Simulation models, in particular agent-based models, are gaining popularity in economics
and the social sciences. The considerable flexibility they offer, as well as their capacity to …

We show that a maximum likelihood approach for parameter estimation in agent-based
models (ABMs) of opinion dynamics outperforms the typical simulation-based approach …

This paper presents a complex, modular, 1: 1 scale model of the Hungarian residential
housing market. All of the 4 million households with their relevant characteristics and all of …

Agent-based models (ABMs) are simulation models used in economics to overcome some of
the limitations of traditional frameworks based on general equilibrium assumptions …

Economic agent-based models (ABMs) are becoming more and more data-driven,
establishing themselves as increasingly valuable tools for economic research and …

Calibrating agent-based models (ABMs) in economics and finance typically involves a
derivative-free search in a very large parameter space. In this work, we benchmark a …

BlackBIRDS is a Python package consisting of generically applicable, black-box inference
methods for differentiable simulation models. It facilitates both (a) the differentiable …

Explanatory relationships between data and hypotheses have been suggested to play a role
in the formation of posterior probabilities. This suggestion was tested in a toy environment …

BeforeIT is an open-source software for building and simulating state-of-the-art
macroeconomic agent-based models (macro ABMs) based on the recently introduced macro …

Central banks and other financial policymakers rely on macroeconomic models to
understand transmission channels of policy decisions, forecast the economy under different …