The unconscious application of sophisticated tools, and in particular the popular reverence to data as the source of knowledge, seems to be the rule in many scientific activities in which the application of tools replaces thinking and data analysis replaces understanding. In this respect, system dynamics has much to offer, though sometimes it is tricky to appreciate its full value and scope. One of its trademarks is known as operational thinking. This paper underlines that operational thinking drives a distinct epistemic posture. This posture, unlike traditional scientific practice that seeks to explain the world by means of data analysis, intends to understand the world in terms of its operations. In this paper I explore the significance of such a posture for the domain of human systems, I highlight its epistemic value in particular with respect to the prevalent observational approach to science, the Humean problem of induction and determinism. Operational thinking means to recognize that human systems do not obey laws to be discovered by observation and data analysis, instead, it acknowledges agency, that is, the fact that a social system is the result of the consequences of actions taken by free decision-makers
The paper introduces a system dynamics Taylor rule model for monetary policy feedback between the real interest rate, inflation and GDP growth for the 2004 to 2011 period in Brazil. The nonlinear Taylor rule for interest rate changes considers gaps and dynamics of GDP growth and inflation as well as monetary policy sluggishness. The results outline a high degree of endogenous feedback for monetary policy and inflation, while GDP growth remains strongly exposed to exogenous economic conditions. Furthermore, stocks of absolute monetary policy flows provide a new mean for assessing empirical monetary policy moves. The stocks show that Brazilian monetary policy has been more driven by growth than by inflation considerations in the period under investigation. Moreover, simulation exercises highlight the potential effects of the new BCB strategy initiated in August 2011 and also consider a recession avoidance Taylor rule. In total, the strong historical fit of the Taylor rule model calls for an application of the model to other economies.
This research is to provide a methodology for making policy scenarios based on the system dynamics. The authors deem this new methodology would be a useful tool for policymakers to make policy scenarios. As for the case study, this research deals with the policy scenarios for managing polioviruses in Japan as an example. This methodology includes both the simulation part of using System Dynamics and the conversation part related to Scenario Planning. Through using this methodology, we had structural understanding of the problem with the visible simulation results and conversation with member which was focusing on the parameters that would be a part of suggested scenarios. This methodology is expected to improve the public deliberations for making policy scenario based on data.
It is known that the presence of a supply line delay may lead to unwanted oscillatory stock behavior. It is also well known that fully considering the supply line in the ordering decisions, which means using the same adjustment time for stock adjustment and supply line adjustment terms, prevents unwanted oscillations. The effect of using the same or different adjustment times is relative. Therefore, in the literature, it is suggested that a weight coefficient should be used instead of explicitly using two separate adjustment times. This weight is simply equal to stock adjustment time divided by supply line adjustment time and it is named as weight of supply line. In this paper, we defined one more decision parameter that we call relative aggressiveness, which is equal to acquisition delay time divided by stock adjustment time. The existence or non-existence of stable or unstable oscillations is a function of the order of the supply line delay structure, weight of supply line, and relative aggressiveness. Usually, acquisition delay time and order of the supply line delay structure are not decision parameters; weight of supply line and stock adjustment time are. In this paper, we aim to give more insight to the readers about the selection of these two important parameters.
Understanding historical overshoots is vital for policy-making, not least when assessing potentials for future global overshoots. For this purpose a simple, unifying theory of overshoots is described and discussed for a variety of observed overshoots. For undesired and avoidable overshoots, misperception at some level must be a major cause. Laboratory experiments support this hypothesis and point to dynamics as the main complicating factor. The theory suggests that misperceptions may cause global overshoots both because of climate change and scarcity of cheap fossil energy. New generations of simulation models are needed to study overshoots, test policies for sustainable development, and to aid information dissemination.
