Empirical vs Theoretical Probability in Human Binary Choices from Go Games

21,212 professional Go matches among 311 masters and 586 amateur games from 293 participants provide data on stone selection. The analysis by Yong-Hwan Kim, published in 2026, investigates discrepancies between empirical and theoretical probability in human binary choices. Results show higher-ranked players have a significantly higher chance of selecting black stones, challenging the assumption of equal odds.

Use Cases

• Modeling strategic interaction in binary choice games based on the stone selection process described.
• Analyzing the influence of player skill level on choice probability based on the ranking data mentioned.
• Studying deviations from theoretical probability in human decision-making based on the large-scale game data.

Strengths

• Dataset includes a large sample of 21,212 professional Go matches.
• Analysis incorporates data from both professional (311 masters) and amateur (293 participants) player levels.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data tables is unknown, which may limit suitability assessment.
• The dataset is small in file size (360.7 KB), indicating limited scope or highly summarized data.

Provenance

Source

Yong-Hwan Kim via figshare

Collection Method

Large-scale data analysis of recorded professional Go matches and in situ games with amateur players.

Freshness

Last updated 2026-04-30 05:24:37; freshness should be verified.