Social Network Analysis
Generally defined, Social Network Analysis (SNA) is the study of the structure of interaction as it occurs between persons and / or other social units. The goal of most SNA is to understand how these configurations of relationships relate to some phenomenon of interest, such as actor behaviours or attitudes. SNA uses mathematical tools like statistics, network theory, graph theory, or game theory.
SEEMI Project (March 2017 - February 2020)
In close synergy with POLISES, the SEEMI research project (Social-Ecological Effects of Microinsurance) has recently been granted funding by the German Research Foundation (DFG). With this project, we want to contribute to a much-needed enhanced understanding of the effects of microinsurance on social-ecological systems.
Specifically, we will address the following research questions:
- Under which conditions will microinsurance crowd out informal safety nets? Could this decrease communal welfare and exacerbate social inequalities?
- Under which circumstances can microinsurance act as a sensible complement to informal safety nets?
- Will land users change their land use strategies given access to microinsurance? Under which conditions will this lead to a degradation of natural resources?
- Can microinsurance schemes enhance land users’ resilience to shocks in the presence of global change processes such as climate change?
To address these questions, we will combine the approach of agent-based modelling with social network analysis to develop a stylized dynamic simulation model. The innovation of this approach is that it simultaneously takes into consideration feedbacks in the social-ecological system, interactions between actors, and changes in social structures. Our model-based analysis is guided by two case studies: (a) drought insurance in Kenya and Ethiopia and (b) health insurance in Cambodia. With these case studies, our analysis comprises shocks of different magnitude.
Thanks to our systematic approach of gradually increasing complexity, we expect an original contribution to knowledge on two levels. First, we offer process-based explanations of empirically observable patterns from the case studies; and second, we advance a thorough understanding of the effects of microinsurance at the systems level – particularly regarding the relevance of social-ecological feedbacks.