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Iscram summerschool12 decisions

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Iscram summerschool12 decisions

  1. 1. Decision Making and Scenario Planning 2012 ISCRAM Summer School on Humanitarian Information Management Tina Comes Research Group: Risk Management Institute for Industrial Production (IIP) KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association www.kit.edu
  2. 2. Risk Management? Aim: support decision-makers in complex and uncertain situations  bridge the gap between formal models and transparent, ready-to-use evaluations  collaborative and distributed decision support tools based on modern ICT systems Tina Comes Decision Making and Scenario Planning 2 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  3. 3. Making decisions… What is the current situation? How will the future unfold? Yes No Tina Comes Decision Making and Scenario Planning 3 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  4. 4. How to improve the crystal ball? Each action has consequences Which of them are relevant? How do they evolve? How to compare different consequences? 200 60 people, %, beca because use … … Tina Comes Decision Making and Scenario Planning 4 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  5. 5. Making decisions 1. Identify objectives System disaster what would you ideally achieve? • environment 2. Describe the system • actors and their decisions what 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 of and food casualties 4. 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 decision Tina Comes Decision Making and Scenario Planning 5 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  6. 6. ... but this is difficult in emergencies! Multiple stakeholders and decision makers Heterogeneous information on various aspects of the situation Uncertainty: unforeseen events and reactions Limited time to make a decision and pressure Actors possibly geographically dispersed Bounded availability of experts Risk of information overload and lack of information Tina Comes Decision Making and Scenario Planning 6 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  7. 7. Strategic decisions 60 % 1. Multiple goals, diverse actors 200  how to make trade-offs people explicit?  how to build 100 consensus? people 2. 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 Comes Decision Making and Scenario Planning 7 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  8. 8. An approach for scenario-based decisions Collecting information: a distributed system with heterogeneous experts Human and artificial  different skills, backgrounds and knowledge Scenario-Based Multi-Criteria Decision Analysis Orchestrate distributed scenario generation Generate relevant, consistent, plausible and coherent scenarios Use the decision-makers‟ and experts‟ information needs as rationale for information filtering and sharing Provide understandable decision analyses and evaluations Tina Comes Decision Making and Scenario Planning 8 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  9. 9. Challenges 1. Improving the crystal ball: objectives and information needs 2. 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 Comes Decision Making and Scenario Planning 9 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  10. 10. More concretely... http://www.bbc.co.uk/news/world-asia-pacific-12149921 http://www.theaustralian.com.au/in-depth/queensland-floods Tina Comes Decision Making and Scenario Planning 10 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  11. 11. Example Situation Flood currently controlled by levee Risk: quick flooding if water rises higher Threat current uncertain situation developments Time 1. Do nothing? What to do? 2. Protect buildings, provide supplies? 3. Evacuation? The Kia Ora Levee http://www.crikey.com.au/2011/02/28/levees- and-the-lack-of-regulation-that-could-cost- millions/ Tina Comes Decision Making and Scenario Planning 11 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  12. 12. What is best decision ? 5 Groups 1. Residents 2. Local industry and infrastructure providers 3. EM staff (fire fighters, health care, police, ...) 4. Political authorities (responsible to make the decision) 5. Moderators Your aim: Establish a consensus about what to do! 1. Preparation and analysis of options 2. Discussion and consensus building  one member per team Tina Comes Decision Making and Scenario Planning 12 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  13. 13. CHALLENGE #1 Improving the crystal ball: objectives and information needs Tina Comes Decision Making and Scenario Planning 13 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  14. 14. Determining possible futures Relevant consequences Situation information What goes here? Ranking of Alternatives alternatives for action Tina Comes Decision Making and Scenario Planning 14 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  15. 15. http://www.theaustralian.com.au/news/nation/queenslands-flood-disaster-a- long-way-from-over-warns-anna-bligh/story-e6frg6nf-1225979264551 Tina Comes Decision Making and Scenario Planning 15 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  16. 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 Comes Decision Making and Scenario Planning 16 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  17. 