Many researchers hypothesize that language adaptation, as with other evolutionary processes, entails both directed selection and random drift (Sapir, 1921; McMahon, 1994; Croft, 2000; Baxter et al., 2006; Van de Velde, 2014; Steels and Szathmáry, 2018). However, the specific contributions of these processes to language evolution remains an open question. It is well established that language evolution is not necessarily driven by selection, for example, speakers preferring specific word variants (Andersen, 1987; Blythe, 2012; Hamilton et al., 2016; Newberry et al., 2017).
Extending related work (Kandler et al., 2017), we use computational agent-based models to elucidate the impact of individual-level bias (speaker prestige) on population-level dynamics (average word similarity), where word diversity is measured by Levenshtein similarity (Levenshtein, 1966). Agents interacted in iterative language games (Kirby et al., 2014), to name and thus converse about resource types (A, B). Such object types represented conversation topics (Karjus et al., 2020c), where resource value indicated agent bias for conversing about (evolving words for) popular topics. For a null model comparison, we comparatively evaluated random drift versus directed word evolution on evolving word similarity, where using directed evolution, agent bias for adopting specific words (about resource types) increased with speaker agent social prestige (fitness).
While previous work has demonstrated selective advantages of various forms of speaker sociolinguistic prestige including competing word variants and borrowed words (Abrams and Strogatz, 2003; Labov, 2011; J. Hernández-Campoy and J. Conde-Silvestre, 2012; Kauhanen, 2017; Calude et al., 2017; Monaghan and Roberts, 2019; Karjus et al., 2020a,c), there has been little research on the impact of speaker prestige on word diversity in language evolution.