There's a particular strain of game theory, Colonel Blotto, that many believe could predict the outcomes of everything from elections to sports matches. It asks two sides to distribute soldiers over a battlefield, and hands wins to whoever has the most soldiers in a given area. However, it has one glaring problem: there hasn't been a way to get a firm solution. Well, computer scientists have finally found that last piece of the puzzle. They've developed an algorithm that can solve the Colonel Blotto game, making it useful as a strategic tool whenever there's a one-on-one situation.
The trick was to scale things back. Rather than try to account for every possible strategy, the code limits itself to "representative" strategies that are likely to cover the bases. While this might not be best for very specific conditions (and won't work at all for three or more sides), it's genuinely effective at handling general situations.
If adapted for real life, the algorithm could be helpful across the board. A political candidate could have a better sense of how much campaigning they need in given areas, and a company could decide whether or not it's devoting enough attention to key parts of its product. This isn't going to produce surefire predictions, of course -- you need to know what criteria to consider in the first place, and it can't account for the wildcard factors that might creep up. All the same, it could provide some direction when human analysis and educated guesses aren't enough.