Running a Simulation
Simulations are a powerful tool for understanding complex systems, supporting decision-making, and exploring alternative outcomes. By stepping through modeled processes, you can reveal cause-and-effect relationships, timelines, and key parameters. Running simulations allows you to explore different scenarios, analyze uncertainties, and test hypotheses.
Simulation Results
In the simulation window, you’ll see all primitives you’ve defined as output parameters.
Results can be visualized in two main ways:
Time Series
- Displays how data changes over time in a line chart
- Hover to see specific values
- Toggle series on or off by clicking the legend
Scatter Plot
- Shows agent behaviors and state changes on a grid
- Hover to see specific values
- Toggle series on or off by clicking the legend
A simulation can only run if at least one output parameter is defined and there are no logic or syntax errors in the model.
Validation
Even if your model runs without errors, you should still critically assess whether its behavior is realistic and represents the real-world system appropriately.
By default, stock values can mathematically drop below zero. For certain variables—like a population count—this wouldn’t make sense.
- Compare your results with real measurements or statistical data to check whether proportions and magnitudes seem reasonable
- Consult subject matter experts to verify that the model and its outputs are plausible and technically sound
- Use historical data to see if your model can reproduce known developments
- Perform a sensitivity analysis to see how small parameter changes impact model behavior
Model validation is not always straightforward. You may lack reliable real-world data, your model might be intentionally abstract and difficult to compare, or you may be modeling social behavior—which can be especially tricky to define.