Chess is often considered a science due to the logical, analytical, and systematic nature of the game. The principles of chess can be studied, explored, and even advanced, similar to scientific disciplines. Here's a detailed explanation of why chess is regarded as a science:

1. Systematic Study and Theories:

• Structured Knowledge: Chess has been studied systematically for centuries. Players and theorists have developed and codified various opening principles, middle-game strategies, and endgame techniques. This mirrors scientific fields where knowledge is accumulated, organized, and refined over time.

• Opening Theory: Chess opening moves are like scientific hypotheses. Different opening systems (e.g., Sicilian Defense, King's Indian) are analyzed for their potential effectiveness, and vast libraries of opening theory exist, similar to scientific research papers.

• Endgame Theory: In chess, specific endgames have been thoroughly studied, and many have exact solutions based on well-known principles (e.g., King and Pawn vs. King). This is akin to a scientific body of knowledge, where certain situations are completely understood and can be predicted based on the current position.

2. Logical Structure and Analytical Thinking:

• Cause and Effect: Chess, like science, operates on a clear cause-and-effect relationship. Every move has direct consequences, which can be calculated and predicted, much like scientific experiments where a change in variables affects the outcome.

• Analysis of Variations: Scientific experimentation involves testing hypotheses, and in chess, players analyze multiple variations of potential moves, evaluating the outcomes and choosing the optimal one. This is akin to forming, testing, and refining hypotheses in science.

• Logical Sequences: Chess problems and puzzles often require following strict logical steps to reach a solution, much like solving mathematical equations or scientific problems. Moves in chess can often be deduced through logical reasoning, which mirrors scientific analysis.

3. Patterns and Predictability:

• Pattern Recognition: In science, recognizing patterns is crucial in understanding natural laws, and in chess, pattern recognition is essential for mastering tactical combinations, strategic plans, and typical positional motifs. Players develop the ability to spot recurring tactical themes (e.g., pins, forks) or strategic concepts (e.g., weak squares, pawn structures).

• Predictable Outcomes: In certain positions, the outcome of the game can be predicted with a high degree of certainty, similar to how scientists can predict outcomes of experiments based on known variables. Endgame databases, for example, allow computers to determine the exact result of some positions (win, draw, or loss), just as science uses formulas to predict specific outcomes.

4. Use of Mathematics:

• Chess and Algorithms: Chess can be studied through mathematical models and algorithms. Concepts such as game theory and decision trees apply directly to chess. In fact, computers use algorithms and brute-force calculations to evaluate millions of positions in seconds, applying logical rules in a systematic way that resembles scientific modeling.

• Quantitative Analysis: Evaluating a chess position involves numerical assessments, whether by humans or computers. Material imbalances (counting pawns and pieces) and positional elements (e.g., control of space, weak pawns) can be quantitatively assessed, much like mathematical models in scientific analysis.

5. Research and Experimentation:

• Scientific Approach to Improvement: Chess improvement, especially at a high level, is similar to scientific research. Players experiment with new openings, strategies, and techniques to see what works in practice. They collect data from their games, analyze mistakes, and refine their approach.

• Data-Driven Analysis: With the rise of powerful chess engines and databases, chess has become increasingly data-driven. Players study massive collections of games, learn from historical data, and refine their strategies based on patterns that emerge over time, similar to the way scientific research advances through experimentation and analysis of large data sets.

6. Use of Technology and Artificial Intelligence:

• Chess Engines and AI: Computer programs like Stockfish and AlphaZero use advanced artificial intelligence and algorithms to calculate millions of possibilities and find the best moves. These engines analyze chess positions with mathematical precision, simulating the scientific process of testing hypotheses, analyzing outcomes, and adjusting strategies.

• Machine Learning in Chess: AlphaZero, for instance, uses machine learning techniques to teach itself chess by playing millions of games against itself. This resembles the way scientists develop theories through trial and error, refinement, and deeper understanding over time.

7. Problem Solving and Creativity:

• Problem-Solving Processes: In science, problems are solved through inquiry, experimentation, and systematic analysis. In chess, players face complex problems (how to win or defend in a particular position) and must find creative solutions using logic and calculation.

• Creative Discovery: Many chess principles and concepts have been discovered through analytical problem-solving, just as scientific discoveries arise from exploration and logical deduction. Players "discover" new ideas, such as previously unknown tactical combinations or strategic plans, that expand the understanding of the game.

8. Theoretical Advancements:

• Evolution of Chess Theory: Chess theory, much like scientific theory, evolves over time. New discoveries are made, old concepts are revised, and the understanding of the game deepens. For example, certain opening strategies that were popular decades ago have been refuted or improved upon through deep analysis, just as outdated scientific theories get replaced by new ones based on further research.

• Research in Chess: Chess grandmasters and researchers spend years developing and refining new strategies, testing them in games, and publishing their findings in books and journals, similar to academic research in the sciences.

9. Chess as a Study of Complexity:

• Complex Systems: Chess is an extremely complex game with 10^120 possible moves (Shannon number), making it a perfect domain for studying complexity. Just as scientists study complex systems like weather patterns or the stock market, chess players study the dynamics of ever-changing positions where small variations can lead to dramatically different outcomes.

• Chaos Theory and Chess: Chess can also exhibit traits of chaos theory, where small changes in a position can lead to wildly different game outcomes. Understanding the intricacies of these changes is akin to understanding chaotic systems in nature.

10. Scientific Rigor and Objective Truth:

• Objective Truths in Chess: Like scientific inquiry, chess often seeks objective truths. For example, in some positions, there is only one correct move or a clearly best strategy. This mirrors the pursuit of objective, verifiable truths in science.

• Rigorous Study: Chess positions can be rigorously studied to the point where the best course of action becomes a proven fact, especially in endgame scenarios. This methodical approach parallels scientific methodology.

Conclusion:

Chess is considered a science because it involves systematic study, logical analysis, problem-solving, and theory-building, much like scientific disciplines. The game is based on predictable patterns, calculable outcomes, and complex theories, making it a subject of intense study and research. Just as science progresses through hypothesis testing and data analysis, chess evolves through deep analytical exploration, experimentation, and refinement of strategies.

Ali Eskici
24.10.2024