Co-Evolutionary Method For Modelling Large Scale Socio-Technical Systems Evolution

Exactly predicting the future of an evolving large scale socio-technical system is impossible. Yet, if we are to sustainably manage the industrial and infrastructure systems our society depends on, we must understand how the actions we take today will affect the evolution of these systems. Simulating how the social and technical networks co-evolve over time allows us to explore possible system futures. This knowledge can help today’s decision makers to steer the system away from undesirable evolutionary pathways. Creating models that capture the complexity of socio-technical systems co-evolution requires multiple formalisms to be encoded in a modeling framework that itself evolves. This thesis presents a method for creating Agent Based Models that suitably represent complex evolving systems. The method involves a co-evolution between the technical aspects of model development, the social process involving the stakeholders, the collection of relevant domain knowledge and the encoding of facts. Through seven case studies the method is demonstrated to yield subsequent generations of richer and ever more useful simulation models.Section Energy and IndustryTechnology, Policy and Managemen

On the development of Agent-Based Models for infrastructure evolution

Infrastructure systems for energy, water, transport, information etc. are large scale socio-technical systems that are critical for achieving a sustainable world. They were not created at the current global scale at once, but have slowly evolved from simple local systems, through many social and technical decisions. If we are to understand them and manage them sustainably, we need to capture their full diversity and adaptivity in models that respect Ashby's law of requisite variety. Models of evolving complex systems must themselves be evolving complex systems that can not be created from scratch but must be grown from simple to complex. This paper presents a socio-technical evolutionary modeling process for creating evolving, complex agent based models for understanding the evolution of large scale socio-technical systems such as infrastructures. It involves the continuous co-evolution and improvement of a social process for model specification, the technical design of a modular simulation engine, the encoding of formalized knowledge and collection of relevant facts. In the paper we introduce the process design, the requirements for guiding the evolution of the modeling process and illustrate the process for Agent Based Model development by showing a series of ever more complex models.Section Energy and IndustryTechnology, Policy and Managemen

Infrastructure network design with a multi-model approach: Comparing geometric graph theory with an agent-based implementation of an ant colony optimization

Network infrastructures, such as roads, pipelines or the power grid face a multitude of challenges, from organizational and use changes, to climate change and resource scarcity. These challenges require the adaptation of existing infrastructures or their complete new development. Traditionally, infrastructure planning and routing issues are solved through top-down optimization strategies such as mixed integer non linear programming or graph approaches, or through bottom up approaches such as particle swarm optimizations or ant colony optimizations. While some integrated approaches have been proposed int he literature, no direct comparison of the two approaches as applied to the same problem have been reported. Therefore, we implement two routing algorithms to connect a single source node to multiple consuming nodes in a topology with hard boundaries and no-go areas. We compare a geometric graph algorithm finding an (sub)optimum edge-weighted Steiner minimal tree with a Ant Colony Optimization algorithm implemented as an Agent Based Model. Experimenting with 100 randomly generated routing problems, we find that both algorithms perform surprisingly similar in terms of topology, cost and computational performance. We also discovered that by approaching the problem from both top-down and bottom-up perspective, we were able to enrich both algorithms in a co-evolutionary fashion. Our main findings are that the two algorithms, as currently implemented in our test environment hardly differ in the quality of solution and computational performance. There are however significant differences in ease of problem encoding and future extensibility.Energy & IndustrySystem Engineerin

It’s in the social network: The Social Neighbourhood model to unravel local social structures for liveable and safe neighbourhoods

Fast growth of cities decreases the quality of life in these places. In response, Municipalities install policies aiming to improve local livability. While literature suggests social structures to have a defining impact on policy effectiveness, current evaluation metrics are not able to take this into account. This paper presents the Social Neighbourhood model, an agent-based model used to simulate and explore how livability changes in a neighbourhood given various social structures and policies. The model is applied to a neighbourhood in The Hague, Netherlands. The main result of the modelling experiments is that social structures have a very strong influence on whether or not a policy to improve livability is effective. Three hypotheses, concerning this relationship between social structures, livability, and policy interventions are drawn up as a starting point for future research.System Engineerin

Diffusion: Key to Horticulture Innovation Systems

Horticulture, a pillar of the Dutch economy, has already achieved remarkable productivity increases through the use of natural gas for heating, lighting and CO2. Further innovative technologies that could aid the transition toward sustainable energy use, including heat/cold storage and deepgeothermal heat sources, are currently in development and spreading. However, there is a need to better understand the processes of technology diffusion in this industrial cluster to help stakeholders retain their competitive advantage and establish the best way to influence the energy future in the region and in the sector. This presentation discusses the experimental results of a series of agent based models of the greenhouse horticulture sector in the Netherlands, simulating the technological innovation decisions of greenhouse growers. Surveys of greenhouse growers suggest that innovation decisions are made on the basis of personal experience and information shared from other growers. In the model, each greenhouse grower must learn how to operate a greenhouse by evaluating their repertoire of technologies, exchanging information with other growers about their technological evaluations and purchasing new technologies to augment, expand or replace the existing selection. The interactions of greenhouse growers and the flow of information between them lead to emergent patterns, including diversity, adaption and complexity, in the diffusion of technologies throughout the community. These emergent patterns of diffusion indicate that technological innovations develop and spread according to evolutionary mechanisms, suggesting that influencing, supporting or advocating the diffusion of sustainable technologies in this sector must also follow evolutionary mechanisms. As an evolving system, the reality of technology, innovation and transitions may require new approaches to management that work with, rather than against, the properties of evolving systems. Survey results, horticulture cluster background, model design and simulation results will be presented and implications for regional industrial management are discussed.Infrastructures, Systems and ServicesTechnology, Policy and Managemen

