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.
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.
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.
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 shows the changing happened in forest industry and mountain tourism in Derby Tasmania. Logging will degrade mountain tourism while benefit the forestry industry. Simulation borrowed from the Easter Island simulation.
According to the analysis, logging does not reduce tourism income. With the increase of number of bike guide, tourism income will increase as well. Also, in forest industry, timber income is higher than the harvest spending which means the industry always gain profits from logging. Therefore, the main concern is that the logging should be balanced between the Mountain Tourism and the forest industry.
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
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 model situmalte the relationship between moutain bikes and logging industry in Derby, Tasmania, It explains more when the number of visitors increases or decreses.
How the model works
The left side shows when the number of travellers increase, the income from travellers rental of bike and stay of hotel increase simultaneously. However, there is a capacity for both parking lots and hotel venues, which means that the top ability of hospitality of Derby. The right side shows the logging industry of Derby and income from logging. It has a impact on how travellers would value Derby moutain.
Insights
As the number of travellers increase, it increases the total income of Derby, and in return, the local government will re-revest in Derby Moutain and will also maintain the forrestry logging industry.
The model simulates the comparison between mountain biking industry and forestry/logging in Derby Tasmania.
How the model works
On the left-hand side, Derby Mountain biking, tourists visit the mountain according to reviews and recommendation of mountain scenery and entertainment activities. The number of people who hire bikes and who choose to dine on the mountain are limited by bike availability. Both bike hiring and biker dining contribute to tourist revenue in Derby. On the right-hand side, forest trees grow at certain rates, but are negatively affected by timber demand. Timber logging generate revenue, which depends on sale price and associated cost.
Interesting insights
Although forestry contributes more revenue in a certain time, it seems that Derby Mountain bike generate more tourist revenue from dining services and bike hiring in a long term.
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
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.
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.
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.
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 Flow connection with a connection from Trig. function. Repeat (A) & (B).
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
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.
