Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes.

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Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes

Transcript of Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes.

Page 1: Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes.

Models in IE Lecture 13

System Dynamics

2nd Lecture

Systems Archetypes

Page 2: Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes.

Lecture 13: Outline• More examples of causal-loop diagrams

• Glucose and Insulin– What is wrong?– How could such a bad system evolve?

• Natural systems, man-made systems– What to look for when systems behave poorly

• Common Patterns: System Archetypes– Success to the successful– Escalation

Page 3: Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes.

Analysis of an argument

or

How to make a mountain out of a molehill

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A’sanger

B’sharshness

A’s harshness

B’sanger

+

+

+

+

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Mountains out of Molehills

• Whose fault is it?– “You started it”

• Cause, effect, and blame are not clear in complex systems

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Eat Candy

BloodGlucose

Level

HungerLevel

+

-

+

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Eat Candy

BloodGlucose

Level

HungerLevel

+

-

+

InsulinProduction

Delay

-+

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0

5

10

15

20

25

30

35

40

45

Candy MoreCandy

Candy

GlucoseInsulin

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Oscillations that increase in amplitude over time are

a danger signal

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Glucose and Insulin

• How could nature be so stupid?

• How could such an unfit system survive?

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Nature isn’t stupid -- We are!

• 1st appearance of insulin regulation– 10,000,000-70,000,000

years ago

• 1st appearance of refined sugar– 1500 to 2400 years ago

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Eat Complex

Carbohydrates

BloodGlucose

Level

HungerLevel

+

-

+

InsulinProduction

Delay

-+

Delay

Page 13: Models in IE Lecture 13 System Dynamics 2nd Lecture Systems Archetypes.

Eat Complex

Carbohydrates

BloodGlucose

Level

HungerLevel

+

-

+

InsulinProduction

Delay

-+

Delay

Eat Candy

+

+

+

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Natural & Man-Made Systems

合Natural systems have usually evolved so that timings work well

› Introduce a new element into a smoothly running system and you are lucky if timings remain good› As change rate of technology this

problem occurs more frequently

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“The road to hell is paved with good intentions”

“The hurrieder I go,

the behinder I get”

“The harder we run, the more weseem to stay in the same place”

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“When your best isn’t good enough…”

“I keep banging my head

against the same wall”

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Understanding System Performance

GoodB en ch m ark

Im p roveU n d ers tan d w h y

F o cu s o ncom p on en t(tes t b e fo rein ves tm en t)

UnsatisfactoryF a ilu re ap p aren t

in on ecom p on en t

L in kag esTim in g s

F eed b ack

UnsatisfactoryN o ap p aren t

fa ilu re incom p on en ts

AssessPerform ance

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System Archetypes

• Recurring, classic patterns

• Identified in Peter Senge’s book, The Fifth Discipline (Chapters 6-16, Appendix 2)

• Should become part of your permanent vocabulary

• Escalation• Balancing loop with

delay• Success to the

successful• Tragedy of the

commons• Limits to growth• Shifting the burden

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For Next Class…

• Read about the different system archetypes

• Bring your book to class– open book QuizQuiz forecast w.p. .8

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Feedback: class performance on homework

• Good but not excellent overall

• Don’t complain about 6 hours/week!– 2nd half of term much harder

• Many of the harder problems are difficult conceptually, not technically– try harder– try more than once (many already do).

• Most work on Little’s Law problem, (6a.m, 6pm) was unsatisfactory.

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