Groups can outperform individuals by sharing and aggregating information through diverse social structures and learning strategies that shape how innovations are discovered and transmitted. At the same time, groups must solve problems that vary in complexity, from relatively simple decisions to complex tasks with many interacting components such as scientific discovery. Interdisciplinary research shows that collective outcomes depend on learning strategies, group structure and problem complexity, yet it remains unclear how these factors together influence the success of collective search and problem-solving. We develop an evolutionary agent-based model in which selection operates at the group level: networks compete based on collective performance and reproduce, selecting for successful proportions of learning strategies. We use this framework to study the co-emergence of learning strategies across network sizes and densities and across tasks of varying complexity. Our results show that payoff-biased and frequency-biased social learning can co-exist, with their relative prevalence shifting as a function of network structure and task complexity. These findings suggest that no single learning strategy distribution is universally the best; instead, network structure and task complexity jointly shape the proportions of different learning strategies to support collective search. This article is part of the theme issue 'The evolution of collective intelligence'.
Pykälä et al. (Thu,) studied this question.