Jose J Gonzalez, Geir Bøe, and John Einar Johansen on "A System Dynamics Model of the 2005 Hatlestad Slide Emergency Management" at ISCRAM 2013 in Baden-Baden. 10th International Conference on Information Systems for Crisis Response and Management 12-15 May 2013, Baden-Baden, Germany

1. A System Dynamics Model
of the 2005 Hatlestad
Slide Emergency
Management
ISCRAM 2013
Jose J Gonzalez, Geir Bøe, John Einar Johansen
Centre for Integrated Emergency Management
(CIEM)
University of Agder, Norway

2. 3
The 2005 Hatlestad slide
• Landslide hitting neighborhood of Bergen Sept 14
• Extreme precipitation for weeks breaking all records
• Slide of clay, mud and rock hit a row of houses
• Ten people buried, four casualties
• 225 people evacuated
• Rescue operation from 02:05 am until noon
• Agenda-setting event, with deep impact:
• Norwegian policies for housing construction on hills
• Triggered mapping of housing potentially at risk
• Norwegian preparedness toward extreme weather
• Thorough studieslessons learned for emergency
management

3. 4
The Hatlestad slide as case
• Thorough study by Lango (master thesis 2010, book
chapter 2011)
• Hatlestad case qualitatively similar in reference
behavior, to Palau case (Hutchings “Cognition in the
wild”, 1995)
• Pioneer system dynamics simulation of Palau case by
Tu, Wang, & Tseng, 2009) based on Complexity
Theory
• Disorder, Improvisation, Self-Organization
• Data for key emergency handling parameters:
• Cognitive Load,
• Local Innovation and Changes,
• Mutual Understanding

4. 6
The system dynamics modeling
procedure
• Develop simulation that for the right reasons reproduces the
observed reference behavior of the Hatlestad slide emergency
management
• “Right reasons”:
• The model structure should contain the variables
corresponding to the observed behavior of the emergency
management team (the “observables”)
• The observables should be causally linked according to a
parsimonious “dynamic hypothesis”
• The simulations must reproduce the reference behavior
• The model should pass standard tests

5. 7
The system dynamics modeling
procedure – Reference behavior
• Qualitative reference behavior derived from Lango (2010, 2011)
• Criticism from scientists at home in natural sciences ignores science
history
Total reference behaviour
1
0.75
0.5
0.25
0
3 3 3
3
3 3 3
2
2 2
2
2
2
1 1 1 1
1 1 1 1 1 1 1 1
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600
Time (Minute)
Cognition
Cognitive Load : Reference Behaviour 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Local Innovations and Changes : Reference Behaviour2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
MU : Reference Behaviour 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Maximum/minimum
times knownOnset times
known
Return to
normal times
known

6. 8
The system dynamics modeling
procedure – Dynamic Hypothesis
• We hypothesize that the reference behavior can be
explained by a disequilibrium–experimenting–emergence
process (MacIntosh and MacLean 1999) (Dynes and
Quarantelli 1976)
• Accordingly, the causal structure of the model must
contain feedback loops generating
1. disequilibrium
2. experimenting (i.e., innovation and changes)
3. emergence (i.e., self-organization)

7. 10
The system dynamics modeling
procedure – Model development
• Simplified view with the main feedback loops
Increase of Mutual
Understanding
Mutual
Understanding
(MU)
+
+
R: Self-
referencing
Errors
generated
Errors from
mismatch
-
+
Decrease of
Mutual
Understanding
-
Local Innovations
and Changes
-
Potential Work
Rate
+
Actual
Work Rate
-
+
Performance
Gap
-
Desired Work
Rate
+
Errors
-
Cognition Resource
Allocation
+Cognitive
Load
+
+
B:
Performance
adjustment
Error
Correction Rate
-
Available Cognition
Resource
-
+
Average Error
Rate
+
Cognition Resource
Allocating to Avoid
Errors
+
-
+
Cognition for Error
Detection and
Recovery
+
Error
Detection Rate+
+
Error Detection
Skill
+
+
Error
Generation Rate
-
+
B: Team
learning
B: Error
detection
and
discovery
Required Effort for
Each Computation-
Change Rate of
Pressure
+ +
Cognitive Load
Pressure
+
+
B: Local
innovation
A
B: Local
innovation
B
+
-
R:
Loop
A
R:
Loop B
ManPower
+
Manpower
Allocation Rate

8. 11
The system dynamics modeling
procedure – Verification and validation
• Verification
• Checking that the variables and their causal connections
represent the selected case
• Validation
• Checking that the model is able to simulate the reference
behavior (following a calibration procedure)
• Checking that the model simulates extreme conditions
correctly
• Sensitivity analysis
• What happens if you vary the variables obtained by
calibration?

