IE604 Causal Loop Diagram Jayendran Venkateswaran IEOR @ IIT - - PowerPoint PPT Presentation

IE604 Causal Loop Diagram

Jayendran Venkateswaran IEOR @ IIT Bombay

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Causal Loop Diagram (CLD)

p Visual representation of the cause-effect

relationships between the various elements

• f the system, forming feedback loops

n Conceptualize the problem n Capture hypothesis about causes of dynamics n Communication with others

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

CLDs

p CLDs consists of variables connected by

causal links or arrows

p Variable at tail of arrow

n Causal or independent variable

p Variable at head of arrow

n Affected or dependent variable

p Arrows show the direction of causation p Causal link is associated with a link polarity n + or S n — or O

X Y

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Causal Link Polarity

p + or S

n If all other things being equal, a change

in causal variable generates a change in the same direction in affected variable relative to its prior value

n If X increases, then Y increases above

what it would have otherwise been

n If X decreases, then Y decreases below

what it would have otherwise been

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Causal Link Polarity

p — or O

n If all other things being equal, a change

in causal variable generates a change in the opposite direction in affected variable relative to its prior value

n If X increases, then Y decreases below

what it would have otherwise been

n If X decreases, then Y increases above

what it would have otherwise been

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

CLD Example 1

(Goodman, M. R., Study Notes in System Dynamics)

p Hypotheses of dynamics in urban region

n Job availability attracts migrants to the city n New arrivals to city expand the labor population n Population absorbs available jobs, decreasing

job availability

n In long run, as labor also creates demand for

additional services & facilities, a further increase in the number of jobs in the city comes about

n More jobs increase job availability

p The above description is reference mode!

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Loop Polarity

p When feedback loop response opposes the

• riginal perturbation, the loop is negative or

goal seeking

p When feedback loop response reinforces

the original perturbation, the loop is positive or reinforcing

p Fast way:

n If the number of negative links in a loop is even

à loop is _______________

n If the number of negative links in a loop is odd

à loop is _______________

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Core Concepts

p Cause and Effect p Feedback loops

n Makes system complicated n Key causal links are those inside feedback loops

p Positive Feedback loop

n Even number of “-” signs; Amplifying,

reinforcing, growth (rapid decline), unstable

p Negative Feedback loop

n Odd number of “-” signs; Balancing, Stabilizing,

Stable equilibrium; often good.

p Delays in Feedback loops

n E.g., carrying cup of tea; taking a shower; etc.

time units important

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Guidelines for CLD (1)

p All links should have unambiguous polarities

n Ambiguous polarity indicates presence of other

pathways

p Proper Variable names

n Variable names should be nouns or noun phrases.

Action (verbs) are captured by the causal link

n Choose variable names whose normal sense of

direction is positive

p Normal Sense of direction is positive p Make intermediate links explicit

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Guidelines for CLD (2)

p Capture Causation and NOT correlation

n Correlation do NOT represent structure of system.

Correlation among variables reflect the past behavior à may change if circumstances change

n Make sure the relationships are causal, no matter

how strong the correlation is!

p Make goals of negative loops explicit p Distinguish between actual and perceived

conditions

p Indicate important delays in causal links

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Justification of Causal Links

p Conservation consideration

n Laws of conservation

p Accepted Theory p Instructions p Direct observation p Hypothesis or assumptions p Statistical evidence

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Drawbacks of CLD

p Richardson, G. P., 1986, Problems with

causal-loop diagrams, System Dynamics Review, 2(2), 158-170

n http://sysdyn.clexchange.org/sdep/Roadmaps/R

M4/D-3312-2.pdf

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Example: Population, Economy & Land Use (1)

p As employment opportunities increase in a

city, people are attracted into the urban

• area. However, in-migration do not

immediately react to opportunities. Since migrants react to perceived opportunities, there may be 5-10 year lag in response.

p Population growth from the influx of

migrants tends to encourage business expansion in the growing urban area. This business expansion creates demand for additional labor which increases employment opportunities.

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Example: Population, Economy & Land Use (2)

p Population growth also tends to drive housing

construction at a greater pace to match the

• population. Assuming only a fixed land is available

for business and housing use, increasing housing stock makes less land available for business expansion.

p As the unavailability of more land begin to

suppress business expansion in the area, the demand for labor decreases. Consequently local employment opportunities decline. Once potential migrants perceive the lack of opportunities, declining in-migration generates a reduction in the population growth of the area.

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Example of CLD: Factory

p In a factory, the customer order rates are usually

accumulated into order backlog.

p As backlog increases, the production orders to the

shopfloor is increased. After some production delay these orders are converted into finished products and stored in inventory. With more inventory,

• rders will be filled at a higher rate and

correspondingly backlogs will reduce.

p With early filling of orders, the delivery delay will

be reduced which improves customer satisfaction. Higher customer satisfaction leads to more orders from customers.

p An increase in order backlog worsens the delivery

schedule and has an adverse impact on customer satisfaction

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Case Study: Problem of Traffic Congestion

p Trend in road traffic

n Freight transport by road has risen from 6 Billion Tonne

Km (BTK) in 1951 to 1100 BTK in 2000 and passenger traffic has risen from 23 Billion Passenger Km (BPK) to 2875 BPK during the same period.

n The annual growth of road traffic is expected to be 10 to

11%. Current boom in the automobile sector may even increase the future growth rate of road traffic.

p Growth in road network

n NHs carry nearly 40% of

road traffic

n In NH & SH, only 2% of

their length is four-lane, 34% two-lane and 64% single-lane

Source: http://siadipp.nic.in/publicat/books/roads.pdf

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Model for Traffic Congestion

p Open Loop View p Closed loop view

n Travel Time n Pressure to build roads

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Capacity Expansion: Road Traffic Model

Source: Sterman, John D. Business Dynamics (Fig 5-33)

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Traffic Volume dynamics: Road Traffic Model

Source: Sterman, John D. Business Dynamics (Fig 5-34)

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Suburban Development: Road Traffic Model

Source: Sterman, John D. Business Dynamics (Fig 5-35)

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Mass Transit Death Spiral: Road Traffic Model

Source: Sterman, John D. Business Dynamics (Fig 5-36)

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

You can’t get there on bus! Road Traffic Model

Source: Sterman, John D. Business Dynamics (Fig 5-37)

IEOR, IIT Bombay Jayendran Venkateswaran IE604: System Dynamics Modeling and Analysis

Model for Traffic Congestion

p Open Loop View p Closed loop view

n Travel Time n Pressure to build roads

p Dynamics of Traffic volume p Growth of suburban regions p Mass Transit Death Spiral

n You can’t get there on the bus!