Solving Wordle Using Information Theory

Authors

• Talal Aladaileh, School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USAFollow
• Donald Stephens, School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USAFollow
• Mallak Alqaisi, School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USAFollow
• Congyu Wu, School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USAFollow

Author ORCID Identifier

Talal Aladaileh: 0009-0006-5975-6656

Congyu Wu: 0000-0002-6410-1375

Abstract

Wordle, a popular word-guessing game, challenges players to identify a five-letter secret word through iterative guesses and feedback on letter placement. The players must figure out the secret word within six guesses. After each guess, the letters will be color-coded based on different criteria. Optimizing the choice of guesses is critical for maximizing success within the limited attempts allowed. In this study, the application of Shannon entropy is explored as a strategy for selecting words that maximize information gain at each step of the game. By quantifying the uncertainty reduction achieved by potential guesses, this method prioritizes words that are likely to narrow the solution space most effectively. The experimental results demonstrate that entropy-based word selection improves performance compared to a heuristic approach based on selecting words by letter distribution, providing a systematic framework for decision-making in Wordle. This study can be used as a baseline for implementing Shannon entropy in different games.

Recommended Citation

Aladaileh, Talal; Stephens, Donald; Alqaisi, Mallak; and Wu, Congyu (2026) "Solving Wordle Using Information Theory," Northeast Journal of Complex Systems (NEJCS): Vol. 8 : No. 1 , Article 6.
DOI: https://doi.org/10.63562/2577-8439.1146
Available at: https://orb.binghamton.edu/nejcs/vol8/iss1/6