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
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.
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
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.
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 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.
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
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.
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.
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.
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.
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 model simulates the competition between logging versus adventure tourism(mountain bike riding) in Derby Tasmania. The purpose of this model is that focus on the relationship between the timber industry and mountain bike tourism in adventure. It also reflects how well these two industries co-exist.
How this model works
This model shows tree grow development. In order to maximize the profits from selling the logging, the demand for timbers will increase.
The mountain bike visits depend on past experience and recommendations. In addition, past experience and recommendations depend on Scenery, which is determined by the number of trees and visitors and adventure number. However, park capacity limits the number of use mountain bikes, because the convince of parking is a consideration for the visitors.
It seems like the high logging sale does not deter mountain bike activities. By reducing the parking capacity, visitor experience and number are increased. Because of the strong relationship between the mountain bike park and the explosion in visitor numbers. With the improvement in the number of visitors, the number of food and restaurants will go up as well. Because of the daily needs of the visitors.
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.
