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System Dynamics Modelling

using Vensim

3 December 2015

Prof Mikhail Prokopenko

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System: behaviour

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System: interconnectivity

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System Dynamics Definition

An approach to understanding the behaviour

of complex systems over time.

It deals with internal feedback loops and time

delays that affect the behaviour of the entire

system.

What makes system dynamics different from

other approaches to studying complex

systems is the use of feedback loops and

stocks and flows.

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Stock and Flow Diagram

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Black box diagram

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Positive Feedback

- Exponential growth – More begets more – Less begets

less

- The “vicious cycle”

- Snowball rolling down a hill

- Bank account interest

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Negative Feedback

- Goal seeking behaviour

- Pouring water into a glass

- Initial growth leads to an undersupply of resources

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Dynamics of real systems

- Systems often combine feedbacks

- Growth and limitation

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?

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Modelling:

sensitivity to initial conditions

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Butterfly effect:

sensitivity to initial conditions

http://demonstrations.wolfram.com/SensitivityToInitialConditionsInChaos/ http://pixshark.com/chaos-theory-butterfly-effect.htm

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Core Concepts

Simple processes can generate complicated

behaviour

System dynamics provides unified approach for

understanding problems

Assists with your own mental models by making

dynamic problems explicit

– Accumulations (Stocks), Change (Flows),

Feedback (interactions between the two)

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How assets build and decay

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Accumulation itself changes according to inflow and

outflow

image:

http://fixingtheeconomists.wordpress.com/

- inflow > outflow: level rises

- outflow > inflow: level sinks

- outflow = inflow: no change

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Identifying Stocks and Flows

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How can you tell which concepts are stocks and which are

flows?

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Stocks are quantities of material or other accumulations.

They are the states of the system.

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The flows are the rates at which these system states

change. Imagine a river flowing into a reservoir. The

quantity of water in the reservoir is a stock.

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If you drew an imaginary line across the point where the

river enters the reservoir, the flow is the rate at which water

passes the line.

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Identifying Stocks and Flows

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In epidemiology, prevalence measures the number or stock of people who have

a particular condition at any given time, while incidence is the rate at which

people come down with the disease or condition.

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In December 1998 the prevalence of HIV/AIDS worldwide was estimated by the

United Nations AIDS program to be 33.4 million and the incidence of HIV

infection was estimated to be 5.8 million/year. That is, a total of 33.4 million

people were estimated to be HIV-positive or to have AIDS; the rate of addition to

this stock was 5.8 million people per year (16,000 new infections per day).

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The net change in the population of HIV-positive individuals was estimated to be

3.3 million people per year due to the death rate from AIDS, estimated to be 2.5

million people per year in 1998.

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The Snapshot Test

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Stocks characterise the state of the system. To identify key stocks in a system,

imagine freezing the scene with a snapshot. Stocks would be those things you

could count or measure in the picture, including psychological states and other

intangible variables.

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stock of water in a reservoir from a set of satellite images and topographic data,

but cannot determine whether the water level is rising or falling.

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bank statement tells you how much money is in your account but not the rate at

which you are spending it now.

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If time stopped, it would be possible to determine how much inventory a

company has or the price of materials but not the net rate of change in

inventory or the rate of inflation in materials prices.

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Units

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Units of measure can help distinguish stocks from flows. Stocks are usually

a quantity such as widgets of inventory, people employed, or Yen in an

account.

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The associated flows must be measured in the same units per time period

e.g., the rate at which widgets are added per week to inventory, the hiring

rate in people per month, or the rate of expenditure from an account in

$/hour.

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You are free to select any measurement system you like as long as you

remain consistent. You can measure the flow of production into inventory as

widgets per week, widgets per day, or widgets per hour.

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Stocks Change Only

Through Their Rates

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Stocks change only through their rates of flow, no causal link directly into a

stock.

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A model for customer service: Customers arrive at some rate and accumulate

in a queue of Customers Awaiting Service (e.g., a restaurant)

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When service is completed customers depart from the queue, decreasing the

stock of customers waiting for service.

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Rate at which customers can be processed depends on the number of service

personnel, their productivity (in customers processed per hour per person), and

the number of hours they work (the workweek).

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If the number of people waiting for service increases, employees increase their

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The only way a stock can change is via its inflows and

outflows. In turn, the stocks determine the flows.

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Stock change only through

rates

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Stock change only through

rates

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Auxiliary Variables

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It is often helpful to define intermediate or auxiliary variables.

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Auxiliaries consist of functions of stocks (and constants or exogenous

inputs).

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For example, a population model might represent the net birth rate as

depending on population and the fractional birth rate; fractional birth

rate in turn can be modelled as a function of food per capita.

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Ideally, each equation in your models should represent one main idea.

