Governance as Collective Intelligence

Abstract

This research presents a design theory modelling governance as a collective intelligence process. The outcome of this process is a solution to a problem. It can be a decision, policy, product, financial plan, etc. The quality (value) of the outcome solution reflects the quality (performance) of the process. Using an analytical model, I identify five key variables as the channels (mediators) through which, different factors and features of the process can affect the quality of the outcome. Based on this model, I propose an asymmetric response surface method to experimentally improve governance mechanisms by introducing factors to the experimental model considering their plausible effects. As a proof of concept, I implemented a generic collective intelligence process in a web application and measured the effects of a few factors on its performance through online experiments. The results demonstrate the effectiveness of the proposed method. They also show that approval voting is significantly superior to plurality voting. Some studies asserted that not the design process, but the designers drive the quality of the outcome. This study shows that the characteristics of the design process (e.g. voting schemes), as well as the designers (e.g. expertise and gender), can significantly affect the quality of the outcome. Hence, the outcome quality can be used as an indicator of the performance of the process. This enables us to evaluate and compare governance mechanisms objectively free from fairness criteria.

Keywords: Collective Intelligence, Crowdsourcing, Design Science, Mechanism Theory, Response Surface Methodology, Distributed Autonomous Organizations

JEL Classification: C92, D72, D81, D82, D83