The study presents a System Dynamic model of the Nigeria electric power system. The model was developed with a view to using it to evaluate the long-term performance of the system. Both primary and secondary sources were employed to collect the systems baseline information. Results from this formed input to develop a four-sector-model in Vensim software for the long-term evaluation of the NEPS. Leverage points in the model were identified from the validated model using data from 2005 to 2009 in the Base Run. The system behaviour, based on two other scenarios (Scenario 1 representing improved basic level of consumption and Scenario 2 representing industrialization target), was then evaluated on a timeframe of 50 years starting from 2009. The study concluded that Compounded Annual Growth Rate (CAGR) and Economic Growth Rate (EGR) were the most critical policy leverage intervention points for NEPS improvement within the next 50 years.
The paper explores the relevance and use of games for speeding up the energy transition in the Dutch built environment. Since the transition of the Dutch energy system with the current policies is much slower than required given the urgency of the foreseeable problems and the substantive system delays. There seems to be a need for experimentation with innovative policy instruments, governance mechanisms, and systemic conditions. This paper includes applied emphases upon two topics as well as illustrations of the usefulness of games as tools for getting a grip on the energy transition. In this context, a conceptual model has been developed to illustrate the possible causes of the aforementioned slow transition in the built environment. Furthermore, we discuss the potential roles of games for managing the transition in the built environment and illustrate with an interactive experimental game developed for hypothesis testing and learning purposes. Finally, based on the results of the game we explore the possibilities for future research.
We discussed a management of Airline-Airport coexistence for a sustainable air transport system. Governments provide various financial supports for unprofitable regional airways when the airways are essential for local life and economy but providing inefficient subsidies are often criticized worldwide. This paper aims to examine the validity of Load Factor Guarantee (LFG) scheme in which an airline and an airport mutually agree with the load factor of a flight and the airport would compensate for the discrepancy between the actual and the agreed load factor. We analyzed the LFG management using the data of Noto Airport and All Nippon Airways (ANA) from 2005 to 2011. Examining several scenarios with System Dynamics, we found that LFG would be effective to maintain regional airways when combined with appropriate level of subsidy. The results illustrated that only having annual negotiation on a target load factor cannot balance the benefits between an airline and an airport and thus does not sustain the mutualism. Integral management of LFG and monthly demand adjustment is the key to success for the airline-airport coexistence. The SD model can be applicable to airways worldwide and contribute to better design and management of a regional air transport system.
An introduction of the participatory element into the existing policy making scheme challenges the whole policy making practice, since unmanageable stakes have a risk to mask the proper distribution of interests and hide needs wider of the society . The particular interest of this research is to describe participatory modeling procedures and construct the model by means of system dynamics that capacitate an input of policy stakeholders via a rational balance of interest expression in policy making and policy administration streams. The primary intention is to use these modeling techniques for the description of participatory procedures and apply them to governance of wider public policy issues. The model primarily is targeted to introduce such mechanisms to the policy making process that enable control of the completeness of the stake representation and to balance the stake representation. Equally the model has to protect policy makers from narrow interest advocacy against the public interest.
We present a system dynamics model called GT-Mod for assessing the development and management of geothermal energy production. The model simulates the entire geothermal energy cycle by representing the major components as a set of connected sub-systems that include the power plant, the injection well, the geologic reservoir, the production well, the surface feeder and distribution pipes, the pumps, and the economics. GT-Mod uses a Latin Hypercube Monte-Carlo approach to propagate uncertainties in various input parameters to calculate the systems thermal and power performance over the lifetime of the project and to assemble a probability distribution of the levelized cost of electricity (LCOE) that is used to estimate the integrated risk as a function of input uncertainty. Integrated risk is the summation of the product of consequence and probability over all probabilities and represents a comprehensive metric for benefit analysis that includes the full range of uncertainties in a particular problem. GT-Mod is also used to bound viable solution spaces and to identify areas of uncertainty that have the greatest influence on risk. An example based on a hypothetical but realistic geothermal site is presented that demonstrates the models application and highlights the suitability of the system dynamics approach.
