Know-it-all 101 🎓 ➡️ ~ The Minimax Algorithm

 When you play chess, tic-tac-toe, or any two-player strategy game, your brain does something fascinating. You’re not just thinking about your move — you’re thinking about your opponent’s move in response to yours. Then you think about how you’d respond to that. And so on, like an argument in your head where you play both sides.

The minimax algorithm is how computers formalize that mental tug-of-war. It’s the logic engine behind many game AIs, from retro video games to modern chess engines. The name comes from the goal; Minimize the opponent’s maximum possible gain (if they play optimally) and maximize your own minimum guaranteed gain (if you play optimally).

Here’s how it works in a nutshell. Imagine a decision tree, with each node representing a possible game state. On your turn, the algorithm looks at all the moves you can make and picks the one that gives you the best worst-case outcome. On your opponent’s turn, it assumes they will choose the move that’s worst for you. This back-and-forth continues until the algorithm reaches the “end” of the tree — a win, loss, or draw — or until it runs out of search depth and uses an evaluation function to guess which positions are better.

In tic-tac-toe, minimax can search the entire game tree easily, because there are only so many possible positions. That’s why it can play perfectly and never lose. In chess, though, the tree is so vast that even the most powerful supercomputers can’t calculate every possibility — which is why engines use minimax plus clever shortcuts like alpha-beta pruning to ignore obviously bad branches.

If you think about it, minimax is kind of like life advice in disguise: assume the world will respond in the harshest way possible, and prepare the move that still leaves you in the best position.

And now, you know how computers think about games — not with genius intuition, but by relentlessly playing both sides of every conversation until they find the path that hurts the least and helps the most.