# Cultural evolution via iterated learning and communication explains efficient color naming systems

arXiv: https://arxiv.org/abs/2305.10154 · HTML: https://mlsanity.com/paper/2305.10154/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016)
- [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017)
- [Compositional Languages Emerge in a Neural Iterated Learning Model](https://mlsanity.com/paper/2002.01365/) (2020)
- [Semantic categories of artifacts and animals reflect efficient coding](https://mlsanity.com/paper/1905.04562/) (2019)

## Built on by (most cited first, 10 of 10)

- [Why do objects have many names? A study on word informativeness in language use and lexical systems](https://mlsanity.com/paper/2410.07827/) (2024)
- [NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use](https://mlsanity.com/paper/2509.22479/) (2025)
- [Learning Efficient Recursive Numeral Systems via Reinforcement Learning](https://mlsanity.com/paper/2409.07170/) (2024)
- [PACE: Procedural Abstractions for Communicating Efficiently](https://mlsanity.com/paper/2409.20120/) (2024)
- [Evolution and compression in LLMs: On the emergence of human-aligned categorization](https://mlsanity.com/paper/2509.08093/) (2025)
- [When Efficient Communication Explains Convexity](https://mlsanity.com/paper/2602.02821/) (2026)
- [Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning](https://mlsanity.com/paper/2602.21720/) (2026)
- [Agent-based imitation dynamics can yield efficiently compressed population-level vocabularies](https://mlsanity.com/paper/2603.15903/) (2026)
- [Modeling Human-Like Color Naming Behavior in Context](https://mlsanity.com/paper/2604.25674/) (2026)
- [Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations](https://mlsanity.com/paper/2609.06025/) (2026)

## Abstract (arXiv)

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient.