Quantitative agent-based models: a promising alternative for macroeconomics
介绍定量基于主体模型（ABMs）作为主流宏观经济模型的替代方案，通过四个实例（杠杆周期、2008年房地产泡沫、新冠疫情、通用微观-宏观模型）展示其在时间序列预测和解释复杂现象方面的优势。
Abstract Agent-based models (ABMs) are dynamic computer simulations that abandon utility maximization and instead assume that agents are boundedly rational and make decisions using heuristics, myopic reasoning, and/or learning algorithms. Because ABMs do not need to compute optima they are more tractable, allowing a higher level of realism. Recent research has developed quantitative agent-based models that make time series predictions, modelling a specific economy at a specific point in time; some of these address questions that mainstream models cannot even ask, and some make predictions that are superior or equal to their mainstream equivalents. After explaining what ABMs are and how they are built in more detail, I review four examples of models from my own work for leverage cycles, the 2008 housing bubble, Covid, and a general-purpose micro-macro model. I conclude by discussing the advantages and disadvantages of agent-based models in comparison to standard models.