前沿研究揭示：在Colonel Blotto博弈中，人类表现依然优于大语言模型，战略决策的深度解析。
arXiv:2605.22095v1 Announce Type: cross Abstract: The emergence of large language models (LLMs) has spurred economists to study how humans and LLMs be…
提出可测试、可操作的校准方法，解决全交换遗憾的优化难题
arXiv:2605.17749v1 Announce Type: new Abstract: AI generated predictions increasingly inform decision making in critical tasks, and therefore must be …
让LLM在迷你“狼人杀”中玩出欺骗、检测与披露，为多智能体社会智能提供理论新视角。
arXiv:2509.23023v3 Announce Type: replace Abstract: Large language models are increasingly deployed in multi-agent settings whose outcomes hinge on so…
探讨预测与干预博弈中不变集的数学结构，为理解智能体交互提供理论框架。
arXiv:2605.16828v1 Announce Type: cross Abstract: We consider the following two-player game: using observational data, the leader chooses a prediction…
突破二元无环限制，提出支持任意有向加权优先级图的广义Shapley值，解决真实数据估值难题。
arXiv:2605.15018v1 Announce Type: cross Abstract: Shapley value and its priority-aware extensions are widely used for valuation in machine learning, b…
首次系统研究结构化Stackelberg博弈，利用上下文信息预测对手类型，助力领导者在线学习最优策略。
arXiv:2504.09006v4 Announce Type: replace-cross Abstract: We initiate the study of structured Stackelberg games, a novel form of strategic interaction…