Moonseo Park

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Moonseo Park

39동 433 Phone 880-‐5848, Fax 871-‐5518

E-‐mail: [email protected] Department of Architecture College of Engineering Seoul NaJonal University Professor, PhD

Problem-

Solving with a better

decision

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Earth

I meter

Suppose

We measure the circumference of the earth 1 meter above the earth surface, which is ‘A’.

And, the original circumference is ‘B’.

Then, A – B would be? ¹⁾

<

^{10 M}

2) About 10 KM 3) About 1000 KM 4)

>

^{1000 KM}

A B

Anchoring

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3 I meter

A

B

R

B = 2*

π

*R

π

^{= 3.14…}

A = 2*

π

^*(R+1)

A - B = 2*π*(R+1)^-2*π*R = 2 π

= 2*3.14…

= 6.28…

3

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Suppose

You have a paper big enough to fold several times.

If you fold the paper 50 times, what would be the thickness of the paper?

1)

<

^{10 CM}

2) About 1 M

3) about 1 KM

Once,

And so on…

Twice, Three times,

4)

>

^{1000 KM}

Underes/ma/ng Feedback Eﬀect

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5

Suppose

The thickness of the original paper is ‘x’, which was 0.01 CM.

When you fold the paper twice, the thickness will be x*2¹ When you fold the paper 10 times, the thickness will be x*2¹⁰ When you fold the paper 50 times, the thickness will be

x*2¹⁰*2¹⁰ *2¹⁰*2¹⁰*2¹⁰ = 10^-2*10³*10³*10³*10³*10³= 10¹³CM = 10⁸ KM

= 100 Million KM

,where x = 10^-2 CM

2¹⁰ = 1024 = about 10³ 1 KM = 10⁵CM

Earth

Sun 150 Million KM

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Lack of Empathy

A Government Policy Failure Example

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How to reduce air pollution caused by emitting vehicles?

Regulating car driving on an odd and even

number basis

Buying one more car

Increasing registration fee

for new cars

Buying a used car

They found a clue BUT…

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Cogni/on Trap

§  CogniJon (인지): a human being’s ‘conscious’ mental process including aTenJon, solving problems and decision-‐making.

§  CogniJon Trap (인지함정): a frame of thinking that leads to a

mistake by staJc cling (정태적인 집착으로 실책을 이끄는 사고의

틀).

§  Our decision-‐making is prone to a mistake, as many cogni5on traps exist in the uncontrolled real world.

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Cause Confusion

§  More diﬃcult to idenJfy what the problem is than you expect.

§  Once idenJﬁed, less diﬃcult to solve than you worry

§  The problem should be a ‘nightmare’.

Confusing the causes of complex events

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Over-‐simplifying

by missing links

“Charles Lamb’s Essay, 1800”

Missing links in causes

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Causa/on vs Correla/on

Confusing causaJon with

correlaJon

Blunder, Zachary Shore 2007

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Backward Causa/on

You observe the shopping cart of a fat woman, which is full of diet food.

?

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Depression Chemical Imbalance

Drugs

Not always that simple…

What if chemical imbalance is the result of depression?

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Sta/c Cling

§  Longing for things

(prosperity, peace, success) to remain.

§  BUT

§  There is oden something more important than fact.

Refusal to accept a changing world

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Categoriza/on Risk

Is it a bird?

A chicken A robin

An ostrich

Tend to think idealized best examples in prototypes

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§  CategorizaJon is criJcal in daily life

§  Relate all objects, events, and ideas as classes

§  Tend to think idealized best examples in prototypes

Ex. When hearing “bird”, people imagine a robin or another bird like it

§  Thus, allow a thing only inside the category or outside, NO in between

Ex. characterize people according to blood types

§  SuscepJble to ﬂatview and cure-‐allism

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Failure of Sachs’s policy for Soviet

Union’s priva/za/on IMF & Asian Tigers in 1998

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Mirror Imaging

Tendency that the other people think like themselves.

PatrioJc ProsJtutes in Japan ader 2^nd world war Men are from Mars, Women are from Venus

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Flatview

§  Mostly caused by categorizaJon, lack of Empathy and ImaginaJon *empathy (feel with) cf. sympathy (feel for)

§  Lead to ‘black or white view’

§  Important to understand that all people (enemies or allies) are ruled by emoJon and their decision making is ‘opJmized (good or bad)’ to the given situaJon

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§  Not only a communist leader

§  Also, a naJonalist patriot ﬁghJng for his people’s freedom

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Selec/ve Percep/on

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Infomania

§  Amount of informaJon guarantees a good decision?

