Artificial textual traditions are used to test and develop the methods of textual criticism and computer-assisted stemmatology. They try to imitate the different aspects of the actual copying and dissemination process of a real-life text as closely as possible: e.g., in order to simulate a mediaeval textual tradition, the text may be copied by several scribes by hand and its language may be familiar to the scribes but not their mother tongue. Such laborious practices restrict the possible size of an artificial textual tradition created by actual scribes, and the biggest artificial traditions of this kind consist of slightly over a hundred copies of a text (as of May 2015). Therefore, computers have also been and are being used to mutate the original contents of a text to create an artificial tradition. Whatever the means, the basic idea is to have a data set that is comparable to real-life textual traditions but the history and development of which is known in detail. Thus, the artificial data sets help to evaluate the performance of various methods to study textual traditions.
On the other hand, the genealogical tree of a textual tradition can be simulated by defining an original and a probability rate that it is lost and another one that it is copied per time t. Weitzman pioneered this approach and plotted some cases, cf. ill. below. It is interesting to note how the archetype shifts through the loss of witnesses and ends up being equal to witness 13 in this example. Many other initial text branches in Weitzman's experiment became completely extinct soon.
Fig. 1. Illustration from Weitzman, as reproduced in Trovato, p. 87. Here, Ω represents the original; circled manuscripts have been lost, dotted rings show manuscript that are dying in the present step.
– Baret, Philippe V., Caroline Macé, and Peter Robinson. 2006. “Testing Methods on an Artificially Created Textual Tradition.” In The Evolution of Texts: Confronting Stemmatological and Genetical Methods, edited by Caroline Macé, Philippe Baret, Andrea Bozzi, and Laura Cignoni, 255–281. Linguistica computazionale, vols. 24–25. Pisa: Istituti editoriali e poligrafici internazionali.
– Roos, Teemu, and Tuomas Heikkilä. 2009. “Evaluating Methods for Computer-Assisted Stemmatology Using Artificial Benchmark Data Sets.” Literary and Linguistic Computing 24 (4): 417–433.
– Trovato, Paolo. 2014. Everything You Always Wanted to Know about Lachmann’s Method: A Non-Standard Handbook of Genealogical Textual Criticism in the Age of Post-Structuralism, Cladistics, and Copy-Text. Foreword by Michael D. Reeve. Firenze: Libreriauniversitaria.it edizioni.
– Weitzman, Michael P. 1982. “Computer Simulation of the Development of Manuscript Traditions.” ALLC Bulletin 10: 55–59.