arXiv:2606.07308v1 Announce Type: new Abstract: We study off-policy evaluation (OPE) under strategic behavior where decision subjects (or agents) respond to a decision maker's policy by strategically modifying their covariates. Such behavior induces a policy-dependent covariate shift, breaking the standard assumption in existing methods that covariates are exogenous to the policy. Related work addresses this challenge by imposing strong assumptions such as repeated interactions or full knowledge of agents' response behavior, substantially limiting its applicability to OPE. In contrast, we cons
The paper represents an advancement in understanding off-policy evaluation, especially as AI-driven systems become more prevalent in real-world scenarios with strategic human or agent interaction.
This research could lead to more robust and reliable AI systems in complex strategic environments, improving the efficacy of autonomous agents.
Current AI evaluation methods often assume static covariates, but this work directly addresses strategic agent responses, enabling more accurate predictions in dynamic systems.
- · AI developers
- · Reinforcement learning researchers
- · Industries deploying AI in strategic environments
- · AI models without strategic consideration
- · Traditional OPE methods
Improved performance and safety of AI agents in strategic decision-making scenarios.
Accelerated adoption of AI systems in fields like economics, governance, and resource management.
Potentially more sophisticated and adaptive AI agents capable of navigating complex multi-agent interactions.
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Read at arXiv cs.AI