External incentive mechanisms have been studied as a method to promote cooperation in sequential social dilemmas involving multiple autonomous agents. Mutual Acknowledgment Token Exchange (MATE) is one such approach: by enabling agents to exchange acknowledgment tokens, it induces cooperation without additional training. However, MATE’s use of fixed, manually tuned token values limits adaptability to nonstationary environments and can constrain performance. To enable a dynamically adapted token, we introduce Social Influence-based MATE (SI-MATE), which allows agents to share their individual improvement signals and to self-punishment in response to inequality. Experiments in a four-agent environment show that SI-MATE outperforms MATE across multiple metrics, including learning speed.

In this study, we propose two methods to estimate static graphs from a single dynamic graph and integrate them into hybrid Graph Neural Networks (GNNs), which combine long-term static structure with transient dynamic interactions. Since static graphs are often unavailable and attributes may be difficult to use at scale or under privacy constraints, we introduce: (i) a “behavioral similarity” estimator based on normalized co-occurrence, which requires no attributes, and (ii) an attribute-aware K-means + k-NN estimator that is more efficient than cosine similarity. Experiments on multiple real-world datasets show that both methods consistently improve predictive accuracy and training efficiency, underscoring the importance of static graph choice in hybrid GNNs.

The Wizard of Oz (WoZ) method is a widely adopted research approach where a human Wizard “role-plays” a not readily available technology and interacts with participants to elicit user behaviors and probe the design space. With the growing ability for modern large language models (LLMs) to role-play, one can apply LLMs as Wizards in WoZ experiments with better scalability and lower cost than the traditional approach. However, methodological guidance on responsibly applying LLMs in WoZ experiments and a systematic evaluation of LLMs’ role-playing ability are lacking. Through two LLM-powered WoZ studies, we take the first step towards identifying an experiment lifecycle for researchers to safely integrate LLMs into WoZ experiments and interpret data generated from settings that involve Wizards role-played by LLMs. We also contribute a heuristic-based evaluation framework that allows the estimation of LLMs’ role-playing ability in WoZ experiments and reveals LLMs’ behavior patterns at scale.

Agent-based crowd simulation is a widely used technique for designing and evaluating human-in-the-loop situations such as evacuation plans and building design. Valid evaluation requires a correct model of an individual agent's action rule, which causes human behavior in a crowd. However, in general, designing a specific action rule of each agent depends strongly on a trial-and-error approach because a real crowd shows diverse behaviors.