AI News, Data Science meets System Dynamics

Data Science meets System Dynamics

Developed at MIT’s Sloan School of Management in 1950s system dynamics is a methodological approach to model the behavior of complex systems, where change in one component leads to change in others (like the dominos effect with feedback loops added).

Now if the account owner is interested in how much money she is going to save after one period, it is a simple calculation and needs no calculators: Stock 2 = Stock 1 + Flow 1 – Flow 2 However, a calculator may be needed if she’s to calculate savings over 5 years.

System dynamics

System dynamics (SD) is an approach to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, table functions and time delays.[1]

Originally developed in the 1950s to help corporate managers improve their understanding of industrial processes, SD is currently being used throughout the public and private sector for policy analysis and design.[2] Convenient graphical user interface (GUI) system dynamics software developed into user friendly versions by the 1990s and have been applied to diverse systems.

SD models solve the problem of simultaneity (mutual causation) by updating all variables in small time increments with positive and negative feedbacks and time delays structuring the interactions and control.

This model forecast that exponential growth of population and capital, with finite resource sources and sinks and perception delays, would lead to economic collapse during the 21st century under a wide variety of growth scenarios.

From hand simulations (or calculations) of the stock-flow-feedback structure of the GE plants, which included the existing corporate decision-making structure for hiring and layoffs, Forrester was able to show how the instability in GE employment was due to the internal structure of the firm and not to an external force such as the business cycle.

These hand simulations were the start of the field of system dynamics.[2] During the late 1950s and early 1960s, Forrester and a team of graduate students moved the emerging field of system dynamics from the hand-simulation stage to the formal computer modeling stage.

The Club of Rome is an organization devoted to solving what its members describe as the 'predicament of mankind'—that is, the global crisis that may appear sometime in the future, due to the demands being placed on the Earth's carrying capacity (its sources of renewable and nonrenewable resources and its sinks for the disposal of pollutants) by the world's exponentially growing population.

In the system dynamics methodology, a problem or a system (e.g., ecosystem, political system or mechanical system) may be represented as a causal loop diagram.[4] A causal loop diagram is a simple map of a system with all its constituent components and their interactions.

By understanding the structure of a system, it becomes possible to ascertain a system’s behavior over a certain time period.[5] The causal loop diagram of the new product introduction may look as follows: There are two feedback loops in this diagram.

However, in general a causal loop diagram does not specify the structure of a system sufficiently to permit determination of its behavior from the visual representation alone.[6] Causal loop diagrams aid in visualizing a system’s structure and behavior, and analyzing the system qualitatively.

The steps involved in a simulation are: In this example, the equations that change the two stocks via the flow are: List of all the equations in discrete time, in their order of execution in each year, for years 1 to 15 : The dynamic simulation results show that the behaviour of the system would be to have growth in adopters that follows a classic s-curve shape. The

List of the equations in continuous time for trimesters = 1 to 60 : System dynamics has found application in a wide range of areas, for example population, ecological and economic systems, which usually interact strongly with each other.

However, system dynamics typically goes further and utilises simulation to study the behaviour of systems and the impact of alternative policies.[8] System dynamics has been used to investigate resource dependencies, and resulting problems, in product development.[9][10] A

Developing a Stock and Flow Model

Systems science has been instrumental in breaking new scientific ground in diverse fields such as meteorology, engineering and decision analysis.

This seminar is designed to introduce students to basic tools of theory building and data analysis in systems science and to apply those tools to better understand the obesity epidemic in human populations.

The central organizing idea of the course is to examine the obesity epidemic at a population level as an emergent properties of complex, nested systems, with attention to feedback processes, multilevel interactions, and the phenomenon of emergence.

System Dynamics Modeling for Public Health: Background and Opportunities

We start with a challenging question: Why is it that, despite repeated calls for a greater emphasis on primary prevention of chronic disease (including a prominent recent example54), the vast majority of health activities and expenditures in the United States are made not for such prevention but rather for disease management and care?55 This dominance of “downstream”

health activities appears to have grown ever greater during the era of modern medicine and is now seen as a pressing problem by public health agencies such as the Centers for Disease Control and Prevention (CDC).56 To illustrate how system dynamics simulation might shed light on this question, we have built a relatively simple model exploring how a hypothetical chronic disease population may be affected by 2 types of prevention: upstream prevention of disease onset, and downstream prevention of disease complications.

The model has only a single aggregated population stock, 27 differential and algebraic equations and 12 numerical inputs, and is based on general knowledge rather than on any specific case study or other hard data.

For both types of prevention, assumptions are made about the preventable fractions of cases given existing clinical tools, and also about the resource requirements per case prevented, and the time delay between the availability of new tools and their adoption by providers and impact on patients.

presents simulation output, over a period of 50 years, for 4 key variables (onset prevention fraction, complications prevention fraction, people with disease, and deaths from complications) under 3 different policy scenarios we have tested (Status Quo, More Complications Prevention, and More Onset Prevention).

In all 3 scenarios, the model has been initialized in a dynamic equilibrium or steady state in which there are about 1 million people with disease, with 75 000 new cases per year and an equal number of deaths, and with 56 000 of the annual deaths from complications.

This reinforcing (R) loop, seen in the left-hand portion of Figure 1 ▶, ultimately drives out onset prevention entirely, leading to large permanent increases in both disease prevalence and complications deaths relative to their starting points.

To summarize this second scenario, although the complications prevention fraction is in fact permanently increased, the prolongation of life and the squeezing out of onset prevention ultimately cause the prevalence of disease to increase proportionately even more;

Nuts and Bolts of System Dynamics Models

Systems science has been instrumental in breaking new scientific ground in diverse fields such as meteorology, engineering and decision analysis.

This seminar is designed to introduce students to basic tools of theory building and data analysis in systems science and to apply those tools to better understand the obesity epidemic in human populations.

The central organizing idea of the course is to examine the obesity epidemic at a population level as an emergent properties of complex, nested systems, with attention to feedback processes, multilevel interactions, and the phenomenon of emergence.

A system dynamics modelling approach to assess the impact of launching a new nicotine product on population health outcomes

In 2012 the US FDA suggested the use of mathematical models to assess the impact of releasing new nicotine or tobacco products on population health outcomes.

The results suggested an overall beneficial effect from launching e-cigarettes and that system dynamics could be a useful approach to assess the potential population health effects of nicotine products when epidemiological data are not available.

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