Simulation Models
What Is a (Simulation) Model?
Physical and digital models have long been valuable tools in disciplines such as science, engineering, and design.
They simplify, illustrate, and analyze complex relationships by abstracting selected aspects of reality for a specific purpose and transforming them into a manageable and testable form.
This simplification requires defining system boundaries — for example, by selecting which model parameters to include or which assumptions to make.
Especially when modeling social systems, individual perspectives and values inevitably influence the model — meaning there is no such thing as a fully “correct” model.
As British statistician George Box famously put it: “All models are wrong — but some are useful.”
Today, digital simulation models are becoming increasingly important.
Thanks to advances in computing power and capacity, it is now possible to approximate and investigate dynamic, complex processes and multiple future scenarios in new ways.
Scenarios and Simulations
Digital simulation models allow you to explore scenarios without building physical prototypes or making real-world interventions — reducing risks and saving resources.
At the same time, the use of generative algorithms increases both the number and variety of optimal solutions.
Digital models can support informed decision-making based on data-driven forecasts, and they offer significant potential to integrate datasets from different disciplines in an interoperable way — generating new insights and enabling multidimensional problem-solving.
The Urban Model Builder is a tool for collaboratively modeling complex systems.
This means that many assumptions will be made — both in the overall design of the model and in the specific values and function definitions required.
- Document your assumptions explicitly and transparently
- Explain why you made them and what alternatives could be considered
- Check whether they remain valid in light of new insights
Uncertainty is an unavoidable part of modeling — whether due to imprecise data, variable parameters, or unclear model structures.
It’s important to identify these uncertainties and communicate them transparently.