The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels
This addresses the incremental problem of language evolution for researchers in multi-agent simulations and linguistics.
The study tackled the problem of how shared concepts emerge in a community of language users by extending prior work on negated assertions to include linguistic hedges, finding that hedged assertions improve convergence and reduce concept overlap but slow development speed.
We investigate the emergence of shared concepts in a community of language users using a multi-agent simulation. We extend results showing that negated assertions are of use in developing shared categories, to include assertions modified by linguistic hedges. Results show that using hedged assertions positively affects the emergence of shared categories in two distinct ways. Firstly, using contraction hedges like `very' gives better convergence over time. Secondly, using expansion hedges such as `quite' reduces concept overlap. However, both these improvements come at a cost of slower speed of development.