Facilitator Guide

Facilitator Guide

Owner: Bill Eisenhauer Last Updated: August 6, 2026 Status: 🚧 Ready for field testing Contributors: Bill Eisenhauer

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Use the simulator to surface mental models, not to identify a winner. The most valuable conversation is usually why participants interpreted the same evidence differently.

Choose a format

Format	Time	Suggested mode	Purpose
Solo orientation	10–15 minutes	Easy, then Hard	Learn mechanics before testing judgment
Pair exercise	20–30 minutes	Hard	Compare evidence and intervention theories
Team workshop	45–60 minutes	Hard first	Make assumptions and disagreements visible
Leadership discussion	30–45 minutes	Hard	Connect investment choices to outcome evidence

Easy versus Hard

Easy mode previews every affordable card's marginal next-cycle effect against the current bundle. Use it to learn model relationships and discover combination effects.

Hard mode hides those results. Participants must interpret incomplete telemetry, make an allocation, and predict the post-intervention constraint before seeing the receipt.

Neither mode says what a real organization should do. Easy teaches the model; Hard tests reasoning inside the model.

Workshop flow

flowchart LR
    A["State the outcome boundary"] --> B["Play cycle 1 silently"]
    B --> C["Compare diagnoses"]
    C --> D["Name assumptions"]
    D --> E["Play remaining cycles"]
    E --> F["Debrief constraint movement"]
    F --> G["Draft one real measurement experiment"]

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In prose: agree on what counts as an outcome, let participants make the first decision independently, compare their diagnoses, expose assumptions, finish the scenario, then translate one insight into a measurement experiment rather than a broad prescription.

Facilitation script

Before cycle 1

Ask:

• What does this system count as value?
• Which signal are you trusting most?
• Which evidence is absent?
• Is your proposed intervention adding capacity, reducing load, improving yield, or improving diagnosis?

Before running each cycle

Require a sentence in this form:

We will invest in [intervention] because [mechanism]; afterward we expect [stage] to govern throughput, and we expect [outcome and burden change].

After each receipt

Ask:

• Did the mechanism work even if the prediction missed?
• Did the intervention move the constraint or only the queue?
• What new demand did the intervention create downstream?
• Which human attention pool changed?
• What would you instrument before spending again?

Avoid these traps

• Do not equate the largest queue with the system constraint.
• Do not maximize the simulator score without articulating a causal theory.
• Do not treat a green Easy-mode card as universally good.
• Do not map simulator stages directly onto team performance.
• Do not use the exercise to justify a predetermined reorganization.
• Do not replace real discovery with the model's constants.

Convert play into a field experiment

End with one small receipt template:

Field	Team entry
Outcome boundary
Probable constraint
Confidence and missing evidence
Bounded intervention
Expected mechanism
Expected next constraint
Learning-window length
Metrics and event sources
Named owner
Review date

The desired result is not agreement about the simulator. It is a safer, faster way to test a real operating assumption.

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Related: Constraint Learning Loop · Simulator Model