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ExploratoryModelling:EmergingApproachesfortheTreatmentofDeepUncertain;esinSystemsModelling
EnayatA.Moallemi28,September,2017
Outline• Backgroundonexploratorymodelling• Twoexploratorymodellingapproaches• ApplicaEonsanddecisioninsights• Concludingremarks
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SystemsModelling&DeepUncertainty
Themodellingofreal-worldsystemischallengingbycomplexityanddeepuncertainty.TradiEonalSystemsmodellingapproachesfailtocopedeepuncertain2es(Lempertetal.2003).
Credit:duckfarmondeviantART
Lempert,R.J.,Popper,S.W.,&Bankes,S.C.(2003).Shapingthenextonehundredyears:newmethodsforquanEtaEve,long-termpolicyanalysis:RandCorporaEon.
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ExploratorymodellingExploratorymodellingresultsinaporXolioofwhatcouldhappen,asopposedtowhatwillhappen(Bankes1993).
Itisgrowingrapidlyintoseveralapproaches(e.g.RDM,DAPP,MORDM,EEA).ItisadoptedinvariousapplicaEondomains(e.g.planninginwater,energy,defence,climate,infrastructure).
Bankes,S.(1993).Exploratorymodelingforpolicyanalysis.Opera2onsResearch,41(3),435-449.
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TwoexploratorymodellingpracEcesinCSC
Approaches
• RobustDecisionMaking• MulE-ObjecEveRobustOpEmisaEon
Applica;onAcquisiEonandmaintenancemanagementofaircra]fleets
• Decision:Numberofaircra]&sizeofmaintenance• Systemperformance:availabilityofaircra]&totalcosts
∼RobustDecisionMaking(RDM)∼
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RobustDecisionMakingParEcipatoryscoping(defineuncertainEes,
strategies,relaEonships,andobjecEves)
CasegeneraEon(esEmatetheperformanceof
strategiesinmanyfutures)
ScenarioexploraEonanddiscovery(characterise
vulnerabiliEesofstrategies)
Trade-offanalysis(displayandevaluatetrade-offsbetween
strategies)
PlanforsimulaEonmodelling
DatabaseofsimulaEonresults
InformaEonon
vulnerabiliEes
Insightsintomorerobuststrategies
Robuststrategy
InformaEonon
vulnerabiliEes
InformaEontohelpchoose
candidatestrategies
Scenariosthatilluminate
vulnerabiliEes
(Lempertetal.2003)
Lempert,R.J.,Popper,S.W.,&Bankes,S.C.(2003).Shapingthenextonehundredyears:newmethodsforquanEtaEve,long-termpolicyanalysis:RandCorporaEon.
RDMfocusesonextremecondi2onsandeventswithlesslikelihoodratherthanmost-likelyscenarios.EmphasisesonidenEficaEonfailurescenariosratherthansuccessscenarios.
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TheRDMprocess ParEcipatoryscoping(defineuncertainEes,
strategies,relaEonships,andobjecEves)
CasegeneraEon(esEmatetheperformanceof
strategiesinmanyfutures)
ScenarioexploraEonanddiscovery
(characterisevulnerabiliEesofstrategies)
Trade-offanalysis(displayandevaluatetrade-offsbetween
strategies)
PlanforsimulaEonmodelling
DatabaseofsimulaEonresults
InformaEononvulnerabiliEes
Insightsintomorerobuststrategies
Robuststrategy
InformaEononvulnerabiliEes
InformaEontohelpchoosecandidatestrategies
Scenariosthatilluminate
vulnerabiliEes
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ParEcipatoryscoping(defineuncertainEes,
strategies,relaEonships,andobjecEves)
CasegeneraEon(esEmatetheperformanceof
strategiesinmanyfutures)
ScenarioexploraEonanddiscovery
(characterisevulnerabiliEesofstrategies)
Trade-offanalysis(displayandevaluatetrade-offsbetween
strategies)
PlanforsimulaEonmodelling
DatabaseofsimulaEonresults
InformaEononvulnerabiliEes
Insightsintomorerobuststrategies
Robuststrategy
InformaEononvulnerabiliEes
InformaEontohelpchoosecandidatestrategies
Scenariosthatilluminate
vulnerabiliEes
TheRDMprocess
10
ParEcipatoryscoping(defineuncertainEes,
strategies,relaEonships,andobjecEves)
CasegeneraEon(esEmatetheperformanceof
strategiesinmanyfutures)
ScenarioexploraEonanddiscovery
(characterisevulnerabiliEesofstrategies)
Trade-offanalysis(displayandevaluatetrade-offsbetween
strategies)
PlanforsimulaEonmodelling
DatabaseofsimulaEonresults
InformaEononvulnerabiliEes
Insightsintomorerobuststrategies
Robuststrategy
InformaEononvulnerabiliEes
InformaEontohelpchoosecandidatestrategies
Scenariosthatilluminate
vulnerabiliEes
TheRDMprocess
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ScenarioDiscovery
𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝑡𝑜𝑡𝑎𝑙
𝐷𝑒𝑛𝑠𝑖𝑡𝑦= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑡𝑜𝑡𝑎𝑙_𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝐵𝑜𝑥 1
IdenEfyingandexplainingfailurescenariosusingtherelevantspaceofuncertaintyintheinputparameters(BryantandLempert2010).
