China Chapter – Events – 2022ISDC

In the past ten years, China High-speed Railway has achieved a great success on construction and development, according to the research report from world bank 2019. However, the report also indicates the quite low EIRR (8%) and FIRR (6%). The achievement is guaranteed by a continual large invest of China railway company and China Government assistance. In this project, we use SD model to catch main structure which covers “Invest-Construction-Operation” and how government assistance works with the construction and operation of High-speed railway. The dynamics of structure indicates the significant meaning both and problems from government assistance. Firstly, in construction stage, abundant invest and fast construction would lead to overshooting which causes resource waste and social problems. Then in operation stage, the structure shows how China government realize the positive effect of high-speed railway to its most to economics and livelihood with financing assistance. On the other hand, the government intervene would also lead to an independent management problem to railway company. Therefore, based on the structure analysis and simulation result from SD model, the paper would give a detailed demonstration process for these findings in project and try to give a constructive proposal.

In this paper, we aim to develop a systematic method to aid modelers in designing a survey (or interview) to quantify variables in order to create and validate reference modes. We call our method the “REference MOde DEvelopment ALgorithm” (REMODEAL). We present an overview of REMODEAL and delve into our three graph process to enable modelers to check reference modes for internal consistency and inter-participant consistency.

The consumption of energy and the energy source creates emissions beyond where it should be in China today. With this, pollution accumulates in the atmosphere energy is generated from carbon-intensive energy sources and dissipates through natural processes. With several high-intensive energy sources as the main source of energy in China today, emissions run high. To decrease these emissions, a policy is imagined to increase the renewable energy capacity in China, while decreasing the fossil fuel capacity, particularly coal. By a system dynamics approach to the issue, we have modelled a wishful thinking model including implementation and obstacles structure. The investment to change country as big as China is huge, and will also include obstacles like job loss, opposition and the use of critical minerals. Therefore, for a successful implementation, a policymaker needs to be aware of these obstacles. The dynamics of energy systems and its relationship with pollution is nothing new and has been studied extensively in many different sciences. The present research briefly explores the problem of the Chinese electricity pollution and provides a detailed analysis of the policy to enable a transition.

In complex safety-critical systems, mental fatigue resulting from high levels of stress or boredom during monitoring tasks may lead to a higher risk of human errors and eventually accidents. However, the estimation and quantification of the operator’s mental workload have been challenging. We present a dynamic model of the suboptimality of workload and its interaction with fatigue through time in safety-critical monitoring roles. Using the context and data of the Traffic Controllers (TC) in the Belgian railway network, we have set to examine the dynamic hypotheses of mental fatigue affecting the workers’ overload and underload thresholds of TCs and consequently their comfort range of workload through time. The simulated model results show that 85-86% of work-hours in the morning and afternoon shifts are in the comfort range, in contrast with only 59% of the work hours in the night shift. This is due to the large portion of underload work-hours in the night shift, which brings about accumulated fatigue and boredom, resulting in the comfort range shrinking with an increased pace. Finally, our model takes the first leap to utilize simulation models and quantify the suboptimality of workload by considering the changes in the operator’s mental fatigue through time.

System dynamics solves social, natural and economic real-world problems to describe relationships among variables by mathematical and non-mathematical models of complex systems. However, it is difficult to identify the key areas in a large and dense system to generate significant changes in the whole system. This paper targets in the analysis of system related to large and complex issues, exploring the contributions of an integration approach combining social network analysis (SNA) and system dynamics (SD) modelling. SNA is a method based on graph theory, focusing on actors and their interrelationships, which can help identify key issues and help the constitution of a SD model boundary. This can help modellers prioritise areas where they can focus their interventions to apply actions. This paper first reviews the theoretical coherence and contribution of the integration of SNA and SD. Then methodological approaches are proposed and to be tested through case studies with multi-stakeholder collaboration in urban greenspace regeneration to validate the methodology.

In this paper, we aim to develop a systematic method to aid modelers in designing a survey (or interview) to quantify variables in order to create and validate reference modes. We call our method the “REference MOde DEvelopment ALgorithm” (REMODEAL). We present an overview of REMODEAL and delve into our three graph process to enable modelers to check reference modes for internal consistency and inter-participant consistency.

