Valuing urban natural capital for health and resilience: A stakeholder-informed system dynamics model for Dublin

Natural capital plays a central role in urban functioning, reducing flooding, mitigating urban heat island effects, reducing air pollution, and improving urban biodiversity through provision of habitat space. There is also evidence on the role played by blue and green space in improving physical and mental health, reducing the burden on the health care service. Yet from an urban planning and development view, natural capital may be considered a nice to have, but not essential element of urban design; taking up valuable space which could otherwise be used for traditional built environment uses. While urban natural capital is largely recognised as a positive element, its benefits are difficult to measure both in space and time, making its inclusion in urban (re)development difficult to justify. Here, using a London case study and information provided by key stakeholders, we present a system dynamics (SD) modelling framework to assess the natural capital performance of development and aid design evaluation. A headline indicator: Natural Space Performance, is used to evaluate the capacity of natural space to provide ecosystem services, providing a semi-quantitative measure of system wide impacts of change within a combined natural, built and social system. We demonstrate the capacity of the model to explore how combined or individual changes in development design can affect natural capital and the provision of ecosystem services, for example, biodiversity or flood risk. By evaluating natural capital and ecosystem services over time, greater justification for their inclusion in planning and development can be derived, providing support for increased blue and green space within cities, improving urban sustainability and enhancing quality of life. Furthermore, the application of a SD approach captures key interactions between variables over time, showing system evolution while highlighting intervention opportunities.

System dynamics perspectives and modeling opportunities for research in operations management

168 Impact of Connecticut’s Good Samaritan Laws in Preventing Opioid Overdose Deaths – An Applied System Dynamics Approach

Urbanization is rapidly emerging as one of the most potent forces to shape the global environment. There is already immense pressure on urban natural capital; local biodiversity has been isolated and overshadowed by urban sprawl. The irreversible loss of native biodiversity has an impact on multi-scale ecosystem functioning. It has become critical to reconcile these issues to ensure that urban development is sustainable and, in fact, betters the quality of life. This essay explores the interactions between urban biodiversity, natural capital, and people in urban human ecosystems. A strong scientifi c basis through continual mapping, monitoring, and assessment is required to communicate the implicit connection between biodiversity and the quality of life to the public and policymakers. Grassroots innovation, public participation, and local governance are critical to maintaining urban biodiversity. A natural capital and ecosystem approach are strongly recommended to ensure that urbanization becomes more sustainable and cities are able to reduce their ecological footprint.

Purpose Both system dynamics (SD) and agent-based modeling (ABM) have been used in simulation-based group dynamics research. To combine the advantages of both simulation approaches, the concept of SD-ABM hybrid simulation has been proposed. However, research efforts to compare the effectiveness of modeling approaches between the hybrid and non-hybrid models in the context of group dynamics study are rare. Against this background, this study aims to propose an agent-embedded SD (aeSD) modeling approach and demonstrate its advantages when compared to pure SD or ABM modeling approaches, based on a research case on construction workers’ social absenteeism. Design/methodology/approach The authors introduce an aeSD modeling approach to incorporate individual attributes and interactions among individuals in an SD model. An aeSD model is developed to replicate the behavior of an agent-based model previously developed by the authors to study construction workers’ group behavior regarding absenteeism. Then, the characteristics of the aeSD model in comparison with a pure ABM or SD model are demonstrated through various simulation experiments. Findings It is demonstrated that an aeSD model can capture the diversity of individuals and simulate emergent system behaviors arising from interactions among heterogeneous agents while holding the strengths of an SD model in identifying causal feedback loops and policy testing. Specifically, the effectiveness of the aeSD approach in policy testing is demonstrated through examples of simulation experiments designed to test various group-level and individual-level interventions to control social absence behavior of workers (e.g. changing work groupings, influencing workgroup networks and communication channels) under the consideration of the context of construction projects. Originality/value The proposed aeSD modeling method is a novel approach to how individual attributes of agents can be modeled into an SD model. Such an embedding-based approach is distinguished from the previous communication-based hybrid simulation approaches. The demonstration example presented in the paper shows that the aeSD modeling approach has advantages in studying group dynamic behavior, especially when the modeling of the interactions and networks between individuals is needed within an SD structure. The simulation experiments conducted in this study demonstrate the characteristics of the aeSD approach distinguishable from both ABM and SD. Based on the results, it is argued that the aeSD modeling approach would be useful in studying construction workers’ social behavior and investigating worker policies through computer simulation.

