Emerging small-world referral networks in evolutionary labor markets

We model a labor market that includes referral networks using an agent-based simulation. Agents maximize their employment satisfaction by allocating resources to build friendship networks and to adjust search intensity. We use a local selection evolutionary algorithm, which maintains a diverse population of strategies, to study the adaptive graph topologies resulting from the model. The evolved networks display mixtures of regularity and randomness, as in small-world networks. A second characteristic emerges in our model as time progresses: the population loses efficiency due to over competition for job referral contacts in a way similar to social dilemmas such as the tragedy of the commons. Analysis reveals that the loss of global fitness is driven by an increase in individual robustness, which allows agents to live longer by surviving job losses. The behavior of our model suggests predictions for a number of policies