Shumiao Ouyang

Large Language Models (LLMs) exhibit diverse and stable risk preferences in economic decision tasks, yet the drivers of this variation are unclear. Studying 50 LLMs, we show that alignment tuning for harmlessness, helpfulness and honesty systematically increases risk aversion. A ten percent increase in ethics scores reduces risk appetite by two to eight percent. This induced caution persists against prompts and affects economic forecasts. Alignment therefore promotes safety but can dampen valuable risk taking, revealing a tradeoff risking suboptimal economic outcomes. Our framework provides an adaptable and enduring benchmark for tracking model risk preferences and this emerging tradeoff.