erik pruyt

Lyme disease due to infection with Lyme borreliosis poses an uncertain dynamic threat to the Dutch and their public health system. This risk was used to develop and illustrate two variants of a National Risk Assessment approaches for slumbering/latent risks. This paper explains and illustrates the System Dynamics-based variant using the societal risk posed by Lyme disease. Thousands of plausible evolutions of lyme disease are generated using a System Dynamics model in order to assess the societal risk posed by Lyme disease. The risk is scored in the Dutch National Risk Assessment framework adapted to deeply uncertain dynamically complex risks, and mapped in a new type of risk diagram developed for uncertain complex risks in order to compare the risk posed by Lyme disease to other plausible risks. Finally, scenario discovery techniques are used to identify a small set of representative scenarios that could be used in a capability analysis.

Foreign fighters, wannabe foreign fighters, and returned foreign fighters have occupied the European news bulletins for many months. Foreign fighters voluntarily join fighting parties in conflict zones, like in Syria and Iraq. Both wannabe foreign fighters and returning foreign fighters from these and other regions directly pose a substantial real threat to Western nations. And actual foreign fighters pose a direct foreign threat as well as an indirect domestic threat. In spite of the fact that the general public opinion supports the perspective that there is a domestic foreign-fighter related security problem, perspectives between and within Western nations as to the causes and solutions differ. In this paper, we present SD models that correspond to three of these perspectives. These models are simulated in view of finding robust policies, i.e. policies that work across different perspectives.

The increasing need for kidney transplants and the structural gap between kidney donation and demand is virtually a universal problem, and has been challenging policy makers in the US and worldwide for years. Although improvements for kidney procurement have been made over the years, still, on average 13 people on the waiting list die every day while awaiting a kidney transplant. At the same time, illegal kidney transplants are flourishing. In this study, an attempt was made to explore the various dynamics involved in the kidney transplant system by means of System Dynamics simulation, to identify leverage points for policy interventions and explore various policies and their effectiveness under highly uncertain conditions. Key focus was on the transplant waiting list, donor registrations, and both legal and illegal transplants performed. The model was validated in accordance with Organ Procurement and Transplantation Network Data. Simulation results showed that without intervention...

Although System Dynamics modelling is sometimes referred to as data-poor modelling, it often is –or could be– applied in a data-rich manner. However, more can be done in the era of ‘big data'. Big data refers here to situations with much more available data than was until recently manageable. The field of data science makes big(ger) data manageable. This paper provides a perspective on the future of System Dynamics with a prominent place for bigger data and data science. It discusses different approaches for dealing with bigger data. It reviews methods, techniques and tools for dealing with bigger data in System Dynamics, and sheds light on the modelling phases for which data science is most useful. Finally, it provides several examples of current applications in which big data, data science, and System Dynamics modelling and simulation are being merged.

This paper introduces a novel approach for (i) exploring the spectrum of behaviors system dynamics models could generate and (ii) identifying and selecting exemplar simulation runs that are representative in terms of their dynamics and their origin in the input space. Our overall approach consists of two phases: a first phase of iterative behavior-based sampling and simulation; and a second phase of behavior-based classification or clustering, input-space identification and selection of representative exemplars. For this approach, we introduce new ways to characterize the dynamic complexity of simulation runs, develop a new iterative output-oriented sampling approach, and combine classification, clustering, data visualization and machine learning techniques. Using two well-known system dynamics models, we show how our approach enables one to generate a wide spectrum of behaviors and group runs based on their type of behavior, and identify distinct areas in the input space based on the particular type of behavior they generate. Copyright © 2016 System Dynamics Society

In an ever more complex and uncertain world, integrated risk-capability analysis methodologies to deal with increasing degrees of complexity and deep uncertainty are needed more than ever before. Today, some governments and organizations use scenario approaches, risk assessment methods, and capability analysis methods, but few use truly integrated risk-capability approaches, and almost none use integrated risk-capability approaches that take deep uncertainty seriously into account. This paper presents and illustrates a novel integrated risk-capability analysis approach for dealing with deeply uncertain dynamically complex risks, and discusses near future developments related to integrated risk-capability analysis for such issues. It illustrates a multi-method consisting of Exploratory Modeling and Analysis, System Dynamics Modeling, and Scenario Discovery and Selection, and discusses the use of MCDA and Robust Optimization for simultaneous all-hazard capability-based planning.

Starting from the state-of-the-art and recent evolutions in the field of system dynamics modeling and simulation, this chapter sketches a plausible near term future of the broader field of systems modeling and simulation. In the near term future, different systems modeling schools are expected to further integrate and accelerate the adoption of methods and techniques from related fields like policy analysis, data science, machine learning, and computer science. The resulting future state of the art of the modeling field is illustrated by three recent pilot projects. Each of these projects required further integration of different modeling and simulation approaches and related disciplines as discussed in this chapter. These examples also illustrate which gaps need to be filled in order to meet the expectations of real decision makers facing complex uncertain issues.

In the 2014 Ebola outbreak in West Africa, sociocultural, psychological and higher-order disease-related dynamics play an important role in the speed of virus transmission. Although such effects may strongly affect outbreaks, they are often not included in transmission models. Here, we include different combinations of these effects in an extended system dynamics transmission model to generate and explore ensembles of plausible future dynamics of the Ebola outbreak and test the effectiveness of sets of policies in the presence of these effects under deep uncertainty. Accounting for these effects, it seems that policies currently being implemented to curb the ongoing Ebola epidemic are, or could be made, sufficient to curb the epidemic by early 2015 unless psychological and sociocultural effects remain adverse. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACT It has recently been argued that standard multiobjective algorithms like NSGA-II, SPEA2, and SMS-EMOA, are not well suited for solving problems with symmetries and/or multimodal single objective functions due to their concentration onto one Pareto set part. We here deliver a real-world application that shows such properties and is thus hard to solve by standard approaches. As direct tuning of the algorithms is too costly, we attempt it via constructive modeling (algorithm-based validation), but succeed only partly in improving performance, which emphasizes the need to integrate special operators for boosting decision space diversity in future algorithms. KeywordsEvolutionary multi-criterial optimization-Decision space diversity-Constructive surrogate modeling

Abstract. In an ever more complex and uncertain world, integrated risk-capability analysis methodologies to deal with increasing degrees of complexity and deep uncertainty are needed more than ever before. Today, some governments and organizations use scenario approaches, risk assessment methods, and capability analysis methods, but few use truly integrated risk-capability approaches, and almost none use integrated risk-capability approaches that take deep uncertainty seriously into account. This paper presents and illustrates a novel integrated risk-capability analysis approach for dealing with deeply uncertain dynamically complex risks, and discusses near future developments related to integrated risk-capability analysis for such issues. It illustrates a multi-method consisting of Exploratory Modeling and Analysis, System Dynamics Modeling, and Scenario Discovery and Selection, and discusses the use of MCDA and Robust Optimization for simultaneous all-hazard capability-based planning.