assumed that the order rate has reached its saturation limit and thus, it will not be
affected even when the delivery delay is reduced below 2 months. At the other
extreme, it may be assumed that increase in delivery delay will decrease the
order rate only less than proportionately, as perhaps, some customers will still
remain loyal to the company’s product.
(2) a table function proposes an explicit assumption (visually displayed) which may
be discussed and debated, before changing it, if required.
6.6 Modelling Principles
6.6.1 Causal Loops
Causal loop diagrams represent visually the cause-effect connections among the
system elements which form structures of feedback loops. As such diagrams are easy
to produce and show clearly the overall system causal structure, they often help to
conceptualize real-life problems, to write the model equations, to interpret the results
from simulations, to design new policies, and eventually, to communicate with
persons unfamiliar with the system dynamics method. Causal loop diagrams are
therefore quite popular in system dynamics modelling.
While developing a causal loop diagram, it must not be forgotten that there is no
unique diagram for a given problem. The number of variables incorporated in a
diagram will vary from person to person. If the model builder opts to make a skeleton
representation of the problem situation, he will choose very few variables in the
causal loop. In contrast, if the modeller desires a detailed diagram, he will select a
number of variables.
6.6.2 The Diagramming Approach
A causal loop diagram records a succession of cause-effect relationships in a circular
fashion. Therefore, to know about causal loop diagrams, we have to first know about
cause-effect relationships and the way they are identified and represented.
6.6.3 Simple to Complex Modelling
This chapter has described briefly that infrastructure in a country comprises
sub-systems which sometimes interact in complex ways. These subsystems need
to be studied, as seen in Chap. 5, some decades before actual implementation, and
will be functioning for even many more decades.
6.6 Modelling Principles
183
affected even when the delivery delay is reduced below 2 months. At the other
extreme, it may be assumed that increase in delivery delay will decrease the
order rate only less than proportionately, as perhaps, some customers will still
remain loyal to the company’s product.
(2) a table function proposes an explicit assumption (visually displayed) which may
be discussed and debated, before changing it, if required.
6.6 Modelling Principles
6.6.1 Causal Loops
Causal loop diagrams represent visually the cause-effect connections among the
system elements which form structures of feedback loops. As such diagrams are easy
to produce and show clearly the overall system causal structure, they often help to
conceptualize real-life problems, to write the model equations, to interpret the results
from simulations, to design new policies, and eventually, to communicate with
persons unfamiliar with the system dynamics method. Causal loop diagrams are
therefore quite popular in system dynamics modelling.
While developing a causal loop diagram, it must not be forgotten that there is no
unique diagram for a given problem. The number of variables incorporated in a
diagram will vary from person to person. If the model builder opts to make a skeleton
representation of the problem situation, he will choose very few variables in the
causal loop. In contrast, if the modeller desires a detailed diagram, he will select a
number of variables.
6.6.2 The Diagramming Approach
A causal loop diagram records a succession of cause-effect relationships in a circular
fashion. Therefore, to know about causal loop diagrams, we have to first know about
cause-effect relationships and the way they are identified and represented.
6.6.3 Simple to Complex Modelling
This chapter has described briefly that infrastructure in a country comprises
sub-systems which sometimes interact in complex ways. These subsystems need
to be studied, as seen in Chap. 5, some decades before actual implementation, and
will be functioning for even many more decades.
6.6 Modelling Principles
183