Igor Nikolic

Dematerialization: Not Just a Matter of Weight

This article contains the results of a study performed to support the Dutch environmental policy of dematerialization. The aim of the study was to develop and apply a methodology to identify the materials that contribute most to the environmental problems in the Netherlands. The developed methodology combines aspects of material flow accounting (MFA) and life‐cycle assessment (LCA) and aims at adding a set of environmental weights to the flows of the materials. The methodology was applied to a number of materials. For these materials, impacts per kilogram were extracted from a standard LCA database in combination with standard LCA software. These impacts per kilogram are then multiplied with the yearly throughput of each material in the Netherlands to obtain an indication of the environmental impacts associated with each material. This article contains a discussion of dematerialization as background for the research, a description of the methodology followed by the results of its application, and a discussion of the comparison of impact‐based versus mass‐based indicators. Materials vary many orders of magnitude in their impacts per unit mass. In general, the impact per unit of mass of bulk materials is lower than that of materials used in small quantities. This implies that the variation in orders of magnitude of impact multiplied by mass is much less than either mass or impact per kilogram separately. High‐priority materials based on impact multiplied by mass are either small‐quantity materials with very high impacts per kilogram (such as heavy metals) or large‐quantity materials with not‐so‐low impacts per kilogram (such as materials from agriculture and plastics).

Agent-Based Modelling of Socio-Technical Systems

Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at TU Delft and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.

Industrial Ecology 2.0

Industrial ecology (IE) is an ambitious field of study where we seek to understand systems using a wide perspective ranging from the scale of molecules to that of the planet. Achieving such a holistic view is challenging and requires collecting, processing, curating, and sharing immense amounts of data and knowledge. We are not capable of fully achieving this due to the current state of tools used in IE and current community practices. Although we deal with a vastly interconnected world, we are not so good at efficiently interconnecting what we learn about it. This is not a problem unique to IE, and other fields have begun to use tools supported by the World Wide Web to meet these challenges. We discuss these sets of tools and illustrate how community driven data collection, processing, curation, and sharing is allowing people to achieve more than ever before. In particular, we discuss standards that have been created to allow for interlinking of data dispersed across multiple Web sites. This is currently visible in the Linking Open Data initiative, which among others contains interlinked datasets from the U.S. and U.K. governments, biology databases, and Wikipedia. Since the types of technologies and standards involved are outside the normal scope of work by many industrial ecologists, we attempt to explain the relevance, implications, and benefits through a discussion of many real examples currently on the Web. From these, we discuss several best practices, which can be enabling factors for how IE and the community can more efficiently and effectively meet its ambitions - an agenda for Industrial Ecology 2.0.

Modeling Metal Flow Systems

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.

Critical infrastructures: a review from a complex adaptive systems perspective

Critical infrastructures are complex systems that may be studied in large interdisciplinary teams. Interaction between researchers in these interdisciplinary teams can suffer from the lack of a shared view on the object of study or the meaning of words. To facilitate the understanding of complex infrastructure systems, we reviewed literature on complex systems from different fields. We created a three-layered framework that clarifies the relationships between the properties and the different levels of complex systems. We illustrated our framework with an example of a transportation system that shows how this complex system may be understood in terms of the framework.

A method for developing agent-based models of socio-technical systems

Agent-based modeling is one of the popular tools for analyzing complex socio-technical systems. Because of the complex nature of such systems a systematic methodology is required to guide the modeling process. By studying the existing methodologies in MAS we distinguished four major differences between MAS and ABM regarding goals, system scale and diversity, level of system understanding and verification and validation concerns. In this paper we take these differences into account and based on more than 25 case studies, we present a methodological framework for developing agent-based models that consists of five general iterative phases. These phases namely: system analysis, model design, detailed design, implementation and evaluation further consists of smaller step that are also addressed in this paper. This methodology provides a tool independent template while respecting the specific requirements for ABM.

Assessing business continuity risks in IT

Over the past years, IT has grown in importance in business operations. For most companies it has become vital for their functioning, with large losses and the potential for default if the systems fail. This growth was not precisely planned, it occurred in an organic fashion; IT systems have evolved. Because of this evolutionary process, companies might not be aware of the risks that are hidden in their IT systems. This paper presents a model based approach to quantify the risk. This approach builds on the risk process, which sees risks as a chain of causes and effects. For IT systems the phases are causes, resources, processes and consequences. The model allows for large numbers of items. However, for business purposes it is important to keep the numbers low. The approach also allows for ranking within the categories. The approach was applied in Essent Netwerk, a large Dutch energy network manager. The application of the approach led to a review of the current IT fallback scenarios.

Facilitating Interdisciplinary Modelling of Complex Problems

In this paper the system-decomposition method is presented, which facilitates the process of modeling of complex systems by multidisciplinary teams. Such modeling is non-trivial, because these teams must codify knowledge from different scientific disciplines in order to create models that exhibit realistic behaviour. The system-decomposition method has been developed to facilitate the creation of a standard interface for and between researchers, model parts find computer model, code. The method was applied to three different complex system modeling efforts. The remits demonstrate that It reduces modeling costs, improves model quality and enables the integration of knowledge in multidisciplinary teams, the models they produce and the actual written computer code

New Methods for Analysis of Systems-of-Systems and Policy: The Power of Systems Theory,Crowd Sourcing and Data Management

Our world is a complex socio-technical system-of-systems (Chappin & Dijkema, 2007; Nikolic, 2009). Embedded within the geological, chemical and biological planetary context, the physical infrastructures, such as power grids or transport networks span the globe with energy and material flows. Social networks in the form of global commerce and the Internet blanket the planet in information flows. While parts of these global social and technical systems have been consciously engineered and managed, the overall system-of-systems (SoS) is emergent: it has no central coordinator or manager. The emergence of this socio-technical SoS has not been without consequences: the human species is currently facing a series of global challenges, such as resource depletion, environmental pollution and climate change. Tackling these issues requires active policy and management of those socio-technical SoS. But how are we to design policies if policy makers and managers have a limited span of control over small parts of the global system of systems?

General Methodology for Action-Oriented Industrial Ecology Complex Systems Approach Applied to the Rotterdam Industrial Cluster

A new approach for the understanding and shaping of the evolution of large scale socio-technical systems is presented. A proof-of-concept knowledge application has been developed, based on the industrial Rotterdam-Rijnmond case. The knowledge application includes the design of a model of industry-infrastructure evolution. Such networks are modeled via a system decomposition, formalization in an ontology and implementation of an agent based model. In simulation runs several network metrics are presented. The results provide insights in real world system behavior and show the validity and potential of the approach