Experiences creating three implementations of the repast agent modeling toolkit
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Specialization, or division of labour, leads to increased productivity in systems. We study the effect of social influence on the level of agent specialization in complex systems connected via social networks. There are several methods that explain the emergence of specialization, with the most prominent being the genetic threshold model. This model posits that agents possess an inherent threshold for task stimulus, and when that threshold is exceeded, the agent will perform that task. The idea of social influence is that an agent's choice of which task to specialize in when multiple ones are availabe, is influenced by the choices of its neighbours. Using the threshold model and an established metric that quantifies the level of agent specialization, we found that social influence leads to an increase in the division of labour.