Influence Benchmarks for AI Systems

This topic seeks to develop a simulated market as a test environment to characterize the latent behavioral preferences of AI systems.As warfighters increasingly engage with AI systems, particularly agents that rely on large-language models (LLMs), concern has arisen that the systems may encourage cognitive behavior in users that impart hidden biases, impair judgement, and ultimately degrade warfighting capacity. For example, overreliance on AI-powered decision support tools may induce users to accept erroneous suggestions or change from correct decisions to incorrect decisions (Buçinca et al, 2021). LLM use may also induce novel cognitive biases (Alessa et al. 2025) and amplify delusional beliefs (Dohnány et al. 2025). Compounding the threat, deceptive strategies frequently emerge among interacting AI systems (Ying et al. 2026). Few tests of AI systems account for their adaption to dynamic data, which obfuscates such adaptive strategies. To detect and combat these risks, the DoW requires a universal approach to elicit, characterize, and compare the behavior of AI agents amid changing contexts (Li et al. 2026). Such a testbed shall rely only on queries and outputs of the subject AI system, rather than direct access to the model itself. Economic frameworks permit the measurement and comparison of decisions. As the basis of extensive prior research, models of auctions, markets, and other economic arenas provide a critical baseline of organic patterns of human behavior (e.g. Hausch 1986, Martinez-Saito 2019). Emerging open-source tools, such as Magentic Marketplace (Bansal et al. 2026) have shown promise in revealing behavioral variations in market-focused contexts. Furthermore, unlike existing assessments of AI risk, economic frameworks do not rely on a priori definitions of “harmful” or “helpful” traits (Vijayvargiya et al. 2026), and these testbeds allow for diverse social strategies such as deception and collaboration.