Causality and diagrams for system dynamics

1
Causality and diagrams for system dynamics
Martin Schaffernicht Facultad de Ciencias
Empresariales Universidad de Talca Talca
Chile martin_at_utalca.cl

• Students have a hard time learning to correctly
  use polarity.
• Most approaches use causality between events, not
  behavior. System dynamics focuses on behavior,
  but intuitive thinking focuses on events the
  relationship between them is not clearly defined.
• System dynamics has several definitions of
  polarity
• the popular one is simple but allows false
  statements
• the complete one avoids false statements, but
  is hard to learn and ambiguous with respect to
  the relationship between behavior and event.
• Causal loop diagrams (CLDs)
• allow to model fast-and-dirty
• can represent mental models that contradict basic
  system dynamics truths
• cannot represent some of the basic system
  dynamics truths.
• CLDs are easy and risky to use
• the expert dynamicist does not need external help
  to avoid errors and can use CLDs safely
• the beginner needs external help but resists the
  effort of learning stock-and-flow modeling before
  thinking about a problem.

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Causality in different approaches
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Approach Main use Nodes Links Driving force Degree of abstrac-tion
Concept mapping Structure knowledge in form of propositions Concepts in general Any type Undefined Low
Cognitive mapping Uncover, structure and analyze problem accounts Constructs Qualitative causal links, negative and positive type Events, actions Low
Causal mapping Represent beliefs about the causal structure. Variables Qualitative, including popular polarity Undefined High
Causal diagrams Determine causes of events or consequences of actions Variables with discrete values Quantified causal links Events High
System dynamics Structure decision policies Variables with continuous values Quantified causal links Behavior High
3
What is behavior (that it is not an event)?
2
Quantitative
Computer
the sequence of quantities
the trend (quality of behavior)
Levels of description
Specialization
first descends, then grows
decreases
Event
the change of the trend
switches from decreasing to growth
Qualitative
Modeler
Behavior
4
The popular definition (between behavior and
event)
3
when the independent variable changes, then the
dependent variable changes in the same direction
positive
when the independent variable changes, then the
dependent variable changes in the opposite
direction
negative
dV1
- dV2
Change
Change dV
Behavior as change of quantities
Behavior as change of the quality of behavior
(event)
5
The complete definition (between behavior and
event)
4
when the independent variable changes with a
particular sign ( or -), then the following
values of the dependent variable will be above
(or less) than what they would have been.
positive
when the independent variable changes with a
particular sign ( or -), then the following
values of the dependent variable will be less(or
above) than what they would have been.
negative
Behavior as change of quantities
Behavior as change of the quality of behavior
(event)
6
Famous example population dynamics
5
-

population

-


births
deaths


This part of the CLD is used to critique the
causal loop diagram language as a tool for
systems thinking in pedagogical contexts.
natality
mortality


birth control policy
public health policy
?See the population case.itm model in the
additional material.
7
What a CLD can say (but should not)
6

population
Popular definition
Complete definition
Is the moment of bifurcation an event?
?
births
?
?
?
If births decrease, then population will
decrease.
If births decrease, then population will be less
than what it would have been.
Complete definition seems to avoid
contradiction. However, less than what would
have been does not imply no absolute decrease.
8
What a CLD should say (but cannot)
7

population
Any definition
births
0
0
No flow variables that are inflow or
outflow, no quantities
No bounds on quantities (like a negative inflow).
No stock variables that maintain values
A constant independent variable can lead to a
growing dependent variable.
No flow-stock integration
9
8
Famous example once more population decreases!
What does ceteris paribus then mean
population has no other influences or all
other influences on population remain as they
are?
Population depends on various variables and also
influences them. births ? population is too
reduced.
-

population

-
births
deaths




natality
mortality


birth control policy
public health policy
?Experiment with the population case.itm model
in the additional material.
10
Concluding arguments (cog.map)
9
CLDs allow event-oriented (intuitive) thinking.
CLDs have few implied concepts.
CLDs allow to go fast and do not require much
previous study.
Use of CLDs does not educate proper systems
thinking.
Thinking with CLD is popular.
We generally cannot avoid to make behavioral
interpretations
CLDs allow false behavioral interpretations.
A beginner is likely to think in terms of
constructs and events. He is not aware of the
hazard zones.

An expert can think in terms of variables and
behavior he embodies the rules and will not err
CLDs as shorthand.
Use by experts OK.
Only disciplined use in teaching!
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Concluding arguments (cog. map)
10
A beginner is likely to think in terms of
constructs and events. He is not aware of the
hazard zones.
System dynamics has several complicated
concepts.
We generally cannot avoid to make behavioral
interpretations
-
Learning stock-and-flow thinking and modeling
takes time.
Adults have few time to learn.
Improve definition of relationships with event
thinking approaches.
Start teaching earlier.
An expert can think in terms of variables and
behavior he embodies the rules and will not err
CLDs as shorthand.
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System dynamics references
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Other references (selcted)
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• General sources
• Hume, D. 1984 Enquiry concerning the human
  Understanding Investigación sobre el
  conocimiento humano, (Translated by Jaime de
  Salas Ortueta), Alianza Editorial, Madrid
• Gopnik, A. Glymour, C. Sobel, David M.
  Schulz, L. E. Kushnir, T. and Danks, D. 2004.
  A theory of causal learning in children Causal
  maps and Bayes nets. Psychological Review, 2004,
  111, 1, 1-30
• Allan. L. and Tangen, J. 2005. Judging
  Relationships Between Events How Do We Do It?
  Canadian Journal of Experimental Psychology Mar
  2005 59, 1 pg. 22
• Heider, F., 1958. The Psychology of Interpersonal
  Relations. New York Wiley
• López, F., Cobos, P. and Caño, A. 2005.
  Associative and causal reasoning accounts of
  causal induction Symmetries and.asymmetries in
  causal predictions and diagnostic inferences,
  Memory Cognition Dec 2005 33, 8 pg. 1388
• Kahnemann, D. 2002. Maps of bounded rationality
  a perspective on intuitive judgment and choice,
  Nober Prize Lecture, December 8, 2002
• Kelley, H. H. 1973. The processes of causal
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• Causal diagram sources
• Halper, J. and Pearl, J. 2005a. Causes and
  Explanations A Structural-Model Approach. Part
  I Causes, Brit. J. Phil. Sci. 56 (2005), 843887
• Halper, J. and Pearl, J. 2005b. Causes and
  Explanations A Structural-Model Approach. Part
  II Explanations, Brit. J. Phil. Sci. 56 (2005),
  889911
• Pearl, J. 1955. Causal diagrams for empirical
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• Cognitive mapping
• Eden, C. 1990. Using cognitive mapping for
  Strategic Options Development and Analyses
  (SODA), in J. Rosenhead (Ed.), Rational Analysis
  for a Problematic World, Wiley Chchester
• Howick, S. Ackermann, F. and Andersen, D., 2006.
  Linking event thinking with structural thinking
  methods to improve client value in projects,
  System Dynamics Review Vol. 22, No. 2, (Summer
  2006) 113140
• Mental model sources