Emergent Complexity in Agent-Based Computational Economics

Abstract

In this paper, we shall review two kinds of emergent complexity in agent-based computational economics (ACE). The first kind is based on the complex systems initiated in the 1980s or even earlier by mathematicians and physicists, whereas the second kind is based on the idea of complex adaptive systems composed of autonomous agents, for which many representative works have been collected in Rosser. For the latter, we shall go further to examine the two elements which have just recently been incorporated in agent-based economic modelling, namely, intelligence and modularity. This augmentation leads to the development of neurocognitive software agents, which can guide the generation and direction of future ACE studies with a multistage 'brain/behaviourto-emergency-to-brain/behaviour' approach.

Agent‐based Macroeconomics and Dynamic Stochastic General Equilibrium Models: Where Do We Go From Here?

“…In particular, two identical impulses, differing only in sign, will cancel each other out. With non‐linear models this property fails to hold, and as such the response of a non‐linear system to an impulse is not necessarily proportional to the size of the shock, and the state of the system will matter in determining the response to any given shock (see also Al‐Suwailem, ; Chen and Wang, ). In addition, some scholars argue that complexity requires neutral equilibria, such that complex dynamics are a midpoint between chaos and asymptotic stability (Foley, ).…”