«Between Simulation and Reality in the Digital Age»
Between Simulation and Reality in the Digital Age
In recent years, concepts like cloud computing, big data, and artificial intelligence have become familiar terms. Now, the phrase «digital twin» is also gaining traction. This idea, introduced almost 20 years ago by Michael Grieves in engineering, started as a virtual copy that mirrors a physical process in real time. Today, however, «digital twin» is applied to digital models that represent social, economic, and industrial systems. This broad use raises questions about what truly defines a digital twin—especially when dealing with complex systems like cities.
Digital Models vs. Digital Twins
A digital model is a simplified version of a real system, used to focus on key aspects for analysis. In contrast, a digital twin should be an accurate, real-time replica of its physical counterpart. This means not all digital models are digital twins—the distinction lies in their detail and accuracy. When a model becomes highly detailed and synchronized with reality, the line between the two starts to blur. Establishing clear links or patterns between simulation and reality is essential in today’s digital world, as this helps achieve greater productivity in many areas.
The Paradox of the Perfect Digital Twin
A perfect digital twin is one that completely merges with the physical system. At that point, it is no longer just a model or tool, but becomes the system itself. This creates a dilemma: if the digital twin is identical to the real system, how can it be used to test or improve it? The solution is to balance precision and practicality. A digital twin should be accurate enough to represent the system, but also flexible enough to serve as a tool for experimentation and improvement.
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