- 1. Decision Making and Scenario Planning2012 ISCRAM Summer
School on Humanitarian Information ManagementTina ComesResearch
Group: Risk ManagementInstitute for Industrial Production (IIP)KIT
University of the State of Baden-Wuerttemberg andNational Research
Center of the Helmholtz Association www.kit.edu
2. Risk Management?Aim:support decision-makers in complex
anduncertain situations bridge the gap between formal models and
transparent,ready-to-use evaluations collaborative and distributed
decision support tools based on modernICT systemsTina ComesDecision
Making and Scenario Planning2Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 3. Making decisionsWhat is the
current situation?How will the future unfold?YesNoTina
ComesDecision Making and Scenario Planning3Institute for Industrial
Production (IIP)ISCRAM Summer School 2012 4. How to improve the
crystal ball?Each action has consequencesWhich of them are
relevant?How do they evolve?How to compare different
consequences?200 60people, %, becabecause use Tina ComesDecision
Making and Scenario Planning4Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 5. Making decisions1. Identify
objectivesSystem disaster what would you ideally
achieve?environment2. Describe the systemactors and their
decisionswhat are the constitutent elements?how are they related?3.
Derive relevant consequences from the higher- level objective
Actions Consequences how to compare consequences? supply water
number ofand foodcasualties4. Find actions to improve number of
evacuate the consequences people evacuated ... what can be done?5.
Compare and analyze what to do? improve actions and iterate make
decisionTina ComesDecision Making and Scenario Planning 5Institute
for Industrial Production (IIP)ISCRAM Summer School 2012 6. ... but
this is difficult in emergencies!Multiple stakeholders and decision
makersHeterogeneous information on various aspects of the
situationUncertainty: unforeseen events and reactionsLimited time
to make a decision and pressureActors possibly geographically
dispersedBounded availability of expertsRisk of information
overload and lack of informationTina ComesDecision Making and
Scenario Planning6Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 7. Strategic decisions60 %1. Multiple goals,
diverse actors 200 how to make trade-offs peopleexplicit? how to
build 100consensus?people2. Uncertainty and complexity what could
the consequences of a decision be?50 % what can go wrong? why?3.
How to integrate uncertainty into the decision-making? what is the
best option given limited knowledge?Tina ComesDecision Making and
Scenario Planning7Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 8. An approach for scenario-based
decisionsCollecting information:a distributed system with
heterogeneous expertsHuman and artificial different skills,
backgrounds and knowledgeScenario-Based Multi-Criteria Decision
AnalysisOrchestrate distributed scenario generationGenerate
relevant, consistent, plausible and coherent scenariosUse the
decision-makers and experts information needs as rationalefor
information filtering and sharingProvide understandable decision
analyses and evaluationsTina ComesDecision Making and Scenario
Planning8Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 9. Challenges1. Improving the crystal ball: objectives
and information needs2. How to get relevant information?3. How to
combine and process information?4. How to manage the
combinatorics?5. Supporting decision makers: how to analyse,
interpret and communicate the results?Tina ComesDecision Making and
Scenario Planning9Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 10. More
concretely...http://www.bbc.co.uk/news/world-asia-pacific-12149921
http://www.theaustralian.com.au/in-depth/queensland-floodsTina
ComesDecision Making and Scenario Planning10Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 11. Example
SituationFlood currently controlled by leveeRisk: quick flooding if
water rises higherThreatcurrent
uncertainsituationdevelopmentsTime1. Do nothing?What to do? 2.
