Service system performance depends on how participants respond to design choices, but modeling these responses is hard due to the complexity of human behavior. We introduce an LLM‑powered multi‑agent simulation (LLM‑MAS) framework for optimizing …
LLMs are emerging tools for simulating human behavior in business, economics, and social science, offering a lower‑cost complement to laboratory experiments, field studies, and surveys. This paper evaluates how well LLMs replicate human behavior in …
With the rapid progress of large language models (LLMs), LLM-powered multi-agent systems (MAS) are drawing increasing interest across academia and industry. However, many current MAS frameworks struggle with reliability and scalability, especially on …
We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that …