Stylized innovation: generating timelines by interrogating incrementally available randomised dictionaries
This work addresses the problem of analyzing innovation processes for researchers in fields like linguistics and technology, but it is incremental as it builds on prior studies of innovation in language and gastronomy.
The paper tackles the challenge of understanding innovation as a dynamic process by generating synthetic innovation web dictionaries to simulate timelines and analyze their statistics and dependencies on structure or algorithms, finding insights into how new symbol discovery scales with dictionary generation models and parameters.
A key challenge when trying to understand innovation is that it is a dynamic, ongoing process, which can be highly contingent on ephemeral factors such as culture, economics, or luck. This means that any analysis of the real-world process must necessarily be historical - and thus probably too late to be most useful - but also cannot be sure what the properties of the web of connections between innovations is or was. Here I try to address this by designing and generating a set of synthetic innovation web "dictionaries" that can be used to host sampled innovation timelines, probe the overall statistics and behaviours of these processes, and determine the degree of their reliance on the structure or generating algorithm. Thus, inspired by the work of Fink, Reeves, Palma and Farr (2017) on innovation in language, gastronomy, and technology, I study how new symbol discovery manifests itself in terms of additional "word" vocabulary being available from dictionaries generated from a finite number of symbols. Several distinct dictionary generation models are investigated using numerical simulation, with emphasis on the scaling of knowledge as dictionary generators and parameters are varied, and the role of which order the symbols are discovered in.