A model of infectious diseases has been developed for integration within a larger simulation structure to assess the interdependencies of critical infrastructures. The model has been parameterized to model a disease outbreak a large metropolitan area. The model subsequently calculates the spread of the infection and the influence of vaccination policies, quarantine and isolation procedures. Consequences are deaths, illnesses, and a variety of economic costs. Sensitivity analysis is a statistical technique to investigate how uncertainty in the input variables affects the model outputs and which input variables tend to drive variation in the outputs. Such analysis can provide critical information for decision makers and public health officials who may have to deal with the realities of a virulent infectious disease. This paper presents the results of preliminary analyses of the effects of inputs to the infectious disease model on the calculated consequences.
The Critical Infrastructure Protection Decision Support System (CIP/DSS) simulates the dynamics of individual infrastructures and couples separate infrastructures to each other according to their interdependencies. For example, repairing damage to the electric power grid in a city requires transportation to failure sites and delivery of parts, fuel for repair vehicles, telecommunications for problem diagnosis and coordination of repairs, and the availability of labor. The repair itself involves diagnosis, ordering parts, dispatching crews, and performing work. The electric power grid responds to the initial damage and to the completion of repairs with changes in its operating characteristics. Dynamic processes like these are represented in the CIP/DSS infrastructure sector simulations by differential equations, discrete events, and codified rules of operation. Many of these variables are output metrics estimating the human health, economic, or environmental effects of disturbances to the infrastructures.
A generic representation of the telecommunications infrastructure in a metropolitan area designed to be integrated into a much larger simulation of the seventeen key infrastructures[1] has been implemented in Vensim[2]. This Critical Infrastructure Protection Decision Support System (CIP/DSS) is designed to provide insights for the Department of Homeland Security (DHS) in making decisions about investments related to critical infrastructure protection[3]. Although a system dynamics representation was well suited to representing the dynamics and interdependencies in this complex system of systems, it was recognized early on that collaborations with key infrastructure domain experts and organizations would be important to the success of the project. This paper summarizes the results of a collaborative effort with Bell Laboratories, Lucent Technologies to leverage a detailed switched network simulation to inform the telecommunications system dynamics model in CIP/DSS.
A demonstration and discussion of the process and benefits of building a 'high level' strategic simulation tool for a UK Health Authority seeking to build an evidence base to support strategic investment decisions across the primary and secondary health care system. The presentation will discuss the key drivers for change and performance expectations in the UK health system. It will describe the rationale behind the Health Authority's request and the means of delivering on this. Particular emphasis will be placed on the learning process that took place during model construction, the key links to strategic decision making, evidence of the benefits to the Authority during and since the simulator development and the way in which the simulator tool has been placed locally to inform and influence performance monitoring. The workshop will demonstrate the model itself as well as outputs generated by at least one 'future scenarios' workshop held locally.
Problems of strategic human resource management, such as proposed reductions in prospective retirement benefits under an American pension plan, present complex challenges. Typically, a firm pursues strategic objectives such as Cost control through changes in focal policies and programs affecting its workforce. At such times a firm should be wary of long-term consequences among individual employees, since the firm, its programs, and its employees, comprise a three-level feedback system. Unintended consequences at the micro-organizational level may lead in turn to additional unpleasant surprises at the program or firm levels. This paper discusses the development of a simulation model combining the approaches of agent based and system dynamics disciplines in addressing a clients multi-level concerns. It articulates differences between the agent based and system dynamics modeling disciplines in the service of this task, and discusses issues of implementation and praxis which have governed our design at points of tangency between these approaches in such a hybrid model.
There are some industries whose strategic innovations are easier and faster to replicate because the products are mostly commodities, one of such industries is the fast-moving consumer goods industry (FMCG). In the fast-moving consumer goods industry, companies face a very difficult, if not almost impossible, task developing a competitive advantage based on differentiation or low cost strategies. The main reason is that competitors match or, even overtake innovations and costs reductions in a very short period. A case study illustrates the competitive dynamics of a market segment of this industry using a behavioral model (Morecroft, Lane, and Viita, 1991). The model captures the management team understanding of the competitive dynamics of the industry.
The resource-based view of strategy (RBV) seeks to explain why some firms consistently outperform rivals in the same industry by acquiring a unique set of strategic assets (or resources). We suggest firms achieve competitive advantage through cognitive asymmetries (differences between dominant managerial mental models) that lead rival management teams to implement distinct resource building strategies. This managerial and cognitive view of competition and rivalry lends itself to investigation through problem structuring methods. We suggest that resource maps, as a problem structuring method, can be used to interpret managerial mental models for strategic decision-making in terms of resource building processes. Through resource maps, we represent the system of asset stocks believed to be most important for driving business performance. We illustrate the framework by comparing and contrasting maps of the system of resources (asset stocks) that best characterise the four leading firms in the UK Commercial Radio Broadcasting Industry.
There is an economic reason to extend the useful life of the landfill because once the old landfill is filled, the new one can be found only at greater distance, and this increases remarkably the transportation cost. Therefore, waste has to be recovered as much as possible. To do this, in developing countries context, small scale composting promotion is widely accepted and the contribution of informal recycling is also widely recognized. The question remains to what extent composting and informal recycling contribute to the waste diversion. To create a platform for discussion and learning, a model is established. The model in this paper is based on the system dynamics (SD) approach. The simulation results with the data collected in Phnom Penh city, Cambodia, show that waste recovery through small-scale composting and informal recycling cannot contribute significantly to the waste diversion without other supporting policies.
One aim of this paper is to show the mode of action of the integrated transport-economic-environment model ASTRA that is based on the System Dynamics Modelling method. Furthermore two policy scenarios - one in which a tax per ton of CO2 emitted by transport will be introduced and another with a significant increase of crude oil price until 2020 - will be compared to a reference case. The reference scenario results will be analysed to identify long-term trends in transport, economy and environment. The scenarios results show the impact on a sustainable development by an introduction of transport emission pricing by policy makers on the one hand and of a stronger increase of crude oil prices based on the fact of growing scarcity of oil resources on the other hand.