An agent-based simulator driven by variants of Self-Organizing Maps

This paper introduces an agent-based simulator driven by variants of Self-Organizing Maps (SOMs), specifically designed to model agents learning in economic systems, as well as to render how they interact and the way such interaction can affect the system general behavior. As a consequence, we developed an environment with SOMs nodes treated as agents that are suitable to simulate economic systems and their evolution over time; moreover, in this way we were able to study within the SOM framework the impact of spatial connections on individual decisions. The effectiveness of this framework has been tested in the formalization of a model of economic growth. Agents behavior is simulated when the production efforts are a direct consequence of how individuals (in our simulation: SOM nodes) allocate their time and energies between working and studying, thus defining corresponding consumption and savings patterns. We then tested the model coherence with respect to observable data. The results confirm that, in order to simulate economic systems dynamics, it is relatively easy to mold SOM so that the simulation framework highlights significant patterns. Furthermore, in the examined case being the patterns consistent with the existence of dichotomous growth, i.e. the combination of convergence within regions and divergence among regions, they can be of help to rulers to effectively address their policy intervention.