Towards Better Prioritization of Governmental Objectives using Artificial Swarm Intelligence

Published on February 18, 2020


Groups often struggle to reach decisions, especially when populations are strongly divided by conflicting views. Traditional methods for collective decision-making involve polling individuals and aggregating results. In recent years, a new method called Artificial Swarm Intelligence (ASI) has been developed that enables networked human groups to deliberate in real-time systems, moderated by artificial intelligence algorithms. While traditional voting methods aggregate input provided by isolated participants, Swarm-based methods enable participants to influence each other and converge on solutions together. In this study we compare the output of traditional methods such as Majority vote and Borda count to the Swarm method on a set of divisive policy issues. We find that the rankings generated using ASI and the Borda Count methods are often rated as significantly more satisfactory than those generated by the Majority vote system (p<0.05). This result held for both the population that generated the rankings (the “in-group”) and the population that did not (the “out-group”): the in-group ranked the Swarm prioritizations as 9.6% more satisfactory than the Majority prioritizations, while the out-group ranked the Swarm prioritizations as 6.5% more satisfactory than the Majority prioritizations. This effect also held even when the out-group was subject to a demographic sampling bias of 10% (i.e. the out-group was composed of 10% more Labour voters than the in-group). The Swarm method was the only method to be perceived as more satisfactory to the “out-group” than the voting group.


Gregg Willcox

Gregg Willcox is the Director of Research and Development at Unanimous AI, a California company focused on the amplification of human intelligence using AI algorithms modeled after natural swarms. He studied Physics and Systems Engineering before discovering robotics and AI, earning getting his Masters in Robotics from Washington University in St. Louis. Gregg has published numerous papers on artificial intelligence, machine learning, and the use of Swarm Intelligence as a basis for amplifying the intelligence of human groups.

Louis Rosenberg

Louis Rosenberg, PhD is CEO and Chief Scientist of Unanimous AI, a California company focused on amplifying the intelligence of networked human groups using AI algorithms modeled after natural swarms. Rosenberg attended Stanford University where he earned his Bachelor’s, Master’s, and PhD degrees. His doctoral work focused on robotics, virtual reality, and human-computer interaction. While working as a researcher at the U.S. Air Force’s Armstrong Labs in the early 1990s, Rosenberg created the ‘Virtual Fixtures’ system, the first immersive Augmented Reality platform ever built. Rosenberg then founded Immersion Corporation to pursue virtual reality technologies. As CEO of Immersion, he brought the company public in 1999 (NASDAQ: IMMR). Rosenberg also founded Microscribe, maker of the world’s first desktop 3D digitizer – the Microscribe 3D – which has been used in the production of many feature films, including Shrek, Ice Age, A Bugs Life, and Titanic. Rosenberg has also worked as a tenured Professor at California State University (Cal Poly), teaching design and entrepreneurship. Rosenberg has been awarded more than 300 patents for his technological efforts.

Mark Burgman

Imperial College London

Alexandru Marcoci

University of North Carolina, Chapel Hill

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