Governance as Collective Intelligence

45 Pages Posted: 16 Jun 2020 Last revised: 10 Jun 2022

See all articles by Daniel Armani

Daniel Armani

McCombs School of Business - University of Texas at Austin; The Eli Broad Graduate School of Management, Michigan State University

Date Written: December 31, 2019


This research presents a design theory that models 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. However, 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

Suggested Citation

Armani, Daniel, Governance as Collective Intelligence (December 31, 2019). Available at SSRN: or

Daniel Armani (Contact Author)

McCombs School of Business - University of Texas at Austin ( email )

2110 Speedway
McCombs School of Bussiness
Austin, TX 78705
United States
5174022026 (Phone)

The Eli Broad Graduate School of Management, Michigan State University ( email )

632 Bogue Street , North Business College Complex
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East Lansing, MI TX 48825
United States
5174022026 (Phone)

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