Tom Mitchell, head of the Machine Learning Department at Carnegie Mellon University, called computer model predictions based on historical evidence "one of more positive trends we're going to see this century. ... We're just beginning."
Take a look at baseball, where Silver got his start as a stats geek. The Oakland A's, a team that famously uses computer statistics in selecting players, surprised everyone by getting into the playoffs despite one of the lowest payrolls in baseball.
Computer modeling tells the government what happens when a nuclear bomb explodes, helped Goodyear make a better tire and helped the makers of Pringles figure out how to keep the potato crisps from breaking in the can, said Bill Tang, program director for the Princeton Plasma Physics Laboratory simulation program.
Every time you swipe a credit card, a computer is using predictive models based on past evidence to determine if it's really you or if it is fraud, Mitchell added.
For about 40 years, climate scientists have used computer models to predict what global warming will look like with dead-on accuracy, said climate computer modeler Andrew Weaver of the University of Victoria in British Columbia.
For computer models to make predictions, three things are needed: computer power, mathematical formulas designed to mirror real world cause-and-effects and current conditions converted into numbers that can be used in formulas.
Experts input the data of current conditions into the formulas that say if X and Y happen, then it will produce Z. Then the computers run those what-if simulations over and over again, with slight variations changing the end results. These scenarios are run tens of thousands of times, giving a whole range of outcomes.
The key is seeing what happens most often and why. It's not a dead-on prediction, but breaks down the future into probabilities.