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Mizuki Yokoyama

Publications and source records attributed to Mizuki Yokoyama.

2 recordsLinked to original sources

Movement synchronization in complex and dynamic team coordination: Relationship with mutual anticipation

Mutual anticipation of teammates' actions enables efficient interactions in team coordination that achieves a common goal and high performance. In team sports involving direct competition, such implicit and non-verbal interactions within short periods are required. If players begin moving only after observing their teammates, gaps may emerge, allowing opponents to interfere. When mutual anticipation functions properly, players' interactions are smooth without gaps, and their movements are expected to become synchronized. Synchronization represents a temporally stable structure in interactions and its mechanisms have been examined in previous studies. However, few studies have investigated synchronization in real-world coordination involving heterogeneous roles and interactions evolving over time, or quantitatively examined how temporally stable structures differ from a baseline. In our approach, we utilized team sports and introduced a statistical method to probabilistically examine these differences. The purpose of this study was to extract the temporal components of movement using 3-on-3 basketball. We calculated the relative phases in which players approached or moved away from their teammates during mini-games in a field experiment that investigated the effects of advice on offensive coordination. These frequency distributions were estimated using Bayesian inference and were compared before and after advice. The results showed that the probability of a synchronization trend among the offensive players after advice compared with before advice reached 70\% or higher. This may be a typical case that is related to mutual anticipation based on the team strategy established through coaching. These findings contribute to a quantitative understanding of coordination processes.

stat.OT↗

Effects of personality steering on cooperative behavior in Large Language Model agents

Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects cooperation under controlled conditions remains unclear. In this study, we examine the effects of personality steering on cooperative behavior in LLM agents using repeated Prisoner's Dilemma games. Based on the Big Five framework, we first measure basic personality scores of three models, GPT-3.5-turbo, GPT-4o, and GPT-5, using the Big Five Inventory. We then compare behavior under baseline and personality-informed conditions, and further analyze the effects of independently manipulating each personality dimension to extreme values. Our results show that agreeableness is the dominant factor promoting cooperation across all models, while other personality traits have limited impact. Explicit personality information increases cooperation but can also raise vulnerability to exploitation, particularly in earlier-generation models. In contrast, later-generation models exhibit more selective cooperation. These findings indicate that personality steering acts as a behavioral bias rather than a deterministic control mechanism.

cs.AI↗