Learning library

Master the Idea Simulator in three chapters

Move from a quick simulation to a structured business model and pick the right engine for the job. Bookmark this page for onboarding teammates or refreshing your workflow.

Start with essentialsPick a model
Chapter 1

Platform essentials

Understand the main workspace, navigation, and roadmap so you can explore with confidence.

Quick simulation run

Use the default workspace when you need a fast probability check.

  • Set your inputs Adjust Simulation Runs (100-50,000) and Months to Project (12-120) to match the level of fidelity you need.
  • Stay reproducible Lock a seed before sharing results so collaborators can rerun the same scenario without drift.
  • Run the engine Hit "Run simulation" and watch the percentile bands, tables, and narrative load in a couple of seconds.
  • Interpret and iterate Read the summary, identify top drivers in the sensitivity cards, and tweak one variable at a time.

End-to-end workflow

Follow this path when you want the full modelling experience.

Step 1

Create a new idea

Capture the concept at a high level so you can iterate in context.

  • Visit /ideas and click "+ New Idea".
  • Fill in the idea name, optional description, and select a currency.
  • Save to create the workspace shell.
Open /ideas
Step 2

Complete the Lean Canvas

Document the problem, solution, and customer insights your simulation will rely on.

  • Describe the problem, solution, UVP, and key customer segments.
  • Outline go-to-market channels, revenue streams, costs, and metrics.
  • Save frequently - draft mode keeps partial progress safe.
View canvas editor
Step 3Coming soon

Quantify KPIs & assumptions

Translate market size, pricing, costs, and churn into numbers the engine can use.

  • Fill TAM, SAM, SOM, and target customers for year 1 and 3.
  • Specify pricing tiers, ARPU, CAC, churn, and marketing budget.
  • Mark the idea complete to auto-generate starter variables.
Step 4Coming soon

Run tailored simulations

Use the idea-specific simulation view to compare scenarios and export results.

  • Switch to the simulate tab to run Monte Carlo on saved variables.
  • Save scenarios (e.g., conservative vs aggressive) for side-by-side comparison.
  • Export CSVs or share links once satisfied with the insight.

Capabilities & roadmap

Available now

What you can do today

  • Quick simulation canvas with percentile charts, narrative, and sensitivity insights.
  • Lean Canvas editor for capturing qualitative context and KPIs.
  • Admin controls for membership levels, upgrades, and invitations.
In progress

On the near-term roadmap

  • Per-idea variable management and scenario storage.
  • Idea-specific simulation dashboards with comparisons and exports.
  • Collaboration enhancements such as comments, sharing, and report generation.

Key URLs & resources

Bookmark these routes while developing locally.

Core pages
PageURLPurpose
Homehttp://ideasimulator.comRun a quick Monte Carlo simulation.
Ideas listhttp://ideasimulator.com/ideasManage and open saved ideas.
Idea detailhttp://ideasimulator.com/ideas/{ideaname}Edit Lean Canvas content for a specific idea.
API docshttp://ideasimulatorapi/docsTest backend endpoints interactively.
Health checkhttp://ideasimulatorapi/healthVerify the backend service status.
Chapter 2

Idea modelling playbook

Structure ideas with the Lean Canvas, quantify assumptions, and prepare data for simulation.

Multi-step idea flow

📋

Step 1 - Basic information

  • Idea name (required) and description (optional).
  • Choose the operating currency (USD, EUR, GBP, JPY, CAD, AUD).
🎯

Step 2 - Lean Canvas fundamentals

  • Problem, solution, and unique value proposition.
  • Customer segments and channels.
  • Revenue streams, cost structure, key metrics, competition.
📊

Step 3 - Market & KPI inputs

  • Market sizing (TAM, SAM, SOM) and target customers for year 1 and year 3.
  • Pricing mix, ARPU, CAC, churn, marketing budget, and growth assumptions.
  • Break-even targets and margin goals for later analysis.
✅

Step 4 - Review & confirm

  • Check the summary of inputs and guidance before finalising.
  • Mark the idea complete when you are ready to generate variables.

Save modes & when to use them

Save as draft

Stores progress without validating required fields or generating variables.

Use when: Use while you are still shaping ideas or collaborating on qualitative inputs.

Save as complete

Validates Name, Problem, and Solution then generates starter variables from KPIs.

Use when: Use when you want the Monte Carlo engine to create defaults and unlock simulations.

Auto-generated Monte Carlo variables

Completing Step 3 generates default distributions you can refine later.

KPI to variable mapping
KPI inputVariableDistributionParameters
Target customers Y1customers_y1Triangularmin: 0.5x, mode: x, max: 2x
ARPU monthlyarpu_monthlyTriangularmin: 0.7x, mode: x, max: 1.5x
Fixed costs monthlyfixed_costs_monthlyTriangularmin: 0.8x, mode: x, max: 1.3x
Variable cost per customervariable_cost_per_customerTriangularmin: 0.7x, mode: x, max: 1.5x
CACcacPERTmin: 0.6x, mode: x, max: 2x, λ = 4
Churn rate monthlychurn_rate_monthlyTriangularmin: 0.5x, mode: x, max: 2x
Marketing budget monthlymarketing_budget_monthlyTriangularmin: 0.7x, mode: x, max: 1.5x

Next steps after saving an idea

  1. Fine-tune generated variables - adjust distributions, ranges, or add new drivers.
  2. Run Monte Carlo simulations and review percentile outputs together.
  3. Save contrasting scenarios (e.g., conservative vs aggressive) once scenario storage is available.
  4. Export CSVs or share summaries with stakeholders to drive decisions.