Energy transitions are a matter of competitions between multiple emerging systems and a dominant, established system. Understanding the complex dynamics of these interactions can assist better-informed decision making and policy interventions. This paper presents a coupled modelling-narrative approach, consisting of a System Dynamics (SD) model interlinked with a narrative transitions-theoretical framework. The approach is geared at understanding the dynamics of emerging on-grid electricity sources, such as renewables, in power sector transitions.\nThe value of implementing such a coupled approach is twofold. Firstly, it empowers the SD modelling process. As SD modelling itself is agnostic to the conceptualisation of the (societal) system under study, it is left to the modeller to design an appropriate SD structure - i.e. Causal Loop Diagram. The approach presented in this paper provides a narrative theoretical framework based on the state-of-the-art of Sustainability Transitions literature and a generic SD model (applicable to similar energy transition cases) which directly translates the key concepts and dynamical hypotheses. The theoretical framework enables the creation of highly structured narratives that not only provide a clear overview of the case, but also assist the identification of case specific boundary conditions, parameters, feedback loops and therefore in setting up and validating the SD model. Secondly, the close connection between the narrative theoretical framework and the SD model enables considerable explanatory power that cannot be obtained from simply using a model or a narrative. Where the narrative case description, for example, outlines the developments following a certain policy intervention, the SD model allows interrogating the detailed interactions of the chain of causes and consequences following the intervention. SD models are able to represent and reproduce complex causal relations including feedbacks, non-linearity, threshold effects and time delays - dynamics which are impracticable to analyse with human mental models alone.\nThis paper presents how the SD model is structured based on the core concepts of the narrative theoretical framework. Examples from an existing application by the authors of the framework on the case of the emergence of on-grid solar electricity in India are used to illustrate how the coupling of the SD model with the narrative theory helps addressing questions going beyond modelling or narrative analysis in isolation.

The purpose of this chapter is to develop project system dynamic (SD) models that mirror non-linear Monte Carlo N-SCRA models of project Zemblanity. Only schedule part of risk exposure is considered. Required recalculations of parameters is undertaken. As these are no one-to-one relations between the parameters of the SD and Monte Carlo models, required assumptions are applied. These can be used for mutual calibrations of the two types of models. Two SD models are built that reflect on the project risk exposure before and after risk interaction addressing. Limitations of the project SD modelling are revealed. The SD modelling results demonstrate a good alignment of corresponding non-linear schedule and cost risk analysis (N-SCRA) and SD models. One additional SD model is built to explicitly demonstrate a contribution of risk compounding to overall project duration. The three workable SD models are available on the book’s companion website.

Management of freshwater supply and demand systems in coastal areas faces many challenges given the high levels of uncertainty and complexity which follow from dynamic interactions and feedbacks amongst multiple climatic and non-climatic drivers such as sea level rise, changes in precipitation and river flows, and socio-economic development. Temporal and spatial variation among these driving factors further contributes to the highly complex management challenge. These issues are prevalent in coastal areas of developing countries which typically experience high rates of population growth and urbanization. To help inform management of coastal freshwater systems under conditions of high uncertainty and complexity, this thesis developed a coupled top-down and bottom-up modelling framework for a case study setting in close consultation with local stakeholders. A system dynamics (SD) model was applied as a top-down approach to assess the vulnerability of the system under climatic and non-climatic changes, and a Bayesian decision network (BDN) model was employed as a bottom-up approach to identify cost-effective adaptation options in the face of the same climatic and non-climatic changes. This decision-making framework was developed with an understanding of the strengths and weaknesses of top-down and bottom-up approaches, and SD and BDN models as well as in the light of the dynamics and uncertainties inherent in coastal freshwater supply and demand systems. A global systematic quantitative literature review found that Bayesian networks (BNs) have rarely been coupled with SD models in water resource management, and also that BNs have rarely been applied to prioritize cost-effective adaptation measures for managing water supply and water demand under climate change in developing countries and tropical regions. Equally importantly, the literature view found that only in very few instances has the performance of BN models been tested against other modelling approaches for cross-examining model types and outputs. The freshwater supply and demand system in the Da Do Basin in Hai Phong City, Vietnam was used as a case study in this thesis to develop the coupled top-down and bottom-up modelling framework. In addition to historical data collection, causal loop diagrams (CLDs) for the system were constructed during workshops with local stakeholders to better understand how interactions among climatic and non-climatic drivers affect system operation. Stakeholder consultations at these workshops were also used to identify key climatic and non-climatic drivers for inclusion in SD and BDN models of the system, and to select a short list of potential adaptation options to counteract adverse changes in these key drivers. The SD model was developed, calibrated and tested using historical data and stakeholder knowledge. SD simulations indicated that freshwater availability is sufficient to meet existing domestic, industrial and agricultural demands during the six-month dry season under representative current conditions, but that freshwater availability could collapse under some plausible future scenarios. Upstream flow decline was identified as the strongest threat to the system‘s vulnerability, with the consequent reduction in river water level and increase in salinity level severely restricting opening hours for the sluice gates which supply freshwater to the system. The BDN model was developed in close consultation with stakeholders to identify cost-effective adaptation options to counteract climatic and non-climatic changes in key drivers. The BDN model indicated that the cost-effectiveness of adaptation options differed depending on which future scenarios were considered. Building pumping stations individually, or in conjunction with increasing water prices, were identified as the most cost-effective adaptation options to counteract climatic and non-climatic changes in combination. Subsequent simulation of these options in the SD model showed that they should be effective and robust in increasing water availability and recovering system collapse during the dry season. The ultimate objective of this coupled top-down SD and bottom-up BDN modelling approach was to provide a learning tool for stakeholders to assess system vulnerability and identify appropriate adaptation options for this complex coastal freshwater supply and demand system subject to multiple threats. Subsequent applications of this approach are likely to be highly relevant for water resource management in other basins in Hai Phong City, as well as in urban estuarine settings elsewhere in the developing and developed world.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System Dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. SD methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity and focuses on feedback processes arising from endogenous system elements. A variety of data sources may be used in SD model development, and parametric sensitivity analysis with SD models can determine priority information needs in feedback-rich systems when data are lacking. Although numerous applications of SD modeling to agriculture exist, the approach is underutilized as a useful tool for research, instruction and programmatic development. This presentation highlights key elements of SD modeling using two examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model, and illustrates the benefits of the SD approach in animal nutrition research and for farm-level nutritional management decisions. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The presentation concludes with recommendations to increase awareness and training is SD methods to enhance its appropriate use in research and instruction.