Protect buildings, provide supplies?3. Evacuation?The Kia Ora
Leveehttp://www.crikey.com.au/2011/02/28/levees-and-the-lack-of-regulation-that-could-cost-millions/Tina
ComesDecision Making and Scenario Planning11Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 12. What is
best decision ?5 Groups1.Residents2.Local industry and
infrastructure providers3.EM staff (fire fighters, health care,
police, ...)4.Political authorities (responsible to make the
decision)5.ModeratorsYour aim: Establish a consensus about what to
do!1. Preparation and analysis of options2. Discussion and
consensus building one member per teamTina ComesDecision Making and
Scenario Planning12Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 13. CHALLENGE #1Improving the crystal
ball:objectives and information needsTina Comes Decision Making and
Scenario Planning 13Institute for Industrial Production (IIP)
ISCRAM Summer School 2012 14. Determining possible
futuresRelevantconsequences Situation informationWhat goes
here?Ranking of Alternatives alternatives for actionTina
ComesDecision Making and Scenario Planning 14Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 15.
http://www.theaustralian.com.au/news/nation/queenslands-flood-disaster-a-long-way-from-over-warns-anna-bligh/story-e6frg6nf-1225979264551Tina
ComesDecision Making and Scenario Planning15Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 16. What are
the relevant consequences?Discuss in your team:1. From your
perspective, what the relevant consequences? health and safety,
avoid economic losses, efficiency of operations, ...2. Which of
them are the most relevant for you?3. How can the consequences be
measured? Use indicators that quantify the consequences, such as
duration of business interruption for economic losses!Tina
ComesDecision Making and Scenario Planning16Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 17. How are
the consequences related?Aim:structured evaluation of a decisions
consequencestaking into account the decision makers
preferencesmodelling the problem by an attribute tree# people
evacuated per dayhealth 1. do nothing # people exposedto flood 2.
protection and supplies total performancefirefighters [man-h] 3.
evacuationeffort police [man-h]Tina ComesDecision Making and
Scenario Planning 17Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 18. Back to the exampleIn your team, structure
the problem by an attribute tree 1. do nothing 2. protection and
suppliestotalperformance 3. evacuationTina ComesDecision Making and
Scenario Planning18Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 19. Determining the consequences?Decision tables
specify the consequences for all alternatives withrespect to each
attribute # people# peoplefirefighters police evacuated exposed
[man-h][man-h] per day to flood1. donothing2. protect3. evacuate
How to fill in the blanks? 1. collect information 2. manage
uncertaintyTina ComesDecision Making and Scenario
Planning19Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 20. An example from chemical emergencymanagement # pp
unshelt &police [manh]# pp shelt & firefighterslosses [k]
alternative economic[manh]expexpE&S115 0 0247,50123,75 S17 0
0165,0082,50 DN0 0 0 0,000,00Tina ComesDecision Making and Scenario
Planning20Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 21. An example from chemical emergencymanagement
determining the basicinformationWhat information is required to
determine the attributes?
variablesindicatorsvariablesATTRIBUTESaffected* (GVP/d,affected*
(GVP/d,population registry# pp unshelt & exp firefighters
[manh] economic losses # pp shelt & expfirms indirectly
critical objects infrastructure* transportation infrastructure
police [manh]firms directlysource term*
populationalternativepresence*leak size* chemicalweather* building
registry plume[k]k) k) E&S NW none Cl_2 none none 750 05
00,3350,671500247,5 123,8 1 S1 NW none Cl_2 none none500 05
00,3350,677 00165 82,50 0DN NW none Cl_2 none none 0 05 00,3350,670
000Tina Comes Decision Making and Scenario Planning 21Institute for
Industrial Production (IIP) ISCRAM Summer School 2012 22. CHALLENGE
