Module 3: (Social) Networks

Abstract

Module three explores network analysis methods applicable to diverse networks like social, social-ecological, and other relational networks. It introduces social network analysis (SNA), computing variables for interpretation, and understanding network structures within systems. It emphasizes relational mapping's significance in navigating complex systems.

Objective

The learning objective of this module is to explore network analysis methods and their applications in various contexts, including social networks, social-ecological networks, and other relational mappings. The module provides both a quantitative and a qualitative understanding of network structures, emphasizing their relevance to designing resilient regenerative systems (DRRS). Participants will learn how to compute variables and metrics using social network analysis (SNA) and interpret them qualitatively for specific functions such as resilience. By the end of the module, learners should be able to analyze and visualize different types of networks, understand their implications in complex systems, and apply this understanding to contribute effectively to enacting complex systems. The learning objective assessment starts in the preceding MOOC with a multiple-choice quiz. To pass, 70 percent of the questions must be answered correctly across all modules. Active attendance in the live sessions with experts is also required for each module. In addition, the Quest’s progress is monitored continuously in the peer-learning process and through individual discussions with the lecturers.

Content

The learning module on (Social) Networks extensively explores network analysis methods, offering insights into their application across various types of networks within the context of designing resilient regenerative systems (DRRS). The importance of identifying leverage points within complex systems, as espoused by Donella Meadows, is emphasized. These leverage points represent areas where interventions can have a significant impact on the system as a whole. Metaphors and visualization techniques, such as the "View from Above," are introduced as tools to gain perspective on complex systems and discern underlying structures and patterns. The module then introduces the method of social network analysis as a set of techniques applicable to diverse networks, including social networks between individuals or companies, social-ecological networks encompassing natural resources, and networks representing shared interests or concepts among people. The module emphasizes the dual nature of network analysis, comprising both quantitative and qualitative components. Quantitative methods involve computing variables and metrics using specialized computer programs. Qualitative mapping, such as visual dialogue, serves as a powerful tool for understanding the underlying reasoning and motivations within network structures. The module discusses the abstraction of networks, highlighting that any set of related objects can be represented in a network format for analytical purposes. Within the scope of DRRS, social networks are portrayed as representations of communities, groups, companies, and other entities. Social network analysis (SNA) is presented as a method to compute variables and metrics, allowing for the qualitative interpretation of resilience and other specific functions within these networks. The module adopts a functional and quantitative perspective, focusing on elements, relationships, context, and purpose to define systems within network structures. Relational mapping, particularly within social and social-ecological networks, is explored in detail, emphasizing its significance in understanding complex relationships and interactions. Additionally, the module covers techniques for analyzing and visualizing social networks, both quantitatively and qualitatively, providing learners with practical tools to explore and interpret network data effectively. Overall, the module aims to equip learners with a comprehensive understanding of social networks, network analysis methods, and their practical application in designing resilient regenerative systems, integrating quantitative and qualitative approaches for a systemic perspective on networks.

Resources