Behavioural Data in Complex Economic and Business Systems

Glenn Parry

doi:10.1017/9781108939010.030

Understanding behavioural data within complex economic and business systems requires a shift from linear, equilibrium-oriented perspectives to frameworks that accommodate emergence, adaptation and interdependence. Complexity science offers a valuable lens through which the dynamics of modern economic behaviours can be analysed, particularly in an era characterised by interconnected supply chains, automated financial ecosystems and data-intensive marketplaces. This chapter explores how behavioural data can be harnessed to illuminate patterns of interaction within such systems, examining feedback loops, tipping points, network effects and the role of institutional context. Drawing on case studies from financial markets, organisational decision-making and global production networks, this chapter illustrates how Behavioural Data Science can capture the micro-motives and macro-behaviours that define complexity. The work also addresses methodological challenges – including multi-level inference, non-stationarity and limited observability – and outlines new directions in agent-based modelling, behavioural data fusion and computational experimentation. The discussion culminates in a reflection on ethical considerations and the role of behavioural data in governing complex systems, especially under uncertainty.