The sorting and recycling of municipal household waste (MHW) as a complex adaptive system is becoming increasingly popular in current research. However, traditional simulation modeling ignores the important influence of anticipated regret and personality traits on individual choice behavior. This study introduces the degree of regret-joy in agent-based simulation modeling and designs a learning rule that takes into account the personality characteristics of individuals, their neighbors, and historical information to analyze the waste sorting behavior of municipal residents. The results suggest that the number of people participating in formal recycling approaches is significantly higher than informal recycling approaches after accounting for the economic profits of formal recycling approaches. Our results also reveal that subsidies are an important factor influencing the waste sorting behavior of municipal residents. However, simply reducing the time required for a formal recycling approach does not affect the behavioral decisions of municipal residents. Only improving the accessibility of facilities in formal recycling approaches on the premise of regulating the informal recycling market, will the number of municipal residents participating in the former increase significantly. The intensity of communication and learning among municipal residents also affects the volatility of decision-making. When intensity is high, group decision-making changes greatly, but at low intensity, group behavior tends to be stable. These findings are useful as a theoretical reference for the development of waste management policies.

作为一种复杂的自适应系统，城市生活垃圾（MHW）的分类和回收在当前的研究中变得越来越流行。但是，传统的模拟模型忽略了预期的后悔和人格特质对个人选择行为的重要影响。本研究介绍了基于主体的仿真模型中的后悔喜悦程度，并设计了一种学习规则，该规则考虑了个人，邻居的个性特征以及历史信息，以分析市政居民的垃圾分类行为。结果表明，在考虑了正规回收方法的经济利益之后，参与正规回收方法的人数明显高于非正规回收方法。我们的结果还表明，补贴是影响市政居民废物分类行为的重要因素。但是，仅减少正式回收方法所需的时间不会影响市政居民的行为决定。只有在规范非正式回收市场的前提下，以正式回收方法改善设施的可及性，参与前者的市政居民人数才会大大增加。市政居民之间交流和学习的强度也影响决策的波动性。强度高时，群体决策会发生很大变化，但是强度低时，群体行为趋于稳定。这些发现可作为制定废物管理政策的理论参考。