Due to the increase of precipitation, there is an increasing interest to seek sustainable adaptation strategies in urban areas. Green infrastructure has received an increasing amount of attention as an adaptation strategy in order to reduce runoff that is occurred by the increase of precipitation and the impervious surface in urban areas. The city at Buffalo, which is experiencing an increasing amount of precipitation due to global warming has launched a rain check project by Buffalo Sewer Authority to install green infrastructure. There has been effort to install rain barrels at individual households. In order to increase the number of rain barrels starting from this point, the research question is to analyze the change of participants considering the word of mouth and the economic incentive and also analyze the effect on reducing runoff using a system dynamic analysis. The system dynamic analysis is used to consider the environmental, economic, and social perspective by involving different factors in the model. The expected result is to compare different scenarios with different tax incentive levels to find the scenario that meets the objective.

The COVID-19 pandemic poses waves of crisis to the socioeconomic system. Emerging research in the UK links the number of pandemic waves with deterioration in the population’s mental health and well-being, indicating potential accumulative risks of multiple crises on the systems recovery. Resiliency describes how the system recovers from crisis events. Drawing from the reflections of COVID-19, we developed a small system dynamics model exploring how multiple waves of crises challenge individual and organisation resilience during a long pandemic. We found that ongoing environment adjustment and a lack of service provision resources can potentially increase accumulative risks from multiple crises, resulting in mental health and well-being deterioration. We highlighted that prevention and mental health system capacity remains the essential solution in decreasing the number of stressed people.

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, has few other nuances, and offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).

Model parameter calibration is a demanding and time-consuming task that must be completed before the system dynamics (SD) model can be used to understand and improve a system’s dynamic behavior. This paper employs deep learning as a parameter calibration tool for SD models. We test our proposed deep learning calibrator on the SEIRb epidemic model. Specifically, we train our calibrator on the data generated by SEIRb, mapping the system’s infection and death rate dynamics to a set of model parameters. Then, we use the trained deep learning model as a calibrator to estimate the model parameters of a previously unseen infection and death rate data. Our results show that the deep learning calibrator accurately predicts the model parameters, accelerates the calibration process, and offers more robustness to the process and measurement noise.

Buprenorphine is an effective medication for opioid use disorder that can be prescribed and taken at home. We conducted a cost-effectiveness analysis to compare the addition of contingency management and psychotherapy to buprenorphine treatment and increasing the reach of hub and spoke provider training programs. We modeled the effects of each intervention with the Simulation of Opioid Use, Response, Consequences, and Effects (SOURCE), a system dynamics model of opioid and heroin use, treatment, and remission in the United States, calibrated to data from 1999-2020. We found that telehealth averted 8,650 overdose deaths, more than any other single-intervention strategy. However, interventions that increased the duration of buprenorphine initially increased the number of overdose deaths due to insufficient capacity. The portfolio with contingency management, hub and spoke training, ED initiation, and telehealth was the most cost-effective strategy with an incremental cost-effectiveness ratio of $8,607 per quality-adjusted life year (QALY) gained (2021 USD). When expanding buprenorphine treatment in the U.S., strategies to increase treatment duration, treatment initiation, and the sustained capacity of buprenorphine treatment at the same time avert the most overdose deaths and are the most cost-effective.

Fee-For-Service (FFS) is one of the conventional payment systems applied alone or mixed in many countries. The goal of this system is to increase efficiency through financial incentives. However, excessive services and unnecessary or inappropriate care may be encouraged, adversely affecting healthcare expenditure and quality. Although many system dynamics (SD) models investigate the dynamic of health expenditures, they rarely investigate the effect of payment systems on health expenditures. This paper responds to this theoretical gap by developing an SD model that considers crucial feedback loops in FFS. The model shows that this payment system could end in an overshoot and collapse behavior for hospitals’ income, leading to budget overrun and harming the quality of service. The ten most recommended policies are tested at the end to investigate how much they can be helpful to prevent these undesired consequences.