In this paper we explore behavioral issues, coupled with temporary capacity imbalances that could influence the characteristics that a service supply chain may assume in the long run. We look at a service chain in which processing times by human agents are endogenously determined by what constitutes an acceptable and credible backlog, but implicit incentives, particularly within a formal hierarchy, may also impinge upon throughput rates at certain stages of the supply chain when agents are trying not to overwhelm downstream stations with excess work. We explore these issues in the context of a managerial intervention in a judicial service supply chain. Using data from a detailed case study we develop a preliminary model and discuss some results.
Mismanagement of societal aging is an important threat to health care systems, social security systems, and the economy of many nations. A System Dynamics simulation model related to societal aging in the Netherlands and its implications for the Dutch welfare system is used here as a scenario generator for Exploratory System Dynamics Modeling and Analysis - a System Dynamics-based approach for exploring and analysing deeply uncertain dynamically complex issues and testing policy robustness many plausible futures. Key concerns derived from this exploratory research are (i) the existence of plausible futures with severe labour scarcity, especially in health care, (ii) unsustainable evolutions of health care costs, and (iii) insufficient labour productivity, especially in health care. Our analysis shows that labour productivity may be cause of and cure for many of the undesirable evolutions. We conclude that (i) sufficient increases in labour productivity in health care as well as labour productivity in general without pinching the necessary workers in care are needed, and (ii) sufficiently raising the retirement age only helps if both the willingness to work longer and the willingness to keep older employees increase. These conclusions are derived from systematic data analysis which is fully documented in the appendix.
Supply chain collaboration is the key to success in business. A major challenge in such collaborations is the dilemma between locally optimized quick solutions and the more systemic, long-term but slow-acting approaches. Such dilemma in business collaborations has been contemplated in supply chain management research, illustrating their respective benefits and disadvantages. In light of the findings from literature, this study investigates the dynamics of this dilemma using an integrated perspective, utilizing both theories and case studies in supply chain relationships and collaborations. The archetypical dynamics and behavior of relationships over time are modeled, proposing an ever-evolving framework of supply chain collaborations. Along with the model, a prototype of a supply chain collaboration simulation model is also presented, as a customizable environment for policy testing and demonstration of different supply chain collaboration approaches. This model may also be used for training purposes as Microworlds.
The productivity of services has recently become the subject of intensive research. While most contributions here have focused on developing measurement concepts, so far little is known about the dynamics of productivity in service companies. Because productivity tends to increase if the service delivery process is enhanced and improved, there seems to be a link between incremental service innovations and the productivity of services. Therefore, this article analyzes the interaction of innovations and productivity over time in knowledge-intensive business services (kibs). A simple system dynamics model was constructed to examine these dynamics and interactions over the life cycle of an exemplary knowledge-intensive business service offer. First the system structure is developed using literature analysis. Second, several simulation runs and experiments are conducted, to obtain a deeper understanding of the interactions of service innovations and productivity. The paper closes with findings and conclusions.
We report the results of a collaborative decision making exercise using a simulated stability operations task. The exercise allowed Canadian Forces personnel to experience first-hand the benefits and challenges of taking an integrative decision making approach (i.e., with information and resource sharing) compared to a stovepipe approach (no communication and partial view of the whole system). While teams generally achieved greater mission success in the integrated condition, they could only partially cope with the complexity of such an endeavor. A training session on systems thinking and collaborative design generally improved integrated planning effectiveness. We designed a decision support tool capable of suggesting an effective integrated course of action based on qualitative information about system structure and effects. The tool essentially relies on an innovative 'action-oriented' cross-impact matrix and decision matrix that jointly allow deriving a viable resource allocation given a range of intervention options. The prototype tool aims to be simple and generic for use in real-life applications. The system's inputs are based on simple user judgements (i.e., mental models). We show that the tool provides solutions superior to most human teams. Future research will test the generalization of the approach and assess human ability to refine the tools' solutions.
