The emergence of division of labour through decentralized social sanctioning

Anil Yaman, Joel Z. Leibo, Giovanni Iacca, Sang Wan Lee

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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 labour. 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 thus 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 labour 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 labour 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.

Original languageEnglish
Article number20231716
Pages (from-to)1-12
Number of pages12
JournalProceedings of the Royal Society B: Biological Sciences
Volume290
Issue number2009
Early online date25 Oct 2023
DOIs
Publication statusPublished - 25 Oct 2023

Funding

S.W.L. was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grants funded by the Korean government (MSIT) (no. RS-2023-00233251, System3 Reinforcement learning with high-level brain functions, and no. 2019-0-00075, Artificial Intelligence Graduate School Program (KAIST)), and by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2019M3E5D2A01066267).

FundersFunder number
Artificial Intelligence Graduate School Program
NRF-2019M3E5D2A01066267
Ministry of Science, ICT and Future PlanningRS-2023-00233251, 2019-0-00075
National Research Foundation of Korea
Korea Advanced Institute of Science and Technology
Institute for Information and Communications Technology Promotion

    Keywords

    • complexity regularization
    • division of labour
    • evolution of cooperation
    • lifetime-learning
    • social sanctions

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