We aim to present the role of System Dynamics helping to develop sustainable systems for inclusive growth with government setting and social innovations. A nation-wide case study, Taiwan, is a diverse society looking for more equitable and inclusive growth. The dynamics of diversity and development require more dialogues bridging mental models and collaborative leadership. We present three case studies on (1)Building Educational Innovation Ecosystem with the Ministry of Education, (2)Driving Industry Value Chain and Business Innovation Ecosystem with the Ministry of Economic Affairs, and (3)Developing Livable Cities and Social Welfares with Sustainable Urban Management System with Taipei City Government. Reflections for sustainable system development in actions are provided.

Business consulting is a rich field where the system dynamic method thrives in its ability to diagnose obscure issues and provide a rigorous testing ground for potential solutions. With this in mind, an SD (system dynamics) case study approach, involving a local client who had previously reached out to the business department, was used to redesign an introductory consulting course. The course drew on much of the significant body of published literature to setup educational frameworks for the teaching and learning of SD. This paper seeks to support this literature by reconciling proposed SD learning objectives with a student perspective and understanding of the subject matter after instruction.

System dynamics solves social, natural and economic real-world problems to describe relationships among variables by mathematical and non-mathematical models of complex systems. However, it is difficult to identify the key areas in a large and dense system to generate significant changes in the whole system. This paper targets in the analysis of system related to large and complex issues, exploring the contributions of an integration approach combining social network analysis (SNA) and system dynamics (SD) modelling. SNA is a method based on graph theory, focusing on actors and their interrelationships, which can help identify key issues and help the constitution of a SD model boundary. This can help modellers prioritise areas where they can focus their interventions to apply actions. This paper first reviews the theoretical coherence and contribution of the integration of SNA and SD. Then methodological approaches are proposed and to be tested through case studies with multi-stakeholder collaboration in urban greenspace regeneration to validate the methodology. 

Rising healthcare costs in developed countries have been a challenge for health systems for several decades. Cost inflation without improvements in health outcomes could partially be explained by misaligned reimbursement mechanisms (RM) in the traditional fee-for-service (FFS) model in most health systems. In Singapore, capitation, an RM under which payments are based on population served (e.g., per enrolled member per month) and paid out prospectively without regard to volume of services delivered, has emerged as an alternative to the traditional RM model, with the potential to improve the performance of the health system according to the “quadruple aim”. In collaboration with stakeholders of the public healthcare system in Singapore, we developed a causal loop diagram (CLD) to highlight features of RM innovation in the form of capitation and its intended effects on the health system from the perspective of the government, healthcare institutions and providers. The CLD developed shows that an FFS model incentivizes high margin services irrespective of their aggregate health benefits. Though capitation is expected to provide redress for this undesirable effect, there is a need to assess its secondary effects and to design mechanisms for the governance of common pool resources under a future capitation regime.

The field of system dynamics operates in causality. However, opaqueness surrounding the colloquial definition of “causal” can make the seemingly simple phrase “correlation does not imply causation” hard to interpret and a point of contention. When is data causal, and when is it correlational? We examine the problem through measurement theory, and offer a measurement-based definition and framework for determining causation.

Concerns over the creation, preservation and quality of urban natural space are increasing due to the intensification of urban areas, the move towards privatized spaces and the wider societal shifts in nature of the public realm/public life. There is a substantial body of research on the positive impacts of natural space on the urban system, with an increasing focus on the value of urban natural spaces to sustainability, health and wellbeing. Despite such benefits, studies report a recent decline of natural space use in England. The decline results from a number of factors, including socio-demographic and health conditions, the quality and proximity of the natural spaces and their facilities. These factors are often investigated in isolation, whereas their interconnections possibly determine for what and how natural space is used. Within this context, this paper aims to use participatory System Dynamics modelling to understand the factors influencing the use of urban natural space. It presents a highly participatory modelling process and the related model aimed to investigate the use of space, quantifying a large set of intangible variables and uncertain relationships. The paper is based on a participatory SD modelling process for an urban regeneration case study based in Thamesmead, London. 