Objectives. When creating models of system dynamics, the basic construct at the design stage is the representation of the process under study in terms of a causal relationship consisting of a positive feedback loop and a negative feedback loop. The construction of a model of a dynamic environment can experience a number of difficulties in using feedback. This work shows the possibility of designing modules of system dynamics for decision-making systems based on the situational-activity approach. The study proposes the gap in knowledge about models of system dynamics to be filled with a conceptual model of an act of activity, by means of which an expert system can be implemented based on production rules. In this context, conceptual models are applied to human reasoning with reference to certain types of activity. The objective of the study was to investigate the possibility of applying the situational-active approach to designing models of system dynamics of infectious diseases based on particular representations of the conceptual structure of the act of activity.Methods. By synthesizing Bolotova's situational algorithm and Shchedrovitskiy's system-activity approach, the conceptual structure of the act of activity is presented as a methodology of the situational-activity approach. The analysis of this structure leads to the construction of a plan of processual structure and a plan of analytical relationships. The article proposed a hypothesis that the process representations describe the notation of flows and levels, and the analytical relationships implement differential equations. In order to prove this hypothesis, the subject area of infectious diseases was investigated.Results. Based on the set of these plans, a graphic image was synthesized for constructing models of system dynamics, which is identical to the diagram of flows and levels of development of the SIR process. However, the problem of constructing conceptual structures is nontrivial, complex, and laborious. Therefore, the Designer-Solver-Interpreter software suite was implemented. The software tools enable a visualization of the conceptual structures and implementation of the knowledge bases for expert models of system dynamics. It also tests the completeness and viability of the model.Conclusions. To date, there is no single conceptual structure for designing expert systems and situational and simulation dynamic models. The proposed method and software tools allow these problems to be resolved using the situational-activity method. Various types of dynamics in expert systems interact, thus confirming the reliability of knowledge in the models of system dynamics. The conceptual structures of the act of activity are the core part of designing expert systems, while he derivative process and analytical representations of the act of activity are the core part of developing modules of system dynamics.

AimsDepression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have been used to model infectious and chronic disease, but rarely applied to mental health. This scoping review aimed to identify population-based SD models of depression and report on their modelling strategies and applications to policy and decision-making to inform research in this emergent field.MethodsWe searched articles in MEDLINE, Embase, PsychInfo, Scopus, MedXriv, and abstracts from the System Dynamics Society from inception to October 20, 2021 for studies of population-level SD models of depression. We extracted data on model purpose, elements of SD models, results, and interventions, and assessed the quality of reporting.ResultsWe identified 1899 records and found four studies that met the inclusion criteria. Studies used SD models to assess various system-level processes and interventions, including the impact of antidepressant use on population-level depression in Canada; the impact of recall error on lifetime estimates of depression in the USA; smoking-related outcomes among adults with and without depression in the USA; and the impact of increasing depression incidence and counselling rates on depression in Zimbabwe. Studies included diverse stocks and flows for depression severity, recurrence, and remittance, but all models included flows for incidence and recurrence of depression. Feedback loops were also present in all models. Three studies provided sufficient information for replicability.ConclusionsThe review highlights the usefulness of SD models to model the dynamics of population-level depression and inform policy and decision-making. These results can help guide future applications of SD models to depression at the population-level.

