We combine empirical research with modeling in various ways:
Co-developing models of SES to facilitate integration of empirical social and natural science findings
Modelling can be a useful method to integrate understanding about social and ecological dimensions of a SES phenomena into a conceptual model representing key processes that may have caused the phenomenon. We, for instance, developed a generalized model of the Baltic Sea Cod collapse to analyze the importance of social processes for the collapse together with SRC colleagues that have studied the Baltic social and ecological systems for many years. We jointly developed a causal loop diagram representing the experts’ hypotheses about important social and ecological processes and then formalized it in a generalized model to perform stability analyses (Lade et al. 2015). The co-development of the conceptual models allowed us to make explicit, discuss and scrutinize the assumptions about processes influencing the cod collapse and thus develop a empirically well-grounded model.
Using agent-based modeling to find micro-level explanations of empirical SES phenomena
Agent-based modeling is a suitable tool to test possible explanations for observed SES phenomena. Together with colleagues from the Beijer Institute of Ecological Economics, we for example wanted to understand why cooperative groups in behavioral economic experiments often over- or under-harvest (it is often assumed that cooperative groups harvest sustainably). We developed a possible explanation of this finding based on observations and data from the experiment that we then implemented in an agent-based model. We analyzed the model with respect to whether and under which conditions it reproduces the empirical patterns in order to validate our explanation. The validated model was then used to test scenarios that cannot be explored with real participants (Schill et al. 2016). With SMILI we use an agent-based model to identify micro- level explanations of the emergence of cooperative versus non-cooperative forms of self-governance in small-scale fisheries.
Exploring the consequences of empirical social-ecological complexities
We also combine modeling with empirical research to explore the consequences of real-world complexities such as the diversity of human behavior or the relationships between human activities and environmental degradation. Human behavior, for instance, is more diverse and complex than normally represented in dynamic models (which often rely on the rational actor model). We implemented empirical insights about different fishing styles in the Baltic Sea to explore their consequences for sustainable fisheries (FiBe). We also explored different assumptions about financial, natural and cultural aspects and their interactions in causing poverty traps or poverty alleviation (PovertyTraps).
In general, the combination of empirical research with modeling in an iterative processes is useful to improve the realism of formal models of social-ecological systems, develop and test realistic explanations of SES phenomena and enhance understanding of the implications of social-ecological interactions. At the same time the process of model development raises many new questions and insights to be followed up in empirical research. In most cases we start with an empirical phenomenon and a social-ecological system that is well-researched from the social and natural science dimensions and formalize this understanding in a model, however, it can also be useful to start from the theoretical side.