#2Collecting Information:Getting Experts to CooperateTina
ComesDecision Making and Scenario Planning22Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 23. How to
determine a decisions consequences?Monolithic SystemSeems like a
good ideaBuilt exactly to system specificationQuick simulation of
resultsArtificial intelligence techniques are matureHoweverVendor
lock-inSpecification changes over time as problem changesArtificial
Intelligence techniques are expensiveTina ComesDecision Making and
Scenario Planning23Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 24. An alternative approachIn your team
discuss:1. Which information do you need to determine the best
alternative from your perspective?2. Who can provide it?3. How to
combine it?Tina ComesDecision Making and Scenario
Planning24Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 25. Using a Hybrid Heterogeneous Distributed
SystemNetwork of expertsHybrid: both human and artificial
expertsDiverse backgrounds, skills and expertise breaking down
complex problems into manageable sub-problemsExperts cooperate to
determine a set of possible futures: scenarios via a standardized
communication engineTina ComesDecision Making and Scenario
Planning25Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 26. Cooperating experts?What goes here?Tina
ComesDecision Making and Scenario Planning26Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 27. A
distributed problem solving approachCooperation
structureDistributed information processing workflowWorkflow setup:
combined top-down bottom-up approachBased on information need
(backwards): request for informationBased on event (forwards):
information available further processingMatching the experts
processing capabilitiesBased on profiles per expertMatch based
oninformation types(input & output)expertise(e.g., location,
capabilities)Tina ComesDecision Making and Scenario
Planning27Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 28. Orchestrated information processingTina
ComesDecision Making and Scenario Planning28Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 29. Experts in
workflow for the chemicalemergency exampleTina ComesDecision Making
and Scenario Planning29Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 30. Another distributed systemSummer
of extreme weather -
sbs.com.au/newshttp://maps.google.com.au/maps/ms?ie=UTF8&hq=&hnear=Bundarra+New+South+Wales&gl=au&t=h&source=embed&oe=UTF8&msa=0&msid=216305641036137584677.000498fa830661a4cbafb.Tina
ComesDecision Making and Scenario Planning30Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 31. Summer of
extreme weather - sbs.com.au/news
http://maps.google.com.au/maps/ms?ie=UTF8&hq=&hnear=Bundarra+New+South+Wales&gl=au&t=h&so
urce=embed&oe=UTF8&msa=0&msid=216305641036137584677.000498fa830661a4cbafb
.Tina ComesDecision Making and Scenario Planning 31Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 32. Local
informationhttp://www.rockhamptonregion.qld.gov.au/Council_Services/News_and_Announcements/Latest_News/Evacuation_Centre_open_8am_Friday_31_DecemberTina
ComesDecision Making and Scenario Planning32Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 33. Tina
ComesDecision Making and Scenario Planning33Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 34. Trying it
outEstablish a rationale for the negotiations referring to the
goals andobjectives you identified!- where would you enforce
evacuation?- recommend evacuation?- recommend sheltering?-
other?Some sources you may find
usefulhttp://www.qldreconstruction.org.au/maps/aerial-imaging-and-mapping-pdfshttp://highload.131940.qld.gov.au/#11http://maps.google.com.au/maps/ms?ie=UTF8&hq=&hnear=Bundarra+New+South+Wales&gl=au&t=h&source=embed&oe=UTF8&msa=0&msid=216305641036137584677.000498fa830661a4cbafbTina
ComesDecision Making and Scenario Planning34Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 35. CHALLENGE
#3Keeping track of the futureTina Comes Decision Making and
Scenario Planning 35Institute for Industrial Production (IIP)
ISCRAM Summer School 2012 36. Why information is not perfect
Uncertainty AmbiguityIncomplete and uncertaininformation in
