# The Spontaneous Emergence of Conventions: An Experimental Study of Cultural Evolution

arXiv: https://arxiv.org/abs/1502.06910 · HTML: https://mlsanity.com/paper/1502.06910/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2015.

## Built on by (most cited first, 17 of 17)

- [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024)
- [From partners to populations: A hierarchical Bayesian account of coordination and convention](https://mlsanity.com/paper/2104.05857/) (2021)
- [Group size effects and collective misalignment in LLM multi-agent systems](https://mlsanity.com/paper/2510.22422/) (2025)
- [The Role of Social Learning and Collective Norm Formation in Fostering Cooperation in LLM Multi-Agent Systems](https://mlsanity.com/paper/2510.14401/) (2025)
- [Shaping New Norms for AI](https://mlsanity.com/paper/2307.08564/) (2023)
- [Static network structure cannot stabilize cooperation among Large Language Model agents](https://mlsanity.com/paper/2411.10294/) (2024)
- [Exploring the Topology and Memory of Consensus: How LLM Agents Agree, Fragment, or Settle When Forming Conventions](https://mlsanity.com/paper/2606.04197/) (2026)
- [Emergence of Biased Consensus in Multi-Agent LLM Debates](https://mlsanity.com/paper/2608.02827/) (2026)
- [Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima](https://mlsanity.com/paper/2303.04544/) (2023)
- [Reply to "Emergent LLM behaviors are observationally equivalent to data leakage"](https://mlsanity.com/paper/2506.18600/) (2025)
- [Copying explains the collective behavior of AI agents in the wild](https://mlsanity.com/paper/2609.09150/) (2026)
- [Indirect tipping: a social attack surface in AI agent populations](https://mlsanity.com/paper/2609.25194/) (2026)
- [How social reinforcement learning can lead to metastable polarisation and the voter model](https://mlsanity.com/paper/2406.07993/) (2024)
- [Benchmarking large language model agent societies against human behavioural distributions](https://mlsanity.com/paper/2608.28182/) (2026)
- [AI agents reshape consensus formation in human groups](https://mlsanity.com/paper/2609.02122/) (2026)
- [Collective creativity in hybrid societies](https://mlsanity.com/paper/2609.02620/) (2026)
- [Drivers of LLM Consensus Formation Under Competing Incentives: A Simulation Study](https://mlsanity.com/paper/2610.20200/) (2026)

## Abstract (arXiv)

How do shared conventions emerge in complex decentralized social systems? This question engages fields as diverse as linguistics, sociology and cognitive science. Previous empirical attempts to solve this puzzle all presuppose that formal or informal institutions, such as incentives for global agreement, coordinated leadership, or aggregated information about the population, are needed to facilitate a solution. Evolutionary theories of social conventions, by contrast, hypothesize that such institutions are not necessary in order for social conventions to form. However, empirical tests of this hypothesis have been hindered by the difficulties of evaluating the real-time creation of new collective behaviors in large decentralized populations. Here, we present experimental results - replicated at several scales - that demonstrate the spontaneous creation of universally adopted social conventions, and show how simple changes in a population's network structure can direct the dynamics of norm formation, driving human populations with no ambition for large scale coordination to rapidly evolve shared social conventions.