The multi-dimensional nature of urban regeneration implies that decision-making for health and sustainability is not easy. Through an in-depth case study with a large English housing association, we found that the focus of decision makers’ attention changes over time. Specifically, the attention to the social mission of a housing association is not sustained as the regeneration project proceeds from early to delivery stage, potentially risking the delivery of health and sustainability outcomes. Drawing on decision-making and institutional theories, in this paper we summarise the tensions of decision-making that the organisation faces in urban regeneration projects. We develop a qualitative systems model highlighting the underlying decision-making rules that explain why attention changes over time. Our findings provide insights of managing competing demands in decision-making. Our method of integrating of change-over-time analysis into qualitative analysis and causal loop diagrams offers a novel approach to connect causal hypothesis and systems behaviours.

Organisations often face multiple demands in decision-making. This paper synthesises existing theoretical explanations of competing and contradicting needs in organisational decision-making. We review three strands of theories: the attention-based view, institutional theory and paradox theory and use causal loop diagrams to represent the core mechanisms. Based on the theoretical models and a case study at an English housing association, a hybrid organisation, we develop a theoretical stock-and-flow model to elicit a systems perspective in theorising and modelling the competing demands.

Nowcasting has become more and more important in our fast moving world. We present two of our real-world nowcasting models, one from the construction industry, another for the COVID-19 statistics. The models focus on characterizing the delay and incorporate additional data. Parameterizations can be carried out promptly, in some cases automatically. It is also relatively easy to create and further develop nowcasting models of this kind, since the treatment of delays is part of t

Many mountain communities in Hindu Kush Himalaya (HKH) region are shifting from traditional modes of livelihood to new sources of income. Our fieldwork in Bhutan, Nepal and Myanmar informs that such transitions are driven by context-specific combinations of human aspirations, factors driving labor productivity, national policies, development and planned climate adaptation practices and resource bases. Social-ecological systems (SES) framework provides a conceptual lens to investigate such interdependencies between society and eco-systems and strives to explore the dynamic complexity across scales of household economy, resource systems and landscapes, and policy and development practices. Though it strives beyond the traditional indicator-based studies the vocabulary of this approach is difficult to translate to policymaking. There is an urgent need to integrate social-ecological resilience into Monitoring and Evaluation (M&E) frameworks of development and climate adaptation projects in the HKH region to avoid maladaptation and sustain the well-being and development of mountain communities. Our study uses Systems thinking and Systems Dynamics modelling as a method to explore the dynamics of livelihood transitions in three Himalayan contexts, their tradeoffs and opportunities for human well-being, and derives three-interdependent-Change Areas for M&E to target as resilience markers in future projects for climate adaptation and development in the region.

As a heavily populated megacity, Shanghai faces major epidemic risks. However, Shanghai’s control of COVID-19 has been successful owing to both government policy and community participation. We extended the classic SEIR model to incorporate the transmission of SARS-CoV-2 in various scenarios reflecting the lockdown policy implemented in Shanghai and the community participation happened, with residents’ cooperation and enhanced community fever clinics for the early identification of infected individuals, which cut infection chain. The simulation results showed that without lockdown policy, the new confirmed cases would gradually increase to more than 7,000 [292/1,000,000]. However, with currently implemented lockdown policy but without community participation, the new confirmed sharply decreased to 30 [1.2/1,000,000] during the first several months, but when lockdown policy was gradually lifted, the new confirmed cases increased exponentially, reaching more than 17,000 [708/1,000,000]. Therefore, government lockdown policy was necessary for the rapid control of COVID-19 during the outbreak stage while community participation played a particularly important role in keeping the number of new confirmed cases low when resuming normal life.

System Dynamics simulation models are commonly used for exploring, structuring and managing complex problems in order to design more effective policies and inform decision-making. They are often used to investigate areas in which limited knowledge is available, describing nonlinear relationships and including variables representing intangible elements of the system. Indeed, SD practitioners build and depend on formal simulation models to overcome the cognitive limitations to grasp the dynamic complexity of the problem situation, and to make reliable behavioural inferences. While this explorative nature is one of the key advantages of SD models, it also represents a major challenge for modellers working on the quantification and parametrization of the qualitative aspects of a (participatory) model, namely soft variables and data scarce contexts, especially when it is not possible to apply conventional analytical methods. There is a limited availability of procedures to obtain and analyse qualitative information. This paper investigates quantification good practices; on the other side, it describes a quantification process carried out during a participatory SD modelling process on the use of natural space in Thamesmead, an area undergoing urban regeneration in London, United Kingdom.