The field of large language models (LLMs) is rapidly advancing, with a focus on improving their ability to simulate human decision-making and behavior in social science simulations. Recent research has highlighted the importance of considering process-level realism and behavioral fidelity when evaluating LLMs. This includes assessing their ability to adapt to different levels of external guidance and human-derived noise, as well as their capacity to replicate human-like diversity in decision-making. Noteworthy papers in this area include: Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making, which proposes a process-oriented evaluation framework to examine LLM adaptability. Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics, which introduces a persona-based approach to adjust model biases and improve alignment with human behavior. Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance, which analyzes the effectiveness of expert persona prompting and proposes mitigation strategies to improve robustness.