Analysis of Human Behaviors in Real-Time Swarms

Abstract

Many species reach group decisions by deliberating in real-time systems. This natural process, known as Swarm Intelligence (SI), has been studied extensively in a range of social organisms, from schools of fish to swarms of bees. A new technique called Artificial Swarm Intelligence (ASI) has enabled networked human groups to reach decisions in systems modeled after natural swarms. The present research seeks to understand the behavioral dynamics of such “human swarms.” Data was collected from ten human groups, each having between 21 and 25 members. The groups were tasked with answering a set of 25 ordered ranking questions on a 1-5 scale, first independently by survey and then collaboratively as a real-time swarm. We found that groups reached significantly different answers, on average, by swarm versus survey ($\mathrm{p}=0.02$). Initially, the distribution of individual responses in each swarm was little different than the distribution of survey responses, but through the process of real-time deliberation, the swarm's average answer changed significantly ($\mathrm{p} < 0.001$). We discuss possible interpretations of this dynamic behavior. Importantly, the we find that swarm's answer is not simply the arithmetic mean of initial individual “votes” ($\mathrm{p} < 0.001$) as in a survey, suggesting a more complex mechanism is at play—one that relies on the time-varying behaviors of the participants in swarms. Finally, we publish a set of data that enables other researchers to analyze human behaviors in real-time swarms.