17. How are the consequences related? Aim: structured evaluation of a decisions consequences taking into account the decision makers preferences modelling the problem by an attribute tree # people evacuated per day health 1. do nothing # people exposed to flood 2. protection and supplies total performance firefighters [man-h] 3. evacuation effort police [man-h] Tina Comes Decision Making and Scenario Planning 17 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  18. 18. Back to the example In your team, structure the problem by an attribute tree 1. do nothing 2. protection and supplies total performance 3. evacuation Tina Comes Decision Making and Scenario Planning 18 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  19. 19. Determining the consequences? Decision tables specify the consequences for all alternatives with respect to each attribute # people # people firefighters police evacuated exposed [man-h] [man-h] per day to flood 1. do nothing 2. protect 3. evacuate How to fill in the blanks? 1. collect information 2. manage uncertainty Tina Comes Decision Making and Scenario Planning 19 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  20. 20. An example from chemical emergency management # pp unshelt & police [manh] # pp shelt & firefighters losses [k€] alternative economic [manh] exp exp E&S1 15 0 0 247,50 123,75 S1 7 0 0 165,00 82,50 DN 0 0 0 0,00 0,00 Tina Comes Decision Making and Scenario Planning 20 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  21. 21. An example from chemical emergency management – determining the basic information What information is required to determine the attributes? variables indicators variables ATTRIBUTES affected* (GVP/d, affected* (GVP/d, population registry # pp unshelt & exp firefighters [manh] economic losses # pp shelt & exp firms indirectly critical objects infrastructure* transportation infrastructure police [manh] firms directly source term* population alternative presence* leak size* chemical weather* building registry plume [k€] k€) k€) E&S NW none Cl_2 none none 750 0 5 0 0,33 5 0,67 15 0 0 247,5 123,8 1 S1 NW none Cl_2 none none 500 0 5 0 0,33 5 0,67 7 0 0 165 82,50 0 DN NW none Cl_2 none none 0 0 5 0 0,33 5 0,67 0 0 0 0 Tina Comes Decision Making and Scenario Planning 21 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  22. 22. CHALLENGE #2 Collecting Information: Getting Experts to Cooperate Tina Comes Decision Making and Scenario Planning 22 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  23. 23. How to determine a decision’s consequences? Monolithic System Seems like a good idea Built exactly to system specification Quick simulation of results Artificial intelligence techniques are mature … However Vendor lock-in Specification changes over time as problem changes Artificial Intelligence techniques are expensive … Tina Comes Decision Making and Scenario Planning 23 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  24. 24. An alternative approach In 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 Comes Decision Making and Scenario Planning 24 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  25. 25. Using a Hybrid Heterogeneous Distributed System Network of experts Hybrid: both human and artificial experts Diverse backgrounds, skills and expertise  breaking down complex problems into manageable sub-problems Experts cooperate… … to determine a set of possible futures: scenarios … via a standardized communication „engine‟ Tina Comes Decision Making and Scenario Planning 25 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  26. 26. Cooperating experts? What goes here? Tina Comes Decision Making and Scenario Planning 26 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  27. 27. A distributed problem solving approach Cooperation structure Distributed information processing workflow Workflow setup: combined top-down bottom-up approach Based on information need („backwards‟): request for information Based on event („forwards‟): information available  further processing Matching the experts‟ processing capabilities Based on profiles per expert Match based on information types (input & output) expertise (e.g., location, capabilities) Tina Comes Decision Making and Scenario Planning 27 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  28. 28. Orchestrated information processing Tina Comes Decision Making and Scenario Planning 28 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  29. 29. Experts in workflow for the chemical emergency example Tina Comes Decision Making and Scenario Planning 29 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  30. 30. Another distributed system 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 Comes Decision Making and Scenario Planning 30 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  31. 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 Comes Decision Making and Scenario Planning 31 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  32. 32. Local information http://www.rockhamptonregion.qld.gov.au/Council_Services/New s_and_Announcements/Latest_News/Evacuation_Centre_open_ 8am_Friday_31_December Tina Comes Decision Making and Scenario Planning 32 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  33. 33. Tina Comes Decision Making and Scenario Planning 33 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  34. 