As energy systems grow more complex, modeling efforts spanning multiple scales, disciplines and perspectives are essential. Improved methods are needed to guide the development of not just individual models, but also multi-model ecologies - systems of interacting models. Currently there is a lack of knowledge concerning how multi-model ecologies can and should be designed to facilitate adequate understanding of energy system complexity and its consequences. Via an analysis of twelve multi-model initiatives both within and outside the energy domain, this paper elucidates possible design patterns and development paths for multi-model ecologies.The results highlight two broad paths to developing energy system multi-model ecologies, one prioritizing interoperability and the other prioritizing diversity. The former path facilitates the efficient development of models spanning multiple scales and (to a degree) disciplines, and can ease systematic testing of assumptions. The latter is suited to bridging traditional disciplines and perspectives and advancing knowledge within the interstices of different knowledge communities. It is furthermore suggested that a combination of diversity, connectivity and hierarchy in multi-model ecology composition is central to enabling the development of complex webs of models capable of addressing the complexity of real-world energy systems.

@en

The modern world is dependent on complex evolving networks of production chains that convert raw resources into the products and services required for the functioning of society. Dealing with issues of the environmental sustainability of these systems can be difficult due to their large scale and dynamic nature. Understanding these systems requires large amounts of diverse data and proper information management techniques to help make sense of them. We propose a way forward based on Semantic Web standards, and illustrate the use of Semantic Wiki software to help facilitate a collaborative community process of gathering and managing this data, and enablingtransparent peer review of it. This type of system could greatly enablethe use of tools such as Life Cycle Analysis, which have very large data demands and have struggled to build larger databases, yet still maintain quality of the data.@en

Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation-based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent-based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models¿an agent-based model and a system dynamics model¿both addressing the following research question: What conditions foster the development of a closed-loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent-based modeling (ABM) and equation-based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.@en