The emergence of division of labor through decentralized social sanctioning

التفاصيل البيبلوغرافية
العنوان: The emergence of division of labor through decentralized social sanctioning
المؤلفون: Yaman, Anil, Leibo, Joel Z., Iacca, Giovanni, Lee, Sang Wan
سنة النشر: 2022
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
الوصف: 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.
نوع الوثيقة: Working Paper
DOI: 10.1098/rspb.2023.1716
URL الوصول: http://arxiv.org/abs/2208.05568
رقم الأكسشن: edsarx.2208.05568
قاعدة البيانات: arXiv