The integration of Large Language Models (LLMs) into various simulation environments is significantly advancing the field of social and behavioral studies. Researchers are leveraging LLMs to create sophisticated simulations that can replicate human behavior in diverse scenarios, from individual decision-making to complex societal interactions. These models are enabling more nuanced analyses of influence dynamics, communication effectiveness, and adaptive learning behaviors, particularly in areas like social media, esports, and transportation. The use of LLMs in these simulations allows for the exploration of fine-grained behavioral patterns and the emergence of opinion leaders, which are crucial for understanding and predicting social phenomena. Additionally, the application of LLMs in pre-testing surveys and optimizing research design is proving to be a valuable tool for social scientists. The advancements in this area are not only enhancing our understanding of human behavior but also providing practical tools for policymakers and researchers to make informed decisions. Notably, the development of AI-driven route choice models and the simulation of day-to-day adaptive learning behaviors are particularly innovative, offering new insights into transportation planning and policy-making. Furthermore, the creation of LLM-based social agents in game-theoretic scenarios is expanding the scope of social intelligence research, paving the way for more comprehensive evaluations of agent performance in complex, real-world settings.