consequences and evaluationConstraints in Time Constraints
resourcesTina ComesDecision Making and Scenario Planning
36Institute for Industrial Production (IIP)ISCRAM Summer School
2012 37. Robust Decision-MakingAim: Find the alternative that
performs satisfactory in many (all) scenarios. ScoreScore
Satisfactorythreshold Time TimeConsidering one scenario per
Considering multiple scenarios peralternative results in one
scoring.alternative results in spread of scoring.Tina ComesDecision
Making and Scenario Planning 37Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 38. Considering several futuresA
A$BBE 1.2C2.5C25512 E D DTina Comes Decision Making and Scenario
Planning 38Institute for Industrial Production (IIP) ISCRAM Summer
School 2012 39. The flood?Tina ComesDecision Making and Scenario
Planning39Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 40. Media CoverageAt the scene: Nick Bryant BBC
News,RockhamptonAlmost completely encircled by muddy
floodwaters,Rockhampton risked being entirely cut off if those rose
muchfurther, but they peaked slightly lower than the authoritieshad
feared, enough to keep the one highway thats open frombeing
inundated. Many of the citys low-lying suburbs willremain flooded
for more than a week, but a local official saidthe city as a whole
had "dodged the bullet".Longer term consequencesNow attention is
shifting to the economic
http://www.bbc.co.uk/news/world-asia-pacific-12116919impact of the
flooding on Australias two most vital sectors, mining and
agriculture.Operations at some 40 mines have been interrupted and
many of the railway lines thattransport coal to the ports have been
severed. Queensland is responsible for more thanhalf of the
countrys coal exports. With farms flooded and crops ruined, the
price of freshfruit and vegetables is also forecast to rise, by as
much as 50%.State Premier Anna Bligh predicted this disaster could
have a global impact, partly becauseQueensland supplies half of the
worlds coking coal for steel manufacturing. At least onesenior
economist here thinks this could be Australias most costly natural
disaster, largelybecause of the impact on exports.Tina
ComesDecision Making and Scenario Planning40Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 41. Trying it
outRevisit your recommendation and rationale- is it optimal?- is it
robust?- which are the most important scenarios you want to use in
thediscussions? why?Tina ComesDecision Making and Scenario
Planning41Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 42. Managing the experts work in distributedreasoning
frameworkOld situation New situationWhat goes here?Information
flowTina ComesDecision Making and Scenario Planning42Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 43. Keeping
track of (partial) scenariosScenarios capture
uncertaintyRequirementsConsistency and comparability Not mixing
scenario valuesCoherence: Keeping track of the
scenarioconstructionTina ComesDecision Making and Scenario Planning
43Institute for Industrial Production (IIP)ISCRAM Summer School
2012 44. Consistency in the example Combination of information
Combination of informationabout independent variablesabout related
variables Changing the workflow mechanisms to keep track of partial
scenarios correctly merge partial scenariosTina ComesDecision
Making and Scenario Planning 44Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 45. An extract from the chemical
emergencymanagement examplevariables indicatorsvariables FOCUS
transportationpolice [manh] infrastructureinfrastructuresource
term* (GVP/d, k) (GVP/d, k) # pp shelt & # pp unshelt
population firefighterslosses [k] populationalternative presence*
leak size*affected* economicindirectly
weather*affected*chemicalregistry registry directly building
objects critical[manh] plume& expfirmsfirms exp *E&S1 NW
noneCl_2 nonenone 7500 5 00,3350,67 150 0 247,50123,75E&S1 NW
noneCl_2 nonenone 7500 5 00,3350,85 180 0 247,50123,75E&S1 NW
med Cl_2 Big Area-big-1 2500 2 2000,2540 0,67 72,00 925,004262,50
437,50218,75E&S1 NW med Cl_2 Big Area-big-1 2500 2 2000,2550
0,67 90,00 925,004262,50 437,50218,75E&S1 NW med Cl_2 Big
Area-big-1 2500 2 2000,2540 0,85 72,00 1375,00 2687,50