Don’t try to economise on the number of equations by writing long

ones that embed multiple concepts, they will be hard to understand.

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Equations with multiple components and ideas are hard to change if

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System Dynamics (Vensim):

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System Dynamics (Vensim):

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?

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Vensim first steps

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Vensim PLE Quick Reference and Tutorial

http://ocw.mit.edu/courses/sloan-school-of-

management/15-988-system-dynamics-self-study-fall-1998-spring-1999/readings/formulating.pdf

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Developing Stock, Flow

and Feedback Structure

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?

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?

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Predator – prey model

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Predator – prey model

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Predator – prey model

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Predator – prey model

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Predator – prey model

rabbit births = Rabbits * birth rate c

?

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Predator – prey model

rabbit births = Rabbits * birth rate c

d

rabbit deaths = Rabbits * Foxes * catching rate d

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Predator – prey model

rabbit births = Rabbits * birth rate c

rabbit deaths = Rabbits * Foxes * catching rate d

change in Rabbits = rabbit births - rabbit deaths

∆R = c R – d R F

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Predator – prey model

fox birth rate

fox births

fox death rate

fox deaths

Fox

population

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Predator – prey model

fox birth rate

fox growth

fox deaths

Fox

population

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Predator – prey model

fox growth = Foxes * Rabbits * growth rate a

fox birth rate

fox growth

fox deaths

Fox

population

fox death rate b

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Predator – prey model

fox growth = Foxes * Rabbits * growth rate a

fox birth rate

fox growth

fox deaths

Fox

population

fox deaths = Foxes * death rate b

fox death rate b

fox birth rate

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Predator – prey model

fox growth = Foxes * Rabbits * growth rate a

fox birth rate

fox growth

fox deaths

Fox

population

fox deaths = Foxes * death rate b

fox death rate b

change in Foxes = fox growth – fox deaths

∆F = a F R – b F

fox birth rate

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Predator – prey model

a

b

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Predator – prey model

∆R = c R – d R F

∆F = a F R – b F

a

b

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Predator – prey model:

Lotka-Volterra model

∆R = c R – d R F

∆F = a F R – b F

 the simplest model of predator-prey interactions

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∆R = c R – d R F

∆F = a F R – b F

Predator – prey model:

Equilibrium

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Predator – prey model:

Equilibrium

0 = ∆R = c R – d R F

0 = ∆F = a F R – b F

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0 = ∆R = c R – d R F

0 = ∆F = a F R – b F

c R = d R F

a F R = b F

Predator – prey model:

Equilibrium

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c R = d R F

a F R = b F

F = c / d

R = b / a

Predator – prey model:

Equilibrium

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Predator – prey model

F = c / d

R = b / a

a

b

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?

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Equilibrium population

sizes

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Building the model

a

b

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Equilibrium population

Population

300

225

150

75

0

0

100

200

300

400

500

600

700

800

900

1000

Time (seasons)

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Equilibrium population

Population

1500

1125

750

375

0

0

100

200

300

400

500

600

700

800

900

1000

Time (seasons)

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Equilibrium population

Selected Variables

300

2000

150

1000

0

0

0

100

200

300

400

500

600

700

800

900

1000

Time (seasons)

Foxes : Current

Rabbits : Current

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Equilibrium population

Population

1500

1125

750

375

0

0

100

200

300

400

500

600

700

800

900

1000

Time (seasons)

Rabbits : Current

Foxes : Current

"eq-rabbits" : Current

"eq-foxes" : Current

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Equilibrium population

Oscillations are observed in both population sizes

Oscillations occur around the equilibrium population values

Dynamic equilibrium (not static)

Oscillatory behaviour is similar to many natural,

socio-technological, and socio-economic systems

(Pure) competition between the species, when one species

(predator) grows at the expense of the other (prey)

Dynamics and equilibria of each population are affected by

dynamics of the others

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Equilibrium population:

phase diagrams

Phases

300

225

150

75

0

250

360

470

580

690

800

910

1020

1130

1240

1350

Rabbits

Foxes : Current

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Equilibrium population:

phase diagrams

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Equilibrium population:

phase diagrams

Phases

300

225

150

75

0

250

360

470

580

690

800

910

1020

1130

1240

1350

Rabbits

Foxes : Current

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Equilibrium population:

phase diagrams

Phases

300

225

150

75

0

250

360

470

580

690

800

910

1020

1130

1240

1350

Rabbits

Foxes : Current

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?

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Faculty of Engineering and IT: Complex Systems Research Cluster

Thank you!

Prof. Mikhail Prokopenko

Starting in 2017:

Master of Complex Systems (MCXS)

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Two PhD Scholarships

One Post-doc

ARC Discovery Project:

Large-scale computational modelling of

epidemics in Australia

University of Sydney &

Monash University

References

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