This review presents machine learning (ML) approaches from an applied economist's
perspective. We first introduce the key ML methods drawing connections to econometric …

Agent-based modeling is a computational approach in which agents with a specified set of
characteristics interact with each other and with their environment according to predefined …

Which policies can increase the resilience of the financial system to climate risks? Recent
evidence on the significant impacts of climate change and natural disasters on firms, banks …

The proliferation of agent-based models (ABMs) in recent decades has motivated model
practitioners to improve the transparency, replicability, and trust in results derived from …

Since the survey by Windrum et al.(Journal of Artificial Societies and Social Simulation 10: 8,
2007), research on empirical validation of agent-based models in economics has made …

Departing from the comprehensive reviews carried out in the field, we identify the key
challenges that agent-based methodology faces when modeling coupled socio-ecological …

Mathematical modeling of innovation diffusion has attracted strong academic interest since
the early 1960s. Traditional diffusion models have aimed at empirical generalizations and …

Existing methodologies of sensitivity analysis may be insufficient for a proper analysis of
Agent-based Models (ABMs). Most ABMs consist of multiple levels, contain various …

While social media make it easy to connect with and access information from anyone, they
also facilitate basic influence and unfriending mechanisms that may lead to segregated and …

Agent-based simulation has become increasingly popular as a modeling approach in the
social sciences because it enables researchers to build models where individual entities …