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Uncertaintya
Uncertaintyb
Box1
FailurecasesSuccesscases
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Box16
Coverage
Density
Bryant,B.P.,&Lempert,R.J.(2010).Thinkinginsidethebox:AparEcipatory,computer-assistedapproachtoscenariodiscovery.TechnologicalForecas2ngandSocialChange,77(1),34-49.
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ScenarioDiscovery
𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝑡𝑜𝑡𝑎𝑙
𝐷𝑒𝑛𝑠𝑖𝑡𝑦= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑡𝑜𝑡𝑎𝑙_𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝐵𝑜𝑥 1
IdenEfyingandexplainingfailurecasesusingtherelevantspaceofuncertaintyintheinputparameters(BryantandLempert2010).
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Uncertaintya
Uncertaintyb
Box1
FailurecasesSuccesscases
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Box16
Coverage
Density
Bryant,B.P.,&Lempert,R.J.(2010).Thinkinginsidethebox:AparEcipatory,computer-assistedapproachtoscenariodiscovery.TechnologicalForecas2ngandSocialChange,77(1),34-49.
13
ScenarioDiscovery
𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝑡𝑜𝑡𝑎𝑙
𝐷𝑒𝑛𝑠𝑖𝑡𝑦= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑡𝑜𝑡𝑎𝑙_𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝐵𝑜𝑥 1
IdenEfyingandexplainingfailurecasesusingtherelevantspaceofuncertaintyintheinputparameters(BryantandLempert2010).
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Uncertaintya
Uncertaintyb
Box1
FailurecasesSuccesscases
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Box16
Coverage
Density
Bryant,B.P.,&Lempert,R.J.(2010).Thinkinginsidethebox:AparEcipatory,computer-assistedapproachtoscenariodiscovery.TechnologicalForecas2ngandSocialChange,77(1),34-49.
14
ScenarioDiscovery
𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝑡𝑜𝑡𝑎𝑙
𝐷𝑒𝑛𝑠𝑖𝑡𝑦= #𝑐𝑎𝑠𝑒𝑠_𝑜𝑓_𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑛 𝐵𝑜𝑥 1 /#𝑡𝑜𝑡𝑎𝑙_𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝐵𝑜𝑥 1
IdenEfyingandexplainingfailurecasesusingtherelevantspaceofuncertaintyintheinputparameters(BryantandLempert2010).
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Uncertaintya
Uncertaintyb
Box1
FailurecasesSuccesscases
Box1
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Box16
Coverage
Density
Bryant,B.P.,&Lempert,R.J.(2010).Thinkinginsidethebox:AparEcipatory,computer-assistedapproachtoscenariodiscovery.TechnologicalForecas2ngandSocialChange,77(1),34-49.
15
ParEcipatoryscoping(defineuncertainEes,
strategies,relaEonships,andobjecEves)
CasegeneraEon(esEmatetheperformanceof
strategiesinmanyfutures)
ScenarioexploraEonanddiscovery
(characterisevulnerabiliEesofstrategies)
Trade-offanalysis(displayandevaluatetrade-offsbetween
strategies)
PlanforsimulaEonmodelling
DatabaseofsimulaEonresults
InformaEononvulnerabiliEes
Insightsintomorerobuststrategies
Robuststrategy
InformaEononvulnerabiliEes
InformaEontohelpchoosecandidatestrategies
Scenariosthatilluminate
vulnerabiliEes
TheRDMprocess
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• StrategyI:HighAcquisiEon
LowMaintenance
• StrategyII:MediumAcquisiEonMediumMaintenance
• StrategyIII:LowAcquisiEonHighMaintenance
Howvulnerableareourstrategiesinsecuringaverageflyinghours>5000hoursandtotalcosts<$1300billion?
