
BAM Engine is a high-performance Python framework that implements the BAM (Bottom-Up Adaptive Macroeconomics) agent-based model from "Macroeconomics from the Bottom-up" (Delli Gatti et al., 2011). The model simulates interactions between households, firms, and banks across labor, credit, and consumption goods markets. The framework features an Entity-Component-System (ECS) architecture with vectorized operations for performance and a modular extension system. It includes built-in validation and calibration tools to support reproducible computational economics research.
If you use this software, please cite it along with the original BAM model (Delli Gatti et al., 2011).
computational economics, agent-based computational economics, bottom-up macroeconomics, agent-based modeling, emergent behavior, CATS models, heterogeneous agents, BAM model, macroeconomics, complex adaptive systems
computational economics, agent-based computational economics, bottom-up macroeconomics, agent-based modeling, emergent behavior, CATS models, heterogeneous agents, BAM model, macroeconomics, complex adaptive systems
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
