Earticle

AlphaGo’s landmark victory over Lee Sedol in March 2016 triggered an unprecedented paradigm shift in the game of Go, prompting widespread revaluation of joseki sequences—locally optimal opening patterns refined over centuries of human tradition. Despite this upheaval, systematic quantitative research into precisely how inefficient traditional joseki are—measured in concrete point (目) differentials—remains scarce in the academic literature. Most existing discourse has operated at the level of qualitative judgment (“this move is good/bad”) without rigorously measuring the numerical stakes. This study addresses that gap by extracting approximately 70 key moves from 20 fuseki patterns widely used in the pre-AlphaGo era and quantifying the efficiency difference between traditional sequences and AI-recommended moves using KataGo’s Expected Score metric. A central contribution is the independent design and development of Joseki Analyzer—a purpose-built program integrating a FastAPI backend with the KataGo engine—enabling automated, large-scale, reproducible analysis under standardized conditions (1,000 visits, Chinese rule set, komi 7.5). The core metric Δ Score is defined as the Score Lead of the AI’s top-recommended move minus the Score Lead of the traditional move at the same position; a negative value indicates that the traditional move is less efficient by the corresponding number of points. Results show an overall mean Δ Score of approximately −0.28 points across 20 patterns, indicating that traditional moves incur an expected-score loss of roughly this magnitude per move relative to AI recommendations. The largest divergences occur in the Komoku Approach–Pincer Response (Ⅱ) (−1.20 pts), Komoku Approach–Aggressive Response (−0.68), Hoshi One- Space Pincer–3-3 Invasion (−0.60), and Komoku Corner Enclosure Fuseki and Komoku Approach–High Extension (both −0.53). The single largest move-level loss is −2.59 points. Conversely, four patterns achieve Δ Score = 0—Hoshi Approach–Knight’s-Move Response, Komoku Enclosure–Development Variation, Hoshi Approach–Contact-Play Joseki, and Komoku Approach– Contact Play (Ⅱ)—indicating perfect alignment with AI evaluation. A consistent typological finding emerges: corner-enclosure and extension patterns show the largest divergence from AI, while contact-play (붙임수) patterns show the smallest. Across all patterns, KataGo systematically prioritizes claiming empty corners over reinforcing one’s own established positions—a finding that runs counter to a core axiom of classical Go strategy. This study represents the first systematic, tool-assisted effort to quantify the inefficiency of traditional fuseki joseki in point-based terms, offering both empirical findings and a replicable methodological framework for evaluating classical Go theory against modern AI computation.

This paper presents a critical analysis of Lee Sedol’s 2025 Memoir, published a decade after his historic match against AlphaGo. Unlike Garry Kasparov’s Deep Thinking (2017), which seeks the truth behind defeat, Lee’s narrative is characterized by ‘narrative revisionism’ and memory reconstruction designed to provide a post-hoc justification for a heroic myth. By retroactively framing Move 68 as a pre-planned gambit to justify the Divine Move (Move 78), Lee’s account exemplifies the cognitive distortions Charan Ranganath warns against and instantiates the logic of the Orwellian memory hole. Drawing on Pierre Bourdieu’s theory of symbolic power, this study examines how Lee’s authority as a dominant narrator neutralizes public critical distance and entrenches an unverified narrative. Ultimately, the paper argues that the replacement of criticism with myth leads to a profound intellectual poverty in discourse on human-machine relations in the AI era and calls for a correction of this constructed and subversive narrative that has taken hold amid collective social silence.

This article investigates whether recurrent formal structures in Go gameplay can be statistically related to structured configurations derived from the Szondi test. Rather than treating psychological constructs as directly observable entities, we approach both playing style and drive organization as constructed systems of indices, each governed by internal regularities. The study examines inter-domain correlations between quantified stylistic dimensions of play (territoriality, orthodoxy, thickness, aggressiveness) and drive-based variables (acting index, symptomatic response rates, factor-level distributions), based on a shared participant corpus. Statistical analyses test linear and non-linear associations across domains. The results suggest non-random correspondences between specific stylistic tendencies and particular drive-related configurations. These findings do not warrant psychological reductionism; instead, they suggest that strategic behavior in Go may function as a structured expressive field in which latent organizational constraints become formally instantiated. From an epistemological perspective, the study argues that correlations between heterogeneous index systems do not validate underlying constructs directly, but rather suggest structural compatibility between distinct formalizations of human behavior.

Due to increasing digital distractions, maintaining attention has become essential for effective learning and work performance. This cross-sectional study examined whether experience with the strategic board game Go is associated with enhanced attentional control in young adults. Ninety-nine university students (49 with substantial Go experience and 50 with minimal experience) completed the Sustained Attention to Response Task and a background questionnaire. Participants with extensive Go experience demonstrated higher overall accuracy than those with minimal experience and performed better on response trials than on withhold trials. Accuracy further increased with both Go proficiency and years of exposure. These results suggest an association between Go experience and sustained attention and inhibitory control in young adults, with implications for educational contexts.

Starting from an empty board, the number of legal positions in a Go game grows exponentially as the number of moves increase. During the early moves, a very large subset of the possible positions have balanced win rates. This suggests that the choice of the opening is not crucial to decide a Go game. Nevertheless it is important for players to know what kind of openings they will most frequently face. To answer this question, we collected half a million games played on Fox Go server from 2013 to 2019 amongst players ranked between 10 kyu and 9 dan. A data-informed methodology was developed to classify and compute the frequency of the different openings before and after the introduction of AI. The most popular first moves are nirensei and hoshi+34. Deeper openings popularity depends on rank. Sanrensei fre-quency decreases with rank, Chinese opening is most popular below 5 dan and Kobayashi between 3 dan and 7 dan (Fox Go Server ranks). The results also show a change in opening playstyle for players ranked above 4 dan after the rise of Go AI. Up to 2019, a similar change is not observed amongst lower ranked players.

Little is known empirically about casual Go players. Their numbers are difficult to estimate and there are no systematic studies about their characteristics (demographics, personality, intelligence etc.). We use data from an international survey among university students including all major Go nations besides Korea (China, Taiwan and Japan) and additional three other countries (Germany, Estonia and Vietnam) to estimate the size of the casual player base and their main characteristics. We find a surprisingly large number of casual players also in countries where Go is not traditionally played. Moreover, we find high masculinity as the strongest predictor for casual Go players while demographic and personality variables are not significant.