Over the last two hundred years, the United States has experienced dramatic losses in wetland coverage and quality. In 1987, the National Wetlands Policy Forum recommended that U.S. wetlands policy should achieve overall no net loss of the countrys remaining wetland acreage and function. Since then, regulations requiring compensatory mitigation for wetland losses, often through wetland creation or restoration, have become an essential component of federal wetland protection efforts. Recent reports have concluded that no net loss policy has been successful, citing the virtual elimination of wetland losses experienced in certain areas. However, these reports have not assessed the temporal nature of wetland loss and restoration. Delays in initiating and completing restoration activities mean that frequent temporary wetland losses can contribute to a consistent net loss over time. This paper analyzes wetland loss and compensation as dynamic processes that include temporal lags endemic to various mitigation techniques. Here, a system dynamics model of the mitigation process is used to explore wetland alteration and mitigation data collected between 1993 and 2004 for the Chicago, IL region. By analyzing wetland change dynamically, it becomes possible to adjust wetland mitigation methods to more effectively eliminate temporal net loss of wetlands.
As the natural world has become dominated by human influences the need for the public involvement in natural resource management decisions has become vital. In addition, scientists are now viewing nature as a dynamic rather than in balance. To accommodate these paradigms, natural resource managers have been encouraged to look holistically at the problems they manage through the lens of adaptive management. Current adaptive management theory incorporates variability, uncertainty, the relationship of impacts with respect to potential temporal and spatial disconnects, and social concerns. Add the obvious need for modeling and the stage is set for participatory system dynamics modeling.
Participatory SD modeling is a process that can integrate science and local knowledge with policy, and open the lines of communication between potentially different world views. We will use case studies to highlight three characteristics of participatory environmental modeling to illustrate the flexibility of process and the effectiveness of a broad range of interventions. 1) Interventions may take place anywhere on the problem identification to solution producing continuum. 2) Stakeholder involvement in the actual building of the model varies; the hands on continuum. 3) The type of data required varies on the qualitative to quantitative continuum.
As part of an integrated system, healthcare infrastructure should be planned and evaluated in conjunction with the services it supports. However, this is challenging because of uncertainty about future requirements due to technological, demographic, medical and policy change. Long lasting infrastructure needs to support healthcare processes that change rapidly. In the UK public private partnership models provide additional challenges for the National Heath Service. Contractual agreements between public sector providers of care services and the organisations responsible for the provision and maintenance of the built infrastructure typically last 30 years or more. Over this period both the demands for care services and the technologies used to deliver them are likely to change considerably. Infrastructure needs to be able to adapt to these changes, and planning tools need to recognise the interdependencies within the care service and care infrastructure system. System dynamics modelling offers the potential to plan for these challenges. It can help to guide the planning process of new healthcare infrastructure under conditions of uncertainty, so that the services it enables can meet present and future needs. The stylised model presented considers care service delivery over time depending on infrastructure flexibility options (e.g. to increase capacity) under different scenarios.
This paper provides an overview of the projections simulated by the base case of the Threshold 21 (T21) model customized to the United States of America. The present study highlights the main results of the simulation of the model for the three spheres, society, economy and environment and shows more in detail the behavior of the energy sector.
The purpose of this study is to develop an understanding of the dynamics of the real estate market in Istanbul. As a result of the unavoidable delays in perception of the real estate condition and construction of new buildings, the market variables are strongly oscillatory. A system dynamics model is constructed to understand the reasons of oscillations in real estate prices in Istanbul from the perspective of a construction company. The model includes the factors effecting the speed and size of the construction side (supply) and sales (demand). The model focuses on the balance/imbalance between housing supply and demand as a result of fundamental material and information delays, rather than costs and interest rates. Different scenarios are tested to improve the current oscillatory behavior. This model can serve as a simulation basis for predicting the reaction of the market to changes in the decisions of internal players and external inputs.
there are conflicting reports regarding the relationship between dose of dialysis or patient outcome, and machine maintenance. In this article, we will discuss the impact of hemodialysis machine maintenance on dialysis adequacy Kt/V and session performance by building a system dynamics model to evaluate the effect of machine maintenance on session performance
Paradigm of the El Farol bar for modeling bounded rationality is undertaken. The memory horizon available to the agents and the selection criteria they utilize for the prediction algorithm are the two essential variables for agent strategies. The latter is enriched by including various rewarding schemes during decision making. Playing with the essential variables, one can maneuver the overall outcome between the comfort level and the endogenously identified limiting state. The distribution of algorithm clusters varies considerably for short memory. This affects the long-term aggregated dynamics of attendances. A transition occurs in the attendance distribution at the critical memory where the correlations of the attendance deviations take longer time to decay. A larger part of the crowd becomes more comfortable while the rest of the bar-goers still feel congestion for long memories. Introducing direct local interactions within the attendees by forming different types of networks, we create extremes in the attendance distributions. Delayed feeding of the data to the agents or their inclination of to absorb failure by insisting on unsuccessful algorithms introduces significant correlations, hence predictability of attendances. We additionally explore the extent of agents manipulation, achieved by modulating the threshold in accordance with the correlations in the data.
The Egyptian stock market has been experiencing a set of fluctuations over the past two years. This was caused by several factors that include the behavior of the Egyptian Economy, as well as the performance of the other Middle Eastern stock markets. In this paper we use System Dynamics as a tool for mapping the performance of the CASE 30, Egyptian Stock market index, to identify the various interacting feedback loops that triggered such performance. These loops are then incorporated in a system dynamics model that is used to understand the causes of such performance as well as developing several scenarios for managing the various factors affecting the Egyptian stock market.
This paper describes a system dynamics model developed to facilitate design of nutrient reduction trading (NRT) programs. NRT is a form of cap-and-trade policy that has been strongly promoted in recent years to address diffuse source nutrient pollution. Despite its wide appeal and the enthusiasm of its proponents, very few trades have occurred to date. We propose that impediments to learning encountered in real-world pilot studies have contributed to lack of consensus in design and implementation of NRT. The model we offer is intended as a demonstration of how the design process in this instance can benefit from system dynamics modeling.
System dynamics is a suitable method in education for problem-oriented learning and for improving overall system thinking skills. It is proposed that integrated learning environments consisting of system dynamics models and additional didactical material have positive learning effects. This is exemplified by the illustration and validation of a learning sequence concerning market processes.