Adamo Cerioli — University of Parma #
AI versus humans in complex games: the dynamics of strategic tension from chess to Go #
Understanding how intelligent agents navigate competitive environments is a fundamental challenge in complex systems. We investigate the macroscopic dynamics of "strategic tension" across two structurally distinct model systems: chess and Go. We introduce a generalizable network framework to quantify this tension dynamically. In chess, tension emerges from the spatial network of interacting threats; in Go, from a network of group vulnerabilities and ownership instability under topological perturbations. In both domains, strategic tension is defined via the spectral properties of interaction matrices, rooted in topological entropy to quantify the exponential diversity of strategic trajectories. Analyzing games by human experts and state-of-the-art AI (e.g., Stockfish, AlphaGo), we reveal distinct profiles of complexity management. Engines consistently sustain significantly higher tension for longer durations than elite humans. This behavior reflects AI's capacity to defer immediate resolution and maintain high-dimensional latent options, scaling dynamically with computational depth. Conversely, human players actively simplify high-tension positions—an adaptive response to biological cognitive limits rather than a mere stylistic choice. Extending this framework from the hierarchical interactions of chess to preliminary data on the spatial dynamics of Go suggests that this divergence between artificial and biological problem-solving may be a universal phenomenon. Beyond cognitive insights, this framework enables the design of pedagogical AI tailored to human limits. Extrapolating to broader complex systems, such as economic markets or geopolitics, our findings suggest AI integration could fundamentally alter systemic stability, sustaining highly complex standoffs while drastically increasing the stakes of miscalculation.