sfctools - A toolbox for stock-flow consistent, agent-based models

Abstract

One of the most challenging tasks in macroeconomic models is to describe the macro-level effects from the behavior of meso-or micro-level actors. Whereas in 1759, Adam Smith was still making use of the concept of an 'invisible hand' ensuring market stability and economic welfare (Rothschild, 1994), a more and more popular approach is to make the 'invisible' visible and to accurately model each actor individually by defining its behavioral rules and myopic knowledge domain (Castelfranchi, 2014;Cincotti et al., 2022). In agent-based computational economics (ACE), economic actors correspond to dynamically interacting entities, implemented as agents in a computer software (Axtell & Farmer, 2022;Klein et al., 2018;Tesfatsion, 2002). Such agent-based modeling (ABM) is a powerful approach utilized in economic simulations to generate complex dynamics, endogenous business cycles and market disequilibria. For many research topics, it is useful to combine agent-based modeling with the stock-flow consistent (SFC) paradigm (Caiani et al., 2016;Caverzasi & Godin, 2015;Nikiforos & Zezza, 2018). This architecture ensures there are no 'black holes', i.e. inconsistent sources or sinks, in an economic model. SFC-ABM models, however, are often intransparent and rely on very peculiar, custom-built data structures, thus hampering accessibility (Bandini et al., 2009;Hansen et al., 2019). A tedious task is to generate, maintain and distribute code for ABM, as well as to check for the inner consistency and logic of such models.