Coastal communities dependent upon groundwater resources for drinking water and irrigation are vulnerable to salinization of the groundwater reserve. The increasing uncertainty associated with changing climatic conditions, population and economic development, and technological advances poses significant challenges for freshwater management. The research reported in this paper offers an approach for investigating and addressing the challenges to freshwater management using innovative exploratory modeling techniques. We present a generic system dynamics model of a low lying coastal region that depends on its groundwater resources. This systems model covers population, agriculture, industry, and the groundwater reserve. The system model in turn is coupled to a powerful scenario generator, which is capable of producing a comprehensive range of plausible future scenarios. Each scenario describes a unique future pathway of the evolution of population, the economy, agricultural and water purification technologies. We explore the behavior of the systems model across a wide range of scenarios and analyze the implications of these scenarios for freshwater management in the coastal region. In particular, the results are summarized in a decision tree that provides insights into the expected outcomes given the various uncertainties, thus supporting the development of effective policies for managing the coastal aquifer.
Rockström et al. (2009) introduced the concept of a safe operating space for humanity that will not push the planet out of the Holocene state. Establishing the limits of this operating space is ongoing for various earth bound systems. Estimates of these limits are plagued by uncertainty. In case of the limits to the world water system, these uncertainties arise out of conflicting models, regional variations, limitation of expansion of water use through financial and institutional capacity, uncertainty about the realization and efficiency of trans-boundary water transfers, and interdependency between the water system and other earth systems. This paper aims at investigating the limits to global freshwater use. To this end, the behavior of a System Dynamic model of the world water balance is explored across a wide variety of uncertainties. Active non-linear testing is used to identify the best case and worst case for water stress and world population. We find counter intuitive results related to the occurrence of maximum water stress, conclude that global limits can be investigated with a spatially aggregated model and are strengthened in our hypotheses that exploratory modeling adds to the understanding of complex and uncertain issues in a way that predictive approaches cannot.
Urban mobility is a prevalent problem in many cities around the world. Cycling offers a fast and cheap transportation option for short-distance trips, with smaller carbon and physical footprint than driving a car. Cycling can also encourage a modal shift from private car to public transport by providing efficient last mile connections. This has led to a renewed interest to promote cycling in cities, manifesting in a growing number of bike-sharing projects with larger bicycle fleets. However, the economic sustainability of these bike-sharing systems has not been demonstrated. Moreover, city governments may invest resources in bike-sharing projects at the expense of developing policies or infrastructure to improve cycling safety and convenience. We take a systems perspective to study how bike-sharing and other policies can influence cycling as a transport mode in the urban mobility problem. We observe that while bike-sharing projects may increase cycling level and generate public demand for better cycling infrastructure in the short run, loss-making bike-sharing projects can discourage the infrastructure investments over the long-run, thereby hampering cycle adoption. Public funds should not be invested in bike-sharing programs at the cost of cycling infrastructure. Instead, governments should facilitate economically viable bike-sharing systems by the private sector through adoption of appropriate policies. Investments in cycling infrastructure should come first.
In the passenger car sector purchasing decisions are driven by economic factors and acceptance. Based on cost analysis, the factors which can be dedicated to different technologies are fuel costs and purchase price. The decision to buy a new car is always accompanied by a cost comparison of each alternative. This leads to a compilation of costs for each technology in terms of negative utility. The decision process is solved by a Logit-Model. The realization within a system dynamics model allows the modeling of feedback loops.
Assessing impacts of policies and strategies to reduce CO2 emissions from road transport requires an integrated modeling approach. System Dynamics suits perfectly as methodology to simulate the dynamics determined by feedbacks between transport, energy, economic and environmental systems. The ASTRA model incorporates these capabilities. The paper at hand describes the structure and the dynamics of the ASTRA model and zooms into the vehicle fleet model. The dynamics considered in the technological diffusion model is explained in detail. Finally, the paper presents a set of different scenarios which should create a common understanding on the complexity of the transport and energy system and the potential contribution of policies and technologies to reduce the carbon footprint of car transport.
