Understanding Human and Machine Interaction from Decision Perspective: An Empirical Study Based on the Game of Go

The authors aim to interpret human and AI interactions from the decision perspective. The authors decompose the interaction analysis into the following main components in the context of interactions: Individual behavior patterns, interaction relationships, and comprehensive analysis. The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study. About the individual behavior patterns, the authors find that human players prefer short-term periods to AI in decision-making. The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run. The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy. The main behavior pattern of players in the game of Go is switching between careful and daring behaviors. The differences in the decision hierarchies imply a discrepancy of patience between humans and AI.