用群体感知行为建模人类在策略网络中的复杂互动。
Modeling Human Behavior in a Strategic Network Game with Complex Group Dynamics
- 基于群体意识与行为分布建模，而非平均行为。
- hCAB模型能准确还原小社会的人群动态，误差较小。
- 生成的行为难区分真人，适合模拟真实社交场景。
人类网络深刻影响财富与健康不平等、贫困及欺凌等社会结果，理解其机制对改善社会成果至关重要。本文以初中生游戏（JHG）为场景，比较多种人类行为建模方法，考察其对行为假设（行为匹配 vs. 群体感知）和建模范式（均值 vs. 分布）的依赖。结果显示，名为hCAB的模型表现最优：它建模行为分布而非均值，并假设个体采用群体感知行为。该模型在小规模社会中能高度复现真实人群动态（存在少量差异）。用户研究表明，参与者难以区分hCAB代理与真实人类，表明其生成行为具备高度可信度。
原文摘要 · Abstract (English)
Human networks greatly impact important societal outcomes, including wealth and health inequality, poverty, and bullying. As such, understanding human networks is critical to learning how to promote favorable societal outcomes. As a step toward better understanding human networks, we compare and contrast several methods for learning models of human behavior in a strategic network game called the Junior High Game (JHG) [39]. These modeling methods differ with respect to the assumptions they use to parameterize human behavior (behavior matching vs. community-aware behavior) and the moments they model (mean vs. distribution). Results show that the highest-performing method, called hCAB, models the distribution of human behavior rather than the mean and assumes humans use community-aware behavior rather than behavior matching. When applied to small societies, the hCAB model closely mirrors the population dynamics of human groups (with notable differences). Additionally, in a user study, human participants had difficulty distinguishing hCAB agents from other humans, thus illustrating that the hCAB model also produces plausible (individual) behavior in this strategic network game.
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