System Dynamics Models

These models and simulations have been tagged “System Dynamics”.

Related tagsSterman

 ​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 Dis

​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.

  Overview  A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.     How the model works.   Trees grow, we cut them down because of demand for Timber amd sell the logs.  Wit
Overview
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits.  This depends on past experience and recommendations.  Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users.  Park capacity limits the number of users.  
Interesting insights
It seems that high logging does not deter mountain biking.  By reducing park capacity, visitor experience and numbers are improved.  A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers.  Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
 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.  @ LinkedIn ,  Twit

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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  Overview  A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.     How the model works.   Trees grow, we cut them down because of demand for Timber amd sell the logs.  Wit
Overview
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits.  This depends on past experience and recommendations.  Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users.  Park capacity limits the number of users.  
Interesting insights
It seems that high logging does not deter mountain biking.  By reducing park capacity, visitor experience and numbers are improved.  A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers.  Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
This model explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests. Logging allows the activity in the forest with a negative demand for timber providing an income (with the price variable). The deforestation variable shows us that over time, the forest wil
This model explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.
Logging allows the activity in the forest with a negative demand for timber providing an income (with the price variable). The deforestation variable shows us that over time, the forest will run out if the logging keeps going on this way.
Alternatively, mountain biking allows a demand of visitors who want to see the scenary. They increase the regional tourism which is good for the community as it involves other businesses around too. The charges paid by visitors and tourists allow an income for the activity which makes it productive over time and great for TAS.
As we stimulate the model, we can see that it is better to have more visitors and more tourists rather than more logging as it will be better over time.
   Overview   The model shows the industry connection and conflict between Forestry and Mountain Tourism in Derby, Tasmania. The objective of this simulation is to find out the balance point for co-exist.      How Does the Model Work?   Both industries can provide economic contribution to Tasmania.

Overview

The model shows the industry connection and conflict between Forestry and Mountain Tourism in Derby, Tasmania. The objective of this simulation is to find out the balance point for co-exist.

 

How Does the Model Work?

Both industries can provide economic contribution to Tasmania. Firstly, selling timbers through logging would generate income. Also, spendings from mountain bike riders would generate incomes. However, low tree regrowth rate can not cover up logging, which influences the beautiful vistas and riders' experiences. While satisfaction and expectation depend on vistas and experience, the demand of mountain biking would be influenced through repeat visits and world of mouth as well.

 

Interesting Insights

Although forestry can provide a great amount of economic contribution to Tasmania, over logging goes against ESG framework as well as creating conflict with mountain tourism. As long as the number of rider visits is stable, tourism can always provide a greater economic contribution compared to forestry. Therefore, the government should consider the balance point between two industries.

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:  La salud económica (PIB regional, desempleo, cartera hipotecaria)  Estado de la construcción (Licenciamientos, in
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: 
La salud económica (PIB regional, desempleo, cartera hipotecaria)
Estado de la construcción (Licenciamientos, iniciaciones, obras civiles, despachos de cemento)
En cuanto a la oferta se presenta como principales iniciadores entre otros:
Capacidad de proveedores: (Disponibilidad de fuentes, Calidad)
Aspectos legales (Titulos mineros, socioambiental)
Transporte (Flete, estado de la red vial, precio de combustible, distancia de acarreo)

An Initial System Dynamics Model for GFS in certain region(s) of Africa
An Initial System Dynamics Model for GFS in certain region(s) of Africa
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
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
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
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
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
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
This is a model which explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.
This is a model which explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.
 The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022.         Total population of Brgy. Sicsican - 22625    Total Covid19 cases as of May 27, 2022 - 250    Local transmission - 241    Imported transmission - 9    Recovery - 226    Death Due
The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Total population of Brgy. Sicsican - 22625
Total Covid19 cases as of May 27, 2022 - 250
Local transmission - 241
Imported transmission - 9
Recovery - 226
Death Due to Covid19 - 15
 This forecasting model can be used to predict global data center electricity needs, based on understanding usage growth. Please note that the corresponding problem description, model developments, and results are discussed in the following paper:     Koot, M., & Wijnhoven, F. (2021). Usage impa
This forecasting model can be used to predict global data center electricity needs, based on understanding usage growth. Please note that the corresponding problem description, model developments, and results are discussed in the following paper:

