Stylized innovation: generating timelines by interrogating incrementally available randomised dictionaries

TL;DR

This paper introduces synthetic innovation web dictionaries to simulate and analyze the dynamics of innovation timelines, focusing on how new symbols emerge and influence the growth of knowledge across different models.

Contribution

It develops and tests various dictionary generation models to understand the impact of symbol discovery order and structure on innovation processes.

Findings

Dictionary models show different scaling behaviors of knowledge growth.

Symbol discovery order significantly affects the evolution of innovation timelines.

Synthetic dictionaries can help probe the properties of real-world innovation webs.

Abstract

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.