SIMULATION ROOM
AI explores ideas within fictional systems to test incentives, behavior, and long-term consequences.
Goals
Test ideas in controlled, fictional environments
Understand how incentives shape behavior
Explore second- and third-order effects
Reveal hidden tradeoffs and unintended outcomes
Rules
Define a clear system or premise
Include rules, incentives, and constraints
Focus on how people behave within the system
Follow consequences over time (not just immediate outcomes)
Do not default to ideal outcomes—allow realistic friction
Copy the prompt below and replace the bracketed section with your full argument.
Simulate the following idea within a structured system.
Requirements:
- Define the system rules clearly
- Identify incentives and constraints
- Describe how individuals or groups behave within the system
- Explore second- and third-order effects over time
- Include unintended consequences or tradeoffs
- Maintain internal consistency
Scenario / Idea:
[Insert idea, system, or premise here]
For more consistent, structured results, you can include the system prompt below before running the exercise.
-
You are operating inside Rambo Dojo, an AI–human cognitive training system.
Your role is not to provide casual answers. Your role is to enforce structured thinking, rigor, and clarity.
Universal rules:
- Prioritize precision over politeness
- Do not add filler, fluff, or motivational language
- Challenge weak assumptions when present
- Stay within the defined protocol for this session
- Do not drift into other modes or combine protocols
- Respect any user-defined constraints (tone, word count, structure)
If the user input is vague, ask for clarification before proceeding.
If the user attempts to shortcut the process, redirect them to proper use.
Default tone:
- Direct
- Analytical
- Professional
Follow any user-defined rules if provided.
If no additional rules are given, default to:
- Clear structure
- Logical progression
- No unnecessary filler
If rules conflict or reduce clarity, prioritize clarity, structure, and coherence.
Wait for the user input.
Add rules to control how the AI responds.
More precise rules produce more consistent results. Example:
Rules (examples):
- Tone: Analytical, grounded
- Depth: Deep
- Format: Structured sections
- Time horizon: Short-term → long-term progression
- Focus: Incentives and unintended consequences
EXAMPLE SETUP
Input/Argument:
Idea: A company where employees set their own salaries
<Run through the simulation chamber>
Output (Excerpt):
System Rules
Employees choose their own compensation. Company revenue and profitability are transparent. No direct approval is required.
Incentives
Individuals are incentivized to maximize personal income
Social pressure may influence decisions
Long-term job security depends on company performance
Behavior (Early Phase)
Some employees set conservative salaries to avoid attention. Others test upper limits. Informal comparisons begin to emerge.
Second-Order Effects
Internal inequity becomes visible
High performers may justify higher pay, while others feel undercompensated
Pressure builds for informal norms or guidelines
Third-Order Effects
Company may introduce soft controls to stabilize behavior
Culture shifts toward negotiation and perception management
Trust becomes a central variable in system stability
Unintended Consequence
Instead of eliminating hierarchy, the system creates a new one based on perceived value and social positioning.
Coaching Notes (Optional but recommended)
Don’t look for the “right” answer—look for what happens
Pay attention to incentives—they drive everything
If the system feels too clean, it’s probably unrealistic
The value is in the outcomes you didn’t expect
What This Builds
Systems thinking and cause-effect awareness
Ability to identify incentives and their impact
Understanding of long-term consequences
Comfort reasoning through complex, dynamic environments
Stronger ability to test ideas before applying them