Hybrid social learning in human-algorithm cultural transmission

• DOI: 10.31235/osf.io/pfdcv
• Publication Date: 2021-04-08T12:14:22Z
• Authors: Levin Brinkmann; Deniz Gezerli; Kira von Kleist; Thomas Franz Müller; Iyad Rahwan; Niccolo Pescetelli
• Keywords: Social Learning

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases that enable solutions and behaviours to spread socially from one human next, selecting whom we copy what copy. In a digital society, algorithmic agents both contribute transmission knowledge. One hypothesis is machines may influence patterns not only by providing means for spreading behavior but also novel behaviors themselves. We propose certain algorithms might show (either learning or design) different behaviors, problem-solving abilities than their counterparts. This in turn foster better decisions environments where diversity strategies beneficial. this study, ask whether with complementary humans could boost lab-based planning task, suboptimal biases. conducted large behavioral study an agent-based simulation test performance chains machine players. half chains, bot replaced participant. boosts immediately following participants chain, gain lost further down chain. Our findings suggest can potentially improve performance, bias hinder being preserved, especially under conditions uncertainty high cognitive load. results hybrid be limited task environment