Simulation models can occupy very different roles modelling in the planning of health care infrastructure and services. They can be a close replica of the real world used to produce exact answers or as a tool in building consensus among stakeholders with different views and objectives, as a boundary object. The role a simulation model can have in a decision process is constrained by the degree to which the model and its results are accessible to stakeholders with no or limited experience with modelling. The visual representation of the model and its results are likely to be of a major influence on the accessibility of the model. However, whether a visually more accessible model will result in improved stakeholder buy-in, creative problem solving and more effective decision processes remains an empirical question. This paper draws on a currently ongoing research project on the use of system dynamics and discrete event simulation tools in health care and presents some early conceptual work on the role of modelling in the planning process.
Mauritius, a Small Island Developing State and one of the most successful economies in Africa, has experienced sustained economic growth during the past three decades. Mauritius' power sector has evolved considerably during this period to cater for increases in electricity demand following such growth. The island has continuously invested in additional capacity to meet such increases in demand in the medium term. Approximately 78% of Mauritius' electricity needs are met through imported energy sources, mainly oil and coal. However there are increasing concerns about price volatility and security of supply from such sources. Policymakers are thus committed towards a sustainable energy development plan for the country. In this line, authorities have embarked into elaborating a comprehensive energy policy for Mauritius. Threshold-21 (T21) is a tool that applies system dynamics to aid in policy making in an integrated manner. This paper documents the first iteration in the development of a power sector model for Mauritius based on T21 models. The aim is to provide a description of the Mauritian power landscape and translate the same into a system dynamics model. Future work includes expansion of the model to cover the whole of the Mauritian energy system and to do policy analysis.
For more than 30 years, system dynamics has played a noteworthy role in the making of energy policy for many states, provinces, corporations, and countries. This work has covered the spectrum from predictive forecasting, to strategic planning, to assessing policy options, to negotiating policy, to shaping policy, to defending policy, to swaying policy. While The FOSSIL1 model appears to have begun this process, the ENERGY2020 model continues to influence the policy process in functional, but possibly ambiguous, ways.
This paper describes a laboratory experiment to study the effect of variable capacity utilization over the behavior of an electricity market. Capacity utilization is an important factor in order to explain the multiple cycle periods observed in many industries. Investors make investment and capacity utilization decisions in an expanded Cobweb market of the power generation sector. The experiment has four year investment lag, power generation capacity with a sixteen year lifetime and it does not assume full capacity utilization. Cyclical behavior results in three out six markets. Tests show that one market presents well-defined and pronounced cyclical behavior, while other two markets present minor oscillations. Results suggest that varying capacity utilization favors stability, rather than a cyclical behavior.
Commodity prices cycles have a negative effect in developing countries. This paper applies system dynamics to study the long term cyclical behavior of coffee price. The model is based in Meadows (1970) and Deaton and Laroque (1996, 2003). The model includes the price dynamics, investments, capacity, and demand. Our model is the first stages and not fully calibrated. Nevertheless, the model provides a better understanding of the commodity cycles, focusing on the internal structure of the system. The model replicates the reference mode, thus, coffee price exhibits cyclical behavior in the long term. In particular, we have founded very difficult the estimation of investment functions; therefore, future work will be focus on validation and use of laboratory experiments to estimate investment function with coffee farmers.
This paper is a review of research on the application of laboratory methods to System Dynamics (SD). Although laboratory methods have been used in psychology for many years, our review focuses on the laboratory experiments developed from the experimental economics field and on their contributions to SD and the management sciences. In particular, we examine the use of experimental methods for estimating the decisions used by SD models, and the intersection of SD and laboratory experiments in theory testing and theory building.
A system dynamics model of insurgencies is built using the US. Army and Marine Counterinsurgency Manual (FM 3-24) as a basis. It must, however, be supplemented by additional theory from outside sources to enable calibration to a historical data set. Parameter and policy analyses are conducted. These highlight the criticality of some features of insurgencies described by FM 3-24 such as the importance of obtaining and maintaining popular support and employing sufficient counterinsurgency forces to gather intelligence. Other features, not highlighted by FM 3-24, also are shown to be important such as how quickly does violence escalate during the insurgency and how easily can detained/eliminated insurgents be replaced. Finally, contraindicating the expectations of FM 3-24 and other conventional wisdom, results from optimization simulations suggest that the level of force used in a counterinsurgency should increase only once a preparatory period of intelligence gathering by those same forces has been completed.
We develop a Systems Dynamics model for capturing the key interactions involved in the evolution of the subprime mortgage crisis. In particular, we propose an aggregate modeling resolution that involves three main sub-systems, namely, an aggregate banking system, an aggregate housing market and an economic environment. The model exposes the physics of each individual system as well as influences and interactions among the three systems. The model is useful for developing intuition about the evolution of the crisis as well as the lagged timing and magnitude of the effects of various corrective actions, such as an economic stimulus package. We present three scenarios using simulated data. In the first scenario, we establish an equilibrium state that represents a steady state normal condition. In the second scenario, we introduce a step function for the availability of subprime loans and hold it for certain duration. This practice eventually culminates in a credit crisis, where the aggregate bank experiences insolvency. In the third scenario, we study the application of an economic stimulus, which steers the entire system back to a new equilibrium state. We note that the economic stimulus needs to be larger than a certain critical lower threshold in order to enable the system towards reaching a new equilibrium.