Evaluating LLMs in Executive Decision-Making

This research investigates whether Large Language Models (LLMs) can effectively function as CEOs by performing complex, high-stakes tasks like strategic resource reallocation. Rather than focusing on simple prompt-response tasks, the authors utilize a multi-role agent simulation framework to model the dynamics of a corporate environment. This approach allows for the assessment of how models handle conflicting information, long-term planning, and the trade-offs inherent in executive-level decision-making.

Multi-Role Agent Simulation as a Benchmark

The core of the study is the development of a simulation environment where agents take on specific roles within a firm. By creating a collaborative (or competitive) ecosystem of agents, the researchers can observe how an 'LLM CEO' interacts with other functional roles—such as finance, operations, or marketing—to reach a consensus on resource distribution. The benchmark measures the quality of these decisions against historical or expert-defined outcomes, providing a quantitative look at the model's ability to synthesize data and maintain strategic alignment under pressure. The study highlights that while LLMs demonstrate strong reasoning capabilities, their performance in 'CEO' roles is highly dependent on the quality of the information provided by subordinate agents and the model's ability to avoid hallucinations when faced with complex, multi-variable financial constraints.