Although the use of persona prompting in large language models appears to trigger different styles of generated text, it is unclear whether these translate into measurable behavioral differences. Furthermore, little work has studied whether these differences, when they do exist, can affect decision-making in an adversarial strategic environment. We investigate the impact of persona prompting on strategic performance in PERIL, a world domination board game. Specifically, we compare the effectiveness of persona-derived heuristics to those chosen manually. Our findings reveal that personality traits intuitively associated with strategic thinking do appear to improve game performance, but only when an additional mediator is used to translate personas into heuristic values. We introduce this mediator as a structured translation process, inspired by exploratory factor analysis, that maps LLM-generated inventory responses into strategic heuristics. Results indicate our method enhances heuristic reliability and face validity when compared to directly inferred heuristics, allowing us to better study the effect of persona types on decision-making behaviors. These insights advance our understanding of how persona prompting influences LLM-based decision-making and propose a novel heuristic generation method that adds to the growing body of work applying psychometric principles to LLMs.