  When generating insights from human groups, conversational deliberation is a
key method for exploring issues, surfacing ideas, debating options, and
converging on solutions. Unfortunately, real-time conversations are difficult
to scale, losing effectiveness in groups above 4 to 7 members. Conversational
Swarm Intelligence (CSI) is a new technology that enables large human groups to
hold real-time conversations using techniques modeled on the dynamics of
biological swarms. Through a novel use of Large Language Models (LLMs), CSI
enables real-time dialog among small groups while simultaneously fostering
content propagation across a much larger group. This combines the benefits of
small-scale deliberative reasoning and large-scale groupwise intelligence. In
this study, we engage a group of 81 American voters from one political party in
real-time deliberation using a CSI platform called Thinkscape. We then task the
group with (a) forecasting which candidate from a set of options will achieve
the most national support, and (b) indicating the specific reasons for this
result. After only six minutes of deliberation, the group of 81 individuals
converged on a selected candidate and surfaced over 400 reasons justifying
various candidates, including 206 justifications that supported the selected
candidate. We find that the selected candidate was significantly more supported
by group members than the other options (p<0.001) and that this effect held
even after six minutes of deliberation, demonstrating that CSI provides both
the qualitative benefits of conversational focus groups and the quantitative
benefits of largescale polling.
