The language game

Coevolution of words and meanings

2014-12-29 — 2026-01-13

Wherein semantics in communication is considered as an ecology of mind, colour words are modeled as shared latents in an information‑theoretic toy framework, and links to categorical stochastics are drawn.

adaptive
agents
classification
collective knowledge
communicating
economics
evolution
information
language
learning
mind
networks
social graph
sociology
standards
stringology
virality

Semantics in a communicative context. I suspect that semantics in a communicative context is a special case of ecology of mind.

Figure 1

When do we need to use words? What words can be shared between people? (Baronchelli et al. 2010; Loreto, Mukherjee, and Tria 2012) present a toy model for colour words — a clever choice of domain.

(Steyvers and Tenenbaum 2005) draws a connection to categorical stochastics and evolutionary models.

Eisenstat (2025) proposes an information-theoretic model of word learning as “shared latents” in the probabilistic ML sense.

1 Incoming

Figure 2: The rose has teeth in the mouth of the beast, courtesy Dead Philosophers.

2 References

Baronchelli, Gong, Puglisi, et al. 2010. “Modeling the emergence of universality in color naming patterns.” Proceedings of the National Academy of Sciences of the United States of America.
Bender, and Koller. 2020. “Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
Cao, Lazaridou, Lanctot, et al. 2018. “Emergent Communication Through Negotiation.”
Chaabouni, Kharitonov, Dupoux, et al. 2019. “Anti-Efficient Encoding in Emergent Communication.” In Advances in Neural Information Processing Systems.
———, et al. 2021. “Communicating Artificial Neural Networks Develop Efficient Color-Naming Systems.” Proceedings of the National Academy of Sciences.
Eisenstat. 2025. “Condensation: A Theory of Concepts.” In.
Enfield. 2023. “Scale in Language.” Cognitive Science.
Havrylov, and Titov. 2017. “Emergence of Language with Multi-Agent Games: Learning to Communicate with Sequences of Symbols.”
Hidaka, and Smith. 2011. “Packing: A Geometric Analysis of Feature Selection and Category Formation.” Cognitive Systems Research.
Jiang, and Lu. 2018. “Learning Attentional Communication for Multi-Agent Cooperation.” In Advances in Neural Information Processing Systems.
Lian, Bisazza, and Verhoef. 2021. “The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning.”
Liu, De Deyne, Jiang, et al. 2023. “Understanding the Frequency of a Word by Its Associates: A Network Perspective.” Proceedings of the Annual Meeting of the Cognitive Science Society.
Loreto, Mukherjee, and Tria. 2012. “On the Origin of the Hierarchy of Color Names.” Proceedings of the National Academy of Sciences of the United States of America.
Lowe, Foerster, Boureau, et al. 2019. “On the Pitfalls of Measuring Emergent Communication.”
Petersen, Tenenbaum, Havlin, et al. 2012. “Languages Cool as They Expand: Allometric Scaling and the Decreasing Need for New Words.” Scientific Reports.
Peysakhovich, and Lerer. 2017. “Prosocial Learning Agents Solve Generalized Stag Hunts Better Than Selfish Ones.”
Pleyer, Perlman, Lupyan, et al. 2025. “The ‘design features’ of language revisited.” Trends in Cognitive Sciences.
Resnick, Gupta, Foerster, et al. 2020. “Capacity, Bandwidth, and Compositionality in Emergent Language Learning.”
Solé, Corominas-Murtra, Valverde, et al. 2010. “Language Networks: Their Structure, Function, and Evolution.” Complexity.
Steyvers, and Tenenbaum. 2005. “The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.” Cognitive Science.
Tucker, Li, Agrawal, et al. 2021. “Emergent Discrete Communication in Semantic Spaces.” In Advances in Neural Information Processing Systems.