This paper explores the idea of using artificial adaptive agents in economic theory. In particular, we use Genetic Algorithms (GAs) to model the learning behavior of a population of adaptive and boundedly rational agents interacting in an economic system. We analyze the behavior of a GA in two...

In this paper we use a simple model of evolutionary market dynamics to illustrate how chaotic behavior can be controlled by making small changes in a parameter that is accessible to the decision makers. This approach is commonly referred to as `targeting' - one can easily switch from a chaotic...

The advent of artificial intelligence is changing the task allocation of workers and machines in firms' production processes with potentially wide ranging effects on workers and firms. We develop an agent-based simulation framework to investigate the consequences of different types of automation...

We analyze the learning behaviour of a Simple Genetic Algorithm in an overlapping generations model with one consumption good and fiat money. It is shown by simulations, that in cases where periodic equilibria exist the equilibrium of period two is learned by a Genetic Algorithm and not the...

We consider a differential game of R&D competition and explore the impact of rivalry on the firms' investment behavior over time. Using closed-loop strategies and hence allowing for strategic interactions among rival firms, we show that R&D spending by the individual competitor is increased due...