Uncertain parameter Range The risk that that an aircraft is lost during operation 0.00026 – 0.00234 (-) Lifetime of aircraft 37440 – 336690 (hour) Total required flying hours with a uniform distribution 12 – 109 (hour/week) Expected time spent by an aircraft in CAP 20 – 28 (week) Time between CAP events 16 – 24 (week) Expected time spent by an aircraft in DM 8 – 10 (week) Time (flying hours) between DM events 200 – 1800 (hour) Expected time spent by an aircraft in OM 3 – 5 (week) Time between OM events 50 – 450 (hour) Cost of OM 0.1 – 2.0 ($ billion)
Strategies Uncertain;es
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FlyinghoursobjecEve5000hours
CostobjecEve<$1300B
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FlyinghoursobjecEve5000hours
CostobjecEve<$1300B
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FlyinghoursobjecEve5000hours
CostobjecEve<$1300B
Strategy Measure of quality Uncertainty Range of failure P-value Medium Acquisition-Medium Maintenance
Coverage: 0.36 Time between operational maintenance
50 – 140 (hour) 2.7e-23
Density: 1 Low Acquisition- High Maintenance
Coverage: 0.59 Time between operational maintenance
51 – 220 (hour) 1.4e-6
Density: 0.48 Time between deep maintenance 290 – 1300 (hour) 1.4e-2
strategy Measure of quality Uncertainty Range of failure P-value Medium Acquisition-Medium Maintenance
Coverage: 0.54 Time between deep maintenance 330 – 930 (hour) 1.7e-10
Density: 0.88 Cost of operational maintenance 1 – 2 ($ billion) 1.9e-7
Time between operational maintenance 170 – 450 (hour) 4.1e-03
Low Acquisition- High Maintenance
Coverage: 0.46 Time between deep maintenance 210 – 960 (hour) 9.1e-16
Density: 0.98 Required rate of effort 39 – 110 (hour/week) 7.1e-6 Time between operational
maintenance 140 – 450 (hour) 1.6e-5 Risk of loss 0.00026 – 0.0018 (–) 4.6e-5 Lifetime 44000 – 320000
(hour) 2.5e-2
Time spent in operational maintenance 3 – 4.8 (week) 3.4e-2
Cost of operational maintenance 0.18 – 2 ($ billion) 5.0e-2
Scenariosleadingtolessthan5000(hour)averageflyinghours
Scenariosleadingtomorethan1300($billion)costs
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FlyinghoursobjecEve5000hours
CostobjecEve<$1300B
Strategy Measure of quality Uncertainty Range of failure P-value Medium Acquisition-Medium Maintenance
Coverage: 0.36 Time between operational maintenance
50 – 140 (hour) 2.7e-23
Density: 1 Low Acquisition- High Maintenance
Coverage: 0.59 Time between operational maintenance
51 – 220 (hour) 1.4e-6
Density: 0.48 Time between deep maintenance 290 – 1300 (hour) 1.4e-2
strategy Measure of quality Uncertainty Range of failure P-value Medium Acquisition-Medium Maintenance
Coverage: 0.54 Time between deep maintenance 330 – 930 (hour) 1.7e-10
Density: 0.88 Cost of operational maintenance 1 – 2 ($ billion) 1.9e-7
Time between operational maintenance 170 – 450 (hour) 4.1e-03
Low Acquisition- High Maintenance
Coverage: 0.46 Time between deep maintenance 210 – 960 (hour) 9.1e-16
Density: 0.98 Required rate of effort 39 – 110 (hour/week) 7.1e-6 Time between operational
maintenance 140 – 450 (hour) 1.6e-5 Risk of loss 0.00026 – 0.0018 (–) 4.6e-5 Lifetime 44000 – 320000
(hour) 2.5e-2
Time spent in operational maintenance 3 – 4.8 (week) 3.4e-2
Cost of operational maintenance 0.18 – 2 ($ billion) 5.0e-2
Scenariosleadingtolessthan5000(hour)averageflyinghours
Scenariosleadingtomorethan1300($billion)costs
∼MulE-ObjecEveRobustOpEmisaEon(MORO)∼
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MulE-ObjecEveRobustOpEmisaEon
MOROconsidersmul2plecontradic2ngobjec2vesofstakeholdersandfuturepossibleadapta2ons.MOROselectadap2verobustsolu2onswhichfulfilstakeholderobjecEvesandcanbemodifiedunderchangingcircumstances.
