Collective causality : building solution architectures with a crowd

Abstract

Traditional open innovation has operated on the assumption that by casting a wide net into the crowd, the likelihood of obtaining a desirable solution to a problem increases, due to the greater range of potential solutions that is obtained. This is typically implemented using a competitive format, where the best ideas are selected from a crowd, and the rest are discarded. Unfortunately, the drawback of such a format is that it fails to make use of the efforts behind discarded ideas. Each of these ideas represents a great deal of cognitive effort that has gone towards understanding and solving a problem, and discarding them sacrifices potentially useful insights that might be derived from ultimately unworkable solutions. This thesis explores how a more effective form of collective intelligence might be obtained - one where the half-baked solutions of many participants might be combined to produce something more effective than one participant's fully baked solution that is selected through competition. The specific format of a collaborative causal map is explored, where individuals can each contribute causes and causal links to an overall causal web, building an ever richer architecture of potential solutions (and their sub-solutions) to an overall problem. The goal is to integrate individuals' contributions such that they accumulate to an overall cohesive solution that is better than what any individual could have developed. A series of pilots are conducted to understand the group dynamics in both offline and online collaboration, and determine those factors that are material to the success of an online collaborative causal map. Such factors include how the question is framed, how users attend to others' contributions, or how users' contributions can be curated. These factors are ultimately incorporated into a prototype collaborative causal mapping website, which is developed for public use.