Investment in the future electricity system - An agent-based modelling approach

Now that renewable technologies are both technically and commercially mature, the imperfect rational behaviour of investors becomes a critical factor in the future success of the energy transition. Here, we take an agent-based approach to model investor decision making in the electricity sector by modelling investors as actors with different (heterogeneous) anticipations of the future. With only a limited set of assumptions, this generic model replicates the dynamics of the liberalised electricity market of the last decades and points out dynamics that are to be expected as the energy transition progresses. Importantly, these dynamics are emergent properties of the evolving electricity system resulting from actor (investor) behaviour. We have experimented with varying carbon price scenarios and find that incorporating heterogeneous investor behaviour results in a large bandwidth of possible transition pathways, and that the depth of renewables penetration is correlated with the variability of their power generation pattern. Furthermore, a counter-intuitive trend was observed, namely that average profits of investors are seen to increase with carbon prices. These results are a vivid and generic illustration that outcome-based policy cannot be solely based on market instruments that rely on perfect rational and perfectly informed agents.Energy & Industr

Agent-Based Modelling of the Social Dynamics of Energy End-Use

Agent-based modelling and simulation is now accepted as a valuable tool for describing the dynamics of complex socio-technical systems, and for studying complex adaptive systems. This chapter surveys the opportunities and challenges in agent-based models (ABM) for the social dynamics of energy end use. Following an introduction to the methodology of ABM, we discuss the ability of ABM to capture heterogeneous, boundedly rational and imperfectly informed behavioural factors at the individual, household, and neighbourhood levels, including the usage of micro-generation and local storage. We review three archetypal case studies of ABM: energy efficiency in domestic heating, electric vehicle adoption, and energy management in smart grids. We conclude with directions for the future, pointing out the importance of ABM in the context of multidisciplinary studies of energy behaviours.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Energy & IndustrySystem EngineeringAlgorithmic

Modelling social learning during participatory modeling processes

System EngineeringOrganisation and Governanc

Modeling with stakeholders for transformative change

Coherent responses to important problems such as climate change require involving a multitude of stakeholders in a transformative process leading to development of policy pathways. The process of coming to an agreement on policy pathways requires critical reflection on underlying system conceptualizations and commitment to building capacity in all stakeholders engaged in a social learning process. Simulation models can support such processes by providing a boundary object or negotiating artifact that allows stakeholders to deliberate through a multi-interpretable, consistent, transparent, and verifiable representation of reality. The challenge is how to structure the transdisciplinary process of involving stakeholders in simulation modeling and how to know when such a process can be labeled as transformative. There is a proliferation of approaches for this across disciplines, of which this article identifies Group Model Building, Companion Modeling, Challenge-and-Reconstruct Learning, and generic environmental modeling as the most prominent. This article systematically reviews relevant theories, terminology, principles, and methodologies across these four approaches to build a framework that can facilitate further learning. The article also provides a typology of approaches to modeling with stakeholders. It distinguishes transformative approaches that involve stakeholders from representative, instrumental and nominal forms. It is based on an extensive literature review, supported by twenty-three semi-structured interviews with participatory and non-participatory modelers. The article brings order into the abundance of conceptions of transformation, the role of simulation models in transformative change processes, the role of participation of stakeholders, and what type of approaches to modeling with stakeholders are befitting in the development of policy pathways.Energy & IndustryPolicy Analysi

Participatory multi-modelling as the creation of a boundary object ecology: the case of future energy infrastructures in the Rotterdam Port Industrial Cluster

Finding leverage points for sustainability transformation of industrial and infrastructure systems is challenging, given that transformation is emergent from the complex interactions among socio-technical system elements over time within a specific social, technical and geographical context. Participatory multi-modelling, in which modellers and stakeholders collaborate to develop multiple interacting models to support a shared understanding of systems, is a promising approach to support sustainability transformations. The participatory process of modeling can serve as a leverage point by facilitating social learning amongst stakeholders, in which models can function as boundary objects that facilitate dialogue between stakeholders from different social worlds. We propose that participatory multi-modeling allows for the creation of a boundary object ecology, which involves a set of interacting and co-evolving boundary objects emerging throughout the modeling process. To explore this, we analyse the participatory multi-modelling process in the Windmaster project in the Rotterdam Port industrial cluster to understand which design choices were key to the creation of boundary objects. Our analysis shows that two types of design choices were key: design choices that enabled translations between participants, and those between participants and their organisation. We conclude that conceptualising participatory multi-modelling as a process of an evolving boundary object ecology, creating and adapting multiple interacting boundary objects provides a novel perspective that is useful for analysis and design of future participatory multi-modeling processes.System EngineeringPolicy AnalysisEnergy & Industr