Team: 45
School: Santa Fe High
Area of Science: Information Theory
Proposal: In information theory there are a plethora of techniques and principles such as entropy which can provide a basis for keen insights into the state and order of a system. This principle has been applied to everything from physics to telecommunication and biology. However, to our knowledge, there have been few applications of this principle to a two player game such as chess.
Our group hopes to develop an algorithm in Python which translates chess boards into usable data where we can then track the change in the state of a chess game over time. Thereafter we can calculate entropy and apply other information theoretic measures to see how they change over the course of the game. Additionally we will model the frequency of specific chess movements and their impact on the entropy of the board.
In the future we would like to apply principles of game theory to our model and see if we can gain better insight to the mathematical and informational properties that lie beneath two player games. If we discover a universal law of entropy in zero sum games this could be very important to gambling and other strategic games.
Team Members:
Armando Martinez-Brito
Aengus McGuinness
William Barral
Roman Nappi
Sponsoring Teacher: Brian Smith