34. Trying it out Establish a rationale for the negotiations referring to the goals and objectives you identified! - where would you enforce evacuation? - recommend evacuation? - recommend sheltering? - other? Some sources you may find useful http://www.qldreconstruction.org.au/maps/aerial-imaging-and-mapping-pdfs http://highload.131940.qld.gov.au/#11 http://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=216305641036137584677.000498fa83 0661a4cbafb Tina Comes Decision Making and Scenario Planning 34 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  35. 35. CHALLENGE #3 Keeping track of the future Tina Comes Decision Making and Scenario Planning 35 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  36. 36. Why information is not perfect Uncertainty Ambiguity Incomplete and uncertain information in consequences and evaluation Constraints in Time Constraints resources Tina Comes Decision Making and Scenario Planning 36 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  37. 37. Robust Decision-Making Aim: Find the alternative that performs satisfactory in many (all) scenarios. Score Score Satisfactory threshold Time Time Considering one scenario per Considering multiple scenarios per alternative results in one scoring. alternative results in spread of scoring. Tina Comes Decision Making and Scenario Planning 37 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  38. 38. Considering several futures… A £ A’ $ B B’ E 1.2 C 2.5 C’ 25 512 E’ D D’ Tina Comes Decision Making and Scenario Planning 38 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  39. 39. The flood? Tina Comes Decision Making and Scenario Planning 39 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  40. 40. Media Coverage At the scene: Nick Bryant BBC News, Rockhampton Almost completely encircled by muddy floodwaters, Rockhampton risked being entirely cut off if those rose much further, but they peaked slightly lower than the authorities had feared, enough to keep the one highway that's open from being inundated. Many of the city's low-lying suburbs will remain flooded for more than a week, but a local official said the city as a whole had "dodged the bullet". Longer term consequences Now attention is shifting to the economic http://www.bbc.co.uk/news/world-asia-pacific-12116919 impact of the flooding on Australia's two most vital sectors, mining and agriculture. Operations at some 40 mines have been interrupted and many of the railway lines that transport coal to the ports have been severed. Queensland is responsible for more than half of the country's coal exports. With farms flooded and crops ruined, the price of fresh fruit 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 because Queensland supplies half of the world's coking coal for steel manufacturing. At least one senior economist here thinks this could be Australia's most costly natural disaster, largely because of the impact on exports. Tina Comes Decision Making and Scenario Planning 40 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  41. 41. Trying it out Revisit your recommendation and rationale - is it optimal? - is it robust? - which are the most important scenarios you want to use in the discussions? why? Tina Comes Decision Making and Scenario Planning 41 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  42. 42. Managing the experts’ work in distributed reasoning framework Old situation New situation What goes here? Information flow Tina Comes Decision Making and Scenario Planning 42 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  43. 43. Keeping track of (partial) scenarios Scenarios capture uncertainty Requirements Consistency and comparability  Not mixing scenario values Coherence:  Keeping track of the scenario construction Tina Comes Decision Making and Scenario Planning 43 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  44. 44. Consistency in the example Combination of information Combination of information about independent variables about related variables  Changing the workflow mechanisms to … keep track of partial scenarios … correctly merge partial scenarios Tina Comes Decision Making and Scenario Planning 44 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  45. 45. An extract from the chemical emergency management example variables indicators variables FOCUS transportation police [manh] infrastructure infrastructure source term* (GVP/d, k€) (GVP/d, k€) # pp shelt & # pp unshelt population firefighters losses [k€] population alternative presence* leak size* affected* economic indirectly weather* affected* chemical registry registry directly building objects critical [manh] plume & exp firms firms exp * E&S1 NW none Cl_2 none none 750 0 5 0 0,33 5 0,67 15 0 0 247,50 123,75 E&S1 NW none Cl_2 none none 750 0 5 0 0,33 5 0,85 18 0 0 247,50 123,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0 0,25 40 0,67 72,00 925,00 4262,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0 0,25 50 0,67 90,00 925,00 4262,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0 0,25 40 0,85 72,00 1375,00 2687,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0 0,25 50 0,85 90,00 1375,00 2687,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0 0,6 40 0,67 72,00 925,00 4262,50 1050,00 525,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0 0,6 50 0,67 90,00 925,00 4262,50 1050,00 