System Dynamics Models

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

Related tagsSterman

 Overview:  This model shows the relationship between the tourist industry and forestry industry in Derby Tasmania. It simulates the situation where these two competing industries can co-exist.      Methodologies:   Tourists are attracted by the beautiful scenery, good air quality and biodiversity i
Overview:
This model shows the relationship between the tourist industry and forestry industry in Derby Tasmania. It simulates the situation where these two competing industries can co-exist.

Methodologies:
Tourists are attracted by the beautiful scenery, good air quality and biodiversity in Mountain bike trail in Derby. The tourism income increase when more visitors are attracted to use the mountain bike trail.

The forestry industry generates revenue by logging and selling timber. Increasing timber supplied will destroy the forests, which makes the park less attractive for tourists.

However, the income from forestry and tourism can be used to improve the facilities in the park to attract more visitors. 

Findings:
When the amount of timber supplied is less than the regrowth rate of forests, the tourism industry and forestry industry can co-exist. If amount of timber supplied excesses the regrowth rate, tourism industry will be negative impacted.


   Evolution of Covid-19 in Brazil:  
  A System Dynamics Approach  
 Villela, Paulo (2020) paulo.villela@engenharia.ufjf.br  This model is based on  Crokidakis, Nuno . (2020).  Data analysis and modeling of the evolution of COVID-19 in Brazil . For more details see full paper  here .
Evolution of Covid-19 in Brazil:
A System Dynamics Approach

Villela, Paulo (2020)
paulo.villela@engenharia.ufjf.br

This model is based on Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. For more details see full paper here.

A new archetype, The Tyranny of Small Steps (TYST) has been observed. Explained through a system dynamics perspective, the archetypical behaviour TYST is an unwanted change to a system through a series of small activities that may be independent from one another. These activities are small enough no
A new archetype, The Tyranny of Small Steps (TYST) has been observed. Explained through a system dynamics perspective, the archetypical behaviour TYST is an unwanted change to a system through a series of small activities that may be independent from one another. These activities are small enough not to be detected by the ‘surveillance’ within the system, but significant enough to encroach upon the “tolerance” zone of the system and compromise the integrity of the system. TYST is an unintentional process that is experienced within the system and made possible by the lack of transparency between an overarching level and a local level where the encroachment is taking place.

Reference:

Haraldsson, H. V., Sverdrup, H. U., Belyazid, S., Holmqvist, J. and Gramstad, R. C. J. (2008), The Tyranny of Small Steps: a reoccurring behaviour in management. Syst. Res., 25: 25–43. doi: 10.1002/sres.859 

From the 1988 killian lecture youtube  video  For more detailed biography See Jay Forrester memorial  webpage  For concepts and applications see  IM-185226
From the 1988 killian lecture youtube video For more detailed biography See Jay Forrester memorial webpage For concepts and applications see IM-185226
 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 40 years I dredged up the following set of influences as a thought exercise. This is an example of a   Drifting Goals Systems Archetype  .

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 40 years I dredged up the following set of influences as a thought exercise. This is an example of a Drifting Goals Systems Archetype.

4 months ago
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.
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.
10 months ago
Overview This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.    How the model works  The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to incre
Overview
This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.

How the model works
The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to increased visitors using the forest of mountain biking. Accompanying variables effect the tourism income that flows from use of the bike trails.
On the right side, the forest flow begins with tree growth then a demand for timber leading to the logging production. The sales from the logging then lead to the forestry income.
The model works by identifying how the different variables interact with both mountain biking and logging. As illustrated there are variables that have a shared effect such as scenery and adventure and entertainment.

Variables
The variables are essential in understanding what drives the flow within the model. For example mountain biking demand is dependent on positive word mouth which in turn is dependent on scenery. This is an important factor as logging has a negative impact on how the scenery changes as logging deteriorates the landscape and therefore effects positive word of mouth.
By establishing variables and their relationships with each other, the model highlights exactly how mountain biking and forestry logging effect each other and the income it supports.

Interesting Insights
The model suggests that though there is some impact from logging, tourism still prospers in spite of negative impacts to the scenery with tourism increasing substantially over forestry income. There is also a point at which the visitor population increases exponentially at which most other variables including adventure and entertainment also increase in result. The model suggests that it may be possible for logging and mountain biking to happen simultaneously without negatively impacting on the tourism income.
 Overview 

 A model which simulates the competition between logging versus adventure
tourism (mountain bike ridding) in Derby Tasmania.  

