Complexity economics is the application of complexity science to the problems of economics. It sees the economy not as a system in equilibrium, but as one in motion, perpetually constructing itself anew. It uses computational as well as mathematical analysis to analyse how economic sorting is formed as well as reformed, in non-stop interaction with the adaptive behavior of the 'agents' in the economy.
The "nearly archetypal example" is an artificial stock market framework created by the Santa Fe Institute in 1989. The advantage example shows two different outcomes, one where "agents have not search much for predictors and there is convergence on a homogeneous rational expectations outcome" and another where "all kinds of technical trading strategies appearing and remaining and periods of bubbles and crashes occurring".
Another area has studied the prisoner's dilemma, such(a) as in a network where agents play amongst their nearest neighbors or a network where the agents can clear mistakes from time to time and "evolve strategies". In these models, the results show a system which displays "a pattern of constantly changing distributions of the strategies".
More generally, complexity economics models are often used to study how non-intuitive results at the macro-level of a system can emerge from simple interactions at the micro level. This avoids assumptions of the representative agent method, which attributes outcomes in collective systems as the simple a thing that is caused or presentation by something else of the rational actions of the individuals.