525,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0,1 0,6 40 0,85 72,00 1375,00 2687,50 1056,00 528,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 20 0,1 0,6 50 0,85 90,00 1375,00 2687,50 1056,00 528,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0 0,25 48,00 0,67 86,40 925,00 4262,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0 0,25 60,00 0,67 108,00 925,00 4262,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0 0,25 48,00 0,85 86,40 1375,00 2687,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0 0,25 60,00 0,85 108,00 1375,00 2687,50 437,50 218,75 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0 0,6 48,00 0,67 86,40 925,00 4262,50 1050,00 525,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0 0,6 60,00 0,67 108,00 925,00 4262,50 1050,00 525,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0,1 0,6 48,00 0,85 86,40 1375,00 2687,50 1056,00 528,00 E&S1 NW med Cl_2 Big Area-big-1 2500 2 22 0,1 0,6 60,00 0,85 108,00 1375,00 2687,50 1056,00 528,00 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0 0,25 50 0,67 90,00 590,00 3935,00 312,50 156,25 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0 0,25 80 0,67 144,00 590,00 3935,00 312,50 156,25 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0 0,25 50 0,85 90,00 950,00 2675,00 312,50 156,25 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0 0,25 80 0,85 144,00 950,00 2675,00 312,50 156,25 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0 0,6 50 0,67 90,00 590,00 3935,00 750,00 375,00 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0 0,6 80 0,67 144,00 590,00 3935,00 750,00 375,00 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0,1 0,6 50 0,85 90,00 950,00 2675,00 756,00 378,00 E&S1 NW large Cl_2 Big Area-big-2 2000 3 30 0,1 0,6 80 0,85 144,00 950,00 2675,00 756,00 378,00 ... and this is just a small extract... Tina Comes Decision Making and Scenario Planning 45 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  46. 46. CHALLENGE #4 Handling combinatorics Tina Comes Decision Making and Scenario Planning 46 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  47. 47. Too many possible futures… Given Limited time, effort, available expertise Need for a decision Aim: exploring the space of possible developments Combinatorics… Too many scenarios! What to do? Tina Comes Decision Making and Scenario Planning 47 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  48. 48. Scenario Management During the construction Selection of the most relevant partial scenarios Pruning of invalid scenarios Update to take into account relevant new information Evaluation: Partial scenario Selection of the most relevant scenarios Selected partial Aggregation of results scenario Updated partial scenario Tina Comes Decision Making and Scenario Planning 48 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  49. 49. Which scenarios are the most relevant? Most scenario similarity measures: distance of the variables‟ values Our aim: Explore the space of evaluations  Making risks and chances transparent  Robustness Definition of Scenario classes  Based on the similarity of the evaluation  Selection of a representative per class Tina Comes Decision Making and Scenario Planning 49 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  50. 50. Impact on exploration of scenario space exploiting the network structures 1 0.9 UPDATED 0.8 0.7 ORIG Evaluation 0.6 SEL 0.5 0.4 0.3 0.2 0.1 0 Scenario Tina Comes Decision Making and Scenario Planning 50 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  51. 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 200 150 100 50 0 Complete update Partial update all Partial update of scenarios selected Approach to update Tina Comes Decision Making and Scenario Planning 51 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  52. 52. How a distributed system can work in chemical emergencies Video available on: http://www.pdc.dk/diadem/Video/DiademVideo.wmv Tina Comes Decision Making and Scenario Planning 52 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  53. 53. CHALLENGE #5 Supporting decision makers Tina Comes Decision Making and Scenario Planning 53 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  54. 54. How to develop good alternatives? MCDA: workshops serve Define the - for the identification of Recommendation Problem decision criteria and feasible countermeasures Sensitivity Analysis n Con Identify the ctio - as exercises Attributes clus odu ing her ion Intr Pla - for the identification of Mea nning su Gat ics top be t res to responsibilities and authorities Choose an ake n Se le to implement a rapid response Alternative c to tin pi g Specify Performance top g the ndlin c a ic Measures Ha How to support decision makers in building better Weight Criteria Identify the alternatives and establish Analyse the Alternatives consensus in very Alternatives uncertain situations? Tina Comes Decision Making and Scenario Planning 54 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  55. 55. How to handle trade-offs? Preference models represent the preferences and value judgements of a decision maker by 1. A model that scores each alternative against each individual attribute  concerns all attributes 2. A model that compares the relative importance among the criteria to obtain a ranking of alternatives a. Elicitation of the relative importance (weights) of the criteria b. Aggregation  concerns the complete attribute tree Tina Comes Decision Making and Scenario Planning 55 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  56. 