The conventional method of teaching macroeconomics to undergraduates relies on static graphs, an
approach with documented pedagogical problems. In contrast, the feedback method uses feedback
loop diagrams and interactive computer simulation models. This paper describes the feedback method
and reports on four experiments designed to test its effectiveness. Two experiments examined student
preferences for methods of learning macroeconomics; for example, using static graphs or a feedback
loop diagram. The experimental designs were quite different, but the results were the samea
significant majority preferred the feedback method. The most commonly cited reason: feedback loops
enable students to visualize an economic process. The third and fourth experiments addressed the
performance question. In the third experiment, students showed more understanding of GDP when
they had access to a stock-and-flow feedback diagram of the economy. In the final experiment,
students using feedback loop diagrams displayed more understanding of business cycle dynamics than
those who had access to a conventional aggregate supply and demand graph. Teaching
undergraduates to search for feedback structure in the economy and using computer simulation to
connect structure with behavior appears to be a promising method for teaching macroeconomics.
Building upon previous work in the field of behavioral finance and artificial stock markets, a model incorporating discrete and continuous interrelations is developed. Three different investor types are modeled as individual agents: fundamental analysts, technical analysts and noise traders. They differ in their intrinsic pricing mechanism and represent trading strategies that are observed in financial markets. The developed structure is able to reproduce the formation of speculative bubbles and other stochastical anomalies that are characteristic for financial time series.
This paper discusses five landmark projects which highlighted issues and produced frameworks that became important building blocks in the application of System Dynamics to corporate strategy. The early models were primarily at the level of the firm. The first model described in the paper captures the tension among conflicting performance objectives and shows how the conflicts impact mid-term company performance. The second model represents the behavior of an R&D organization as it responds to changing pressures and direction from corporate management. Over time the focus shifted to market behavior and competition. The third model explains powerful long-term dynamics that lead to "commoditization" of products and services. Recent work analyzed the social dynamics of markets, e.g., as they affect innovation and technology substitution. The fourth model in the paper represents the fundamental dynamics of innovative industries, building on an extensive body of research and publications. The final model focuses on the market impacts of social factors, e.g., trust, fashion, the characteristics of lead users, how trends are perceived and extrapolated, the flow of information, bandwagon effects, and network effects. The most important lessons are about the critical roles of organizational, social, and psychological factors in important business decisions and competitive behaviors.
The growth theory has, through the so-called endogenous or new growth theory, taken on decisive impulses. This contribution delivers an overview of the various extensions without going into detail about the mathematical observations and the main focus on supply-side orientated approaches. The main goals of the growth theory are to understand the exponential climb of the populations income or also the per‑capita income and to draw conclusions for policy makers. The paper uses stock-flow-graphs to visualize the major loops. Because changes tend to be incremental I adopt standard textbook models first. All models are used in economic teaching with additional simulation and extensions. Later on students learn to modify those models.
Motivated by declining fish populations and the apparent inability of regulatory agencies to manage important fisheries, this research measures the accuracy of a fishery model that explicitly models the regulatory process and the resulting degree of compliance by fishers. The method involved careful review and enhancement of a prior model with a more limited regulatory sub model,and then measuring, for both models, the mean absolute error of model calculated values for historical spawning biomass, acceptable biological catch, and harvest. The most recent five years of data were held back so that model prediction error could also be computed. Results indicated that although the fitness error for the enhanced model was significantly less than the prior model (23% vs. 38%), predictions were improved only for one of the three measures. The implications for researchers seeking to endogenously model fishery management processes are sobering. Policy makers on the other hand will likely see the results as support for their instinctual skepticism regarding policy models.
A School of Business located in the northeast United States annually administers the AACSB/EBI Undergraduate Business Exit Survey to all its graduating seniors. One area that has consistently received low marks has been advising. The Associate Dean of the Business School wanted to address the situation and see how the system could be improved. Through interviews with the Associate Dean and the advising staff, a consulting team compiled information about the system and identified the major problem as congestion in the system. Recommendations included changing the criteria for students required to use the system, simplifying the curriculum, better promoting the advising function to students, increasing the use of automated advising tools, expanding the length of the advising period, and adding advising staff or having faculty do advising. So far, the School of Business has adopted only one of these recommendationscurricular simplificationwhich may improve the situation as time passes. The School is currently examining other options, especially changing the criteria for required advising and having the faculty get involved in advising.
Theoretical reflections about System Dynamics (SD) have usually been grounded in the developments of what can be called general philosophy of science. In our paper, a philosophical approach more sensitive to the peculiarities of SD is proposed that is closely linked to the recent constructivist proposal of John Searle and to the expressivist theses of Robert Brandom. We will focus on three very important conceptual problems the ontological problem of realism concerning the structures postulated by SD models, the epistemological problem of the explanatory power of SD models, and the methodological charge of merely producing a kind of patchwork when building of SD models--, arguing that by combining the constructivist and expressivist philosophical perspectives of those authors in a certain way would offer a better understanding of scientific and technical activities such as SD modelling.
After the Islamic Revolution in 1979, Iran had to face another challenge: the war against Iraq. This challenge forced the government to help people by granting subsidy to essential goods such as bread, drugs and different kinds of energy - especially electric power which is one of the major industries in every country. This policy helped people have an easier life during the war, but as the famous law of supply demand tells us, the lower the price of any good, the higher demand for that good is predicted and this low price of energy made Iran one of the most and worst energy consumers in the world. This high rate of consumption will cause lots of problems such as lack of electricity and financial pressure on the government. In this paper, a system dynamics model is developed to simulate the situations of Irans electric power industry since 15 years ago, assuming the effect of peoples pressure on the government and the pressure of the government to decrease subsidy. The main model is built on two positive and negative loops and the results are compared with the real statistics. Then, two policies are applied to the model: education and increasing the price.
Obtaining insight into the effects of policy interventions is often a difficult matter. A new method to obtain a first insight into those effects is presented in this paper. The basis of the method is a Causal Loop Diagram to which information on causal relations and variables is added. Part of the information is expressed in qualitative terms. This Method to Analyse Relations between Variables using Enriched Loops (MARVEL) takes proposed interventions as a starting point. Interventions are interpreted as imposed changes on selected model variables representing intervention points. A new feature is that causal relations are no longer passive but active model elements. They propagate the changes through the model in a time-dispersed way. MARVEL determines how this causes (other) variables representing the models performance to change in the desired direction at selected moments in time. MARVEL can be used for policy development, policy analysis and policy evaluation problems.
Enloe Medical Center is a non-profit community hospital in Chico, California. Among the many services they provide is a Labor and Delivery Department. While mothers are routinely admitted from 1:00pm to 1:00am, they are generally discharged between 10:00am and 5:00pm. This results in a generic bell curve behavior pattern for patient occupancy during the daytime. Hospitals are reimbursed for inpatient services in two major ways: either on a per diem basis, or by diagnosis related groups (DRG). Either way, the revenue to the hospital remains the same, regardless if the patient is discharged at 4:00am or 4:00pm. In California, state mandated nurse to patient ratios require hospitals to maintain a minimum level of nurse staffing for inpatient services. Thus, as the patient census rises during the day, so must the number of nurses on staff. This is the problem studied; costs expended for patient discharge delays.