This paper studies an agent-based model that bridges Keynesian theories of demandgeneration and Schumpeterian theories of technology-fueled economic growth. We employ the model to investigate the properties of macroeconomic dynamics and the impact of public polices on supply, demand and the...

This paper studies an agent-based model that bridges Keynesian theories of demandgeneration and Schumpeterian theories of technology-fueled economic growth. We employ the model to investigate the properties of macroeconomic dynamics and the impact of public polices on supply, demand and the...

In this work we analyze the short- and long-run effects of fiscal austerity policies, employing an agent-based model populated by heterogeneous, boundedly-rational firms and banks. The model, in line with the family of "Keynes+Schumpeter" formalism, is able to account for a wide array of macro...

This paper studies an agent-based model that bridges Keynesian theories of demandgeneration and Schumpeterian theories of technology-fueled economic growth. We employ the model to investigate the properties of macroeconomic dynamics and the impact of public polices on supply, demand and the...

This paper studies an agent-based model that bridges Keynesian theories of demand generation and Schumpeterian theories of technology-fueled economic growth. We employ the model to investigate the properties of macroeconomic dynamics and the impact of public polices on supply, demand and the...

Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and...

Since the influential survey by Windrum et al. (2007), research on empirical validation of agent-based models in economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated...

Efficiently calibrating agent-based models (ABMs) to real data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs by combining machine-learning and intelligent iterative sampling. The proposed approach "learns" a fast surrogate meta-model...

Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and...

Since the influential survey by Windrum et al. (2007), research on empirical validation of agent-based models in economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated...