Plots of where in a phonetic possibility space different words end up after 10,000 rounds of interaction, across 20 independent simulation runs (each cloud of 100 exemplar dots/triangles represents a single word at round 10,000 of a single simulation run). Blue, yellow, green and orange are regular words; purple is the continuer word. On each independent simulation run, all words are initialised at randomly selected positions in the space. A shows a selection of 6 separate simulation runs chosen for illustrative purposes (showing how regular words end up in different positions); B shows the end-state of all 20 simulation runs overlaid. Parameter settings: (i) minimal effort bias 3 times as strong for continuer word (G=1250) than for regular vocabulary words (G=5000), and (ii) the bias for reuse of features (i.e. segment-similarity bias) is not applied to the continuer category.
Dingemanse, M., Liesenfeld, A., & Woensdregt, M. (2022). Convergent cultural evolution of continuers (mmhm). The Evolution of Language: Proceedings of the Joint Conference on Language Evolution (JCoLE), 61–67. PDF
A comparison of computational complexity (in basic computation steps) by agent type and lexicon size. The main take-away from this figure is that complexity increases exponentially with lexicon size for pragmatic agents, but only linearly for interactional agents. Three types of agent are compared, each with three lexicon sizes. The Interactional agent is a model equipped with a simple form of repair and no pragmatic reasoning. The other two agents cannot initiate repair, but instead feature pragmatic reasoning. The Frugally Pragmatic agent is a model that only uses complex pragmatic reasoning above a certain uncertainty threshold; the Fully Pragmatic agent always uses it. For interactional agents with a 6 × 4 lexicon no data is visible as the computation cost is very small (48) relative to the range of the y-axis.
Arkel, J. van, Woensdregt, M., Dingemanse, M., & Blokpoel, M. (2020). A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis. Proceedings of the 24th Conference on Computational Natural Language Learning. doi: 10.18653/v1/2020.conll-1.14PDF