437,50218,75E&S1 NW med Cl_2 Big Area-big-1 2500 2 2000,2550
0,85 90,00 1375,00 2687,50 437,50218,75E&S1 NW med Cl_2 Big
Area-big-1 2500 2 200 0,640 0,67 72,00 925,004262,50 1050,00
525,00E&S1 NW med Cl_2 Big Area-big-1 2500 2 200 0,650 0,67
90,00 925,004262,50 1050,00 525,00E&S1 NW med Cl_2 Big
Area-big-1 2500 2 200,1 0,640 0,85 72,00 1375,00 2687,50 1056,00
528,00E&S1 NW med Cl_2 Big Area-big-1 2500 2 200,1 0,650 0,85
90,00 1375,00 2687,50 1056,00 528,00E&S1 NW med Cl_2 Big
Area-big-1 2500 2 2200,25 48,00 0,67 86,40 925,004262,50
437,50218,75E&S1 NW med Cl_2 Big Area-big-1 2500 2 2200,25
60,00 0,67 108,00925,004262,50 437,50218,75E&S1 NW med Cl_2 Big
Area-big-1 2500 2 2200,25 48,00 0,85 86,40 1375,00 2687,50
437,50218,75E&S1 NW med Cl_2 Big Area-big-1 2500 2 2200,25
60,00 0,85 108,001375,00 2687,50 437,50218,75E&S1 NW med Cl_2
Big Area-big-1 2500 2 220 0,6 48,00 0,67 86,40 925,004262,50
1050,00 525,00E&S1 NW med Cl_2 Big Area-big-1 2500 2 220 0,6
60,00 0,67 108,00925,004262,50 1050,00 525,00E&S1 NW med Cl_2
Big Area-big-1 2500 2 220,1 0,6 48,00 0,85 86,40 1375,00 2687,50
1056,00 528,00E&S1 NW med Cl_2 Big Area-big-1 2500 2 220,1 0,6
60,00 0,85 108,001375,00 2687,50 1056,00 528,00E&S1 NW large
Cl_2 Big Area-big-2 2000 3 3000,2550 0,67 90,00 590,003935,00
312,50156,25E&S1 NW large Cl_2 Big Area-big-2 2000 3 3000,2580
0,67 144,00590,003935,00 312,50156,25E&S1 NW large Cl_2 Big
Area-big-2 2000 3 3000,2550 0,85 90,00 950,002675,00
312,50156,25E&S1 NW large Cl_2 Big Area-big-2 2000 3 3000,2580
0,85 144,00950,002675,00 312,50156,25E&S1 NW large Cl_2 Big
Area-big-2 2000 3 300 0,650 0,67 90,00 590,003935,00
750,00375,00E&S1 NW large Cl_2 Big Area-big-2 2000 3 300 0,680
0,67 144,00590,003935,00 750,00375,00E&S1 NW large Cl_2 Big
Area-big-2 2000 3 300,1 0,650 0,85 90,00 950,002675,00
756,00378,00E&S1 NW large Cl_2 Big Area-big-2 2000 3 300,1
0,680 0,85 144,00950,002675,00 756,00378,00... and this is just a
small extract...Tina ComesDecision Making and Scenario
Planning45Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 46. CHALLENGE #4Handling combinatoricsTina Comes
Decision Making and Scenario Planning 46Institute for Industrial
Production (IIP) ISCRAM Summer School 2012 47. Too many possible
futuresGivenLimited time, effort, available expertiseNeed for a
decisionAim: exploring the space of possible
developmentsCombinatoricsToo many scenarios!What to do?Tina
ComesDecision Making and Scenario Planning47Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 48. Scenario
ManagementDuring the construction Selection of the most relevant
partial scenarios Pruning of invalid scenarios Update to take into
account relevant new informationEvaluation: Partial
scenarioSelection of the most relevant scenarios Selected
partialAggregation of results scenario Updated partial scenarioTina
ComesDecision Making and Scenario Planning48Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 49. Which
scenarios are the most relevant?Most scenario similarity
measures:distance of the variables valuesOur aim: Explore the space
of evaluationsMaking risks and chances
transparentRobustnessDefinition of Scenario classesBased on the
similarity of the evaluationSelection of a representative per
classTina ComesDecision Making and Scenario Planning49Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 50. Impact on
exploration of scenario space exploitingthe network structures 10.9
UPDATED0.80.7ORIG Evaluation0.6 SEL0.50.40.30.20.1 0 ScenarioTina
ComesDecision Making and Scenario Planning50Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 51. Scenario
Updates: Efficiency 400 Upper Bound of Duration [min] 350 Duration
of update from indicator variables to FOCUS 300 250 Duration of
update to indicator variables 200 150 10050 0 Complete
updatePartial update all Partial update of scenarios selected
Approach to updateTina Comes Decision Making and Scenario Planning
51Institute for Industrial Production (IIP) ISCRAM Summer School
2012 52. How a distributed system can work in chemicalemergencies
Video available on:
http://www.pdc.dk/diadem/Video/DiademVideo.wmvTina ComesDecision
