Empirical vs Theoretical Probability in Human Binary Choices from Go Game Data

A study by Yong-Hwan Kim investigates discrepancies between theoretical and empirical probability in human binary choices using the game of Go. The analysis includes data from 21,212 professional matches among 311 Go masters and 586 games from 293 amateur players. The dataset, last updated in April 2026, supports findings that stone selection probability is not equal and varies with player rank.

Use Cases

• Modeling strategic interaction in binary choice games based on the described sequential stone selection process.
• Analyzing the relationship between player skill level and choice probability based on the reported rank-based discrepancies.
• Studying deviations from expected theoretical odds in human decision-making based on the large-scale professional and amateur game data.

Strengths

• Includes data from 21,212 professional Go matches among 311 masters.
• Contains results from 586 amateur games involving 293 participants.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Dataset is a 345.1 KB PDF; the underlying structured data and column definitions are not directly available.
• Row count for the core analysis data is unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred from the paper.

Provenance

Source

Yong-Hwan Kim via figshare.

Collection Method

Analysis of professional Go match records and in situ experiments with amateur players.

Freshness

Last updated 2026-04-30 05:24:37.