AI
LLMs Tested on Long-Term Business Strategy Through Month-Long Retail Simulator
Researchers built an open-source business game benchmark to evaluate how well leading language models make coherent managerial decisions over extended time horizons.
Most benchmarks for large language models focus on isolated, short-term tasks. A new study from arXiv instead asks a more demanding question: how well can LLMs handle strategic business decision-making that unfolds over months, requiring adaptation and consistency?
The Gap in LLM Evaluation#
While models like ChatGPT and Gemini have shown strong performance on language tasks and pattern recognition, their ability to maintain coherent strategy across multiple interdependent decisions remains largely unmeasured. Previous research, including Vending-Bench, demonstrated that LLM results can diverge significantly from short-term benchmarks when tested in longer, more complex scenarios. Yet few reproducible frameworks exist for assessing this kind of multi-month decision-making capability.
How the Benchmark Works#
The study introduces a transparent, open-access management simulator that tasks LLMs with running a simulated retail business over twelve months. Each month, the models receive a detailed business report from the prior period and must decide on critical operational variables: pricing strategy, order quantities, marketing spend, hiring and layoffs, loan decisions, training budgets, R&D investment, and forecasts for sales and income.
The experimental setup uses a transparent spreadsheet model, allowing researchers to audit exactly how each decision flows into the company's financials and competitive position.
Testing Five Major Models#
Five widely accessible LLMs were evaluated: Gemini, ChatGPT, Meta AI, Mistral AI, and Grok. Rather than relying solely on profit or revenue numbers, the analysis examines strategic coherence, how well models adapt when market conditions shift, and the reasoning they provide for their choices—moving beyond simple numerical performance metrics.
The research contributes a reproducible toolkit to the AI research community, offering both standardized evaluation methods and the simulator itself as an open resource for future studies on long-horizon decision-making.
