MSC ENvironment

1. SYSTEMS THINKING AND
ENVIRONMENTAL
MODELLING
Sunil Duwal

2. What is a system?
“A system is an interconnected set of
elements that is coherently organized in
a way that achieves something”
Donella H. Meadows, Thinking in Systems: A Primer (Chelsea Green
Publishing, 2008) 11

3. Examples of Systems
Digestive Forest Community

4. Bathtubs
A bathtub is a simple system
9AM
0
5
10
15
20
25
Water in Bathtub

5. Bathtub System
Draining
11:46 AM Fri, Feb 20, 2009
Water in bathtub
0.00 7.50 15.00 22.50 30.00
Minutes
1:
1:
1:
0
15
30
1: Bathtub
1
1
1
1

6. Understanding Behavior Over Time
Dynamic Equilibrium
11:34 AM Fri, Feb 20, 2009
Water in bathtub
0.00 7.50 15.00 22.50 30.00
Minutes
1:
1:
1:
24
25
26
1: Bathtub
1 1 1 1

7. SYSTEM DYNAMICS
WHERE DO SYSTEM
DYNAMICS FIT IN PROBLEM
SOLVING?

8. System Dynamics
Model
Action
Mental Model Mental Model

9. Models are tools and concepts that help us
understand, explain, and predict systems that are
too complex or difficult to observe, or to
comprehend on our own.
“The most useless scale for a road map is 1:1”
What is a model
Models are simplifications of reality.

10. Modelling
environmental systems

11. Some words on
modelling

12. Problem perception
• Definition of the scope of the model
• Clearly define your objectives
• Allow for incremental model definition
(don’t start with a model which is too
complex)
• Work in strict co-operation with the
Decision Makers

13. Limits to modelling
• We tend to think linear
• System structure influences behaviour
• Structure in human system is subtle
• Leverage often comes from new ways of
thinking
• Reductionist thinking is often hampering

14. Systems thinking
• for seeing wholes, counteract reductionism
• relationships rather than things
• patterns of change rather than static snapshots
• seeing circles of causality
• dealing with complexity and delays
• acknowledging both hard and soft components

15. What is a model?
• A model is any understanding which is used
to reach a conclusion or a solution
• Only mental models exist; all models rest in
the human mind
• There are no computer models, these are
mere mechanical and mathematical pictures
of mental models
• If a model is ”wrong”, then the underlying
understanding is to blame

16. Modelling: the hardest
part
Needed Unnecessary
Sorting the essential from the nonessentials!

17. The quality of a model
is determined by
• how useful it is for it’s purpose
• how well users understand the model and
have trust in it
• NOT the number of details

18. Simplicity and participation
• The major result is understanding (not the
models themselves)
• Simple models ensure understanding
• Modelling is not a one man work!
• The process is everything!
(”The road is the goal”)

19. One question – one model!
Never trust a Swiss Army knife model!

20. Model categories and
classification

21. Breeds of models
• Models are
• conceptual
• physical
• mathematical

22. Model use
• Descriptive models
• Decision models
• Prescriptive models
• Forecast models

23. Conceptual models

24. A conceptual model
• is presented graphically as a compartment
system
• compartments are defined w.r.t morphology,
and physical, chemical and biological states
• connections denote exchange of matter,
energy, information
• compartments may contain sub-models

25. Types of conceptual models
• Word models
• Picture models
• Box-models
• Feedback dynamics, Causal Loop Diagrams
• Energy Circuit Diagrams (Odum)

26. Causal loop diagrams
Feedback dynamics

27. What is a Causal Loop
Diagram?
• A simplified understanding of a complex problem
• A common language to convey the understanding
• A way of explaining cause and effect relationships
• Explanation of underlying feedback systems
• Helps us understanding the overall system behaviour

28. Reinforcing feedback
R
• Reinforcing behaviour
• Something that causes an amplified
condition
• the larger the population the more
births
• the more money in the bank, the more
in interest

29. Balancing feedback
B
• Balancing behaviour
• Something that causes a change which
dampens/opposes a condition,
• Limited amounts of nutrients
• Intensity of competition

30. Reinforcing
An example of system
in growth over time
100
75
• A self-reinforcing system is a
system in growth, e.g. bank
account, economic growth or
population growth, exponential
growth
50
25
0
2001 2002
time
2003 2004

31. Balancing
An example of system that balances over time
100
• In a balancing system
there is an agent which
retards the growth or is
a limiting factor to the
reinforcing growth, e.g.
limited resources in the
soil, limited light or space
for growth etc.
75
50
25
0
2001 2002
time
2003 2004

32. The structure of Causality
Variables change:
”in the same direction”
”in the opposite direction”

33. A very simple example
+
Photosynthesis R
+
Growth

34. Another simple example
-
Nutrient
uptake
+
B
Nutrients
available

35. a bit more complex

36. Some practice with
CLD

37. Atmospheric system

38. Natural
system

39. Social system

40. Economic system

41. Combined
system

42. The difficult transition
from conceptual to
mathematical models

43. Problem formulation
• Conceptual model construction
• System boundaries
• CLD
• Actors, Drivers and Conditions
• Reference behaviour

44. Model construction
• From conceptual model to quantitative
model
• Parameterization
• Sensitivity and robustness testing
• Model validation

45. The modelling process
Scope/
Purpose
Conceptualisation Data collection
Calibration
Validation
Use

46. Problems in
conceptual modelling
• What is relevant? Sorting out essentials
• At what level? Micro- or Macro-level
• Static and dynamic factors?
• System boundaries?
• Time horizon
• Qualitative and/or quantitative factors?
• Problems to ”kill your darlings”
• Perception limitations

47. Adding causes to model
From: Sverdrup & Haraldsson, 2002

48. Model performance
From: Sverdrup &
Haraldsson, 2002

49. Model cost and performance
From: Sverdrup &
Haraldsson, 2002

50. System Levels
From: Sverdrup &
Haraldsson, 2002

51. Modeling Systems with STELLA

52. STELLA – OVERVIEW
Stocks
connectors
Links Sector frames Graph Table
View the math
behind the
model
Edit the values
in stocks and
flows
Visualize the
system in this
tab (start here)
Interact with the
simulation – add
buttons, etc
flows
Modules

53. Stocks & Flows
The bathtub is a stock
Bathtub

54. Stocks & Flows
The faucet and the drain are flows
Bathtub
adding water draining water

55. Other Stocks &
Flows
Same thing, different units
Bank Account
making deposits
C02 In
Atmosphere
adding c02
Self Esteem
building

56. Feedback Loops
A feedback loop occurs when a stock
affects its flows
Bank account
earning interest
interest rate
R

57. Bank account
earning interest
interest rate
Feedback Loops
Reinforcing Loops
R
Population
births
birth rate
R
Size of Snowball
adding snow
speed
R

58. Behavior vs. Data
It’s important to focus on behavior when you’re thinking about
systems
Bank account
earning interest
interest rate
R
1
1
1
1

59. Feedback Loops
Balancing Loop
Room
Temp
adding heat
from furnace
thermostat setting
difference between
desired and actual temp

60. Balancing Loops
0.00 15.00 30.00 45.00 60.00
Time
1:
1:
1:
50
250
450
1: Population
1
1
1
1
Population
reproducing
birth rate
dying
death rate

61. Applying Systems Thinking
Use Systems Thinking Stock & flow Mapping Use Model-based
Concepts Materials
Non-software-based Software-based

62. Let’s make a model!