  
How the model works: 

 Trees grow, and we cut them down because of the demand for Timber and
sell the logs. Mountain bikers and holiday visitors will come t

Overview

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. 


How the model works:

Trees grow, and we cut them down because of the demand for Timber and sell the logs. Mountain bikers and holiday visitors will come to the park and this depends on experience and recommendations.  Past experience and recommendations depend on the Scenery, number of trees compared to the visitor and Adventure number of trees and users.  Park capacity limits the number of users.  To utilize highest park capacity, they need to promote to the holiday visitor segment as well. Again, the visit depends on the scenery. So, both mountain biking and forestry (logging) businesses need to contribute a significant amount of revenue to CSR for faster regrowth of trees.


Interesting insights

It looks like a lot of logging doesn't stop people from mountain biking. 

Faster replantation of the tree will balance out the impact created by logging which will give the visitor a positive experience and the number of visitors is both improved. 

To keep the park's popularity in check, the price of wood needs to be high. 

Also, it looks like mountain biking only needs a narrow path.

CSR contribution to nature can be a crucial factor. 

  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).
A Conveyor is an infinite order exponential delay.  This insight illustrates how increasing the order of an exponential delay begins to approximate a conveyor.
A Conveyor is an infinite order exponential delay.  This insight illustrates how increasing the order of an exponential delay begins to approximate a conveyor.
Based on model discussed by John D. Sterman (p 508) in  All models are wrong: reflections on becoming a systems scientist  (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flo
Based on model discussed by John D. Sterman (p 508) in All models are wrong: reflections on becoming a systems scientist (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flow connection with a connection from Trig. function.  Repeat (A) & (B).
Based on model discussed by John D. Sterman (p 508) in  All models are wrong: reflections on becoming a systems scientist  (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flo
Based on model discussed by John D. Sterman (p 508) in All models are wrong: reflections on becoming a systems scientist (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flow connection with a connection from Trig. function.  Repeat (A) & (B).
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.
  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).
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 t
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.
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.
Based on model discussed by John D. Sterman (p 508) in  All models are wrong: reflections on becoming a systems scientist  (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flo
Based on model discussed by John D. Sterman (p 508) in All models are wrong: reflections on becoming a systems scientist (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flow connection with a connection from Trig. function.  Repeat (A) & (B).
Overview This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.    How the model works  The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to incre
Overview
This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.

How the model works
The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to increased visitors using the forest of mountain biking. Accompanying variables effect the tourism income that flows from use of the bike trails.
On the right side, the forest flow begins with tree growth then a demand for timber leading to the logging production. The sales from the logging then lead to the forestry income.
The model works by identifying how the different variables interact with both mountain biking and logging. As illustrated there are variables that have a shared effect such as scenery and adventure and entertainment.

Variables
The variables are essential in understanding what drives the flow within the model. For example mountain biking demand is dependent on positive word mouth which in turn is dependent on scenery. This is an important factor as logging has a negative impact on how the scenery changes as logging deteriorates the landscape and therefore effects positive word of mouth.
By establishing variables and their relationships with each other, the model highlights exactly how mountain biking and forestry logging effect each other and the income it supports.

Interesting Insights
The model suggests that though there is some impact from logging, tourism still prospers in spite of negative impacts to the scenery with tourism increasing substantially over forestry income. There is also a point at which the visitor population increases exponentially at which most other variables including adventure and entertainment also increase in result. The model suggests that it may be possible for logging and mountain biking to happen simultaneously without negatively impacting on the tourism income.
Based on model discussed by John D. Sterman (p 508) in  All models are wrong: reflections on becoming a systems scientist  (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flo
Based on model discussed by John D. Sterman (p 508) in All models are wrong: reflections on becoming a systems scientist (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flow connection with a connection from Trig. function.  Repeat (A) & (B).
Based on model discussed by John D. Sterman (p 508) in  All models are wrong: reflections on becoming a systems scientist  (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flo
Based on model discussed by John D. Sterman (p 508) in All models are wrong: reflections on becoming a systems scientist (2002). Task: (A) Sketch what you think the resultant graph will be (see directions for drawing in model). (B) Then Run Simulation.  Optional Extension: Replace Graph In/Out Flow connection with a connection from Trig. function.  Repeat (A) & (B).
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
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.