A System Dynamics Model of the 2005 Hatlestad Slide Emergency Management

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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

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A System Dynamics Model of the 2005 Hatlestad Slide Emergency Management

  1. 1. A System Dynamics Modelof the 2005 HatlestadSlide EmergencyManagementISCRAM 2013Jose J Gonzalez, Geir Bøe, John Einar JohansenCentre for Integrated Emergency Management(CIEM)University of Agder, Norway
  2. 2. 3The 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 emergencymanagement
  3. 3. 4The Hatlestad slide as case• Thorough study by Lango (master thesis 2010, bookchapter 2011)• Hatlestad case qualitatively similar in referencebehavior, to Palau case (Hutchings “Cognition in thewild”, 1995)• Pioneer system dynamics simulation of Palau case byTu, Wang, & Tseng, 2009) based on ComplexityTheory• Disorder, Improvisation, Self-Organization• Data for key emergency handling parameters:• Cognitive Load,• Local Innovation and Changes,• Mutual Understanding
  4. 4. 6The system dynamics modelingprocedure• Develop simulation that for the right reasons reproduces theobserved reference behavior of the Hatlestad slide emergencymanagement• “Right reasons”:• The model structure should contain the variablescorresponding to the observed behavior of the emergencymanagement team (the “observables”)• The observables should be causally linked according to aparsimonious “dynamic hypothesis”• The simulations must reproduce the reference behavior• The model should pass standard tests
  5. 5. 7The system dynamics modelingprocedure – Reference behavior• Qualitative reference behavior derived from Lango (2010, 2011)• Criticism from scientists at home in natural sciences ignores sciencehistoryTotal reference behaviour10.750.50.2503 3 333 3 322 22221 1 1 11 1 1 1 1 1 1 10 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600Time (Minute)CognitionCognitive Load : Reference Behaviour 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1Local Innovations and Changes : Reference Behaviour2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2MU : Reference Behaviour 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3Maximum/minimumtimes knownOnset timesknownReturn tonormal timesknown
  6. 6. 8The system dynamics modelingprocedure – Dynamic Hypothesis• We hypothesize that the reference behavior can beexplained by a disequilibrium–experimenting–emergenceprocess (MacIntosh and MacLean 1999) (Dynes andQuarantelli 1976)• Accordingly, the causal structure of the model mustcontain feedback loops generating1. disequilibrium2. experimenting (i.e., innovation and changes)3. emergence (i.e., self-organization)
  7. 7. 10The system dynamics modelingprocedure – Model development• Simplified view with the main feedback loopsIncrease of MutualUnderstandingMutualUnderstanding(MU)++R: Self-referencingErrorsgeneratedErrors frommismatch-+Decrease ofMutualUnderstanding-Local Innovationsand Changes-Potential WorkRate+ActualWork Rate-+PerformanceGap-Desired WorkRate+Errors-Cognition ResourceAllocation+CognitiveLoad++B:PerformanceadjustmentErrorCorrection Rate-Available CognitionResource-+Average ErrorRate+Cognition ResourceAllocating to AvoidErrors+-+Cognition for ErrorDetection andRecovery+ErrorDetection Rate++Error DetectionSkill++ErrorGeneration Rate-+B: TeamlearningB: ErrordetectionanddiscoveryRequired Effort forEach Computation-Change Rate ofPressure+ +Cognitive LoadPressure++B: LocalinnovationAB: LocalinnovationB+-R:LoopAR:Loop BManPower+ManpowerAllocation Rate
  8. 8. 11The system dynamics modelingprocedure – Verification and validation• Verification• Checking that the variables and their causal connectionsrepresent the selected case• Validation• Checking that the model is able to simulate the referencebehavior (following a calibration procedure)• Checking that the model simulates extreme conditionscorrectly• Sensitivity analysis• What happens if you vary the variables obtained bycalibration?
  9. 9. 12The system dynamics modelingprocedure – Reproducing referencebehavior for Cognitive Load10.750.50.25022 2222 22 22 2111 1 111 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 10 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600Time (Minute)CognitionCognitive Load : Hatlestad1 1 1 1 1 1 1 Cognitive Load : Reference Behaviour2 2 2 2 2
  10. 10. 13The system dynamics modeling procedure– Reproducing reference behavior forLocal Innovations and Changes10.750.50.25022 22221111111 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 10 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600Time (Minute)CognitionLocal Innovations and Changes : Hatlestad1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1Local Innovations and Changes : Reference Behaviour2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
  11. 11. 14The system dynamics modelingprocedure – Reproducing referencebehavior for Mutual Understanding10.90.80.7222222 21111111 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 10 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600Time (Minute)MUMU : Hatlestad 1 1 1 1 1 1 1 1 1 MU : Reference Behaviour 2 2 2 2 2 2 2
  12. 12. 15The system dynamics modelingprocedure – Distilling insights throughfeedback analysis• Feedback analysis• Systematic elimination of feedback loops (breaking loops byassigning a zero causal influence)• Feedback analysis shows1. Performance adjustment loop dominates initially2. The reinforcing Loop A acts as a vicious loop, whereby CognitiveLoad and Errors increase, and thereafter Local Innovations andChanges increases and Mutual Understanding decreases3. The reinforcing Loop B starts to dominate, driving MutualUnderstanding further down4. Local Innovations and Changes lead to improvements, wherebyErrors decrease and Mutual Understanding increase
