Simulated results from an agent-based model examining inequality and innovation in social networks

Theories of innovation often balance contrasting views that either smart
people create smart things or smartly constructed institutions create
smart things. While population models have shown factors including
population size, connectivity, and agent behavior as crucial for
innovation, few have taken the individual-central approach seriously by
examining the role individuals play within their groups. To explore how
network structures influence not only population-level innovation but also
performance among individuals, we studied an agent-based model of the
Potions Task, a paradigm developed to test how structure affects a
group's ability to solve a difficult exploration task. We explore how
size, connectivity, and rates of information sharing in a network
influence innovation and how these have an impact on the emergence of
inequality in terms of agent contributions. We find, in line with prior
work, population size has a positive effect on innovation, but that large
and small populations perform similarly per capita; that many small groups
outperform fewer large groups; that random changes to structure have few
effects on innovation; and that the highest performing agents tend to
occupy more central network positions. Moreover, we show that every
network factor which facilitates innovation leads to a proportional
increase in inequality of performance, creating "genius effects"
among otherwise "dumb" agents in both idealized and real-world
networks.