To Populate is To Regulate
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Abstract (arXiv)
We examine the effects of instantiating Lewis signaling games within a population of speaker and listener agents with the aim of producing a set of general and robust representations of unstructured pixel data. Preliminary experiments suggest that the set of representations associated with languages generated within a population outperform those generated between a single speaker-listener pair on this objective, making a case for the adoption of population-based approaches in emergent communication studies. Furthermore, post-hoc analysis reveals that population-based learning induces a number of novel factors to the conventional emergent communication setup, inviting a wide range of future research questions regarding communication dynamics and the flow of information within them.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
- 2023Towards More Human-like AI Communication: A Review of Emergent Communication Researchcited by 13
- 2021Dynamic population-based meta-learning for multi-agent communication with natural languagecited by 10
- 2025VQEL: Enabling Self-Play in Emergent Language Games via Agent-Internal Vector Quantizationcited by 0
- 2025VQEL: Enabling Self-Play in Emergent Language Games via Agent-Internal Vector Quantization6 Mar 2025
- 2024A Review of the Applications of Deep Learning-Based Emergent Communication3 Jul 2024
- 2023Towards More Human-like AI Communication: A Review of Emergent Communication Research1 Aug 2023
- 2021Dynamic population-based meta-learning for multi-agent communication with natural language27 Oct 2021