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Human behaviour is of profound significance in shaping pathways towards sustainability. Yet, the approach to understanding human behaviour in many fields remains reliant on overly simplistic models. For a better understanding of the interface between human behaviour and sustainability, we take work in behavioural economics and cognitive psychology as a starting point, but argue for an expansion of this work by adopting a more dynamic and systemic understanding of human behaviour, that is, as part of complex adaptive systems. A complex adaptive systems approach allows us to capture behaviour as ‘enculturated’ and ‘enearthed’, co-evolving with socio–cultural and biophysical contexts. Connecting human behaviour and context through a complex adaptive systems lens is critical to inform environmental governance and management for sustainability, and ultimately to better understand the dynamics of the Anthropocene itself.
Formal models are commonly used in natural resource management (NRM) to study human-environment interactions and inform policy making. In the majority of applications, human behaviour is represented by the rational actor model despite growing empirical evidence of its shortcomings in NRM contexts. While the importance of accounting for the complexity of human behaviour is increasingly recognized, its integration into formal models remains a major challenge. The challenges are multiple: i) there exist many theories scattered across the social sciences, ii) most theories cover only a certain aspect of decision-making, iii) they vary in their degree of formalization, iv) causal mechanisms are often not specified. We provide a framework- MoHuB (Modelling Human Behavior) – to facilitate a broader inclusion of theories on human decision-making in formal NRM models. It serves as a tool and common language to describe, compare and communicate alternative theories. In doing so, we not only enhance understanding of commonalities and differences between theories, but take a first step towards tackling the challenges mentioned above. This approach may enable modellers to find and formalize relevant theories, and be more explicit and inclusive about theories of human decision making in the analysis of social-ecological systems.
Chapter 21 focuses on controlled behavioural experiments, methods that randomly divide participants into different groups (treatments), controlling conditions across these treatments and allowing only the variable of interest to vary. Controlled behavioural experiments are used to test the conditions under which we can expect collective action to emerge. The chapter discusses controlled behavioural experiments in the form of common-pool resource games and public good games. It goes on to discuss the types of social-ecological systems (SES) problems and research questions commonly addressed by this set of methods, as well as their limitations, resource implications and new emerging research directions. The chapter also includes an in-depth case study showcasing the application of controlled behavioural experiments, and suggested further readings on these methods.