9. 12
The system dynamics modeling
procedure – Reproducing reference
behavior for Cognitive Load
1
0.75
0.5
0.25
0
2
2 2
2
2
2 2
2 2
2 21
1
1 1 1
1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600
Time (Minute)
Cognition
Cognitive Load : Hatlestad1 1 1 1 1 1 1 Cognitive Load : Reference Behaviour2 2 2 2 2

10. 13
The system dynamics modeling procedure
– Reproducing reference behavior for
Local Innovations and Changes
1
0.75
0.5
0.25
0
2
2 2
2
2
21
1
1
1
1
1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600
Time (Minute)
Cognition
Local Innovations and Changes : Hatlestad1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Local Innovations and Changes : Reference Behaviour2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

11. 14
The system dynamics modeling
procedure – Reproducing reference
behavior for Mutual Understanding
1
0.9
0.8
0.7
2
2
2
2
2
2 21
1
1
1
1
1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600
Time (Minute)
MU
MU : Hatlestad 1 1 1 1 1 1 1 1 1 MU : Reference Behaviour 2 2 2 2 2 2 2

12. 15
The system dynamics modeling
procedure – Distilling insights through
feedback analysis
• Feedback analysis
• Systematic elimination of feedback loops (breaking loops by
assigning a zero causal influence)
• Feedback analysis shows
1. Performance adjustment loop dominates initially
2. The reinforcing Loop A acts as a vicious loop, whereby Cognitive
Load and Errors increase, and thereafter Local Innovations and
Changes increases and Mutual Understanding decreases
3. The reinforcing Loop B starts to dominate, driving Mutual
Understanding further down
4. Local Innovations and Changes lead to improvements, whereby
Errors decrease and Mutual Understanding increase

13. 16
The system dynamics modeling
procedure – Model development
Increase of Mutual
Understanding
Mutual
Understanding
(MU)
+
+
R: Self-
referencing
Errors
generated
Errors from
mismatch
-
+
Decrease of
Mutual
Understanding
-
Local Innovations
and Changes
-
Potential Work
Rate
+
Actual
Work Rate
-
+
Performance
Gap
-
Desired Work
Rate
+
Errors
-
Cognition Resource
Allocation
+Cognitive
Load
+
+
B:
Performance
adjustment
Error
Correction Rate
-
Available Cognition
Resource
-
+
Average Error
Rate
+
Cognition Resource
Allocating to Avoid Errors
+
-
+
Cognition for Error
Detection and
Recovery
+
Error
Detection Rate+
+
Error Detection
Skill
+
+
Error
Generation Rate
-
+
B: Team
learning
B: Error
detection
and
discovery
Required Effort for
Each Computation-
Change Rate of
Pressure
+ +
Cognitive Load
Pressure
+
+
B: Local
innovation
A
B: Local
innovation
B
+
-
R:
Loop
A
R:
Loop B
ManPower
+
Manpower
Allocation Rate

14. 17
The system dynamics modeling
procedure – Distilling insights through
feedback analysis
• Feedback analysis
• Systematic elimination of feedback loops (breaking loops by
assigning a zero causal influence)
• Feedback analysis shows
1. Performance adjustment loop dominates initially
2. The reinforcing Loop A acts as a vicious loop, whereby Cognitive
Load and Errors increase, and thereafter Local Innovations and
Changes increases and Mutual Understanding decreases
3. The reinforcing Loop B starts to dominate, driving Mutual
Understanding further down
4. Local Innovations and Changes lead to improvements, whereby
Errors decrease and Mutual Understanding increase

15. 18
The system dynamics modeling
procedure – Model development
Increase of Mutual
Understanding
Mutual
Understanding
(MU)
+
+
R: Self-
referencing
Errors
generated
Errors from
mismatch
-
+
Decrease of
Mutual
Understanding
-
Local Innovations
and Changes
-
Potential Work
Rate
+
Actual
Work Rate
-
+
Performance
Gap
-
Desired Work
Rate
+
Errors
-
Cognition Resource
Allocation
+Cognitive
Load
+
+
B:
Performance
adjustment
Error
Correction Rate
-
Available Cognition
Resource
-
+
Average Error
Rate
+
Cognition Resource
Allocating to Avoid Errors
+
-
+
Cognition for Error
Detection and
Recovery
+
Error
Detection Rate+
+
Error Detection
Skill
+
+
Error
Generation Rate
-
+
B: Team
learning
B: Error
detection
and
discovery
Required Effort for
Each Computation-
Change Rate of
Pressure
+ +
Cognitive Load
Pressure
+
+
B: Local
innovation
A
B: Local
innovation
B
+
-
R:
Loop
A
R:
Loop B
ManPower
+
Manpower
Allocation Rate

