Quantization Games on Social Networks and Language Evolution
Ankur Mani, Lav R. Varshney, and Alex (Sandy) Pentland

TL;DR
This paper models language evolution as a strategic game on social networks, showing how agents develop shared vocabularies through Nash equilibria without full source knowledge, and analyzing the stability of communication chains.
Contribution
It introduces a network game framework for quantizer design in language evolution, demonstrating equilibrium existence, shared vocabularies, and conditions for stable communication.
Findings
Nash equilibrium quantizers exist in the network setting.
Multiple vocabularies can coexist at equilibrium.
Error in translation does not grow along chains with shared vocabularies.
Abstract
We consider a strategic network quantizer design setting where agents must balance fidelity in representing their local source distributions against their ability to successfully communicate with other connected agents. We study the problem as a network game and show existence of Nash equilibrium quantizers. For any agent, under Nash equilibrium, the word representing a given partition region is the conditional expectation of the mixture of local and social source probability distributions within the region. Since having knowledge of the original source of information in the network may not be realistic, we show that under certain conditions, the agents need not know the source origin and yet still settle on a Nash equilibrium using only the observed sources. Further, the network may converge to equilibrium through a distributed version of the Lloyd-Max algorithm. In contrast to…
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