§  Lead to overconﬁdence about their decision

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§  “The Necklace”, Maupassant

§  Job posJng example

§  Occurs when people are afraid that their posiJon is threatened when knowledge is spread

§  But, oden sharing informaJon helps avert disaster

Infomisering

^{(정보독점)}

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§  Believe avoiding informaJon can help achieve goals

§  Shun the informaJon that could keep from blunders

§  Similar with Infomisering in that it retreats from the

wisdom of others

Infovoidering

^{(정보회피)}

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System View

§  System

§  See as a whole

§  See the unseen

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System

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See as a whole NOT as parts

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See the unseen

There is God as unseen wind moves reed (Pascal).

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System thinking can be trained?...

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Mental Process Modeling

System Dynamics

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System Dynamics

§  Extend our mental models through the creaJon of explicit models, which are clear, easily communicaJng and can be compared with each other.

§  Increase the probability that the consequences of how we will decide and act in accordance with how we plan to.

§  Developed to apply control theory to the analysis of industrial systems in the late 1950’s by Jay Forrester, MIT Professor

§  Used to analyze industrial, economic, social and environmental systems of all kinds

Wikipedia

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Finding the Root Cause

by iden/fying missing links in causes

Root cause Missing links in

causes Perceived cause but not

real cause Problem observed

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Decision making

against cogni/on traps

causes AcJons

taken

Decision Making

Perceived cause but not

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Analyzing consequence of ac/ons taken

by simula/ng their chain eﬀect

causes Missing links in

chain eﬀect AcJons

taken

Decision Making

Chain eﬀect of acJons taken

(35)

Also, understa/ng their feedback eﬀect

back into the root cause

causes Missing links in

chain eﬀect AcJons

taken

Decision Making

Chain eﬀect of acJons taken

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As the ﬁnal element of system structure, there are two kinds of variables ...

•  Stocks (also called ‘levels’): deﬁne the state of a system and represent stored quanJJes

•  Flows (also called ‘rates’): deﬁne the rate of change in

system states and control quanJJes ﬂowing into and out of stocks

Stocks & Flows

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Representa/ons

Hydraulic Metaphor:

Stock & Flow Diagram: Stock

Inflow Outflow

Integral EquaJon: Stock(t) =

∫

[Inﬂow(s) – Ouulow(s)]ds + Stock (t₀)

t₀ t

“Clouds” represent stocks outside the system boundary

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Terminologies in Diﬀerent Disciplines

Discipline Stocks Flows

Mathematics, Physics, Engineering

Integrals, states, state variables, stocks

Derivatives, rates of change, flows

Chemistry Reactants, reaction products Reaction rate

Manufacturing Buffers, inventories Throughput Economics

Levels Rates

Accounting Stocks, balance sheet items Flow, cash flow or income statement items

Biology, Physiology Compartments Diffusion rate, flows Medicine, Epidemiology Prevalence, reservoirs Incidence, infection,

morbidity, mortality rates

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•  Auxiliaries: intermediate variables to be used for easy of communicaJon and clarity

•  Break up rates into meaningful components

•  Provide alternaJve measures for stocks or ﬂows

•  Reduce diagram “cluTer”

•  Constants: factors which may be stocks or ﬂows, but which do not change over the Jme span of the simulaJon

Auxiliaries & Constants

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Break Rates Into Components

Hiring = Average ATriJon + (Desired Staff -‐ Staff)/ Time to Adjust Staff

(separate equaJons for components) vs.

Hiring = Average ATriJon + (((Total Work to Do -‐ Work Done) / ProducJvity) -‐

Staﬀ)/ Time to Adjust Staﬀ

Staﬀ

Work Done

Hiring ^ATriJon

Average ATriJon

Desired Staﬀ

Work Remaining

Total Work to Do

ProducJvity

Time to Hire

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Provide AlternaJve Measures

Inventory Coverage = Inventory / Average Sales

For stocks… For ﬂows…

Cash

Revenues Expenses

Proﬁts

Inventory

Inventory Coverage

Average Sales

Proﬁts = Revenues -‐ Expenses

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Reduce Diagram CluTer

Project Costs Labor Cost

Material Cost

U/lity Cost

Site Oﬃce Opera/on Cost

Project

Management Fee Direct Cost

Indirect Cost Project Costs

Labor Cost

Material Cost

U/lity Cost

Site Oﬃce Opera/on Cost Project

Management Fee

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Causal Links (Not Casual …)

•  An arrow with a posiJve sign (+): all else remaining equal, an increase (decrease) in the ﬁrst variable increases (decreases) the second variable above (below) what it would otherwise have been.

