发表机构
Johns Hopkins University; University of Illinois, Urbana-Champaign; University of Toronto（约翰斯·霍普金斯大学; 伊利诺伊大学厄巴纳-香槟分校; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对混合个体平均场博弈中不可观测的利他程度与劳动成本，提出逆向学习框架，从含噪声观测中恢复潜在参数，实验验证其可行性与准确性，助力激励设计与政策分析。
AI 中文摘要
理解人类如何对激励做出反应，无论是在个体层面还是集体层面，对于设计有效的政策至关重要。在针对大规模交互群体的连续时间随机框架内，平均场博弈（MFG）对非合作智能体的群体进行建模，而平均场控制（MFC）则描述了完全合作的基准，在我们的设定中将其解释为完全利他行为。混合个体平均场博弈通过一个控制利他程度的参数在这两个极端之间进行插值。然而，监管者和政策制定者面临的一个核心挑战是，内在的利他水平和其他私人结构参数（如个体劳动成本）通常是不可观测的。为了应对这一挑战，我们为混合个体平均场博弈开发了一个逆向学习框架。我们的方法能够从含噪声的观测中恢复（潜在的）利他水平和劳动成本水平，实验证明了我们方法的可行性和准确性。这些发现强调了逆向MFG方法在揭示大规模群体中潜在偏好结构的潜力，对激励设计、实证行为建模和数据驱动的政策分析具有重要意义。
英文摘要
Understanding how humans respond to incentives, both at the individual and collective levels, is crucial to the design of effective policies. Within the continuous-time stochastic framework for large interacting populations, mean field games (MFGs) model populations of non-cooperative agents, whereas mean field control (MFC) describes the fully cooperative benchmark, interpreted in our setting as fully altruistic behavior. Mixed-individual MFGs interpolate between these two extremes through a parameter governing the degree of altruism. A central challenge for regulators and policymakers, however, is that intrinsic altruism levels and other private structural parameters, such as individual labor costs, are typically unobservable. To address this challenge, we develop an inverse learning framework for mixed-individual MFGs. Our approach enables the recovery of (latent) altruism and labor cost levels from noisy observations, with experiments demonstrating the feasibility and accuracy of our method. These findings underscore the promise of inverse MFG methodologies for uncovering latent preference structures in large populations, with important implications for incentive design, empirical behavioral modeling, and data-driven policy analysis.
CommentsTo appear in the 65th IEEE Conference on Decision and Control