Making and Scenario Planning52Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 53. CHALLENGE #5Supporting decision
makersTina Comes Decision Making and Scenario Planning 53Institute
for Industrial Production (IIP) ISCRAM Summer School 2012 54. How
to develop good alternatives?MCDA: workshops serve Define the-for
the identification of Recommendation Problem decision criteria and
feasible countermeasures SensitivityAnalysis n ConIdentify the
ctio-as exercises Attributesclusodu ing her ion IntrPla-for the
identification ofMea nningsuGat icstop be t res to responsibilities
and authoritiesChoose an aken Se le to implement a rapid response
Alternative c to tinpi gSpecifyPerformancetop gthe ndlinc a ic
Measures HaHow to support decisionmakers in building betterWeight
CriteriaIdentify thealternatives and establish Analyse
theAlternativesconsensus in veryAlternativesuncertain
situations?Tina ComesDecision Making and Scenario
Planning54Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 55. How to handle trade-offs?Preference models
represent the preferences and value judgements of adecision maker
by1. A model that scores each alternative against each individual
attribute concerns all attributes2. A model that compares the
relative importance among the criteria to obtain a ranking of
alternativesa. Elicitation of the relative importance (weights) of
the criteriab. Aggregation concerns the complete attribute treeTina
ComesDecision Making and Scenario Planning55Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 56. Back to
the example attribute treesHow to compare the attributes? 1. do
nothing 2. protection and suppliestotalperformance 3.
evacuationTina ComesDecision Making and Scenario
Planning56Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 57. Some technical details: Value functions allow to
scoreeach alternative against each individual attributeScores si(a)
of the alternatives are measured in different units for
thedifferent attributesto make comparisons, map these scores to a
scale ranging from 0 to 1(where the worst and best possible
outcomes correspond to 0 and 1respectively) by defining value
functions si a : score of alternative a relative to attribute i
vivi si a : value of the score of alternative a relative to
attribute i si amin si a# people protecteda , if max si ahighest
valuemax si amin si a a a a vi max si a si aa , if max si alowest
valuemax si amin si a a a awork effort (# workers)Tina Comes
Decision Making and Scenario Planning57Institute for Industrial
Production (IIP) ISCRAM Summer School 2012 58. Weights
Inter-criteria preferencesDifferent weighting procedures The
simplest way is the DIRECT weighting In the SWING procedure, 100
points are first given to the most important attribute; then, less
points are given to the other attributes depending on the relative
importance of their ranges The SMART method is similar, but the
procedure starts from the least important attribute (assigning 10
points to it) keeping it as the reference In SMARTER, the weights
are elicited directly from the ranking of the alternatives In AHP,
the weights are determined by pairwise comparisonsTina
ComesDecision Making and Scenario Planning58Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 59. Trying it
out...Go back to the attribute tree and the rationales you have
developed.- which are the most important criteria for you?- can you
establish clear preferences within your group (for weights andvalue
functions)?Tina ComesDecision Making and Scenario
Planning59Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 60. Scenario selection: Exemplary resultsSelected
sources of uncertainty: success of chlorine transfer residual
amount of chlorine in tank weather Evaluation of Scenarios 1 Health
Effort0.9 Society0.8results for best and worst Evaluation
R(s)0.70.6scenariosEvaluation R(s)0.50.40.30.20.1 0E S N E S N E S
NE SN E SN ES N ES NE SN E S N E S NScenarios for Alternatives
Evacuation (E), Sheltering (S) and Do nothing (N)Scenarios for
Alternatives Evacuation (E), Sheltering (S) and Do Nothing (N)Tina
ComesDecision Making and Scenario Planning 60Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 61.