1. Problem formulation
1. Uncertainties 2. Decision levers and
solutions 3. Quantitative relationship 4. Performance measures
2. Identification of scenario clusters
1. Generation of future scenarios
2. Clustering of scenarios 3. Identification of the
conditions of scenario clusters
3. Enumeration of robust solutions for each scenario
cluster
1. Identification of failure scenarios
2. Enumeration of Pareto optimal sets
4. Adaptation and trade-off among robust solutions
1. Identification of adaptation tipping point
2. Development of adaptive pathways
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1. Problem formulation
1. Uncertainties 2. Decision levers and
solutions 3. Quantitative relationship 4. Performance measures
2. Identification of scenario clusters
1. Generation of future scenarios
2. Clustering of scenarios 3. Identification of the
conditions of scenario clusters
3. Enumeration of robust solutions for each scenario
cluster
1. Identification of failure scenarios
2. Enumeration of Pareto optimal sets
4. Adaptation & trade-off among robust solutions
1. Identification of adaptation tipping point
2. Development of adaptive pathways
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1. Problem formulation
1. Uncertainties 2. Decision levers and
solutions 3. Quantitative relationship 4. Performance measures
2. Identification of scenario clusters
1. Generation of future scenarios
2. Clustering of scenarios 3. Identification of the
conditions of scenario clusters
3. Enumeration of robust solutions for each scenario
cluster
1. Identification of failure scenarios
2. Enumeration of Pareto optimal sets
4. Adaptation & trade-off among robust solutions
1. Identification of adaptation tipping point
2. Development of adaptive pathways
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1. Problem formulation
1. Uncertainties 2. Decision levers and
solutions 3. Quantitative relationship 4. Performance measures
2. Identification of scenario clusters
1. Generation of future scenarios
2. Clustering of scenarios 3. Identification of the
conditions of scenario clusters
3. Enumeration of robust solutions for each scenario
cluster
1. Identification of failure scenarios
2. Enumeration of Pareto optimal sets
4. Adaptation & trade-off among robust solutions
1. Identification of adaptation tipping point
2. Development of adaptive pathways
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1. Problem formulation
1. Uncertainties 2. Decision levers and
solutions 3. Quantitative relationship 4. Performance measures
2. Identification of scenario clusters
1. Generation of future scenarios
2. Clustering of scenarios 3. Identification of the
conditions of scenario clusters
3. Enumeration of robust solutions for each scenario
cluster
1. Identification of failure scenarios
2. Enumeration of Pareto optimal sets
4. Adaptation & trade-off among robust solutions
1. Identification of adaptation tipping point
2. Development of adaptive pathways
27
• AllpossiblevariaEonofdecision
levers(343strategies)
Whatstrategiestochooseandwhentoadapttomaximiseaveragein-serviceaircra]andtominimisetotalcostsoverEme?
Strategies Uncertain;es
Context uncertainties Discrete range
The risk that an aircraft is lost during operations 0.00026, 0.00078, 0.0013, 0.00182, 0.00234 (-)
Total required flying hours 12, 48, 109 (-)
Expected time spent by an aircraft in CAP 20, 24, 28 (week)
Expected time spent by an aircraft in DM 8, 9, 10 (week)
Expected time spent by an aircraft in OM 3, 4, 5 (week)
Cost of OM 0.1, 0.575, 1.05, 1.525, 2 ($ billion)
Performance measure Objective Threshold Average of available aircraft for service Maximisation No less than 2 (–) Total acquisition and maintenance costs Minimisation No more than 400 (B$)
Performancemeasures
Futurescenarioclusters
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Cluster0
Cluster1
Cluster3
Cluster2
Cluster4
Futurescenarioclusters
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Cluster0
Cluster1
Cluster3
Cluster2
Cluster4
Cluster Uncertainty Range P-value
Cluster 0 Number of purchased aircraft 3 – 5 (–) 8.4e-10 Required flying hours 45 – 109 (hour/week) 3.8e-09 Time between OM events 50 – 290 (hour) 7.0e-3
