Solving Wordle using information theory

Summary by Krater

This video uses the game Wordle to explain information theory and the concept of entropy. It covers how word frequency and entropy can be combined to optimize guesses in Wordle.

From the video

Answers: How does information theory and entropy apply to playing Wordle?

• information theory
• entropy
• Wordle solver algorithm
• expected information value
• word frequency data
• sigmoid function

What it concludes

• Maximizing entropy alone in a Wordle bot results in an average score of 4.124.
• Incorporating word frequency data into the Wordle bot improves the average score to 3.601.
• Using the true Wordle list along with word frequency data achieves a best average score around 3.420.
• The maximum expected information after the first two guesses in Wordle is around 10 bits out of 11.17 bits of initial uncertainty.