Aggregation of results:how important is each scenario?Definition of
weights but how?direct elicitation from the
decision-makersAccording to the Evaluation Goal AttainmentTrying to
satisfice overall or partial goals (Simon, 1979)Deviation from
equal weighting if these goals are not attained: penalty functions
According to risk aversionRisk aversion: relative importance of
scenarios evaluated worst/best (Yager, 2008)Determination of
weights according to the scenarios rankingTina ComesDecision Making
and Scenario Planning61Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 62. Example: Results for varying
levels of risk aversion 11 Evacuation 0.9 Sheltering0.9 Do Nothing
0.8Aggregated weights0.8 0.7aggregated weight ofworst evaluated
scenarios 0.6aggregated weight ofResult(alternative)0.7 best
evaluated scenarios 0.5 0.40.6 0.30.50.2 0.10.40 0 0.1 0.2 0.3
0.40.50.60.70.8 0.91.00.3 Risk level0.200.1 0.2 0.3 0.40.5 0.6
0.70.8 0.91.0 Risk levelTina Comes Decision Making and Scenario
Planning62Institute for Industrial Production (IIP) ISCRAM Summer
School 2012 63. Interpreting the results: scenario
reliabilityNumber of scenarios increases with growing uncertainty
risk of overemphasizing some scenarios results for structural
reasonsScenario ReliabilityModelling the relative uncertainty of
scenarios:uncertainty of the situation: comparison to other
scenariosuncertainty of the specific scenariopreferences of the
decision makers easily manageable measure enables decision-makers
to adapt scenario weights and overcomecognitive biasesTina
ComesDecision Making and Scenario Planning63Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 64. How to
make alternatives better1. How is the quality of an alternative
measured? MCDA!2. What can go well and what can go wrong? SBR!An
iterative approach1. Identification of key weaknesses per
alternative2. Identification of better alternatives to addressthese
weaknessesAnalysis: how can these alternatives be combined?So, all
information is there. But...... large numbers of scenarios and
results... visualisations not easy to interpret need for a clear
and transparent explanation of resultsTina ComesDecision Making and
Scenario Planning64Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 65. Making sense of what you seeTina
ComesDecision Making and Scenario Planning65Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 66.
Communicating decisions under uncertainty Evaluation of Scenarios 1
Health Effort0.9 Society0.80.70.6Evaluation R(s)0.50.40.30.20.1 0E
S N E S N E S NE SN E SN ES N ES NE SN E S N E S NScenarios for
Alternatives Evacuation (E), Sheltering (S) and Do nothing (N)Tina
ComesDecision Making and Scenario Planning 66Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 67. Generation
of natural language reports1. Content determinationInformation
about what? Type of report and variables: alternatives, outcomes,
drivers, ...informationQuestions that should be addressed?
requirements relations: causes and effects, better or worse, ...2.
Discourse planning3. Sentence generationTina ComesDecision Making
and Scenario Planning 67Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 68. Generation of natural language
reports1. Content determination variables Type of report and
relationsinformationrequirements2. Discourse PlanningWhat can be
said about the entities and their relations? determine types of
individual messages ArgumentationHow to combine the messages into
an argumentation? relate and cluster messages into a tree
structure3. Sentence generationTina ComesDecision Making and
Scenario Planning 68Institute for Industrial Production (IIP)ISCRAM
Summer School 2012 69. Generation of natural language reports1.
Content determination variables Type of report and
relationsinformationrequirements2. Discourse Planning types of
individual messages Argumentation tree structurestructure3.
