Translating LLM Capabilities to Human‑Like Judgments and Decisions

Ascertain how the generative and reasoning capabilities of large language models translate when the models are used to produce judgments and decisions intended to resemble human choices.

Background

The paper reviews evidence that LLMs demonstrate strong generative and reasoning performance across many business and research applications, including productivity gains and enhanced content creation. However, these achievements concern task completion and output quality rather than the fidelity of human-like decision-making.

The authors explicitly flag uncertainty about whether, and how, these capabilities carry over when models are tasked with producing judgments and decisions that are meant to mirror human behavior. This motivates their focus on behavioral alignment in simulations.