How simulation modelling can help reduce the impact of COVID-19

Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community

The influence of perceived uncertainty on entrepreneurial action in the transition to a low-emission energy infrastructure: The case of biomass combustion in the Netherlands

Abstract The transition towards renewable energy production will not occur without the involvement of entrepreneurs who dare to take action amidst uncertainty. In an earlier article [1], a conceptual model was introduced for analyzing how perceived uncertainties influence the decisions and actions of entrepreneurs involved in innovation projects that aimed at developing and implementing renewable energy technologies. In this article, the conceptual model is applied to stand-alone biomass combustion projects in the Netherlands. Although none of the biomass combustion projects has been abandoned, some entrepreneurs clearly have more difficulty to turn their project into a success than others. To create insight into the underlying dynamics of these projects, the article analyzes what types of positive or negative interaction patterns occur over time between (internal or external) factors in the project environment, perceived uncertainties, motivation and entrepreneurial action and how these patterns can be stimulated or prevented. The results provide several lessons to take into account when designing policies for stimulating the development and implementation of biomass combustion

Long-Term Functionality of Rural Water Services in Developing Countries: A System Dynamics Approach to Understanding the Dynamic Interaction of Causal Factors

Research has shown that sustainability of rural water infrastructure in developing countries is largely affected by the dynamic and systemic interactions of technical, social, financial, institutional, and environmental factors that can lead to premature water system failure. This research employs systems dynamic modeling, which uses feedback mechanisms to understand how these factors interact dynamically to influence long-term rural water system functionality. To do this, the research first identified and aggregated key factors from literature, then asked water sector experts to indicate the polarity and strength between factors through Delphi and cross impact survey questionnaires, and finally used system dynamics modeling to identify and prioritize feedback mechanisms. The resulting model identified 101 feedback mechanisms that were dominated primarily by three and four-factor loops that contained some combination of the factors: Water System Functionality, Community, Financial, Government, Management, and Technology. These feedback mechanisms were then scored and prioritized, with the most dominant feedback mechanism identified as Water System Functionality – Community – Finance – Management. This research offers insight into the dynamic interaction of factors impacting sustainability of rural water infrastructure through the identification of these feedback mechanisms and makes a compelling case for future research to longitudinally investigate the interaction of these factors in various contexts

Community-Centered Responses to Ebola in Urban Liberia: The View from Below

The West African Ebola epidemic has demonstrated that the existing range of medical and epidemiological responses to emerging disease outbreaks is insufficient, especially in post-conflict contexts with exceedingly poor healthcare infrastructures. This study provides baseline information on community-based epidemic control priorities and identifies innovative local strategies for containing EVD in Liberia.In this study the authors analyzed data from the 2014 Ebola outbreak in Monrovia and Montserrado County, Liberia. The data were collected for the purposes of program design and evaluation by the World Health Organization (WHO) and the Government of Liberia (GOL), in order to identify: (1) local knowledge about EVD, (2) local responses to the outbreak, and (3) community based innovations to contain the virus. At the time of data collection, the international Ebola response had little insight into how much local Liberian communities knew about Ebola, and how communities managed the epidemic when they could not get access to care due to widespread hospital and clinic closures. Methods included 15 focus group discussions with community leaders from areas with active Ebola cases. Participants were asked about best practices and what they were currently doing to manage EVD in their respective communities, with the goal of developing conceptual models of local responses informed by local narratives. Findings reveal that communities responded to the outbreak in numerous ways that both supported and discouraged formal efforts to contain the spread of the disease. This research will inform global health policy for both this, and future, epidemic and pandemic responses

Saving a Bank? Cracking the Case of the Fortis Bank?

This paper presents a simple experimental System Dynamics model of the underlying value and stock market value of a bank to analyze a loss of trust the a bank. The System Dynamics model was developed on 28 September 2008 –the day the governments of the Benelux countries met in a great hurry to rescue the Fortis bank– in order to gain a better understanding of the potential dynamics of bank crises and to test policies for keeping banks from collapsing. The System Dynamics model and an even simpler exam case based on it are interesting because of the actuality and importance of the topic, their small size and simplicity, their potential to generate different dynamic behaviors, and their usefulness for quick fostering of understanding and rough-cut policy exploration.Multi Actor SystemsTechnology, Policy and Managemen

