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Abstract. Natural languages exhibit many so-called semantic universals, properties of meaning shared across all languages. In this talk, we will present an explanation of one very prominent semantic universal, namely that all simple determiners denote monotone quantifiers. While existing work has shown that monotone quantifiers are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that monotone quantifiers regularly evolve in an iterated learning paradigm with neural networks as agents.