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 Comescomes@kit.eduQuestions?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