Strategic Steering of Large Language Models via Game-Theoretic Action Space Optimization

Abstract This paper investigates how large language models can be steered to act more strategically in text-based negotiation settings. Two prompt-based action space designs are compared, namely emotional tone prompts and explicit offer prompts, within a negotiation environment, and outcomes are compared in simulated dialogues. The results show that both approaches improve strategic outcomes compared to a baseline, with tone-based actions yielding higher agreement rates and offer-based actions providing more stable tradeoffs. These findings demonstrate how action space design influences agent behavior, providing insights for deployment of large language models in strategic negotiation scenarios to gain an advantage in, for example, online influence operations.