Oct 05, 2018 | 14:00—15:30
Innovation can often be formulated as a recombinatorial-process where objects from a set are combined to form new objects that are in turn added to the set of objects that can again be recombined to form yet new objects.
In many instances this is a social process where the recombination is performed by a group of collaborators. This is especially true when it comes to the creation of new knowledge or methods as this mostly happens by people exchanging and combining their current knowledge and ideas to create new knowledge.
For this to happen communication between the collaborators is necessary. However in social psychology one can often observe a phenomenon called Ringelmann-Effect, which states that as such groups grow in size the increasing coordination overhead reduces the rate of successful communications. This ultimately leads to less innovative exchanges and a decline in novelty production.
In this talk I want to give a short overview about a simple combinatorial innovation model that mimics the recombination of knowledge elements by a set of agents and an analysis of data from Open-Source-Software projects. The aim is to investigate the influence of the Ringelmann-Effect in the model as well as in the data-set.