System Dynamics | Insight Maker

Mass excitation model

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.

Simulation of Derby Mountain biking versus logging

BS

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

Clone of GFS Raw Input Food Production

gabriel jara

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.

Lancamento Horizontal

Paulo Villela

Overview

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

Clone of Simulation of Derby Mountain biking versus logging

RAJNEESH MAHAJAN

9 months 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 Flow connection with a connection from Trig. function. Repeat (A) & (B).

Sterman Model (2002)

Walter M. Stroup

Infección

Nallely Vargas Zepeda

PostanModel(SD)

NangManCat

6 months ago

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).

Clone of Problém časové alokace

Stanislava Mildeova

Overview

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

Clone of Simulation of Derby Mountain biking versus logging

Steven D'Alessandro

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.

Clone of First SD Model: Predator Prey Model with Squirrels, Mountain Lions, and Hunters

Afiff Zamidee

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).

Clone of Clone of Problém časové alokace

Dmitriy Sosnovich

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)

Clone of Oferta y demanda de materiales de construcción en Cali

david abarca

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.

Clone of Google Adwords Model

TBMOM Agency

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

Clone of Active and Passive Internet of Things - Regulated

Rob

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

Clone of System Dynamics Concepts

Nilo Guimaraes

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).

Clone of Clone of Clone of Sterman Model (2002)

darkdan

A Conveyor is an infinite order exponential delay. This insight illustrates how increasing the order of an exponential delay begins to approximate a conveyor.

Conveyor vs. nth order exp. delay vs DELAY fct.

Rolf Widmer

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

Clone of System Dynamics Concepts

Jeb Eddy

9 months 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 Flow connection with a connection from Trig. function. Repeat (A) & (B).

Clone of Sterman Model (2002)

Elisavet Mavroudi

The rework cycle

Zheng XU

Foxes birth rate is decrease by 50%

Investigation of Predator/Prey Modal 1 Scenario 5

group3

From the 1988 killian lecture youtube video For more detailed biography See Jay Forrester memorial webpage For concepts and applications see IM-185226

Clone of History of System Dynamics Forrester

sub cribed

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.

PHYSICS: Kinematics, Projectile

Alfred Aenishaenslin

System Dynamics Models