It’s Inauguration Day, when everyone is looking to the future, which is always a brighter spot than the particular one we happen to be in. But there are of course people who look to the future every day. They make a profession of anticipating it. These folks, from meteorologists to economists to financiers to farmers, all have a common stock in trade: probability. They are interested in pinpointing the moments when randomness and a particular event can meet.
For their work professional future-watchers use mathematical expressions that trace the path of likelihood through chance to happening: probability functions. These functions can be plotted graphically, and they come in many shapes and carry many names–Wikipedia lists at least a hundred different kinds of probability functions. So, depending on whether you’re trying to calculate value-at-risk, doing statistical analysis for production forecasting, or helping a client with retirement planning, it’s probable that there is a function formulated for that purpose. Choosing the correct probability function is crucial to credible forecasting.
Whether they are standard-issue or designer-created, probability functions have work to do. Introduced into mathematical models (such as those spun out by Monte Carlo software) they mediate the force of chance to specify the future outcomes in, say —? Population trends? Widgets? Income? Depending on which future you’re watching, the curve of a probability function is the shape of things to come.