This paper reports on the current state of a project to develop a system dynamics (SD) model for urban housing markets in China, aimed at facilitating policy analysis and supporting practical educational tools that might reach large numbers of potential entrepreneurs in China. Although numerous academic papers have applied SD models to real estate markets over the past generation, the technique remains relatively unknown and little used both in the academic economics literature and, more to the point, among practitioners and educators in the real estate community. Yet SD has the potential to address key needs among these constituencies, and extend and complement upon traditional economic methods. SD models are focused on modeling market transitions toward long-run equilibria, facilitating the study of the details of causality and the dynamic path of the market and features that are prominent in the history of housing markets in emerging markets. Different from intensive data-driven economic models, SD models are structural-based operational models that can more easily accommodate the actual non-market features and unique institutional components of these emerging real estate markets, where long-range historical data are not readily available. SD can provide intuitive and transparent model structures that should be able to improve pedagogy for educating large numbers of potential real estate entrepreneurs particularly in emerging market countries. For demonstration, in the present paper we choose to focus on the China-specific features of ‘speculative demand’ and ‘land financing scheme’, and use the newly developed SD model to explore the effects of land supply, “command-and-control” versus “market-driven” policies for housing in China. It is important to note, however, that while we chose China for the purposes of our study, the same technique can be applied to any emerging real estate market. Moreover, our research here can be seen as a stepping stone: Before a generalized SD model for emerging markets can be developed, it is both reasonable and appropriate to construct a model that is constrained to a manageable subset of the overall market space.

Portable metabolic units afford a practical utility for field measurements of energy expenditure (EE). This methodology has proven useful to assess EE related to terrain, intensity, and duration during a single event. Similarly, system dynamics (SD) modeling has been used to describe the relationship between exercise and obesity as it relates to EE. However, there is paucity of literature that report SD to predict EE in real time. PURPOSE: To compare actual EE from a portable metabolic unit to predicted EE from a System Dynamics model. METHODS: Seven subjects (4 males, 3 females; 24.4 +/- 1.71) walked selected routes of varied terrain paced by a metronome at 2.7 mph. EE was measured using a Cosmed K4b2 portable metabolic unit with each subject completing four trials per route. An integrated GPS receiver recorded latitude and longitude coordinates of each route. The modeling software STELLA was used to design the SD model which incorporates subjects’ weight, walking pace, route elevation profile and distance. Pandolf’s et al (1977)) prediction equation for EE was run in the model to compare with the real-time K4b2 data. RESULTS: In simulation modeling parameters (stocks and flows) are adjusted to increase accuracy. Model parameters were adjusted to provide agreement for EE to within +/- 1% of the actual total EE as measured by the Cosmed K4b2 unit. A paired t-test comparing the actual versus the SD model predictions of total EE were not significantly different (p = .034). CONCLUSION: It appears that SD modeling can be an effective tool to predict EE of individuals walking on varied terrain. Once user parameters have been entered, simulation modeling can provide feedback on EE with suitable accuracy of a selected route. Compared to a single event measurement, SD allow users to compare EE of multiple defined routes simultaneously. Feedback has been identified as a critical component of adherence and motivation for physical activity. In this case of SD modeling, accurate feedback and route selection may encourage users to engage in regular physical activity. Funded by James Madison University Office of Public Safety.

What lessons might a body of work recognized for “Lifetime Achievements” offer to a new generation of system dynamics modelers? In what areas are future contributions of system dynamics most needed and likely to have the greatest impact? My life goals have been the design of effective public policy systems and the remediation of ineffective ones. Searching for a practice – an “engineering” or “curative” discipline that would facilitate the attainment of those goals led me to system dynamics modeling. A chronicling of three projects focusing, respectively, on global modeling, conflict–development linkages, and Singapore's improbable resilience, illustrate system dynamics modeling principles, applications, and lessons learned. China represents an important new frontier for system dynamics modeling and modelers to contribute to the survival and well‐being of the human species. Copyright © 2013 System Dynamics Society

BackgroundDecision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health.Methods and findingsRelevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions.ConclusionsThis systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further.