A system dynamics model is composed of many variables. These variables simplify complex phenomena and provide a description of a systems current state or problems. Basic variables that describe the real-world urban development can be established from the elements that make up a citys different dimensions such as industry product, population growth and vacancy rate. The urban development framework takes a system-based approach by systemizing the citys internal elements. The systemic variables then provide not only a clear reflection of the interactions between all of the sub-systems but also how they relate to the overall system. It is therefore very important to select the appropriate variables. Most variables of system dynamics models are, however, set up by the designer, served as a subjective and unscientific approach. This study therefore applies the Fuzzy Delphi Method to the selection process of system variables to increase the confidence of the model. This was accomplished by first examining the system relationships as well as the intent and meaning of the sub-system variables to be created. After establishing the criteria for variable selection, an empirical case study was used to devise the evaluation variables for each sub-system.
Maintaining one of the most complex System Dynamics model ASTRA with a group of more than 5 economists, we were facing two main problems. First, collaboration was difficult because all developers had to work with different files and changes had to be manually transferred into one model. Second, calibration was time consuming, since the complete model needs various minutes for only one run even on high end computers. We found a solution to these problems in transferring techniques from distributed software development to SD modelling. We split our complex ASTRA model into more than 40 modules, developed standards for these modules to be able to run them inde-pendently and to enable automatic merging of any amount of modules to one model, developed a tool for automatically executing this merge in order to run the complete ASTRA model and we set up a version controlled repository accessible by all developers via internet to manage the simultaneous development work of our modelling team which is spread out over three institutes in two countries. In this paper, we would like to present in detail the individual task as described above and conclude with our experiences after this major transformation of our ASTRA model.
Illicit drug policy has been the subject of important SD studies addressing the interaction between policing and medical treatment and estimating the prevalence of national cocaine use. Here we modeled the impacts of policy changes associated with wider use of newer opioid pharmacotherapies besides methadone. These newer drugs allow less supervision of dosing and changes in the mix of prescribing and dispensing arrangements. Key aspects of the model were estimation of potential demand for the enhanced range of therapies and the cost and treatment impacts of changes in cycling on and off treatments due to pricing and service configurations.
In this paper, we provide a preliminary, in-depth qualitative analysis of the plausible feedback mechanisms contributing to the high HIV/AIDS rate among young Malawian women by examining the relationship between HIV/AIDS infections, HIV risk categories, economic welfare (and productivity), and the potential impact of increased access to antiretroviral therapy (ART). Additionally, to obtain greater clarity, test assumptions, create a roadmap of data for ourselves and others, and to provide more opportunities for future use, we further distill the qualitative analysis into a simplified preliminary quantitative model. For each model structure (qualitative and quantitative), we review the formulation, testing, and evaluation processes involved. We hypothesize that ART is fundamental to increasing economic welfare of young, HIV-infected women in Malawi and show that our models do provide useful information and feedback for future discussion on social policy and problem-solving.
Although humans are assumed to be rational beings, there has been little consensus regarding the criteria for distinguishing rational from irrational behaviors. For example, overgrazing that often results in a tragedy of the commons is usually considered irrational. A subsequent question may be why there are always some rational people engaging in irrational overgrazing. Based on rationality theories and related research findings, this research analyzes the overgrazing behavior in the National Health Insurance system of Taiwan. The research findings indicate that system sustainability, the effectiveness of control, and the possibility of jumping out of the system are the critical factors that have effects on overgrazing behavior and the rationality orientation. If system sustainability is not a question, rational and opportunistic agents tend to be driven by greed, and the effectiveness of control is crucial in determining the behavior of the agents. However, once the system is perceived to be unsustainable, the motive of fear may dominate and the possibility of jumping out of the system becomes critical for the agents to choose between self-restrained and overgrazing behaviors. In addition, it is suggested that the it wont me effect may be responsible for the eventual collapse of the system.
The purpose of the paper is to investigate the effects of unconscious versus conscious ways of making decisions in a dynamic decision-making task. An experimental setting is used to study this question; three experimental groups are distinguished: immediate decision-making (only limited time for cognitive processing), distracted decision-making (time for unconscious processing), and considered decision-making (time for conscious processing). As experimental stimulus, a simulator based on the Kaibab Plateau model is employed. Findings are not yet clear, since so far only pre-test have been conducted; the actual experiment will be run in April and May 2008. Implications might comprise the usefulness of rational methods for decision-making, for instance modeling and simulation. The value of the paper lies in the fact that it connects to a recent discussion in psychology and transfers it into a domain in the core interest of the system dynamics community: decision-making in situations with dynamic complexity.
Since 2005, there has been an opportunity for joint reflection about the quality of the peer review process at each conference. Last year, for the first time we talked amongst reviewers, thread-chairs, the program committee and the societys head office and policy council.
The paper focuses on diffusion of energy-efficient innovations. A conceptual model is developed that integrates relevant variables and mechanisms to describe and explain innovation diffusion in the building construction industry an exemplary case of a large fragmented, socio-technical system with slow transition characteristics. A considerable amount of literature has been published about innovation diffusion. However, only little attention has been devoted to integrate relevant knowledge from different disciplines to explain the complex phenomenon of innovation diffusion in the building construction industry. This study draws on accepted theories as one source of information; others are existing empirical research, expert interviews, and workshop results. The latter two are based on a case study to obtain insights from a regional industry cluster. We use the grounded theory approach for data collection, analysis and modeling. The result is a validated conceptual model including mechanisms that explain the diffusion phenomenon. The first contribution is the description and explanation of innovation diffusion in a consistent model developed by inductive and deductive analysis. Second, the model provides first insights about policy levers and hypotheses about relative importance of underlying mechanisms. Third, the model can serve as a conceptual frame for a future quantitative simulation study.
The real-world problem the research aims to address is the continuing highly seasonal, exponential electricity demand growth in the Greek islands that are unconnected to the national electricity grid over the past decades. This paper presents only part of the on-going research. It specifically tests an early draft of the sub-model concerned with the interplay of an islands tourism volume & attractiveness, local technological learning-by-using effects and the dynamics of demand-side equipment diffusion. The general assumption is that a tourist chooses a basket of services received at the place visited, one of which is cooling comfort. Cooling-comfort eventually translates to installed cooling capacity and in effect electricity consumption. This paper examines the sub-model which, based on a figure of cooling comfort per person, constructs an indicator of competitiveness to similar destinations and relates the flow of tourists to it. Similarly, a cost comparison incorporating a learning curve between a conventional and an efficient variant of cooling equipment drives the installation stocks at any time and effectively alters the efficiency of the overall service across the island. The sub-model is run for a number of structural and behavioural tests and also assessed for its potential use in policy making.