Wargaming is a key component of military strategic decision-making, providing a means to explore human decision-making processes. However, the military frequently depends on scarce wargaming expertise to ensure the quality of wargames. In this context, we examine the potential of large language models (LLMs) to produce useful texts that support wargaming activities. Contrary to previous studies unfavourably juxtaposing LLMs as decision-makers to their human counterparts, we focused on the potential use of these models for generating wargaming components. We conducted a study with wargaming experts to compare the effectiveness of human-created texts and LLM-generated texts across various tasks within the wargaming lifecycle. For all wargaming tasks for which the LLM-generated texts could be compared to the human-created texts, the LLM was able to match or even surpass human-level quality. This demonstrates that despite the dangers of LLMs when in the driving seat, even with minimal training, they can offer significant benefits in support of strategic wargaming, showing effective performance across multiple phases of the lifecycle. By reducing reliance on scarce wargaming expertise, LLMs can make wargaming more accessible. This allows the wargaming process to be more widely used within military strategic decision-making, ultimately enhancing the quality of human decision-making.
Meerveld et al. (Sun,) studied this question.