The emergence of division of labor through decentralized social sanctioning

Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specialization and division of labor. Unlike most other animals, humans learn by trial and error during their lives what role to take on. However, when some critical roles are more attractive than others, and individuals are self-interested, then there is a social dilemma: each individual would prefer others take on the critical but unremunerative roles so they may remain free to take one that pays better. But disaster occurs if all act thusly and a critical role goes unfilled. In such situations learning an optimum role distribution may not be possible. Consequently, a fundamental question is: how can division of labor emerge in groups of self-interested lifetime-learning individuals? Here we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labor involving all critical roles. Such social norms work by redistributing rewards within the population to disincentivize antisocial roles while incentivizing prosocial roles that do not intrinsically pay as well as others.

Methods
The data is generated by the computational models that aim to model the cultural evolutionary process of social norms on a groups of individuals. The individuals are able to learn during their lifetime by maximizing their payoffs. However, in this situation, a group level cooperation could not emerge. This is because in some tasks, the individuals would be required to select actions to maximize the group level payoff that may not be necessarily the best for them. Social norms, in forms of peer-to-peer social sanctioning rule through rewards and punisments, emerged through the cumulative cultural process to allow group of individuals to coopearte.

The data provides the code and the raw data generated by the algorithms. The results of the social sanctioning rules are shown in Supplementary Material.