  13. 13. 16The system dynamics modelingprocedure – Model developmentIncrease of MutualUnderstandingMutualUnderstanding(MU)++R: Self-referencingErrorsgeneratedErrors frommismatch-+Decrease ofMutualUnderstanding-Local Innovationsand Changes-Potential WorkRate+ActualWork Rate-+PerformanceGap-Desired WorkRate+Errors-Cognition ResourceAllocation+CognitiveLoad++B:PerformanceadjustmentErrorCorrection Rate-Available CognitionResource-+Average ErrorRate+Cognition ResourceAllocating to Avoid Errors+-+Cognition for ErrorDetection andRecovery+ErrorDetection Rate++Error DetectionSkill++ErrorGeneration Rate-+B: TeamlearningB: ErrordetectionanddiscoveryRequired Effort forEach Computation-Change Rate ofPressure+ +Cognitive LoadPressure++B: LocalinnovationAB: LocalinnovationB+-R:LoopAR:Loop BManPower+ManpowerAllocation Rate
  14. 14. 17The system dynamics modelingprocedure – Distilling insights throughfeedback analysis• Feedback analysis• Systematic elimination of feedback loops (breaking loops byassigning a zero causal influence)• Feedback analysis shows1. Performance adjustment loop dominates initially2. The reinforcing Loop A acts as a vicious loop, whereby CognitiveLoad and Errors increase, and thereafter Local Innovations andChanges increases and Mutual Understanding decreases3. The reinforcing Loop B starts to dominate, driving MutualUnderstanding further down4. Local Innovations and Changes lead to improvements, wherebyErrors decrease and Mutual Understanding increase
  15. 15. 18The system dynamics modelingprocedure – Model developmentIncrease of MutualUnderstandingMutualUnderstanding(MU)++R: Self-referencingErrorsgeneratedErrors frommismatch-+Decrease ofMutualUnderstanding-Local Innovationsand Changes-Potential WorkRate+ActualWork Rate-+PerformanceGap-Desired WorkRate+Errors-Cognition ResourceAllocation+CognitiveLoad++B:PerformanceadjustmentErrorCorrection Rate-Available CognitionResource-+Average ErrorRate+Cognition ResourceAllocating to Avoid Errors+-+Cognition for ErrorDetection andRecovery+ErrorDetection Rate++Error DetectionSkill++ErrorGeneration Rate-+B: TeamlearningB: ErrordetectionanddiscoveryRequired Effort forEach Computation-Change Rate ofPressure+ +Cognitive LoadPressure++B: LocalinnovationAB: LocalinnovationB+-R:LoopAR:Loop BManPower+ManpowerAllocation Rate
  16. 16. 19The system dynamics modelingprocedure – Distilling insights throughfeedback analysis• Feedback analysis• Systematic elimination of feedback loops (breaking loops byassigning a zero causal influence)• Feedback analysis shows1. Performance adjustment loop dominates initially2. The reinforcing Loop A acts as a vicious loop, whereby CognitiveLoad and Errors increase, and thereafter Local Innovations andChanges increases and Mutual Understanding decreases3. The reinforcing Loop B starts to dominate, driving MutualUnderstanding further down4. Local Innovations and Changes lead to improvements, wherebyErrors decrease and Mutual Understanding increase
  17. 17. 20The system dynamics modelingprocedure – Model developmentIncrease of MutualUnderstandingMutualUnderstanding(MU)++R: Self-referencingErrorsgeneratedErrors frommismatch-+Decrease ofMutualUnderstanding-Local Innovationsand Changes-Potential WorkRate+ActualWork Rate-+PerformanceGap-Desired WorkRate+Errors-Cognition ResourceAllocation+CognitiveLoad++B:PerformanceadjustmentErrorCorrection Rate-Available CognitionResource-+Average ErrorRate+Cognition ResourceAllocating to Avoid Errors+-+Cognition for ErrorDetection andRecovery+ErrorDetection Rate++Error DetectionSkill++ErrorGeneration Rate-+B: TeamlearningB: ErrordetectionanddiscoveryRequired Effort forEach Computation-Change Rate ofPressure+ +Cognitive LoadPressure++B: LocalinnovationAB: LocalinnovationB+-R:LoopAR:LoopBManPower+ManpowerAllocation Rate
  18. 18. 21The system dynamics modelingprocedure – Distilling insights throughfeedback analysis• Feedback analysis• Systematic elimination of feedback loops (breaking loops byassigning a zero causal influence)• Feedback analysis shows1. Performance adjustment loop dominates initially2. The reinforcing Loop A acts as a vicious loop, whereby CognitiveLoad and Errors increase, and thereafter Local Innovations andChanges increases and Mutual Understanding decreases3. The reinforcing Loop B starts to dominate, driving MutualUnderstanding further down4. Local Innovations and Changes lead to improvements, wherebyErrors decrease and Mutual Understanding increase
  19. 19. 22The system dynamics modelingprocedure – Model development• One more look at the whole modelIncrease of MutualUnderstandingMutualUnderstanding(MU)++R: Self-referencingErrorsgeneratedErrors frommismatch-+Decrease ofMutualUnderstanding-Local Innovationsand Changes-Potential WorkRate+ActualWork Rate-+PerformanceGap-Desired WorkRate+Errors-Cognition ResourceAllocation+CognitiveLoad++B:PerformanceadjustmentErrorCorrection Rate-Available CognitionResource-+Average ErrorRate+Cognition ResourceAllocating to AvoidErrors+-+Cognition for ErrorDetection andRecovery+ErrorDetection Rate++Error DetectionSkill++ErrorGeneration Rate-+B: TeamlearningB: ErrordetectionanddiscoveryRequired Effort forEach Computation-Change Rate ofPressure+ +Cognitive LoadPressure++B: LocalinnovationAB: LocalinnovationB+-R:LoopAR:Loop BManPower+ManpowerAllocation Rate
  20. 20. 23Looking ahead: Status and researchchallenges• The system dynamics model embodies a rudimentary middle-range theory for the transition from disorganization to self-organization in emergencies for an emergency with onetransition 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

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