasters”: Local rules can generate dangerous turbulence at extreme density.
How simple rules determine pedestrian behavior and crowd disasters - official guidance
ICO worker-monitoring limit — Swarm coordination: rights and data protection apply to “Frame the claim narrowly”. Fixture type for “Extract agents and environment”: fictional or consented. Covert use of Trajectory coordination, time, error and collision risk in an authorized declared crowd model is outside scope.
Employment practices and data protection: monitoring workers
Limits
- Swarm coordination boundary: interpret “Trajectory coordination, time, error and collision risk in an authorized declared crowd model” only for Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density.
- A successful result does not establish “Unlock a human hive mind”.
- Swarm coordination limiting finding: Local rules can generate dangerous turbulence at extreme density.
- Swarm coordination limiting finding: Simple walking coordination does not establish deliberate trainable collective intelligence.
- Scope remains Swarm coordination: Audit a published pedestrian-model study and a human-crowd consensus study, asking whether simple local alignment produces safe trajectories at low and extreme density. Recheck the comparator, support and “Trajectory coordination, time, error and collision risk in an authorized declared crowd model” after any configuration change.
Open the complete canonical research register
- Primary empirical supportLimiting / contraryConsensus decision making in human crowds
John R. G. Dyer; Christos C. Ioannou; Lesley J. Morrell; Darren P. Croft; Iain D. Couzin; Dean A. Waters; Jens Krause · 2008 · Primary research
- Primary empirical supportLimiting / contraryHow simple rules determine pedestrian behavior and crowd disasters
Mehdi Moussaïd; Dirk Helbing; Guy Theraulaz · 2011 · Primary research
- Limiting / contraryOfficial boundary contextEmployment practices and data protection: monitoring workers
Information Commissioner’s Office · 2023 · Official guidance
Read the complete evidence interpretation on the Power dossier.
Tutorial delivery controls
Learn, adapt, troubleshoot and resume
Progress is saved only in this browser on this device.
Step-by-step learner mode
Each activity includes its success check, a nearby accessible alternative and an “I’m stuck” correction path. Alternatives preserve the target where possible; when they change the task, Titan labels them as related rather than equivalent.
Frame the claim narrowly
Ask whether the named local rule changes trajectory error or collision risk in the declared crowd size, density and environment.
The question does not use “collective intelligence” as an outcome.
The question does not use “collective intelligence” as an outcome.
I’m stuck on this step
Reset: Re-read this authored instruction — “Ask whether the named local rule changes trajectory error or collision risk in the declared crowd size, density and environment.” — and its success check, then attempt only this step.
Possible snag: Describe “swarm instinct” without a rule.
Correction: Translate the claim into agent inputs and outputs.
Stop / get help: Stop if rule details, density or collision definitions are missing.
Extract agents and environment
Record number of people or simulated agents, area, obstacles, visibility, start points and goal.
Density can be calculated and reproduced.
Density can be calculated and reproduced.
I’m stuck on this step
Reset: Re-read this authored instruction — “Record number of people or simulated agents, area, obstacles, visibility, start points and goal.” — and its success check, then attempt only this step.
Possible snag: The result from “Record number of people or simulated agents, area, obstacles, visibility, start points and goal.” does not yet meet this declared check: Density can be calculated and reproduced.
Correction: Return to the start of “Extract agents and environment”, reduce complexity or pace, and repeat only the part needed to satisfy: “Density can be calculated and reproduced.”
Stop / get help: Stop if rule details, density or collision definitions are missing.
Rebuild each local rule
Write what information each agent senses, update order, speed limit, separation rule and stopping condition.
Another analyst could implement the rule without guessing.
Another analyst could implement the rule without guessing.
I’m stuck on this step
Reset: Re-read this authored instruction — “Write what information each agent senses, update order, speed limit, separation rule and stopping condition.” — and its success check, then attempt only this step.
Possible snag: The result from “Write what information each agent senses, update order, speed limit, separation rule and stopping condition.” does not yet meet this declared check: Another analyst could implement the rule without guessing.
Correction: Return to the start of “Rebuild each local rule”, reduce complexity or pace, and repeat only the part needed to satisfy: “Another analyst could implement the rule without guessing.”
Stop / get help: Stop if rule details, density or collision definitions are missing.
Map informed members
Record who knows the destination, how information spreads and whether consent or authority is modelled.
Informed status and influence are distinct from simple proximity.
