mlsanityData to 7 Oct 2026

The emergence of division of labor through decentralized social sanctioning

arXiv 2208.05568 ↗First posted 10 Aug 2022cs.MA cs.AI cs.NEcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 82 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

5 of 82 references.

References · 5

  1. 2017Learning Sparse Neural Networks through L0 Regularizationcited by 571
  2. 2018Evolving intrinsic motivations for altruistic behaviorcited by 30
  3. 2021A learning agent that acquires social norms from public sanctions in decentralized multi-agent settingscited by 24
  4. 2021Emergent Prosociality in Multi-Agent Games Through Giftingcited by 12
  5. 2021Meta-control of social learning strategiescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.