So as to stabilize the atmospheric greenhouse gas concentrations at tolerable levels, global emissions should dramatically be reduced soon within this century. To achieve this end, a long term global cooperation and developing country participation is essential. In this paper, we take the Contraction and Convergence framework first proposed by the CSE of India as one possible treaty and investigate the long term abatement and trading behavior of countries with economic growth. Dynamic simulation based economic experiments is the method. Seven countries with potential buyers and sellers trade permits in the global market for 25 years. For each simulated year, asks and bids of the countries /regions are collected and permit prices are set at the equilibrium price. In the first treatment, annual national quotas expire each year and the countries cannot save their allowances. In the second treatment, the countries are allowed to transfer quota surplus /deficit up to 30 /20 percent of their annual emissions to the next year. One hypothesis is that, neither the developed nor the developing countries will make sufficient timely reductions and they will create unanticipated costs for their economies as the quota prices increase over the years. An implication of this result is global cooperation being threatened under more stringent reduction requirements and increasing costs of compliance.
Delays are one specific factor contributing to misperceptions of dynamics. An experimental study was conducted to investigate how different representations of delays in the decision making interfaces (DMIs) may affect peoples ability to manage and understand a dynamic system. A simple production-inventory management game was developed with four distinct DMIs, each featuring the production delay in a different way. Subjects were assigned randomly to use one of the four DMIs and a single-subject, think-aloud experimental protocol was deployed to gather data on the decision making process. No vivid impact of the different representations of the delay in the DMI was observed. However, data gathered through the single subject experimental protocol suggest that the subjects do not follow the anchoring and adjustment rule proposed earlier (see Sterman 1989). Rather, they develop a simplified decision rule that is not robust to changes in the task settings but that is successful in the context of the particular experimental task.
This paper presents a System Dynamics approach to analyzing the violence and widespread death and displacement in Darfur, Sudan, as observed since 2003. We lay a foundation for using simulation to investigate the underlying structure and effects of violence; model analysis indicates that presently the dynamics may be driven by the population at risk more so than aggressor intent. This model can aid in future policy analysis and establishes a foundation for using System Dynamics to understand the structure and pattern of genocide. We present several challenges to analyzing the Darfur crisis including observability, information delays, and the choice of metrics. Finally we discuss modeling results and options for intervention and propose several policy questions and areas for future research.
The UK health and social care systems are continuously changing over time. Other authors have previously put a strong case for usage of system dynamics (SD) in this area largely because SD address issues of system complexity and identification of feedback loops, resulting in a greater insight into this problem situation.
This paper presents research carried out in two areas of SD, firstly the conceptualizing of a problem and secondly the building of a SD model related to the dynamic problem of bed blocking in the UK health and social care domain. A case study approach has been applied to a hospital discharge department and elderly wards in a main UK hospital.
This paper provides a useful insight into issues that have occurred when conceptualizing and formulating a health and social care SD model. System behavior has been discussed as has the use of causal loop diagrams and stock and flow diagrams. Causal loop diagrams and stocks and flows have shown to play a useful part in overcoming SD difficulties. SD has proved to be a useful method in helping to gain an insight into the dynamics of a health and social care system. This is a preliminary paper, future papers will expand on this to look at policy experiments and sensitivity tests.
This paper proposes several alternative methods to improve system dynamics models used in the literature for generation expansion planning in liberalised electricity markets. Concretely, these methods provide a better representation of oligopoly structures and market power. These improvements focus on market price and productions calculations, future markets modelling and companies differentiation when deciding new investments. The methods presented in the paper are based on equilibrium approaches and credit risk theory.
Building upon previous work in the field of system dynamics, a generic model of multiple improvement programs is outlined. The model is used to create insightful stories on success and failure in process improvement initiatives. The simulation experiments reveal that plants should strive for implementation patterns that focus on programs exhibiting higher organizational complexity rather than technical complexity. Furthermore, the simulation analyses provide insights in the interplay between organizational learning, program commitment, and process improvement. The value of the conducted approach lies in the explicit investigation of the impact of varying improvement program patterns on plant performance.
This paper is a concept paper about a suggestion proposed by Nathan Forrester, in the last year conference to extend eigenvalue analysis to nonlinear models. His idea was to consider higher order terms of the Taylor series expansion when approximating nonlinear models. In this paper, we demonstrate the feasibility of Nathan's idea. The main contribution of this paper is to devise a pragmatic approach to solve the resulting equations of Taylor series expansion. This pragmatic approach is based on our novel concept of 'smoothed Jacobian' matrix, which is computed from both the ordinary Jacobian matrix and the set of Hessian matrices. Recall that the elements of the ordinary Jacobian matrix represent slopes of relationships, while the elements of the Hessian matrices represent curvatures of relationships. So by integrating the elements the ordinary Jacobian with the elements of the Hessian matrices, we are actually smoothing the slopes given the knowledge about curvatures. Consequently we are smoothing the time trajectories of eigenvalues and eigenvectors in nonlinear models.
This paper revisits the use of trend forecasting in driving policy in social systems by comparing it with
derivative control in classical control theory. While both processes involve use of trend to determine
policies for achieving reliable performance, the outcomes of the former have considerable variability while
those of the later can create improvement in performance with certainty. The similarities and differences
between the two processes are discussed and guidelines suggested for improving the efficacy of trend-
forecasting in policy design in social systems.
Elderly patients use more medications given the prevalence of co-morbidities putting them at risk of experiencing medication errors. A highly anticipated strategy is the implementation of health record solutions, namely a mix of shared electronic health records and personal health records. This intervention provides information for all individuals involved in the medication use system (patients, carer, doctors and pharmacists) and enables them to make more informed decisions throughout the medication use process (prescribing, dispensing, administering, monitoring of medications). However, it is difficult to direct the design of such an integrated health record solution that takes into consideration contextual factorsand its impact on, existing interventions and society. Traditional methods such as random controlled trials lack the capacity to capture the scale of the problem and are inadequate in terms of time frames, cost, resources required, and non-applicability of trial settings. Multi-scale simulations can represent the systems different spatial and temporal resolutions providing a logical and consistent framework for dynamic analysis and a means to design and test health record policies to cover a range of possible futures in a risk-free environment.
This paper elaborates the original theoretical law of capital accumulation that generates scenarios of macroeconomic evolution. The main variables are relative labour compensation, employment ratio, and gross unit rent, produced capital-output ratio, proved non-renewable reserves-output ratio, desired proved non-renewable reserves-output ratio, and depletion of non-renewable reserves per unit of net output.
Worsening profitability and declining employment ratio over the long term characterise an inertia scenario for the U.S. economy over 19912107. Excessive depletion of proved non-renewable reserves contributes to these disadvantageous tendencies.
A forward-looking investment policy based on proportional and derivative control over proved non-renewable reserves brings about their extension, raises profitability and employment over the long term in the first normative scenario in comparison with the inertia scenario.
The initial theoretical law is transformed into control law of capital accumulation. The operational application of this control law to the U.S. economy reveals and explains substantial lasting improvements in profitability, employment ratio, labour productivity and real labour compensation in the second normative scenario compared with the first normative scenario that are achieved together with extending proved non-renewable reserves due to excess income levy.
Keywords: Capital accumulation, proved reserves, long wave, closed loop control, excess income levy, the U.S. economy