Dealing with Multiple Perspectives: Uing (Cultural) Profiles in Systems Dynamics

Technology, Policy and Managemen

The Soft Drugs Debate in The Netherlands: A Qualitative System Dynamics Analysis

The Dutch Soft Drugs policy is currently under attack from all sides. Based on the opinions regarding the future Dutch soft drugs policy, the Dutch population and political arena could be divided into different groups: those in favour of full legalization, those in favour of more (but different types of) regulation, and those in favour of a full ban. The paper focusses on the points of view of those in favour of more regulation, since a full legalization or a full ban on soft drugs are unrealistic in the Dutch context. However, the group in favour of more regulation is also strongly divided into those in favour of more restrictive policies (compared to current policies) and those in favour of more tolerant policies (compared to current policies). The points of view of these two groups are analyzed in this paper using a qualitative System Dynamics perspective.Multi Actor SystemsTechnology, Policy and Managemen

Heart Meets Mind: Smart Transition Management to Smarten Energy Systems in a Deeply Uncertain World

Enormous future investments are needed to replace old energy systems/technologies, and prepare them for future needs. Moreover, smarter technologies/systems are needed. And in this ever more complex, interconnected, and uncertain world, smarter policymaking in the energy field is certainly needed too. After all, current energy policymaking still mainly ignores dynamic complexity under deep uncertainty. This paper illustrates two model-based approaches for supporting policymaking for complex and uncertain issues as well as their combination. First, Exploratory System Dynamics Modeling and Analysis allows exploring and analyzing millions of plausible uncertain dynamic system behaviors and testing the robustness of policies. This approach is illustrated by means of a System Dynamics simulation model related to energy grid investments. Second, Experiential Model-Based Gaming allows policymakers to experience dynamic complexity and deep uncertainty, and helps them feel the need to embrace both in policymaking. Before having experienced different plausible futures, almost all high-level managers and highly-educated students that played such experiential games applied inappropriate strategies in most plausible futures played, and hence failed in the face of uncertainty. Failing repeatedly actually prepared them for thinking outside their old/reactive/predictive modes in subsequent bounce-casting sessions. Exploratory System Dynamics Modeling and Analysis and Experiential Model-Based Gaming may also be mutually beneficial: most subjects only acknowledge the need to take uncertainty and dynamic complexity seriously into account after having participated in experience-oriented gaming sessions.Multi Actor SystemsTechnology, Policy and Managemen

Scarcity of Minerals and Metals: A Generic Exploratory System Dynamics Model

Possible short, medium and/or long term scarcity of minerals/metals may actually pose a threat to modern societies. Its potentially disruptive societal consequences qualify this issue for exploration from a world/regional security point of view. Hence, exploratory System Dynamics modelling and simulation is used in this paper to explore the dynamic complexity of potential mineral/metal scarcity under deep uncertainty and to create useful scenarios for risk management.Multi Actor SystemsTechnology, Policy and Managemen

Small System dynamics models for big issues: Triple jump towards real-world complexity

System Dynamics (SD) is a method to describe, model, simulate and analyze dynamically complex issues and/or systems in terms of the processes, information, organizational boundaries and strategies. Quantitative SD modeling, simulation and analysis facilitates the (re)design of systems and design of control structures (Wolstenholme 1990). SD is in fact the application of the principles and techniques of control systems to organizational and social-economic-environmental-. . . problems. SD starts from the assumption that the behavior of a system is largely caused by its own structure. System structure consists of physical and informational aspects as well as the policies and traditions important to the decision-making process in a system (Roberts 1988). Hence, in order to improve undesirable behaviors, the structure of the system needs to be changed. SD allows to identify desirable system changes and test them in a ‘virtual laboratory’. The SD approach was developed at the end of the nineteen-fifties and the beginning of the nineteen-sixties by Jay W. Forrester, at the Sloan School of Management of the Massachusetts Institute of Technology (Forrester 1995; Forrester 1958; Forrester 1961; Forrester 2007a; Lane 2007). He argued that the traditional methods for solving problems provided insufficient understanding of the strategic processes involved in complex systems. In his writing, Forrester scaled up from the company level in Industrial Dynamics (Forrester 1961) over the city level in Urban Dynamics (Forrester 1969) to the world level in World Dynamics (Forrester 1971). The latter work was the impetus to the well-known Limits to Growth report (Meadows et al. 1972) commissioned by the Club of Rome, and its successive updates. Beyond these important topics, SD is used today for almost any dynamically complex issue. Important application domains in SD are health policy, energy transitions and resources scarcity, environmental and ecological management, safety and security, public order and public policy, social and organizational dynamics, education and innovation, economics and finance, organizational and strategic business management, information science, and operations and supply chain management. Almost all of these domains are addressed in this e-book, but none of them deeply and broadly enough to do them right. Section 21.3 on page 276 therefore contains references to suggested reading in SD, and section 21.4 on page 277 to some good SD entries into each of these application domains.Multi Actor SystemsTechnology, Policy and Managemen