Cluster 1 Number of purchased aircraft 1 – 2 (–) 1.4e-34
Cluster 2 Required flying hours 12 – 37 (hour/week) 1.2e-13 Number of purchased aircraft 5 – 7 (–) 7.8e-8
Cluster 3 Number of purchased aircraft 3 – 4 (–) 3.2e-9 Required flying hours 12 – 55 (hour/week) 3.0e-8 Time between OM events 87 – 450 (hour) 1.0e-2 Time between DM events 470 – 1800 (hour) 1.4e-2
Cluster 4 Number of purchased aircraft 6 – 7 (–) 1.2e-12 Required flying hours 34 – 82 (hour/week) 9.6e-4 Time between DM events 350 – 1600 (hour) 4.2e-3
Condi;onsleadingtoeachscenariocluster
Futurescenarioclusters
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Cluster0
Cluster1
Cluster3
Cluster2
Cluster4
Cluster Uncertainty Range P-value
Cluster 0 Number of purchased aircraft 3 – 5 (–) 8.4e-10 Required flying hours 45 – 109 (hour/week) 3.8e-09 Time between OM events 50 – 290 (hour) 7.0e-3
Cluster 1 Number of purchased aircraft 1 – 2 (–) 1.4e-34
Cluster 2 Required flying hours 12 – 37 (hour/week) 1.2e-13 Number of purchased aircraft 5 – 7 (–) 7.8e-8
Cluster 3 Number of purchased aircraft 3 – 4 (–) 3.2e-9 Required flying hours 12 – 55 (hour/week) 3.0e-8 Time between OM events 87 – 450 (hour) 1.0e-2 Time between DM events 470 – 1800 (hour) 1.4e-2
Cluster 4 Number of purchased aircraft 6 – 7 (–) 1.2e-12 Required flying hours 34 – 82 (hour/week) 9.6e-4 Time between DM events 350 – 1600 (hour) 4.2e-3
Condi;onsleadingtoeachscenariocluster
Cluster0
Inputuncertaintyspace
ParetoopEmalstrategiesineachcluster
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ParetoopEmalstrategiesundereachcluster
32Decisionvariables
ParetoopEmalstrategiesineachcluster
33Performance
ParetoopEmalstrategiesundereachcluster
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35
Requiredflyinghours(40-60hour/week)
Requiredflyinghours(25-40hour/week)
Requiredflyinghours(10-25hour/week)
MonitoringforadaptaEon
36
Requiredflyinghours(40-60hour/week)
Requiredflyinghours(25-40hour/week)
Requiredflyinghours(10-25hour/week)
MonitoringforadaptaEon
37
Requiredflyinghours(40-60hour/week)• Cluster0
Requiredflyinghours(25-40hour/week)• Cluster4
Requiredflyinghours(10-25hour/week)• Cluster1• Cluster2• Cluster3
MonitoringforadaptaEon
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Whatisthetrade-offamongParetoopEmaldecisionsunderlowrequiredflyinghours
whenin-serviceaircraS>6andtotalcosts<B$250?
39
Whatarethechosenstrategiesunderlowrequiredflyinghours
whenin-serviceaircraS>6andtotalcosts<B$250?
40
Whatarethechosenstrategiesunderlowrequiredflyinghours
whenin-serviceaircraS>6andtotalcosts<B$250?
41
Whatarethechosenstrategiesunderlowrequiredflyinghours
whenin-serviceaircraS>6andtotalcosts<B$250?
42
Conclusions:Whatarethebenefitsofexploratorymodellingtosystemsmodelling?
43
Conclusions:Whatarethebenefitsofexploratorymodellingtosystemsmodelling?
• Exploratorymodellingincorporatesthediversityoftheirviewsandprovokesdelibera2on,experimenta2on,andlearning.
44
Conclusions:Whatarethebenefitsofexploratorymodellingtosystemsmodelling?
• Exploratorymodellingcanenhancetheconfidenceofresultsbycapturingawiderangeofpossiblefuturesandconsideringunexpectedcircumstances.
• Exploratorymodellingincorporatesthediversityoftheirviewsandprovokesdelibera2on,experimenta2on,andlearning.
45
Conclusions:Whatarethebenefitsofexploratorymodellingtosystemsmodelling?
• Exploratorymodellingcanenhancetheconfidenceofresultsbycapturingawiderangeofpossiblefuturesandconsideringunexpectedcircumstances.
• Exploratorymodellingproducesan2cipatoryandprotec2veac2onsinsteadofreacEveandmiEgaEngacEons.
• Exploratorymodellingincorporatesthediversityoftheirviewsandprovokesdelibera2on,experimenta2on,andlearning.
46
FutureresearchdirecEons
• Tointegratemoreofexploratorymodellingapproacheswithsystemengineeringtechniques
• ToidenEfythenewusesofexploratorymodellinginthesystemsmodellingprocess
CapabilitySystemsCentre(CSE)SchoolofEngineeringandInformaEonTechnology
TheUniversityofNewSouthWales(UNSWCanberra)
PainEngintheEtlepage:RobertDelaunay,1938,Rythmen°1,DecoraEonfortheSalondesTuileries,oiloncanvas,Muséed'ArtModernedelavilledeParis. 47
@EnayatMoallemi
EnayatA.Moallemi