Complexity

This chapter presents prevailing features of complex systems, including heterogeneous subsystems and/or autonomous entities that are subject to links featuring nonlinear relationships and multiple interactions (feedback loops in particular) among them. As a result, many complexity features may arise, leading to path-dependence, self-organization, contingency, multifinality, equifinality, and difficulty of prediction. When aiming to understand and envision social or social-ecological systems, modeling human decision and behavior remains a hot topic and big challenge despite many advances in this regard. To handle such challenges, agent-based models are very powerful due to their unique power. Building on detailed survey data from Fanjingshan National Nature Reserve (FNNR), China, this chapters shows how complexity features can be captured and addressed, where a payments for ecosystem services program have affected - and been affected by - household demography and outmigration decisions, local people's livelihood strategies, the environment, and their complex linkages over time and space. The development and use of an agent-based model is presented as an example to show how the local social system (people and households), the environmental system (forest cover and wildlife occupancy), and their interactions are represented, verified and validated, and simulated. The ABM is then employed to explore how and in what ways emergence may arise. The chapter offers several "best practices" for complexity scientists and ABM modelers, popular toolkits and platforms for ABM novice, and impending tasks that need to be addressed for better untangling complexity in complex human-environment systems. This chapter concludes by pointing out new opportunities arising from data science and artificial intelligence.