Sentence generationHow to express the message? choose of adequate
text patternsTemplate SystemWhat is the argument for this
case?completion of statementsTina ComesDecision Making and Scenario
Planning 69Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 70. From numbers to verbal expressions:Semantic
quantifiersAim: describe the quality of a decisionsubstantially
better, slightly worse, ...Alternative performs on in the context
of all available scenarios.A relative approach1. set of evaluated
scenarios and relevant objectives2. determine mean and standard
deviation3. set SQs Alternative evacuation performs very poor on
effort in the context of allavailable scenarios.A benchmark
approach: goal programming and satisfaction levelsAlternative
evacuation has an acceptable performance with respect tohealth in
most scenarios.Tina ComesDecision Making and Scenario
Planning70Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 71. Key weaknesses1. What do the worst scenarios for an
alternative have in common?statistical approach: worst % for each
alternativebenchmark approach: scenarios that violate threshold
identify variables var1, ..., varn and their valuesAlternative
performs on for all scenarios that assume for ,..., for .2. How do
other alternatives perform for the same / similar scenarios?3.
Identify better alternatives and describe significance in an SQ
Alternative performs on than for the identified scenarios.Tina
ComesDecision Making and Scenario Planning71Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 72.
Finally...Prepare for thediscussion, collectthe material youneed
and choose therepresentative...... and then, find asolution:which
strategicmeasures shouldbe implementedand where?Tina ComesDecision
Making and Scenario Planning72Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 73. REFLECTIONS AND CONCLUSIONSTina
ComesDecision Making and Scenario Planning73Institute for
Industrial Production (IIP)ISCRAM Summer School 2012 74.
ConclusionIntegrated Scenario-Based MCDADistributed processing of
relevant informationConsideration of interdependenciesFormalization
using set and graph theoryEnsuring comparabilityScenario
management: updating, selection, pruningRespecting constraints and
requirements in emergency managementDecentralised vs. centralised:
Orchestrating emergenceDecentralised experts involved in
workflowDecision-centric management with overviewTina ComesDecision
Making and Scenario Planning74Institute for Industrial Production
(IIP)ISCRAM Summer School 2012 75. Reflections1. What were the main
challengesin your team?in the discussion?2. Social media
applications?Tina ComesDecision Making and Scenario
Planning75Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 76. Thank you!ContactTina
[email protected]?Tina ComesDecision Making and Scenario
Planning76Institute for Industrial Production (IIP)ISCRAM Summer
School 2012 77. ReferencesComes, T., Wijngaards, N. &
Schultmann, F. (2012): Efficient Scenarios Updating inEmergency
Management. 9th International Conference on Information Systems for
CrisisResponse and ManagementComes, T., Wijngaards, N., Maule, J.,
Allen, D. & Schultmann, F. (2012): ScenarioReliability
Assessment to Support Decision Makers in Situations of Severe
Uncertainty.2012 IEEE Conference on Cognitive Methods in Situation
Awareness and DecisionSupportComes, T., Hiete, M., Wijngaards, N.
& Schultmann, F. (2011): Decision Maps: Aframework for
multi-criteria decision support under severe uncertainty. Decision
SupportSystems, 52(1), 108-118.Comes, T., Conrado, C., Hiete, M.,
Wijngaards, N. & Schultmann, F. (2011): A
distributedscenario-based decision support system for robust
decision-making in complexsituations. International Journal of
Information Systems for Crisis Response andManagement, 3(4),
16-35.Simon, H. (1979): Rational Decision Making in Business
Organizations, The AmericanEconomic Review, 69(4), 493-513.Ronald
R. Yager, Using trapezoids for representing granular objects:
Applications tolearning and OWA aggregation, Information Sciences
178(2), 363-380.Tina ComesDecision Making and Scenario Planning
77Institute for Industrial Production (IIP)ISCRAM Summer School
2012