Combining qualitative and quantitative methods in behavioural psychology for complex human-environment systems

This chapter explores the combination of qualitative and quantitative methods in behavioural psychology for complex human-environment systems. Behavioural psychology aims to understand the causes of human behaviour, such as economic contexts, beliefs, attitudes, and individual differences. The discipline employs diverse methodologies, from experimentation and surveys to computational models and large-scale data analysis. In this chapter, we discuss human behaviour within complex contexts, characterised by interdependencies, adaptive behaviours, and feedback loops. These can change how behaviour unfolds qualitatively and quantitatively over time, making it hard to capture with analytic approaches. Agent-based models (ABMs) are a computational tool to simulate individual and collective behaviours within these environments. We emphasise the need for interdisciplinary collaboration and iterative model development, combining qualitative insights with quantitative validation. A case study of the POSEIDON model illustrates the application of ABMs in fisheries management, showcasing the interplay between qualitative interviews and quantitative calibration.