Modeling scenarios has become a pretty big business, but notice that you often get only the numbers out of the end of the model, rarely the assumptions that drive the outcomes, and rarely a range of options.
This has been brought home again as I read the “strategic plan” completed at great expense on behalf of an industry body that covers a wide range of individual sub industries, each with their own characteristics and issues, albeit with some common drivers such as water availability, labour availability and capital generation.
Consultants at great expense produced a model that churned out numbers supposed to show the way forward, but which common sense says has a lot of dodgy assumptions going in, because what is coming out is unusable by any of the individual enterprises in the industries, or the industry bodies supposed to assist them.
Most commercial models I see set out to prove a point of view, and that point of view usually aligns with some preconceived notion of what the outcome should be. In this above case, the outcome was driven by the consultants need to tell the industry body what they wanted, not necessarily what they needed to know.
Scientific method by contrast works in the opposite way, it sets out to disprove a hypothesis, and by that means, advance our knowledge a bit, as you reconsider the hypotheses on the basis on one more thing you know does not work.
Bit more time consuming, but outcomes that are untainted by an existing perspective.
Numbers are only as good as the assumptions that drive them, so next time you are given an argument backed by numbers, don’t look at the numbers, have someone explain and debate the assumptions that have driven them.