Our limited ability to see, experience and understand places reality far beyond our comprehension. To learn, create solutions and manage problems we create models that simplify nature so that it fits our ability well enough for us to solve problems; every model is an approximation.
George E.P. Box famously said “All models are wrong, but some are useful”.
- They reflect the underlying reality
- They provide a common framework for making sense.
- They enable us to better manage and organize
An example of how models fail comes from Sandra K Mitchell’s book, “Unsimple Truths: Science Complexity and Policy”. Galileo’s law of free fall was a reasonable model to manage gravity but it is contingent on the mass of the earth to be what it is. Newton’s law of universal gravitation was a better model but it requires that the bodies not be super massive or dense and not too close to each other. For those situations, we need Einstein’s theory of relativity but that fails when working in the sub-atomic space where quantum physics is more useful. Thus, even in physics, models only work within certain contingent circumstances. The common thread is that more useful models fit the properties of the environment or problem at the deepest level possible.
To know the usefulness of our healthcare delivery model, we need to look at its achievement of quality and sustainable cost. To understand why it has failed, we must revisit what we know about complexity and systems.