A typology of rules for knowledge exchange in higher-order interactions

Social learning is important to humans and other animals as they gather information about their environment. Information and behaviours can therefore spread rapidly through social networks as contagions. However, the way individuals acquire and use social information is highly variable and frequently complex, often shaped by higher-order or multibody interactions that are not straightforwardly described by conventional dyadic networks. There has been considerable recent progress in modeling social contagions across higher-order networks that explicitly quantify these multibody interactions. A challenge for studying social contagion across multibody or higher-order interactions is the diversity of ways in which knowledge can be exchanged within or among groups. Here we provide a typology of knowledge exchange rules in higher-order networks, focusing on both learning and discovery. We also provide a non-exhaustive list of many basic knowledge exchange rules, to demonstrate our typology and its value in distinguishing between different mechanisms of social learning. Our aim is to provide a framework that helps researchers interested in modeling knowledge exchange in higher-order networks to develop new models and adapt existing models to questions of interest. By doing so we hope to promote interdisciplinarity in the study of how multibody interactions shape social contagions, especially at this critically incipient stage - avoiding the inevitable challenges from the eventual need to integrate parallel, independent, complementary advances among different disciplines.Author summary: Learning from each other is important to humans and other animals as it provides safe or quick ways to gather information about the world around you. Because of this ‘social learning’, ideas and behaviours can spread quickly through our social networks. Previously, researchers have mainly described these social networks using ‘dyadic’ approaches that only consider connections between pairs of individuals at a time. However, often social interactions occur between more than two individuals at a time, and this can influence how we learn from each other. Here we classify different ways that social learning occurs in groups and provide a list of and mathematical formulae for many potential rules for how knowledge is exchanged in these contexts. We aim to provide a framework for researchers interested in modeling how we learn from each other in groups, and help to integrate ideas from different areas of research into social learning. Doing so now is valuable as the number of studies using higher-order networks to study group behviour is growing fast.