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This paper argues that large language models used in open-ended strategic wargames carry inherent risks, such as decision laundering and role collapse, which prevent them from serving as direct safety cases for real-world policy or crisis response. Instead of informing doctrine, these simulations should be utilized strictly to stress-test the robustness of decision-influencing agents before deployment. The core contribution lies in identifying specific failure modes where model language dictates simulated reality, necessitating auditable safety protocols rather than relying on standard benchmarks.
Position: AI Is Not Ready for Strategic Conflicts
arXiv:2609.16189v1 Announce Type: new Abstract: Open-ended strategic wargames are high-stakes LM-based social simulations: they model adversaries, institutions, escalation, plan brittleness, doctrine, and crisis response. Language models (LMs) are attractive because they can play agents, generate scenario branches, adjudicate ambiguous actions, and summarize lessons, but the same affordances make open-ended roles dangerous: model language determines both what an actor attempts and what becomes simulated reality. This position paper argues that no LM-enabled wargame should inform planning, doctrine, policy, or crisis response without an auditable safety case, and that the proper use of open-ended wargames today is to stress-test decision-influencing LM agents. We identify five failure modes: decision laundering, adjudication opacity, role collapse, escalation-through-adjudication, and failure of strategic imagination. Ordinary benchmarks cannot establish safety for these settings. Wargames can expose failures as stress tests; they are not themselves safety cases for consequential use.
Abstract:Open-ended strategic wargames are high-stakes LM-based social simulations: they model adversaries, institutions, escalation, plan brittleness, doctrine, and crisis response. Language models (LMs) are attractive because they can play agents, generate scenario branches, adjudicate ambiguous actions, and summarize lessons, but the same affordances make open-ended roles dangerous: model language determines both what an actor attempts and what becomes simulated reality. This position paper argues that no LM-enabled wargame should inform planning, doctrine, policy, or crisis response without an auditable safety case, and that the proper use of open-ended wargames today is to stress-test decision-influencing LM agents. We identify five failure modes: decision laundering, adjudication opacity, role collapse, escalation-through-adjudication, and failure of strategic imagination. Ordinary benchmarks cannot establish safety for these settings. Wargames can expose failures as stress tests; they are not themselves safety cases for consequential use.
Published at the Social Sim'26 Workshop at COLM 2026. 4 pages body (22 pages total including appendices)
Artificial Intelligence (cs.AI)
arXiv:2609.16189 [cs.AI]
(or arXiv:2609.16189v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.16189 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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