Traditional global models address important global problems using highly aggregated measures, but it may be argued that the world is strongly non-homogeneous at least at some fundamental level: developing (South) nations and developed (North) nations may have very different, asymmetric problems, goals and structures. This study aims to investigate these two distinct groups of economies in a context of global sustainability. We identified population, economic growth, welfare gap, energy supply and pollution as key issues and analyzed them in a systems perspective. A dynamic feedback model, which discriminates these two groups of nations, is constructed based on WORLD-3 model in order to study the dynamics of key parameters related to these issues for the period 1975-2050. Simulation experiments reveal that population characteristics of South and current mode of economic activity, which is extensively dependent on non-renewable energy resources constitute serious obstacles for the sustainability of the system. Hence, stabilizing the population growth in South, transition to alternative energy resources and investment support to South for this transition are vital for closing the welfare gap between blocks and sustaining the global system.
Effects-based Operations (EBO) is becoming the centerpiece of Western military thinking. The concept is based on influencing the behavior of adversary complex adaptive systems (such as terrorist networks) in dynamic environments. Mission analysis is the foundation of campaign planning. This paper suggests a process to improve how campaign planners identify effects necessary to yield a desired endstate during EBO mission analysis. The process is based on using a modified version of Soft Systems Methodology to structure the problem by providing planners a high-level initial understanding of the dynamic complexity associated with 4th Generation Warfare threats. Planners use this understanding to identify and diagnose specific adversary behavior inconsistent with the directed endstate. Potential system changes to modify problematic behavior are next identified and debated. Finally, the changes are converted into effects that serve as the input into more detailed planning efforts. The process uses group learning and shared understanding as a hedge against the ambiguity associated with 21st Century military planning.
In this paper, the typical anchor (expected value of the outflow or expected loss) used in the most popular decision rule of the stock management modeling, the Anchoring and Adjustment Rule is studied for structures including a decaying stock. A new anchor (equilibrium value of loss) is proposed and compared with the expected loss formulation. We demonstrate that equilibrium value of loss formulation helps bringing the control stock to its desired level more rapidly. In addition, we show that managing a decaying stock in a stable way is difficult when the supply line is discrete. Standard stock adjustment and supply line adjustment terms anchored around expected loss can yield highly unstable oscillations. Counter-intuitively, for some cases, ignoring the supply line adjustment term may completely eliminate unwanted oscillations. If equilibrium value of loss is selected as the anchor and when the decay time (life time) is small enough, management of the stock can even be done by ignoring all the adjustment terms.
This is a second paper of a series of macroeconomic modeling
that tries to model macroeconomic dynamics such as the determination of GDP (Gross
Domestic Product) and money supply from system dynamics perspective.
Following the first paper on the money supply and creation of deposits,
this second paper tries to model dynamic determination processes of
GDP, interest rate and price level on the same basis of the principle of
accounting system dynamics developed by the author.
For this purpose, a simple Keynesian multiplier model is constructed
as a base model to examine a dynamic determination process of GDP.
It is then expanded to incorporate the interest rate, whose
introduction enables the analysis of aggregate demand equilibria
as well as transactions of savings and deposits, and
government debt and securities.
Finally, a flexible price is introduced to adjust an interplay between
aggregate demand equilibrium and full capacity output level.
A somewhat surprise result of business cycle is observed from
the analysis.
This paper develops a hypothesis that the normal mode of operation for many organisations is well beyond their safe design capacity and that many health and social care organisations in the UK are in this position. This situation arises from having to cope with demand, irrespective of their supply capability.
The irony is that such organisations can appear to cope at the strategic level. This is because operational managers employ a variety of well-intended, informal, survival techniques to meet performance targets. However, such practices can perpetually mask the underlying reality and have serious unintended consequences .
Evidence for the hypothesis has emerged from a number of studies carried out using system dynamics to identify and promote systemic practice in local health communities in the UK. The rigour of quantitative simulation model construction has identified mismatches between how managers claim their organisations work and the observed data and behaviour. The discrepancies can only be explained by surfacing informal coping strategies. Indeed, the data itself becomes questionable as it reflects more the actions of managers than the true characteristics of patient pathways.
If proved wholly or even partially correct there are some important messages in the paper for Health and Social Care management, the meaning of data and for modelling.
Real-world policy analyses efforts indicate repeated behavioral patterns that inhibit systems approaches, such as the time and budget pressures, the trade-off of detail vs. high-level insights, and the tendency to dwell in the familiar rather than delve into the unrevealed. Examining mainstream (non System Dynamic) business and policy processes issues such as these seems critical to increasing the introduction of systems approaches. However, the perspective we as a community of modelers takes is critical to reinventing business and policy analyses. To the extent the barriers are seen as circumstances of the modeling environments there is little leverage towards resolution; if we can see the impediments as being a result of our behavior as analysts, the nature of the barriers change and there is much more opportunity for improvement. The paper examines a nonSystem Dynamics policy analysis for the electric utility industry from both these points of view.
The two most important fundamental needs of towns and cities are a sufficient supply of adequate drinking water and the removal of polluted water. History has shown that if these needs cannot be met, cities rapidly become uninhabitable. New Zealand's current water systems were designed and built in the 19th century and have not been improved much since. Generally, infrastructure has been built on the assumptions of abundant water resources and the unlimited ability to treat and dispose of polluted waters. Especially in Auckland, New Zealand's largest city and one of the most rapidly urbanising cities in the world, there is increasing tension due to rapid urban growth and the costs associated with replacing old water infrastructure and extending it to new urbanised areas. The challenges of managing urban water systems in New Zealand today call for an application of system dynamics. Our proposed research is based on the hypothesis that systems thinking and modelling methodology can be applied to the question of urban development in the Auckland region and is a valid instrument to identify policies that effectively foster the sustainable development of urban structures, in particular urban water infrastructure. This paper discusses the current situation and challenges, and outlines the proposed research.
The objective of this workshop is to provide participants an introduction to agent-based modeling of
crowd dynamics. A summary of pedestrian socio-psychological egress behavior will be presented
together with an outline of existing modeling techniques and software tools. A detailed description of a
simple crowd model that can be implemented using MATLAB will be presented. Participants will learn
how to develop a simple yet fully functional simulation and visualization of crowd dynamics. Skeleton
Matlab scripts will be available for download from www.sanithw.org starting July 1st, 2005 but will also
be available via PC/MAC compatible USB drives during the workshop.
In a constantly changing environment, a Computer Security Incident Response Team (CSIRT) has to evolve over time in order to sustain or improve its effectiveness. The main task of a CSIRT is to help victims mitigate the effects of computer security incidents. A frequently identified problem for a CSIRT is that they are overworked, understaffed and under funded. In this paper, we present a conceptual model of such conditions based on a case study. The model is a first attempt to understand the main factors influencing a CSIRTs ability to handle computer security incidents effectively, and to identify ways to improve their overall effectiveness. Based on theory from process improvement and information from the case study, we have identified that short-term pressure from a growing incident workload prevents any attempts for developing more response capability long-term. Fundamental solutions to solve this problem will typically involve a worse-before-better trade-off for management.
Information revolutions change the world by taping into a positive feedback loop. If we can identify the loops we can understand where they might be going and what their limits might be. We need to know the difference between a short-term trend and a long term dynamic. We need to know where this information might be pushing us so we can know if it is where we want to go.
Trying to look at a category, as broad as information revolutions, to identify patterns requires an approach that will give a broad but well specified picture a way to understand the positive feedback loops that create the growth and also to understand the countervailing loops that come into play in various ways. I believe that causal loop diagrams can give us a clearer picture of this kind of broad, messy problem