I was visiting with a client the other day. We were talking about the possibility of building a model to support a small airline’s operation. A gentleman in the room, a subject matter expert on aviation operations, turned to me and said, “Why do I need a simulation model? I can answer the questions we have by just sitting down and thinking it through.”
I was taken aback at first. Try to design a complex set of routes, maintenance bases, schedules and the like in your head? That seemed so foreign to mathematicians like me.
And yet, it was a great question. Why do we need models? And especially – why models when we have recognized experts in the field, just down the hall? After pondering this for a while I came to rest on a basic philosophy – models don’t replace human intuition, knowledge, and experience – they codify them. Done properly, models and human judgment are seamlessly interwoven into a single, organized body of knowledge. An institutional artifact that is worthy of preservation and continuous use.
Intuition plays a subtle but key role in all we do in the process of building models. First we start with a hypothesis, which is a problem statement that suggests an answer – to be proven or disproven by analysis. How do we come up with the problem statement? The answer, of course, is that humans have grappled with a complex problem and certainly know enough to restate its bounds.
During the course of model building, humans weigh in on how the system works, while the modeler translates that guidance into model-able form.
And finally, when the model is built, experiments are conducted. “What happens to the call center response time if we increase the number of resources in Brazil” or “how does our supply chain performance look when we add that customer in Peoria”. These are questions that arise from hunches about how the system at hand can and should perform under a variety of conditions. Again, not generated in a vacuum – but coming from human experience.
Perhaps pop culture has given us this false “zero sum game” between human brainpower and computer simulation. Hollywood certainly has its share of dark man v. machine portrayals. But best practice in simulation is diametrically opposed to this view – creating models that leverage humans in a way that simply transforms their knowledge into a fast and convenient format.
Pass the popcorn, please.