4. Conclusion
We have modeled the division of labor as a self-organization process. Driven by individual income optimization, the system moves from disorder to order and a division of labor emerges. We do not set system-wide goals in the simulation process but apply a set of rules governing how agents interact with each other and with their environment. Agents seeking to increase their income is the driving factor in this self-organized evolution.
The rational for labor division is as follows.
(1) Information sharing: Resource information is shared in a closed environment. This is a good approximation of the real-world information exchange process and makes the transaction rules between agents rational and realistic.
(2) Learn-by-doing: This nonlinear mechanism enables agents to further improve what they are good at and—through the effect of positive feedback— become more strongly attached to doing it. Overall efficiency increases, which is an important factor in the emergence of labor division.
(3) Random fluctuation: Stochastic fluctuation is a necessary condition for self-organized evolution and determines the state the agent arrives at in the final stage. In the process of evolution, a small change in the initial state of the system can strongly affect the final state. In our study the emergence of labor division does not depend on stochastic fluctuations, but the final outcome of labor division does depend on them.