•  An arrow with a negaJve sign (-‐): all else remaining equal, an increase (decrease) in the ﬁrst variable decreases (increases) the second variable below (above) what it otherwise would have been.

Bank

Balance Interest

Earned +

Price -‐ Orders

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Labeling Link Polarity

Inventory

Produc/on Shipments

Ordered Books

Sales force Price

Popula/on

Births Deaths

+ -‐

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Causa/on vs. Correla/on

Murder Rate Ice Cream

Sales

+

Observed behavior: “…Ice cream sales and murder rise in summer and fall in winter…”

Incorrect

Sales

+

Correct

Average Temperature +

Causal diagrams must include only those rela5onships that capture the underlying causal structure of the system.

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Having Unambiguous Polari/es

Revenue Price

? (+ or -‐)

Incorrect

+

Correct

-‐

All causal links must have unambiguous polari5es.

* Apparently ambiguous polariJes usually imply the presence of mulJple causal pathways that can be represented

separately.

Sales

+

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Iden/fying the Feedback Loop

Bank

Balance Interest

Earned +

Sales

+ Average

Temperature +

+

-‐

Sales

+ A`rac/veness of

Market

Number of Compe/tors

Price Proﬁts

+

-‐

+ +

Feedback Loop

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Loop Polari/es: Reinforcing or Balancing?

Applicant for Admission

School Reputa/on

Capable Student

Research

+

+ +

+

R

Reinforcing Loops: loops with all posiJve or an even number of negaJve causal links

A`rac/veness of Market

Proﬁts

Industry

Number of Compe/tors

+

B

Balancing Loops: loops with an odd number of negaJve causal links

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Naming Variables

Price Rise Costs Rise

+

Incorrect Correct

Names should be nouns or noun phrases.

•  The acJons (verbs) are captured by the causal links

•  A causal diagram captures the structure of the system, not its behaviors

Price Costs

+

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Naming Variables

Mental Abtude Feedback from

the Boss

+

Names should have a clear sense of direc5on.

Choose names for which the meaning of an increase or decrease is clear

Morale Praise from

Boss

+

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Naming Variables

Unhappiness Quarrels with

Friends

+

Choose variables whose normal sense of direc5on is posi5ve.

Avoid the use of variable names containing preﬁxes indicaJng negaJon (non, un, etc.)

Happiness -‐

Quarrels with Friends

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Choosing Right Level of Aggrega/on

Unit Costs Market Share

-‐

If audience is confused by

Use intermediate variables, if they can make communica5on easier and clearer

You might make the intermediate concepts explicit as follows.

Unit Costs Market Share

+ Produc/on Volume

Cumula/ve Produc/on Experience +

-‐

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Making Goals Explicit

Make the goals of nega5ve loops explicit.

•  All negaJve feedback loops have goals (the desired state of the system)

•  They funcJon by comparing the actual state to the goal, then iniJaJng a correcJve acJon in response to the

discrepancy.

•  Making goals explicit encourages people to ask how the goals are formed.

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Making Goals Explicit

Product Quality

Quality Improvement

Programs - +

B

The goal of the loop is determined by management decision (human behaviors).

The goal of the loop is determined by the laws of

thermodynamics (natural processes).

Coﬀee

Temperature

+

-

B

+

Temperature

Diﬀerence

Desired Room

-

+

Coﬀee

Temperature

Cooling

-

B

Temperature

Product

Quality

Improvement

Programs

-

+

B ^Quality

Shordall

Desired

Product

Quality

+

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System

Dynamics

Applications

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DPM

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The US Navy and Ingalls case

A system dynamics model developed by Pugh-‐

Roberts Associates in the US was used to seTle the claim against the US Navy in the late

1970’s.

With help of this modeling approach, the ship builder, Ingalls managed to receive $447

million as compensaJon for their ﬁnancial losses caused by the owner’s design and speciﬁcaJon changes.

Reference: Business Dynamics (Sterman, 2000); Strategic management of complex projects (Lyneis et al., 2001)

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Quan/fying the ripple eﬀects

TradiJonal project management tools such as CPM, PDM, and PERT do not provide a mean to quanJfy the ripple eﬀects that mulJply the direct impact many Jmes, leading to signiﬁcant overall delay and disrupJon.

