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How Minesweeper Can Make Us Think Differently About Data

We live in a world of uncertainty and imperfect information

Michael Grogan
Towards Data Science
4 min readFeb 6, 2021

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Source: Mines (Ubuntu 18.04 LTS)

I often like to play chess and minesweeper in my spare time (yes, don’t laugh).

Of these two games, I have always found minesweeper more difficult to understand, and the rules of play have always seemed very opaque.

However, the latter game is much more resembling of how situations often unfold in the real world. Here is why that is relevant to data science.

Perfect vs. Imperfect Information

Compare that to chess, where in spite of one’s playing ability — all players have perfect information at all times.

One can always see every piece on the board, and neither opponent possesses any informational advantage over the other (expect the potential knowledge gained from experience playing the game).

For this reason, AI has been used extensively to train computers to win at chess. This proved successful all the way back in 1997, when an IBM supercomputer was trained to beat grandmaster Garry Kasparov.

It is easy to extrapolate from this example that AI models can be trained to potentially solve any given problem that is presented to us in this world…

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Michael Grogan
Michael Grogan

Written by Michael Grogan

Statistical Data Scientist | Python and R trainer | Financial Writer | michael-grogan.com

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