This paper deals with prostitution-related human trafficking. After a brief introduction into the problem of prostitution-related human trafficking, this study focuses on the Dutch policy debate. A first dynamic simulation model is presented based on the problem situation in the Netherlands intended to explore the field and give more understanding about the effects of proposed policies. Using this simulation model a short policy analysis is carried out uncovering the dynamics of the system leading to some preliminary conclusions. Finally it is argued that deep uncertainties exist in this problem field and this is just the first model from various plausible models that are currently developed. An in-depth exploration of the uncertainties related to many of the parameters, functions and structural assumptions will be performed using Exploratory System Dynamics Modeling and Analysis.
One of the main goals of system dynamics models is to improve decision making in dynamic systems. This paper addresses the question of how we can measure what people understand about dynamic systems and what benefit people get from exposure to system dynamics models. For this purpose, we use existing literature about assessing understanding and learning in system dynamics to reflect on outstanding research questions in this area. Learning about dynamic systems requires restructuring of existing knowledge into new knowledge as well as re-use of such new knowledge over time and in different contexts. Existing approaches in system dynamics use elements of dynamic systems to represent knowledge.
The impacts on energy generation and use on sustainability, increasing energy demand, and declining natural resources have made energy improvements a top priority for many organizations. But adequate financing for sustainability improvement projects for built infrastructures is not available. The Paid-From-Savings approach can leverage savings to pay for energy improvements. Although well established and adopted by many organizations, an incomplete understanding of the dynamics of these revolving fund programs hinders their effective and efficient use. In the current work the Harvard Green Campus Initiative and a Texas A&M University sustainability improvement programs were used to develop a dynamic model of a revolving sustainability fund. The validated model is used to test the effectiveness of three project planning strategies and two finance alternatives. Results indicate that with adequate funding it was most advantageous to proceed with all projects as quickly as possible and that with insufficient initial funding the best strategy depended upon the program objectives (e.g. earliest completion, largest fund, minimum negative fund balance). Contributions to sustainability and system dynamics modeling and future research opportunities are discussed.
Meeting 21st centurys challenges of climate change and scarcity of crude oil requires the transition to alternatively powered vehicles, such as electric vehicles. As a consequence, car manufacturers have to integrate these vehicles into their product portfolios. Decisions have to be made about, for instance, the power-train to be offered in specific vehicle models and their times of introduction. This is a complex decision making task, especially due to high uncertainties about the future development of the market demand for alternatively powered vehicles.
Introducing System Dynamics(SD) to solve a complex problem is difficult in two ways: 1) modeling the problem behavior, and 2) selling this approach as a desirable alternative to past troubleshooting methods. Adding the new concept of SD to the problem-solving mix often results in resistance. The perception is that other solutions worked in the past and there isn't time to learn new methods. This classic Limited Growth Archetype is best managed by addressing the balancing loop factors that limit adoption of change. In other words, the change agent needs to identify opponents and change their minds. In this paper I suggest that there is another way: provide the concepts and lessons without recipients being aware.
The financial crisis shifted the focus of monetary policy. Whereas before the crisis the main goal of using monetary policy instruments was to keep the inflation rate low after the crisis policy makers put much emphasis on stabilizing the financial system. The economic literature has started to elaborate on the issue of macroprudential regulation only recently. Financial turbulences, by their very nature, constitute a complex dynamic phenomenon. Hence, an analysis employing tools of system dynamics should help to improve our understanding of the underlying feed-backs. In order to link economic reasoning and the systems approach a model of financial behavior developed by Stein is introduced and used to create building blocks for a basic dynamic simulation model.
Alcohol use is prevalent among college students in the US and is the leading cause of many alcohol-related consequences such as injury, driving under influence, and sexual assault. The problem of college drinking involves complex individual, social, and cultural factors. By viewing college drinking as a complex system problem, this paper describes two components necessary for the full development of a simulation-based dynamic agent model for alcohol use in college. The first component is a basic agent-based model that explores the dynamic of college drinking. The second component discusses the use of system dynamic modeling to explore the causal relationship between various personal/environmental factors and alcohol consumption. The paper also discusses important leverage points for intervention strategies, especially in the context of targeting both high-risk and low- to medium-risk drinkers in college.