When solving complex problems, we tend to look for scalable laws that are derived from existing data (a-posteriori). This approach seems intuitive, but can lead to suboptimal designs in complex systems. Approaches based purely on past data run the risk of overlooking causal relationships. However, complex problems require proactive modeling and not just retrospective adaptation. A-priori principles provide a sound basis by introducing predefined laws and systematic considerations. They enable potential solution spaces to be structured and navigated in a targeted manner. The presentation will use various practical examples to illustrate why the use of a-priori principles is efficient and effective in solving complex problems. It will further present some methodologies that can be used in IPPs daily work.
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