System Dynamics Modelling for E-government Implementation: a Case Study in Bandung City, Indonesia

Governments around the world have developed e-Government programs hoping to obtain great benefits. However, many e-Government projects have failed to deliver their promises. Some of such failures are thought to be the results of lack of understanding about the relationships among \u27technologies\u27, \u27information use\u27, \u27organizational factors\u27, \u27social contexts involved in the selection, implementation and use of information and communication technologies (ICT)\u27. These factors stated above might have produced mismatches and unintended consequences. This research draws on not a few precedent studies as to those factors, and the case of the e-Government program in Bandung municipality, Indonesia, is assumed as a typical example of municipalities in developing nations. In this study, a simulation tool which helps to find the best way to create the efficient and useful e-Government is presented. In particular, the model, which is the core of the simulation tool, takes not only the supply side perspective which describes the mechanism of creating and operating the e-Government system but also the demand side perspective which explains the people\u27s intention of communicating with the eGovernment and their behaviors toward it. The simulation tool is constructed based on System Dynamics as an integrated and comprehensive approach to understand the e-Government and its use. Because of lack of suitable statistical data, simulations were carried out by using subjectively estimated but plausible values of parameters after the sensitivity analysis. From the results of simulations, very complicated trade-off relationships among the allocated project budgets to different types of programs were suggested

Which is more appropriate: a multi-perspective comparison between systems dynamics and discrete event simulation

System Dynamics (SD) and Discrete Event Simulation (DES) are two established simulation tech-niques for simulating the dynamics of a system. Both have been widely used in modelling business de-cisions. This paper presents meta-comparison between the two approaches based on literature survey. Upon reviewing the existing literature it has been identified that existing comparisons could be classi-fied under three main perspectives: Systems perspective, Problems perspective and Methodology per-spective. The nature of system and nature of problem have been argued as primary factors for decid-ing modelling methodology. Therefore SD and DES comparisons have been classified on the basis of systems, problems and inherent aspects and capabilities of both modelling methods. It has been ar-gued that development of sound models need fit between system, problem and methodology. The suc-cess of model depends on it’s technical soundness as well as it’s successful implementation. In order to develop successful models this vision has been further extended to incorporate stakeholders, re-sources and time

Applications of system dynamics modelling to computer music

Based on a composer's psycho-acoustic imagination or response to music, system dynamics modelling and simulation tools can be used as a scoring device to map the structural dynamic shape of interest of computer music compositions. The tools can also be used as a generator of compositional ideas reflecting thematic juxtaposition and emotional flux in musical narratives. These techniques allow the modelling of everyday narratives to provide a structural/metaphorical means of music composition based on archetypes that are shared with wider audiences. The methods are outlined using two examples

Nonlinear Modeling and Verification of a Heaving Point Absorber for Wave Energy Conversion

Although the heaving Point Absorber (PA) concept is well known in wave energy conversion research, few studies focus on appropriate modelling of non-linear fluid viscous and mechanical friction dynamics. Even though these concepts are known to have non-linear effects on the hydrodynamic system, most research studies consider linearity as a starting point and in so doing have a weak approach to modelling the true dynamic behaviour, particularly close to resonance. The sole use of linear modelling leads to limited ability to develop control strategies capable of true power capture optimisation and suitable device operation. Based on a 1/50 scale cylindrical heaving PA, this research focuses on a strategy for hydrodynamic model development and experimental verification. In this study, nonlinear dynamics are considered, including the lumped effect of the fluid viscous and mechanical friction forces. The excellent correspondence between the derived non-linear model and wave tank tested PA behaviours provides a strong background for wave energy tuning and control system design

Simulation modelling: Educational development roles for learning technologists

Simulation modelling was in the mainstream of CAL development in the 1980s when the late David Squires introduced this author to the Dynamic Modelling System. Since those early days, it seems that simulation modelling has drifted into a learning technology backwater to become a member of Laurillard's underutilized, ‘adaptive and productive’ media. Referring to her Conversational Framework, Laurillard constructs a pedagogic case for modelling as a productive student activity but provides few references to current practice and available resources. This paper seeks to complement her account by highlighting the pioneering initiatives of the Computers in the Curriculum Project and more recent developments in systems modelling within geographic and business education. The latter include improvements to system dynamics modelling programs such as STELLA®, the publication of introductory textbooks, and the emergence of online resources. The paper indicates several ways in which modelling activities may be approached and identifies some educational development roles for learning technologists. The paper concludes by advocating simulation modelling as an exemplary use of learning technologies ‐ one that realizes their creative‐transformative potential