56. Back to the example attribute trees How to compare the attributes? 1. do nothing 2. protection and supplies total performance 3. evacuation Tina Comes Decision Making and Scenario Planning 56 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  57. 57. Some technical details: Value functions allow to score each alternative against each individual attribute Scores si(a) of the alternatives are measured in different units for the different attributes to make comparisons, map these scores to a scale ranging from 0 to 1 (where the “worst” and “best” possible outcomes correspond to 0 and 1 respectively) by defining value functions si a : score of alternative a relative to attribute i vi vi si a : value of the score of alternative a relative to attribute i si a min si a # people protected a , if max si a highest value max si a min si a a a a vi max si a si a a , if max si a lowest value max si a min si a a a a work effort (# workers) Tina Comes Decision Making and Scenario Planning 57 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  58. 58. Weights – Inter-criteria preferences Different 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 comparisons Tina Comes Decision Making and Scenario Planning 58 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  59. 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 and value functions)? Tina Comes Decision Making and Scenario Planning 59 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  60. 60. Scenario selection: Exemplary results Selected sources of uncertainty: success of chlorine transfer residual amount of chlorine in tank weather Evaluation of Scenarios 1 Health Effort 0.9 Society 0.8 results for best and worst Evaluation R(s) 0.7 0.6 scenarios Evaluation R(s) 0.5 0.4 0.3 0.2 0.1 0 E S N E S N E S N E S N E S N E S N E S N E S N E S N E S N Scenarios for Alternatives Evacuation (E), Sheltering (S) and Do nothing (N) Scenarios for Alternatives Evacuation (E), Sheltering (S) and Do Nothing (N) Tina Comes Decision Making and Scenario Planning 60 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  61. 61. Aggregation of results: how important is each scenario? Definition of weights – but how? direct elicitation from the decision-makers According to the Evaluation  Goal Attainment  Trying to satisfice overall or partial goals (Simon, 1979)  Deviation from equal weighting if these goals are not attained: penalty functions  According to risk aversion  Risk aversion: relative importance of scenarios evaluated worst/best (Yager, 2008)  Determination of weights according to the scenarios„ ranking Tina Comes Decision Making and Scenario Planning 61 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  62. 62. Example: Results for varying levels of risk aversion 1 1 Evacuation 0.9 Sheltering 0.9 Do Nothing 0.8 Aggregated weights 0.8 0.7 aggregated weight of worst evaluated scenarios 0.6 aggregated weight of Result(alternative) 0.7 best evaluated scenarios 0.5 0.4 0.6 0.3 0.5 0.2 0.1 0.4 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.3 Risk level 0.2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Risk level Tina Comes Decision Making and Scenario Planning 62 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  63. 63. Interpreting the results: scenario reliability Number of scenarios increases with growing uncertainty  risk of overemphasizing some scenarios‟ results for structural reasons Scenario Reliability Modelling the relative uncertainty of scenarios: uncertainty of the situation: comparison to other scenarios uncertainty of the specific scenario preferences of the decision makers  easily manageable measure  enables decision-makers to adapt scenario weights and overcome cognitive biases Tina Comes Decision Making and Scenario Planning 63 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  64. 64. How to make alternatives better 1. How is the quality of an alternative measured? MCDA! 2. What can go well and what can go wrong? SBR! An iterative approach 1. Identification of key weaknesses per alternative 2. Identification of better alternatives to address these weaknesses Analysis: 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 results Tina Comes Decision Making and Scenario Planning 64 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  65. 65. Making sense of what you see Tina Comes Decision Making and Scenario Planning 65 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  66. 66. Communicating decisions under uncertainty Evaluation of Scenarios 1 Health Effort 0.9 Society 0.8 0.7 0.6 Evaluation R(s) 0.5 0.4 0.3 0.2 0.1 0 E S N E S N E S N E S N E S N E S N E S N E S N E S N E S N Scenarios for Alternatives Evacuation (E), Sheltering (S) and Do nothing (N) Tina Comes Decision Making and Scenario Planning 66 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  67. 67. Generation of natural language reports 1. Content determination Information about what? Type of report and  variables: alternatives, outcomes, drivers, ... information Questions that should be addressed? requirements  relations: causes and effects, better or worse, ... 2. Discourse planning 3. Sentence generation Tina Comes Decision Making and Scenario Planning 67 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  68. 