16. 19
The system dynamics modeling
procedure – Distilling insights through
feedback analysis
• Feedback analysis
• Systematic elimination of feedback loops (breaking loops by
assigning a zero causal influence)
• Feedback analysis shows
1. Performance adjustment loop dominates initially
2. The reinforcing Loop A acts as a vicious loop, whereby Cognitive
Load and Errors increase, and thereafter Local Innovations and
Changes increases and Mutual Understanding decreases
3. The reinforcing Loop B starts to dominate, driving Mutual
Understanding further down
4. Local Innovations and Changes lead to improvements, whereby
Errors decrease and Mutual Understanding increase

17. 20
The system dynamics modeling
procedure – Model development
Increase of Mutual
Understanding
Mutual
Understanding
(MU)
+
+
R: Self-
referencing
Errors
generated
Errors from
mismatch
-
+
Decrease of
Mutual
Understanding
-
Local Innovations
and Changes
-
Potential Work
Rate
+
Actual
Work Rate
-
+
Performance
Gap
-
Desired Work
Rate
+
Errors
-
Cognition Resource
Allocation
+Cognitive
Load
+
+
B:
Performance
adjustment
Error
Correction Rate
-
Available Cognition
Resource
-
+
Average Error
Rate
+
Cognition Resource
Allocating to Avoid Errors
+
-
+
Cognition for Error
Detection and
Recovery
+
Error
Detection Rate
+
+
Error Detection
Skill
+
+
Error
Generation Rate
-
+
B: Team
learning
B: Error
detection
and
discovery
Required Effort for
Each Computation-
Change Rate of
Pressure
+ +
Cognitive Load
Pressure
+
+
B: Local
innovation
A
B: Local
innovation
B
+
-
R:
Loop
A
R:
Loop
B
ManPower
+
Manpower
Allocation Rate

18. 21
The system dynamics modeling
procedure – Distilling insights through
feedback analysis
• Feedback analysis
• Systematic elimination of feedback loops (breaking loops by
assigning a zero causal influence)
• Feedback analysis shows
1. Performance adjustment loop dominates initially
2. The reinforcing Loop A acts as a vicious loop, whereby Cognitive
Load and Errors increase, and thereafter Local Innovations and
Changes increases and Mutual Understanding decreases
3. The reinforcing Loop B starts to dominate, driving Mutual
Understanding further down
4. Local Innovations and Changes lead to improvements, whereby
Errors decrease and Mutual Understanding increase

19. 22
The system dynamics modeling
procedure – Model development
• One more look at the whole model
Increase of Mutual
Understanding
Mutual
Understanding
(MU)
+
+
R: Self-
referencing
Errors
generated
Errors from
mismatch
-
+
Decrease of
Mutual
Understanding
-
Local Innovations
and Changes
-
Potential Work
Rate
+
Actual
Work Rate
-
+
Performance
Gap
-
Desired Work
Rate
+
Errors
-
Cognition Resource
Allocation
+Cognitive
Load
+
+
B:
Performance
adjustment
Error
Correction Rate
-
Available Cognition
Resource
-
+
Average Error
Rate
+
Cognition Resource
Allocating to Avoid
Errors
+
-
+
Cognition for Error
Detection and
Recovery
+
Error
Detection Rate+
+
Error Detection
Skill
+
+
Error
Generation Rate
-
+
B: Team
learning
B: Error
detection
and
discovery
Required Effort for
Each Computation-
Change Rate of
Pressure
+ +
Cognitive Load
Pressure
+
+
B: Local
innovation
A
B: Local
innovation
B
+
-
R:
Loop
A
R:
Loop B
ManPower
+
Manpower
Allocation Rate

20. 23
Looking ahead: Status and research
challenges
• The system dynamics model embodies a rudimentary middle-
range theory for the transition from disorganization to self-
organization in emergencies for an emergency with one
transition to self-organization
• Challenge
• Refine model using more emergency cases
• However, the necessary data is mostly lacking
• Needed data:
• Numerical, written and mental
• Bottlenecks:
• Getting data from practitioners
• Methodological issues