Quantitative agent-based models: a promising alternative for macroeconomics

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.