SCENARIO MODELING
Simulations & What-If Scenarios
VIRCON's simulation system allows admins to model hypothetical policy changes, staffing adjustments, and moderation scenarios before implementation. Simulations use historical data and predictive models to forecast outcomes, reducing risk and enabling data-driven decision making.
Simulation Commands
Commands for modeling hypothetical scenarios before implementing policy changes.
- ,simulate strike @user [count] - Model staff strike escalation outcomes
- ,simulate staff <+count|-count> - Predict coverage with adjusted staffing
- ,simulate banrate <multiplier> - Model higher/lower ban rate scenarios
- ,simulate list - View all available simulation types
- ,simulate view <simulation_id> - View past simulation results
Simulation Types
Supported what-if scenarios for testing policy changes and operational adjustments.
Strike Escalation
Model what happens when staff accumulates strikes
Example Scenario: What if this mod gets 1 more strike?
Output: Demotion trigger, recommended action, historical precedent
Staffing Adjustments
Predict coverage changes with different staff counts
Example Scenario: What if we add 2 more moderators?
Output: Coverage gaps filled, response time improvement estimate
Ban Rate Changes
Model effects of stricter/looser ban policies
Example Scenario: What if we ban at 1.5x current rate?
Output: Predicted community size impact, retention analysis
Trust Threshold
Model effect of adjusting trust score thresholds
Example Scenario: What if we kick users <30 trust instead of <20?
Output: Additional users affected, false positive risk estimate
Simulation Workflow
Process for running simulations from parameter input through result analysis.
- 1. Admin specifies simulation type and parameters.
- 2. System pulls relevant historical data for baseline comparison.
- 3. Simulation engine runs scenario using predictive models and trend data.
- 4. Results calculated: affected users, coverage changes, risk scores.
- 5. Output formatted with predicted outcomes, confidence levels, warnings.
- 6. Simulation saved to database for future reference and comparison.
- 7. Admin reviews results before making policy decision.
- 8. If simulation reveals issues, parameters adjusted and re-run until viable.
Simulation Database Schema
Database structure for storing simulation runs, parameters, and predicted outcomes.
| Field | Description |
|---|---|
| simulation_id | Unique simulation run identifier. |
| simulation_type | strike, staff, banrate, trust_threshold, custom. |
| initiated_by | Discord ID of admin who ran simulation. |
| initiated_at | Timestamp of simulation execution. |
| parameters | JSON object defining simulation inputs. |
| baseline_data | Current state snapshot for comparison. |
| predicted_outcome | JSON object with simulation results. |
| confidence_score | 0-100 confidence in prediction accuracy. |
| warnings | Array of potential risks or edge cases. |
Detailed Use Cases
Real-world examples of simulation-driven decisions and their outcomes.
Policy Change Validation
Scenario: Head admin wants to tighten warning thresholds
Simulation: ,simulate banrate 1.3 - models 30% increase in bans
Results: Predicts 15 additional bans/week, 85% confidence. Warning: may affect retention.
Decision: Admin reviews, decides to pilot change for 2 weeks with monitoring.
Staffing Optimization
Scenario: Coverage gaps during EU peak hours identified
Simulation: ,simulate staff +2 - adds 2 moderators to model
Results: Predicts 90% gap coverage, response time drops from 12min to 6min.
Decision: Admin recruits 2 EU timezone moderators based on simulation.
Strike Policy Review
Scenario: Mod has 2 strikes, considering issuing 3rd
Simulation: ,simulate strike @mod 1 - models 3rd strike outcome
Results: Triggers auto-demotion to trial mod, requires 30-day probation.
Decision: Admin proceeds with strike, trainee mode activated automatically.
Advanced Features
Enhanced simulation capabilities for sophisticated scenario modeling and analysis.
- Monte Carlo simulation support for probabilistic outcomes.
- Sensitivity analysis: test parameter ranges to find optimal values.
- Scenario comparison: run multiple simulations and compare side-by-side.
- Historical validation: compare past simulations vs actual outcomes.
- Export capabilities: generate PDF reports for leadership review.
Best Practices
Guidelines for using simulations effectively to inform data-driven decisions.
- Run simulations BEFORE policy changes, not after implementation.
- Test multiple parameter values to understand sensitivity and edge cases.
- Compare simulation results against historical data for validation.
- Document simulation findings when making policy decisions (data-driven).
- Re-run simulations periodically as community dynamics change.
- Share simulation results with stakeholders for transparency.
Limitations & Caveats
Important constraints and accuracy limitations of predictive simulations.
- Simulations are predictions, not guarantees - treat as guidance, not law.
- Models assume current trends continue - major events can invalidate predictions.
- Low data periods (<30 days history) reduce accuracy significantly.
- Human behavior variability limits precision of user-focused simulations.
- Simulations don't account for external factors (platform changes, etc.).