1. Scenario Construction Via Cross Impact Prof. Victor A. Bañuls Management Department Pablo de Olavide University Seville, Spain Email: vabansil@upo.esvabansil@upo.es Web: http://webdee.upo.es/vabansil The Network Nation and Beyond A Festschrift in Honor of Starr Roxanne Hiltz and Murray Turoff Distinguished Prof. Murray Turoff Information Systems Department New Jersey Institute of Technology Newark NJ, USA Email: turoff@njit.eduturoff@njit.edu Web: http://web.njit.edu/~turoff/ NJIT – October 2007

2. Index Research motivations Methodological background Basics of the CIM-ISM Generating scenarios Conclusions

3. Research motivations Why do we need scenarios? –Strategic decision making (policy, business, etc.) compromise resources in the long term. –We need to think about what will happen tomorrow before acting today. –A scenario is a tool for managing the uncertainty of the future. –Our proposal is aimed at contributing to this goal.

4. Research motivations What is the aim of our proposal? –Helping decision makers to manage the uncertainty. How? –Structuring and sharing the beliefs and the knowledge of the people involved in decision making. But… how can we do that? –By the structural analysis of the impacts between the atomic events that are relevant to the decision-making problem.

5. Methodological background Cross-Impact Method –Events cannot be analyzed in a isolated way. –Alternative cross-impact approach (Turoff, 1972): Inferring impacts between events based on experts’ hypothesis about their occurrence (or not).

6. Methodological background 12345678..n 1 2 3 4 5 6 7 8 n GiGi 12345678 n C 43 +/- Impacts between events in the model Impacts of the events not included in the model Cross-Impact Matrix

7. Methodological background Interpretive structural modeling –Taking as an input the impacts obtained with the CIM, this methodology will help us to: Making hypotheses about the occurrence or not of the set of events and analyzed them (to generate scenarios). Detecting and analyzing the key drivers (critical events).

8. Methodological background 3 1 5 2 810 6 4 7 9 Occurring eventsNon-Occurring events Key drivers Scenario

9. Methodological background PiPi S ij R ij C ij GiGi CIM EiEi Events Set of probabilities (isolated and conditional) Cross-Impact Matrix Input Output Cross-Impact Method

10. Methodological background PiPi S ij R ij C ij GiGi CIM ISM Scenarios EiEi Events Set of probabilities (isolated and conditional) Cross-Impact Matrix Cross-Impact Method Interpretive Structural modeling Input Output

11. Basics of the CIM-ISM Starting point – Cross-Impact Matrix (Turoff 1972 paper example).

12. Basics of the CIM-ISM 12345678910 1 OVP-0.290.00-0.81-0.331.570.00-0.25-0.220.00 2 -0.50OVP-0.230.460.00-0.770.900.290.250.42 3 -0.410.31OVP0.430.74-0.580.000.270.240.68 4 -0.810.580.07OVP0.33-1.210.330.250.220.33 5 -0.880.58-0.140.81OVP-0.310.740.00 0.36 6 0.88-0.360.00-2.70-0.42OVP-0.38-0.31-0.28-0.38 7 -0.410.990.000.881.16-0.29OVP0.00 0.68 8 -1.62-0.500.000.580.48-1.160.00OVP0.600.58 9 -1.490.00 0.930.00-1.071.251.01OVP1.25 10 -0.410.99-0.140.881.16-0.580.680.00 OVP GiGi 0.23-1.33-0.30-0.05-1.020.88-0.91-0.97-3.29-0.74 Cross-Impact Matrix

13. Basics of the CIM-ISM Starting point – Cross-Impact Matrix (Turoff 1972 paper example). Transforming the Cross-Impact matrix –Transition Matrix (square and positive matrix).

14. Basics of the CIM-ISM Occurring eventsNon occurring events Occurring events + c ij - c ij Non occurring events - c ij + c ij Transforming the Cross-Impact Matrix

15. Basics of the CIM-ISM Starting point – Cross-Impact Matrix (Turoff 1972 paper example). Transforming the Cross-Impact matrix –Transition Matrix (square and positive matrix). Transforming the Transition Matrix –Adjacency Matrix (taking an arbitrary C ij value (0.85)).

16. Basics of the CIM-ISM Starting point – Cross-Impact Matrix (Turoff 1972 paper example). Transforming the Cross-Impact matrix –Transition Matrix (square and positive matrix). Transforming the Transition Matrix –Adjacency Matrix (taking an arbitrary C ij value (0.85)). –Connection Matrix (adding the Identity Matrix).

17. Basics of the CIM-ISM Starting point – Cross-Impact Matrix (Turoff 1972 paper example). Transforming the Cross-Impact matrix –Transition Matrix (square and positive matrix). Transforming the Transition Matrix –Adjacency Matrix (taking an arbitrary C ij value (0.85)). –Connection Matrix (adding the Identity Matrix). –Reachability Matrix (powering until it is stable).

18. Basics of the CIM-ISM Scenario Generation –Determining antecedent and succedent sets –Obtaining the graphical scenario (using graph theory)

19. Basics of the CIM-ISM Scenario Generation –Determining antecedent and succedent sets. –Obtaining the graphical scenario (using graph theory). Interpretation of the scenario –Analyzing key drivers. –Analyzing the set of probabilities.

20. Basics of the CIM-ISM LEVEL 1 LEVEL 5 LEVEL 4 LEVEL 2 LEVEL 3 1 5 2 8 10 9 64 7 Scenario Occurring eventsNon-Occurring events P 9 =0.1 Key drivers Why 0.85? And event 3?

