This review concerns the methodological challenges that industrial ecology faces in integrating natural and social sciences. Network analysis can be seen as the most promising method to mediate between industrial ecology's overall systems approach and the complex structures found in society. It is a well established method across scientific disciplines, including the social sciences. It has been successfully applied in industrial ecology, in which localized phenomena of industrial symbiosis have been a key focus, and where metrics from both the social and natural sciences are used to understand socio-metabolic structures. In this paper we classify such studies as Social-Material Network Analyses and we discuss the body of work, drawing on network analyses from various disciplines. A challenge is the hierarchical nature of industrial networks and how it can be addressed socially. We discuss the opportunities and limitations of metric-driven network analysis and offer a review of methodological options for Social-Material Network Analyses

Fuzzy Cognitive Mapping (FCM) is a widely used participatory modelling methodology in which stakeholders collaboratively develop a ‘cognitive map’ (a weighted, directed graph), representing the perceived causal structure of their system. This can be directly transformed by a workshop facilitator into simple mathematical models to be interrogated by participants by the end of the session. Such simple models provide thinking tools which can be used for discussion and exploration of complex issues, as well as sense checking the implications of suggested causal links. They increase stakeholder motivation and understanding of whole systems approaches, but cannot be separated from an intersubjective participatory context. Standard FCM methodologies make simplifying assumptions, which may strongly influence results, presenting particular challenges and opportunities. We report on a participatory process, involving local companies and organisations, focussing on the development of a bio-based economy in the Humber region. The initial cognitive map generated consisted of factors considered key for the development of the regional bio-based economy and their directional, weighted, causal interconnections. A verification and scenario generation procedure, to check the structure of the map and suggest modifications, was carried out with a second session. Participants agreed on updates to the original map and described two alternate potential causal structures. In a novel analysis all map structures were tested using two standard methodologies usually used independently: linear and sigmoidal FCMs, demonstrating some significantly different results alongside some broad similarities. We suggest a development of FCM methodology involving a sensitivity analysis with different mappings and discuss the use of this technique in the context of our case study. Using the results and analysis of our process, we discuss the limitations and benefits of the FCM methodology in this case and in general. We conclude by proposing an extended FCM methodology, including multiple functional mappings within one participant-constructed graph.

Enterprise risk management (ERM) was originally developed to manage financial risks and was later transferred to other businesses, sectors and, crucially, government. ERM aims at a maximum of comprehensiveness suggesting the integration of all risks to an organisation’s objective in a portfolio to inform organisational strategy. However, the concept suffers from unknown interdependencies between risks, implementation strategies that lack empirical validation and ambivalences and uncertainties arising from their management. It is only weakly rooted in organisational theory. Drawing on knowledge generation, theory key aspects for the empirical study of risk management in organisations are identified. These address the commensuration of risks, the comprehensiveness of the risk portfolio and the communication of explicit and tacit knowledge enabling organisational learning processes in different institutional contexts.