68. Generation of natural language reports 1. Content determination • variables Type of report and • relations information requirements 2. Discourse Planning What can be said about the entities and their relations?  determine types of individual messages Argumentation How to combine the messages into an argumentation?  relate and cluster messages into a tree structure 3. Sentence generation Tina Comes Decision Making and Scenario Planning 68 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  69. 69. Generation of natural language reports 1. Content determination • variables Type of report and • relations information requirements 2. Discourse Planning • types of individual messages Argumentation • tree structure structure 3. Sentence generation How to express the message?  choose of adequate text patterns Template System What is the argument for this case? completion of statements Tina Comes Decision Making and Scenario Planning 69 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  70. 70. From numbers to verbal expressions: Semantic quantifiers Aim: describe the quality of a decision “substantially better”, “slightly worse”, ... Alternative <name of alternative> performs <semantic quantifier> on <objective> in the context of all available scenarios. A relative approach 1. set of evaluated scenarios and relevant objectives 2. determine mean μ and standard deviation 3. set SQs  Alternative evacuation performs very poor on effort in the context of all available scenarios. A benchmark approach: goal programming and satisfaction levels Alternative evacuation has an acceptable performance with respect to health in most scenarios. Tina Comes Decision Making and Scenario Planning 70 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  71. 71. Key weaknesses 1. What do the worst scenarios for an alternative have in common? statistical approach: worst % for each alternative benchmark approach: scenarios that violate threshold  identify variables var1, ..., varn and their values Alternative <name of alternative> performs <semantic quantifier> on <objective> for all scenarios that assume <value of var1> for <var1>,..., <value of varn> for <varn>. 2. How do other alternatives perform for the same / similar scenarios? 3. Identify better alternatives and describe significance in an SQ Alternative <name of alternative2> performs <semantic quantifier> on <objective> than <name of alternative> for the identified scenarios. Tina Comes Decision Making and Scenario Planning 71 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  72. 72. Finally... Prepare for the discussion, collect the material you need and choose the representative... ... and then, find a solution: which strategic measures should be implemented and where? Tina Comes Decision Making and Scenario Planning 72 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  73. 73. REFLECTIONS AND CONCLUSIONS Tina Comes Decision Making and Scenario Planning 73 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  74. 74. Conclusion Integrated Scenario-Based MCDA Distributed processing of relevant information Consideration of interdependencies Formalization using set and graph theory Ensuring comparability Scenario management: updating, selection, pruning Respecting constraints and requirements in emergency management Decentralised vs. centralised: Orchestrating emergence Decentralised experts involved in workflow Decision-centric management with overview Tina Comes Decision Making and Scenario Planning 74 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  75. 75. Reflections 1. What were the main challenges in your team? in the discussion? 2. Social media applications? Tina Comes Decision Making and Scenario Planning 75 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  76. 76. Thank you! Contact Tina Comes comes@kit.edu Questions? Tina Comes Decision Making and Scenario Planning 76 Institute for Industrial Production (IIP) ISCRAM Summer School 2012
  77. 77. References Comes, T., Wijngaards, N. & Schultmann, F. (2012): Efficient Scenarios Updating in Emergency Management. 9th International Conference on Information Systems for Crisis Response and Management Comes, T., Wijngaards, N., Maule, J., Allen, D. & Schultmann, F. (2012): Scenario Reliability Assessment to Support Decision Makers in Situations of Severe Uncertainty. 2012 IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support Comes, T., Hiete, M., Wijngaards, N. & Schultmann, F. (2011): Decision Maps: A framework for multi-criteria decision support under severe uncertainty. Decision Support Systems, 52(1), 108-118. Comes, T., Conrado, C., Hiete, M., Wijngaards, N. & Schultmann, F. (2011): A distributed scenario-based decision support system for robust decision-making in complex situations. International Journal of Information Systems for Crisis Response and Management, 3(4), 16-35. Simon, H. (1979): Rational Decision Making in Business Organizations, The American Economic Review, 69(4), 493-513. Ronald R. Yager, “Using trapezoids for representing granular objects: Applications to learning and OWA aggregation,” Information Sciences 178(2), 363-380. Tina Comes Decision Making and Scenario Planning 77 Institute for Industrial Production (IIP) ISCRAM Summer School 2012

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