Design/

SpeciﬁcaJon Change

Altering work sequence

Requiring overJme Unscheduled

hiring

Work out of sequence Lowered

producJvity

Resource idling

Lowered quality

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0 200 400 600

1976 1978 1980 1982 1984 1986 1988 1990

Thousand people/year

Total Arrests

Sale/Manufacture Possession

0 20 40 60

1976 1978 1980 1982 1984 1986 1988 1990

Thousand people/year

ER Mentions

ME Mentions * 10

0 20 40 60 80 100

1976 1978 1980 1982 1984 1986 1988 1990

Purity (%)

The War on Drugs

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Simulated vs. actual cocaine epidemic. Dashed lines, data; solid lines, model.

0 0.01 0.02 0.03 0.04 0.05

1976 1980 1984 1988 1992 1996

Prevalence (fraction of population)

NHS Past Month User Fraction Simulated

Actual Past Month User Fraction

Simulated

Reported Past Month User Fraction

History Forecast

Past month user fric/on decreased…

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Source: Homer (1993, 1997)

0 200 400 600 800

1976 1980 1984 1988 1992 1996

Thousand people/year

Sale/Mfr Possession Total Arrests History Forecast

But, drug possession, arrests, sales increased

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0 25 50 75

1976 1980 1984 1988 1992 1996

Thousand people/year ER Mentions

ME Mentions * 10 History Forecast

Even worse…

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Where to Get More

•  System Dynamics Group at MIT

hTp://sysdyn.mit.edu/sd-‐group/

•  System Dynamics Society

hTp://www.albany.edu/cpr/sds/

•  System Dynamic Review SNU Library

•  Ventana Systems: download Vensim PLE version hTp://www.vensim.com/

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References

•  Avraham Shtub, Jonathan F. Bard, Shlomo Globerson, “Project management : engineering, technology, and implementa/on”, Englewood Cliﬀs, NJ, Pren/ce Hall, 1994

•  Frederick E. Gould, Nancy Joyce, Chapter 8, “Construc/on project management”, Upper Saddle River, NJ, Pren/ce Hall, 1999

•  James M. Lyneis *, Kenneth G. Cooper, Sharon A. Els, “Strategic management of complex projects: a case study using system dynamics”, System Dynamics Review, Vol. 17, No. 3, 2001

•  Christopher M. Gordon, “Choosing appropriate construc/on contrac/ng method”, J. of Construc/on Engineering & Management, Vol. 120, No. 1, 1994

•  Feniosky Pena-‐Mora, Jim Lyneis, “Project control and management”, MIT 1.432J Lecture Material, 1998

•  Barrie, D.S., and Paulson, B.C., “Professional Construc/on Management”, McGraw Hill, 1992

•  Halpin, D.W., “Financial and Cost concepts for construc/on management”, John Wiley & Sons, 1995

•  Yehiel Rosenfeld, “Project Management”, MIT 1.401J Course Material, 2000

•  Sarah Slaughter, “Innova/on in construc/on”, MIT 1.420 Course Material, 1999

•  Chan, Albert P. C.; Ho, Danny C. K.; Tam, C. M, “Design and Build Project Success Factors: Mul/variate Analysis”, J. of Construc/on Engineering & Management, Vol. 127, Issue 2, 2001

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Where did the gasoline go?

NO GAS

Moonseo Park

Problem-

Suppose

Anchoring

Lack of Empathy

Cogni/on Trap

Cause Confusion

Causa/on vs Correla/on

Sta/c Cling

Mirror Imaging

Selec/ve Percep/on

Infomisering

§ See as a whole

System Dynamics

System Dynamics

Finding the Root Cause

Analyzing consequence of ac/ons taken

Stocks & Flows

Auxiliaries & Constants

Provide AlternaJve Measures

Causal Links (Not Casual …)

Labeling Link Polarity

Having Unambiguous Polari/es

Loop Polari/es: Reinforcing or Balancing?

Naming Variables

Naming Variables

Making Goals Explicit

Making Goals Explicit

System

Quan/fying the ripple eﬀects

The War on Drugs

But, drug possession, arrests, sales increased

• Ventana Systems: download Vensim PLE version hTp://www.vensim.com/

Where did the gasoline go?

§  See as a whole

•  Ventana Systems: download Vensim PLE version hTp://www.vensim.com/