Koot, M., & Wijnhoven, F. (2021). Usage impact on data center electricity needs: A system dynamic forecasting model. Applied Energy, 291, 116798. DOI: https://doi.org/10.1016/j.apenergy.2021.116798.
  Problém časové alokace     Semestrální práce      V této simulaci můžeme pozorovat přibližnou dobu na dokončení projektu, který má zadané parametry, jenž ovlivňují dobu jeho dokončení. Zároveň také znázorňuje zjednodušené nabývání znalostí a nárůst (případně pokles) mzdy v poměru se znalostmi.
Problém časové alokace
Semestrální práce

V této simulaci můžeme pozorovat přibližnou dobu na dokončení projektu, který má zadané parametry, jenž ovlivňují dobu jeho dokončení. Zároveň také znázorňuje zjednodušené nabývání znalostí a nárůst (případně pokles) mzdy v poměru se znalostmi.

Celý model obsahuje 3 hladiny - vývojový čas, plat a znalosti vývojářů. Mezi parametry, jenž lze zadávat a jenž ovlivňují celkovou dobu vývoje, patří: počet vývojářů (1 - 10), základní mzda (35.000 - 120.000), termín (1 - 6) a obsáhlost projektu (0.4 - 2).

Celkový počet vývojářů a znalosti vývojářů ovlivňují výslednou mzdu jednotlivých vývojářů. Termín určuje za jak dlouhou dobu si přeje klient projekt dokončen (pravý čas se dozví v simulaci) a obsáhlost projektu představuje o jak velký projekt se jedná.

V simulaci lze pozorovat tři grafy. První porovnává požadovaný čas s reálným časem stráveným na projektu, spolu s křivkou komplexnosti jednotlivých prvků, které se vyskytly během vývoje. Druhý graf nám ukazuje nárůst znalostí aktuálního týmu (tým se znalostí 1 dokonale rozumí dané problematice) a na třetím grafu lze vidět vývoj mzdy vývojářů během projektu (mzda je závislá na znalostech, tedy graf má stejný tvar).
  Format: Given  pre-conditions  when  independent variables(s)  then  dependent variable         Given  Earnings Decline (0.25), Spending Variance (55), Initial Investment (500) and Rate of Return (RandNormal(0.06, 0.12))  when  one of these independent variables change  then  how   sensitive   is
Format: Given pre-conditions when independent variables(s) then dependent variable

Given Earnings Decline (0.25), Spending Variance (55), Initial Investment (500) and Rate of Return (RandNormal(0.06, 0.12)) when one of these independent variables change then how sensitive is Investment (22) over a 30 year time period (-1,000)

H1: if you Earn more then Investment will last much longer => rejected

H2: if you Spend less then Investment will last much longer => accepted

H3: if your Initial Investment is higher then Investment will last much longer => accepted

H4: if you reduce your Spend when Investments are declining then Investment will last much longer => accepted

Given Earnings Decline (0.25), Spending Variance (55), Initial Investment (500) and Rate of Return (RandNormal(0.06, 0.12)) when one of these independent variables are optimised then Investment will last exactly 30 years by minimising the absolute investment gap

H1: if you set an appropriate Spending Base then remaining Investment is 0 => rejected

H2: if you set an appropriate Spending Reduction then remaining Investment is 0 => rejected

Source for investment returns: https://seekingalpha.com/article/3896226-90-year-history-of-capital-market-returns-and-risks
A Conveyor is essentially an infinite order exponential delay.  This insight illustrates how increasing the order of an exponential delay begins to approximate a conveyor.  The 10th order delay very closely aligns to the Delay 10 Conveyor.
A Conveyor is essentially an infinite order exponential delay.  This insight illustrates how increasing the order of an exponential delay begins to approximate a conveyor.  The 10th order delay very closely aligns to the Delay 10 Conveyor.
From the 1988 killian lectures youtube  video  describing eroding goals in corporate growth. For more detailed biography See Jay Forrester memorial  webpage  For more eroding goals insights see  search results
From the 1988 killian lectures youtube video describing eroding goals in corporate growth. For more detailed biography See Jay Forrester memorial webpage For more eroding goals insights see search results
Internet of Things and Data Collection - Active and Passive Data under Conditions of Regulation.
Internet of Things and Data Collection - Active and Passive Data under Conditions of Regulation.