Developer endpoints & schema

REST endpoints

POST /api/v1/ideas
GET /api/v1/ideas
GET /api/v1/ideas/{id}
PUT /api/v1/ideas/{id}
POST /api/v1/ideas/{id}/variables

Idea payload

{
  "name": "string (required)",
  "description": "string (optional)",
  "currency": "USD|EUR|GBP|JPY|CAD|AUD",
  "status": "draft|complete",
  "lean_canvas": {
    "problem": "string",
    "solution": "string",
    "unique_value_proposition": "string",
    "segments": "string",
    "channels": "string",
    "revenue_streams": "string",
    "cost_structure": "string",
    "key_metrics": "string",
    "competition": "string"
  }
}

Troubleshooting checklist

Save as complete fails

Ensure the idea has a name plus problem and solution fields completed in the Lean Canvas.

No variables appear after saving

Only KPIs entered in Step 3 are converted - fill in market, pricing, CAC, churn, and budget values.

Simulation controls are disabled

Ideas must be marked complete and contain at least one variable before the Monte Carlo engine unlocks.

Chapter 3

Choosing the right simulation model

Match your business question with Monte Carlo, System Dynamics, or (soon) Agent-Based modelling.

Model overview

  • Monte Carlo: statistical uncertainty modelling with rapid iteration.
  • System Dynamics: stocks, flows, and feedback loops for structural insight.
  • Agent-Based: individual agents and emergence - perfect for network effects.

Model cheat sheets

Fastest

Monte Carlo

Statistical sampling for probabilistic outcomes.

Best for
  • Financial projections with uncertain inputs.
  • Risk analysis requiring percentile bands.
  • Sensitivity checks across up to ~20 assumptions.
Strengths
  • Runs in seconds even with thousands of iterations.
  • Produces P5/P50/P95, probabilities, and correlation-based sensitivity.
  • Supports correlations and Latin Hypercube sampling.
Limitations
  • Treats periods independently - no feedback loops or memory.
  • Assumes relationships come from explicit formulas.
Use when...
  • You need quick ranges before a presentation or review.
  • You are iterating on pricing, growth, or cost assumptions frequently.
Structured

System Dynamics

Stocks, flows, and feedback loops to model behaviour over time.

Best for
  • Retention, adoption, or supply dynamics with accumulation.
  • Scenarios where delays and reinforcing loops matter.
Strengths
  • Explicitly models cause-and-effect relationships.
  • Monte Carlo mode adds uncertainty bands on top of structural logic.
Limitations
  • Requires XMILE models and more upfront design.
  • Longer run times than pure Monte Carlo.
Use when...
  • You need to explain why a KPI changes, not just how much.
  • You must communicate system structure to stakeholders.
Coming soon

Agent-Based

Simulates individual agents and emergent behaviour.

Best for
  • Network effects, referrals, or heterogeneous customer cohorts.
  • Spatial or social adoption where peer influence dominates.
Strengths
  • Captures emergence from micro-level rules.
  • Represents diverse agent types and decision triggers.
Limitations
  • Computationally expensive and sensitive to micro assumptions.
  • Requires rich data to calibrate behaviours.
Use when...
  • You need to model viral growth, marketplace matching, or complex interactions.
  • You are ready to invest in calibration and compute budget.

Decision framework

  1. Start with Monte Carlo: Use it for fast probability ranges and to validate assumptions before adding complexity.
  2. Upgrade to System Dynamics: Move to SD when feedback loops, delays, or accumulations drive outcomes.
  3. Layer in Agent-Based modelling: Adopt ABM (when available) when individual behaviours and network effects shape results.
Combine models when needed: use Monte Carlo for financials, System Dynamics for retention loops, and Agent-Based for network effects once available.

Membership & performance matrix

Model availability by plan
ModelFreeProEnterprise
Monte Carlo✅✅✅
System Dynamics❌✅✅
Agent-Based (planned)❌❌✅
Performance characteristics
ModelExecution timeComplexitySetup effort
Monte Carlo< 1s for 1,000 runsLowLow
System Dynamics (deterministic)< 1s for 12-60 monthsMediumMedium
System Dynamics (Monte Carlo)1-3s for 100 runsMed-HighMedium
Agent-Based10s+ for 100 agentsHighHigh

Practical tips

Monte Carlo essentials

  • Start with 10-20 runs for quick smoke tests before scaling to 1,000+ iterations.
  • Use Latin Hypercube sampling when dealing with tight run budgets.
  • Add correlations for variables that move together (e.g., price and volume).

System Dynamics notes

  • Sketch the stock-and-flow diagram on paper before encoding it in XMILE.
  • Validate a deterministic run first, then introduce parameter uncertainty.
  • Keep the time step (dt) consistent with the story you tell stakeholders.

General guidance

  • Document assumptions and link to research so teammates trust the numbers.
  • Focus sensitivity analysis on the top 2-3 drivers to guide mitigation plans.
  • Use scenario comparisons to communicate trade-offs clearly.

Last updated: November 2025