Macroeconomics must take radical uncertainty into account, if it aims at contributing to the solution of serious real-world problems such as climate change. Allowing for radical uncertainty must happen at two levels: the level of modeling and the level of the scientific discipline. I argue that...

The tools and concepts of the emerging field of complexity science - like agent-based modeling, network theory, and machine learning - can offer powerful insights to economists and crafters of public policy. Complexity science enables us to explicitly model relationships between individuals and...

The paper compares and contrasts complexity economics and neoclassical economics. It proposes a framework for modelling complex systems and, accordingly, utilizes agent‐based simulation to examine consumption behaviour in a complex model and in a neoclassical model. Results suggest that...

Complex systems' approach and methodologies emphasize interactions, diversity and dynamics. Complex systems perspectives also enable public policies to be considered comprehensively and simulated in all their multiplicity of sectors and scales, of cause and effect. This paper attempts to...

Education systems can be viewed as complex systems, by considering that learning, teaching, cognition and education are phenomena resulting from interactions between the heterogeneous agents that compose such systems. Given the complex nature of education systems, new approaches seem relevant,...