ABSTRACT
The economic system is a supremely complex one. The traditional approach to understanding it has been to reduce complexities to simple rules and behaviors. However, there is nowadays an alternative approach: the one proposed by complexity economics, a fast-growing field in economic analysis.
Agent-Based Models (ABM) focus on the computational study of economic processes where the individual agents are given explicit rules of behavior; repeated interactions among heterogeneous agents induce microeconomic patterns which, once aggregated, generate a macro dynamics for the aggregate variables.
For ABM realism is more important than conceptual simplicity. In the modeling of the financial sector, there are examples of the adoption of a complexity-based approach. It can also be used to test policies.
The real-world economies are most of the time out of equilibrium, at best in search of equilibrium. It is natural then to investigate how the economy behaves when it is not at a steady state. Out-of-equilibrium analysis is economics done in a more general way.
Some sources of complication in economics are expectations and network of networks. It is explained how complexity economics deals with them.
Finally, the subjects of multiple equilibria and path dependency are analyzed.