Informed status and influence are distinct from simple proximity.
I’m stuck on this step
Reset: Re-read this authored instruction — “Record who knows the destination, how information spreads and whether consent or authority is modelled.” — and its success check, then attempt only this step.
Possible snag: The result from “Record who knows the destination, how information spreads and whether consent or authority is modelled.” does not yet meet this declared check: Informed status and influence are distinct from simple proximity.
Correction: Return to the start of “Map informed members”, reduce complexity or pace, and repeat only the part needed to satisfy: “Informed status and influence are distinct from simple proximity.”
Stop / get help: Stop if rule details, density or collision definitions are missing.
Measure trajectories and collisions
Extract path length, time, spacing, contacts, near misses and any severity rule.
Coordination gains cannot hide rising collision risk.
Coordination gains cannot hide rising collision risk.
I’m stuck on this step
Reset: Re-read this authored instruction — “Extract path length, time, spacing, contacts, near misses and any severity rule.” — and its success check, then attempt only this step.
Possible snag: Use visual orderliness as proof of safety.
Correction: Extract contacts, near misses and failure states.
Stop / get help: Stop if rule details, density or collision definitions are missing.
Stress the density boundary
Compare the studied density with a documented extreme-density condition and note turbulence or model failure.
The safe-looking claim weakens when density moves outside the tested range.
The safe-looking claim weakens when density moves outside the tested range.
I’m stuck on this step
Reset: Re-read this authored instruction — “Compare the studied density with a documented extreme-density condition and note turbulence or model failure.” — and its success check, then attempt only this step.
Possible snag: Transfer a low-density result to a packed venue.
Correction: Treat density as a boundary, not a footnote.
Stop / get help: Stop if rule details, density or collision definitions are missing.
Test alternative mechanisms
Consider shared visual cues, leader following, boundary effects and simulation assumptions instead of emergence alone.
At least one alternative remains viable if controls are absent.
At least one alternative remains viable if controls are absent.
I’m stuck on this step
Reset: Re-read this authored instruction — “Consider shared visual cues, leader following, boundary effects and simulation assumptions instead of emergence alone.” — and its success check, then attempt only this step.
Possible snag: The result from “Consider shared visual cues, leader following, boundary effects and simulation assumptions instead of emergence alone.” does not yet meet this declared check: At least one alternative remains viable if controls are absent.
Correction: Return to the start of “Test alternative mechanisms”, reduce complexity or pace, and repeat only the part needed to satisfy: “At least one alternative remains viable if controls are absent.”
Stop / get help: Stop if rule details, density or collision definitions are missing.
Write the ethical gate
Require specialist crowd-safety review, validated models, venue data, consent and emergency procedures before any real-world test.
The output is a research decision, not instructions for moving a crowd.
The output is a research decision, not instructions for moving a crowd.
I’m stuck on this step
Reset: Re-read this authored instruction — “Require specialist crowd-safety review, validated models, venue data, consent and emergency procedures before any real-world test.” — and its success check, then attempt only this step.
Possible snag: Run an unsupervised real crowd experiment.
Correction: Require accountable crowd-safety and ethics approval.
Stop / get help: Stop if rule details, density or collision definitions are missing.
Correct versus incorrect execution
These accessible process diagrams are built from the tutorial’s own right/wrong teaching. They are not anatomical illustrations and do not add technique beyond the canonical tutorial.
Specify sensed neighbours, update timing and speed.
Describe “swarm instinct” without a rule.
Report people per area and obstacle geometry.
Transfer a low-density result to a packed venue.
Count path and severity-weighted collision measures.
Use visual orderliness as proof of safety.
Keep work in simulation and research review.
Run an unsupervised real crowd experiment.
Method-structure checklist
10 of 10 structural checks present
- Ordered, Power-specific instructions — present
- Every activity has a success check — present
- Materials or supplied records are declared — present
- Measurement or assessment rule is present — present
- Tutorial-specific troubleshooting is present — present
- Stopping or escalation boundary is present — present
- Every activity has an adjacent alternative — present
- Correct-versus-incorrect comparison is present — present
- Evidence context is bound to the Power record — present
- Planning metadata is present — present
The method-readiness band and presence checklist assess tutorial presentation and are separate from evidence quality for the underlying Power. They are automated editorial aids, not human approval.
Manual editorial sign-off: Pending. This tutorial must not display a human-approved state until an identified editor signs the exact content hash.