​This model attempts to understand the behavior of average lifetime of companies in the S&P500 index. The reference mode for the model is a graph available at this link: https://static-cdn.blinkist.com/ebooks/Blinkracy-Blinkist.pdf (page 5) which was discussed in the System Thinking World Discussion forum.

Mergers & Acquisitions can be one of the reasons for older companies to be replaced with newer companies in the Index. With M&A of older companies, the empty slots are taken over by newer companies. However, overtime, these new companies themselves become old. With steady M&A, the stock of older companies decreases and stock of newer companies increases. The result is that average age of the companies in the S&P Index decreases.

The oscillations in the diagram, according to me, is due to oscillations in the M&A activity.

There are two negative feedback loops in the model. (1) As stock of new companies increases, the number of companies getting older increases which in turn decreases the stock. (2) As M&A increases, stock of older companies decreases which in turn decreases M&A activities.

Limits of the model

The model does not consider factors other than M&A in the increase in number of new companies in the Index. New companies themselves may have exceptional performance which will result in their inclusion in the Index. Changes in technology for example Information Technology can usher in new companies.

Assumptions

1. It is assumed that M&A results in addition of new companies to the Index. There could be other older companies too, which given the opportunity, can move into the Index. Emergence of new technologies brings in new companies.

Based on a dialogue on the System Dynamics mailing list regarding the current level of acceptance of System Dynamics after it has been promoted for over 70 years I dredged up the following set of influences as a thought exercise. This is an example of a Drifting Goals Archetype.

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From Jay Forrester 1988 killian lectures youtube video describing system dynamics at MIT. For Concepts See IM-185226. For more detailed biography See Jay Forrester memorial webpage For MIT HIstory see IM-184930

WIP Overview model structures of Khalid Saeed's 2014 WPI paper Jay Forrester’s Disruptive Models of Economic Behavior  See also General SD and Macroeconomics CLDs IM-168865

Internet of Things and Data Collection - Active and Passive Data.

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

From Jay Forrester 1988 killian lectures youtube video describing system dynamics at MIT. For more detailed biography See Jay Forrester memorial webpage For MIT HIstory see IM-184930 For Applications se IM-185462

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

A model that shows how the digital advertising market is growing and how Google's share in this market, and subsequently their financial results, are influenced by investing in either three of the policy variables.

From SDR Jan 2012 article  and 1988 killian award lectures Youtube

This model explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.

At first, I cloned the System Dynamics Model from the "Predator-Prey Interactions" tutorial. After I did this for populations of squirrels and mountain lions instead of moose and wolves, the model showed that the more squirrels mountain lions catch, the more the mountain lion population grows, and the squirrel population declines. The squirrel death rate, therefore, depends on the number of mountain lions and the mountain lion birth rate depends on the number of squirrels. 

I complicated the model by adding 15 hunters to the landscape. Now, the model starts with 150 squirrels, 100 mountain lions, and 15 hunters. This model operates under the assumption that hunters want to kill mountain lions, who presumably try to eat the farm animals that represent the hunters' livelihoods. I made the mountain lion death rate dependent on the number of hunters, and the model changed such that the squirrel population exploded and the mountain lion population approached extinction every 20 years. I based this model on a real event, which took place and is still taking place in the Sierra Nevada. Squirrel populations there apparently reached record levels when farmers seeking to protect their land killed off the vast majority of the mountain lion population there. Now, hunters in the area kill squirrels for sport because they disrupted the food chain so irrevocably.

We can observe the Covid19 flows or transitions and linkages from healthy to infected and immune in this system dynamics model, or SDM. It conducts a flow known as infection from healthy to infected. The diseased then initiates a flow known as recovery to immunity. It means that the covid19 infects healthy people first, and then they become immune once they recover from covid19 infections. We can conduct a simulation to observe how they interact to get a more useful analysis.

Here is the Covid 19 Statistics model based on the Philippines.

An overview of this quantitative systems science method based on Kurt Kreuger's workshops for public health

This model represents a repair contract to fix a group of houses with unresolved construction defects.

The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Foxes birth rate  is increase by 50%

Este modelo busca simular la demanda y oferta de materiales de construcción en la ciudad de Calí (Colombia), En cuanto a la demanda se presenta como principales iniciadores entre otros: