This is a system dynamic model to
describe relationship between local logging industry and biking tourism in
Tasmanian Derby Mountain.  In the dynamic model, the left-hand side shows how Derby
get income from local biking tourism. The biking visitors number are influenced
by scenery evaluation whic

This is a system dynamic model to describe relationship between local logging industry and biking tourism in Tasmanian Derby Mountain.

In the dynamic model, the left-hand side shows how Derby get income from local biking tourism. The biking visitors number are influenced by scenery evaluation which depend on local size of forest and influenced government policy support when Biking Tourism income is over 1000 unit. Biking visitors with good recommendation will also back to Mountain Derby and bring income for local in twice or more times.  In the right-hand side, we found the income of logging industry was influenced by local logging growth rate and government policy if local Biking Tourism income is over 1000 unit. The increase of logging industry will also increase local employment which will influence employee cost. This factor will also affect total logging income in Derby Mountain.

 

The simulation results show, with governments support the Biking tourism will increase sharply in the first few years and finally instead local logging industry, at same time bring good environment and save local forest under local increase logging industry. The recommendation graph shows that, the number of good recommendation & bad recommendation for Derby Mountain biking tourism will also increase in high speed in front of few years with data fluctuation but finally maintain in a stable line. Last simulation graph shows that how policy factor influences logging and biking industry. The Government has strong support in local tourism, however, as number of tourists increase, the positive impact from government support will continue decrease. On the contrary, the government support influence will also decease to local logging industry when logging been instead by tourism. 

 
			 
				 
					  Physician Workforce Model    Modelo baseado na Figura 1 do paper " Forecasting the need for medical specialists in Spain: application of a system dynamics model " (*) de Patricia Barber (**), Beatriz González López-Valcárcel.   (*)  https://human-resources-health.biomedcentral

Physician Workforce Model

Modelo baseado na Figura 1 do paper "Forecasting the need for medical specialists in Spain: application of a system dynamics model" (*) de Patricia Barber (**), Beatriz González López-Valcárcel.

(*) https://human-resources-health.biomedcentral.com/articles/10.1186/1478-4491-8-24

(**) pbarber@dmc.ulpgc.es - University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de G.C., Canary Islands, Spain

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).
This model represents a repair contract to fix a group of houses with unresolved construction defects.
This model represents a repair contract to fix a group of houses with unresolved construction defects.
An overview of this quantitative systems science method based on Kurt Kreuger's workshops for public health
An overview of this quantitative systems science method based on Kurt Kreuger's workshops for public health
11 months ago
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 

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).
  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).
 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.
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   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.

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.
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.
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
 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.
8 4 weeks ago
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).
 Instructions  Adjust values by using the sliders below or typing in values, then press "Simulate"     To find total cases or total cost with or without WGS, run the simulation twice with WGS = 0 and WGS = 1 (make sure you record the values each time)      Refresh page to restore default values
Instructions
Adjust values by using the sliders below or typing in values, then press "Simulate"

To find total cases or total cost with or without WGS, run the simulation twice with WGS = 0 and WGS = 1 (make sure you record the values each time)

Refresh page to restore default values

Warning:
Initial proportion of asymptomatically colonised patients + Initial proportion of symptomatically infected patients must be < 1

Proportion of admissions asymptomatically colonised + Proportion of admissions with symptomatic infection must be <1

Email amy.buchanan-hughes@costellomedical.com with queries or comments
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.
   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.