​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.

Business System Dynamics

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

Internet Internet Of Things System Dynamics Data Collection Data Analysis

From SDR Jan 2012  View article...  and 1988 killian award lectures youtube

Health Care Rubik System Dynamics Dynamic Mental Models Methods Forrester

Internet of Things and Data Collection - Active and Passive Data under Conditions of Regulation.

Internet Internet Of Things System Dynamics Data Collection Data Analysis

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.

Systems Archetype Drifting Goals System Dynamics

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)

Demand System Dynamics Regional

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.

VCT System Dynamics SD Modeling

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 

Agent Based Modelling ABM System Dynamics Archetype

HOT SHOWER
Faucet control system to regulate the temperature of a shower. The system has a loop that naturally leads to equilibrium, that is, the CURRENT TEMPERATURE tends over time to the desired TEMPERATURE. However due to the delay in water flowing in the pipe, from the faucet to the shower, the system oscillates but still tends to balance. In fact, what makes CURRENT TEMPERATURE fluctuate is the relationship between WATER DELAY and FAUCET ADJUSTMENT TIME. The oscillation frequency is higher the higher the relationship between WATER DELAY time and FAUCET ADJUST TIME.
This model may be cloned and modified without prior permission of the authors.Thanks for quoting the source.

K12 Education System Dynamics

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).

Sterman System Dynamics

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.

System Dynamics Exponential Delay Conveyor Delay

System Dynamics

OBLIQUE THROW IN VACUUM

A body is thrown obliquely into the vacuum at an initial velocity of 100 m / s, in a direction that forms with the horizontal an angle x, such that sin (x) = 0.8 and cos (x) = 0.6. Adopting g = 10m / s², determine:

(a) the horizontal and vertical velocity component modules at the moment of launch;

(b) the instant at which the body reaches the highest point of its trajectory;

c) the maximum height reached by the body;

d) The range of the throw.

Source: RAMALHO, NICOLAU AND TOLEDO; Fundamentos de Física, Volume 1, 8th edition, pp. 12 - 169, 2003.

This model may be cloned and modified without prior permission of the authors. Thanks for quoting the source.

K12 Education Physics Kinematics System Dynamics

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.

System Dynamics

An Initial System Dynamics Model for GFS in certain region(s) of Africa

Food Security Africa SoS GFS System Dynamics

Ciclo 1 extra repair consturction errors rework

Construction System Dynamics

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.

Epidemiology COVID-19 Brazil System Dynamics SIR Model

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).

Time Allocation Development Knowledge System Dynamics

Rabits birth rate  is decrease 50%

System Dynamics

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).

Sterman System Dynamics

Foxes birth rate  is increase by 50%

System Dynamics

HORIZONTAL THROW IN VACUUM

After a flood, a group of people were left in one area. A rescue plane, flying horizontally at a height of 720 m and maintaining a speed of v = 50m / s, approaches the scene for a packet of medicines and food to be launched to isolated people. How far in the horizontal direction should the package be dropped so that it falls with people? Disregard air resistance and adopt g = 10m / s².

Source: RAMALHO, NICOLAU AND TOLEDO; Fundamentos de Física, Volume 1, 8th edition, pp. 12 - 169, 2003).

This model may be cloned and modified without prior permission of the authors. Thanks for quoting the source.

K12 Education Physics Kinematics System Dynamics

Foxes birth rate  is decrease by 50%

System Dynamics

The main scope in this model is seeing how several variables can affect the amounCicl

Construction System Dynamics