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 language | English |
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Article number | 20231716 |
Pages (from-to) | 1-12 |
Number of pages | 12 |
Journal | Proceedings of the Royal Society B: Biological Sciences |
Volume | 290 |
Issue number | 2009 |
Early online date | 25 Oct 2023 |
DOIs | |
Publication status | Published - 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).
Funders | Funder number |
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Artificial Intelligence Graduate School Program | |
NRF-2019M3E5D2A01066267 | |
Ministry of Science, ICT and Future Planning | RS-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