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Abstract: Individuals vary widely in how they categorize novel and ambiguous phenomena. This individual variation has led canonical theories in cognitive and social science to suggest that communication in large social networks leads populations toward divergent category systems. Yet, anthropological data indicates that large, independent societies consistently arrive at highly similar categories across a range of topics. How is it possible for diverse populations, consisting of individuals with significant variation in how they categorize the world, to independently construct similar category systems? Through a series of online experiments, I provide a novel answer to this puzzle that rests on the counterintuitive effects of communication networks on category formation. I designed an online “Grouping Game” that allows me to observe how people construct categories in both small and large populations when presented with the same novel and ambiguous images. I replicate this design for English-speaking subjects in the U.S. and Mandarin-speaking subjects in China. I find that solitary individuals and small social groups produce highly divergent category systems. Yet, I find that large social groups separately and consistently arrive at highly similar category systems, both within and across cultures. These findings are accurately predicted by a simple formal model of critical mass dynamics. In this way, I show how large communication networks can filter lexical diversity among individuals to produce replicable society-level patterns, yielding unexpected implications for cultural evolution. Network-based approaches to promoting shared understanding across cultural boundaries are proposed.
Bio Douglas Guilbeault is an Assistant Professor in the Management of Organizations at the Berkeley Haas School of Business. His research focuses on how people learn, challenge, develop, and invent categories by communicating in social networks. His studies on this topic have been published in a number of top journals, including Nature Communications and The Proceedings of the National Academy of the Sciences. As well, his research has received top awards from the International Conference on Computational Social Science, the Cognitive Science Society, and the International Communication Association. Douglas was among the first to identify the use of algorithmic bots on social media to manipulate elections around the world, and his exposés have appeared in prominent public venues including the Atlantic and Wired. Douglas teaches People Analytics at Haas, and he has served as a People Analytics consultant for a variety of organizations. Recently, he was the winner of Stanford’s “Art of Science” competition for the visual piece, “Changing Views in Data Science over 50 Years,” coauthored with the research collective, comp-syn.