Understanding the costs and benefits of social behaviour is a long-standing, and crucially important, topic to many branches of life sciences. It is now widely recognised that real social systems exist as intricately structured networks of social connections (‘social networks’), and that many key social processes ranging from direct social interactions (mating, competition etc) to contagions (infectious diseases, transmission of behaviours) rely on these social networks. Despite the significance, there remains little knowledge of how real-world social systems can mitigate the costs of social connections (e.g. competition, or disease exposure) while simultaneously promoting the benefits (e.g. cooperative actions, or useful social information). Understanding of the cost-benefit trade-offs that exist within populations’ social networks is not only important for conceptual comprehension of sociality, but also of upmost and immediate concern to societal issues. For instance, the COVID19 pandemic instantly exampled the costs of social connections (i.e. disease transmission), and was met with global efforts to decrease social connectivity across various contexts (physical distancing, closing schools and workplaces etc). However, through removing the social connections that are important for societal functioning, learning, and health, these attempts to disrupt the disease contagion unfortunately quickly exampled the social trade-offs that societies face when considering contagions: how can social systems reduce the costs of connectivity while retaining the vital benefits? which social connections must be removed to control destructive contagions? which connections should be retained to allow functioning? how can connections be rewired to optimise social architecture?
Although the recent pandemic unexpectedly illustrated the stark importance of social trade-offs in an unprecedented and unexpected manner to modern humans, the problem of social trade-offs is conceptually fundamental to any kind of social system which experiences both costs and benefits of social connections. The Firth research group (www.FirthNetwork.com) aims to build interdisciplinary approaches to understanding social behaviour in natural, real-world settings, and to capitalise the various levels of impact these insights and analytical approaches can generate. In particular, the research aims to advance knowledge of how ecology and individual behaviour interact to govern social structure, and the outcome of processes in societies. Throughout this, two complementary approaches are used: First, we develop and apply a wide range of analytical techniques (e.g. simulation modelling, big data processing) to large observational datasets of animal and human social systems to examine how ecological factors and individuals’ traits underpin social structure and how arising networks affect various biological processes. Second, we design experimental manipulations of social networks in natural populations to allow causal tests of sociality within natural ecological settings. Further, across this academic research, we aim to maximize impact by continually exploring how findings and approaches can be applied to human health and activity (such as through collaborations with industry and biomedical practitioners), disease (working closely with applied epidemiologists within policy settings), and also how we can promote accessible science to the general public.
Qualifications and Experience
DPhil Biology, Research Fellow since 2016, Industry experience as Senior Associate in Data Science, Teaching experience including lectures/courses and tutorials, Supervisory experience of 4 DPhil students (1 as lead supervisor) and multiple MSc/BSc research projects