System dynamics modelling in systems biology and applications in pharmacology

El modelado matemático de sistemas biológicos complejos es uno de los temas clave en la Biología de Sistemas y varios métodos computacionales basados ​​en la simulación computarizada han sido aplicados hasta ahora para determinar el comportamiento de los sistemas no lineales. La Dinamica de Sistemas es una metodología de modelado intuitivo basada en el razonamiento cualitativo por el cual un modelo conceptual se puede describir como un conjunto de relaciones de causa y efecto entre las variables de un sistema. A partir de esta estructura, es posible obtener un conjunto de ecuaciones dinámicas que describan cuantitativamente el comportamiento del sistema. Centrándose en los sistemas farmacológicos, el modelado compartimental a menudo se utiliza para resolver un amplio espectro de problemas relacionados con la distribución de materiales en los sistemas vivos en la investigación, el diagnóstico y la terapia en todo el cuerpo, los órganos y los niveles celulares. En este artículo presentamos la metodología de modelado de Dinámica del Sistema y su aplicación al modelado de un modelo compartimental farmacocinético-farmacodinámico del efecto de profundidad anestésica en pacientes sometidos a intervenciones quirúrgicas, derivando un modelo de simulación en el entorno de simulación orientada a objetos OpenModelica. La Dinamica de Sistemas se puede ver como una herramienta educativa poderosa y fácil de usar y en la enseñanza de Biología de Sistemas.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Ocelet: a modelling language and a simulation environment for studying landscape dynamics

Modelling spatial dynamics in landscapes can be a means for better understanding the multiple and complex ongoing processes that underlie important issues facing societies today. Hypotheses and independent knowledge inferred from ground observations can be confronted for consistency, and the mechanisms requiring finer descriptions can also be identified. Different scenarios of landscape management can then be simulated and the possible consequences of the measures taken assessed. However, modelling landscape dynamics at different temporal and spatial scales remains a challenging task. Various approaches have been proposed to address this, including cellular automata, agent-based systems, discrete event systems, system dynamics and geographic information systems, each displaying specific benefits in some domains of application, and weaknesses in others. In this area of research, we are exploring an approach based on the manipulation of graphs (mathematical object expressing a set of entities, some of which are linked) that are employed here in an innovative way for modelling landscape dynamics. Concepts essential for modellers had to be identified and formally defined. A modelling computer language (called Ocelet) was then developed, together with the grammar and syntax needed to manipulate these concepts, the compiler, and the environment/interface for building models and running simulations. Ocelet is thus both a modelling language and a simulation tool. To illustrate its usage in diverse situations, four case studies are presented: 1) land cover changes in an agroforestry landscape; 2) coastal dynamics of mangrove ecosystems; 3) the dissemination of a pathogen among neighbouring agricultural plots; and 4) temporary pond and mosquito population dynamics for understanding Rift Valley Fever (RVF) occurrence. (Texte intégral

The integrated use of enterprise and system dynamics modelling techniques in support of business decisions

Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulationmodelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink

Ocelet modelling language and simulation tool: possible applications in pest management

Modelling spatial dynamics may be used to gather understanding on how insect populations develop in a given environment. Hypotheses and independent knowledge inferred from ground observations can be confronted for consistency, and the mechanisms requiring finer descriptions can also be identified. Different scenarios of pest management can then be simulated and the possible consequences of the measures taken assessed. However, spatial dynamics are expressions of multiple and complex ongoing processes, and their modelling at different temporal and spatial scales remains a challenging task. Various approaches have been proposed to address this, including cellular automata, agent-based systems, discrete event systems, system dynamics and geographic information systems, each displaying specific benefits in some domains of application, and weaknesses in others. In this area of research, we are exploring an approach based on the manipulation of graphs (mathematical object expressing a set of entities, some of which are linked) that are employed here in an innovative way for modelling landscape dynamics. Concepts essential for modellers had to be identified and formally defined. A modelling computer language (called Ocelet) was then developed, together with the grammar and syntax needed to manipulate these concepts, the compiler, and the environment/interface for building models and running simulations. Ocelet is thus both a modelling language and a simulation tool. To illustrate its usage, two case studies possibly pertinent for pest management are presented: 1) the dissemination of a pathogen among neighbouring agricultural plots, and 2) temporary pond and mosquito population dynamics for understanding Rift Valley Fever (RVF) occurrence. (Texte intégral

Nonlinear quantum input-output analysis using Volterra series

Quantum input-output theory plays a very important role for analyzing the dynamics of quantum systems, especially large-scale quantum networks. As an extension of the input-output formalism of Gardiner and Collet, we develop a new approach based on the quantum version of the Volterra series which can be used to analyze nonlinear quantum input-output dynamics. By this approach, we can ignore the internal dynamics of the quantum input-output system and represent the system dynamics by a series of kernel functions. This approach has the great advantage of modelling weak-nonlinear quantum networks. In our approach, the number of parameters, represented by the kernel functions, used to describe the input-output response of a weak-nonlinear quantum network, increases linearly with the scale of the quantum network, not exponentially as usual. Additionally, our approach can be used to formulate the quantum network with both nonlinear and nonconservative components, e.g., quantum amplifiers, which cannot be modelled by the existing methods, such as the Hudson-Parthasarathy model and the quantum transfer function model. We apply our general method to several examples, including Kerr cavities, optomechanical transducers, and a particular coherent feedback system with a nonlinear component and a quantum amplifier in the feedback loop. This approach provides a powerful way to the modelling and control of nonlinear quantum networks.Comment: 12 pages, 7 figure