# Using Models as Anchor Points > **Canonical models** from the field of complexity science give us a rigorous way of **exploring** and **meditating** on various aspects of complex causality and associated assumptions. This yields useful **anchor points** to relate real systems and conditions, and should not be confused with modeling in an attempt to sufficiently _capture_ a real-world complex system. Hence, as we go forward, we will use various conceptual, mathematical, and computational models to anchor various concepts. It is my experience that there is a sinking-in effect associated with repeated exposure to relatively simple models that nonetheless display complex behaviors — it eventually sinks into your gut. (Especially if you program your own simulations!). This is a way to begin unlearning. When you think you understand what is going on, yet still get surprised, you start to feel complexity. An exploration of formal modeling also yields several rigorous bounds with respect our own epistemic abilities and degree of certainty. This is perhaps its most important contribution in answering the question of what to do. It can tell us what _not_ to do. What to cease from trying over and over, always with disaster, always justifying disaster with the varying details, and always missing the invariant, that it is the approach that is the problem, the assumptions simply don’t fit. --- Date: 20211104 Links to: [Complexity Theory](Complexity%20Theory.md) [Big Ideas MOC](Big%20Ideas%20MOC.md) Tags: References: * Email from Joe Norman, The Big Blurry Picture