21. Generating scenarios Sensitivity Analysis –Studying the C ij distribution.

22. Percentile 901.1581 Percentile 800.9198 Percentile 700.8109 Percentile 600.6450 Percentile 500.5389 Percentile 400.4132 Percentile 300.3409 Percentile 200.2950 Percentile 100.2508 Generating scenarios Normal distribution with a reliability of 99% (using K-S test)

23. LEVEL 2 LEVEL 1 LEVEL 3 1 98 6 4 10 Generating scenarios Percentile 901.1581 Percentile 800.9198 Percentile 700.8109 Percentile 600.6450 Percentile 500.5389 Percentile 400.4132 Percentile 300.3409 Percentile 200.2950 Percentile 100.2508

24. LEVEL 2 LEVEL 1 LEVEL 3 LEVEL 4 1 5 8 10 9 7 6 4 2 Generating scenarios Percentile 901.1581 Percentile 800.9198 Percentile 700.8109 Percentile 600.6450 Percentile 500.5389 Percentile 400.4132 Percentile 300.3409 Percentile 200.2950 Percentile 100.2508

25. LEVEL 1 LEVEL 5 LEVEL 4 LEVEL 2 LEVEL 3 1 5 2 8 10 9 64 7 Generating scenarios Percentile 901.1581 Percentile 800.9198 Percentile 700.8109 Percentile 600.6450 Percentile 500.5389 Percentile 400.4132 Percentile 300.3409 Percentile 200.2950 Percentile 100.2508

26. LEVEL 2 LEVEL 1 LEVEL 3 5 810 3 7 6 4 1 2 9 Generating scenarios Percentile 901.1581 Percentile 800.9198 Percentile 700.8109 Percentile 600.6450 Percentile 500.5389 Percentile 400.4132 Percentile 300.3409 Percentile 200.2950 Percentile 100.2508

27. Generating scenarios Sensitivity Analysis –Studying the C ij distribution Solving the forecasted scenario –Determining the limit of the forecasted scenario

28. LEVEL 2 LEVEL 1 5 8 10 3 76412 9 Limit = |0.4975| Forecasted Scenario Generating scenarios

29. -1,-6,2,4,5,7,1038,9 -1,-6,2,4,5,7,10OPV00 31,99OPV0 8,911,530OPV G'G'-9,78-0,70-9,56 Cross-Impact Matrix for the Forecasted Scenario Generating scenarios

30. Sensitivity Analysis –Studying the C ij distribution. Solving the forecasted scenario –Determining the limit of the forecasted scenario. Solving the alternative scenarios –Determining the limit of the alternative scenarios.

31. Generating scenarios Sensitivity Analysis –Studying the C ij distribution. Solving the forecasted scenario –Determining the limit of the forecasted scenario. Solving the alternative scenarios –Determining the limit of the alternative scenarios. Interpretation of results –Analyzing the information included in each scenario.

32. Forecasted Scenario Alternative Scenario I Alternative Scenario II Alternative Scenario III Limits (|  |, |0.4975|) (|0.4975|, |0.3804|)(|0.3804|, |0.2318|)(|0.2318|, 0) Interval of reliability 0.52530.10190.14000.2327 c ij sum35.25273.92726.14141.0224 EventPiPi Clusters of Events 10.5AAAA 20.3BBBA 30.6BBBB 40.5BBBB 50.4BABA 60.3ABA 70.6BBB 80.2BAB 90.1BBB 100.6BBBA Generating scenarios

33. Forecasted Scenario Alternative Scenario I Alternative Scenario II Alternative Scenario III Limits (|  |, |0.4975|) (|0.4975|, |0.3804|)(|0.3804|, |0.2318|)(|0.2318|, 0) Interval of reliability 0.52530.10190.14000.2327 c ij sum35.25273.92726.14141.0224 EventPiPi Clusters of Events 10.5AAAA 20.3BBBA 30.6BBBB 40.5BBBB 50.4BABA 60.3ABA 70.6BBB 80.2BAB 90.1BBB 100.6BBBA Generating scenarios

34. Forecasted Scenario Alternative Scenario I Alternative Scenario II Alternative Scenario III Limits (|  |, |0.4975|) (|0.4975|, |0.3804|)(|0.3804|, |0.2318|)(|0.2318|, 0) Interval of reliability 0.52530.10190.14000.2327 c ij sum35.25273.92726.14141.0224 EventPiPi Clusters of Events 10.5AAAA 20.3BBBA 30.6BBBB 40.5BBBB 50.4BABA 60.3ABA 70.6BBB 80.2BAB ? 0.1BBB 100.6BBBA Generating scenarios

35. Forecasted Scenario Alternative Scenario I Alternative Scenario II Alternative Scenario III Limits (|  |, |0.4975|) (|0.4975|, |0.3804|)(|0.3804|, |0.2318|)(|0.2318|, 0) Interval of reliability 0.52530.10190.14000.2327 c ij sum35.25273.92726.14141.0224 EventPiPi Clusters of Events 10.5AAAA 20.3BBBA 30.6BBBB 40.5BBBB 50.4BABA ?0.3ABA ?0.6BBB ?0.2BAB 90.1BBB 100.6BBBA Generating scenarios

36. Conclusions Aims of the model –Handle complex systems. –Obtain a set of plausible snapshots of the future. –Analyze interaction between events. –Detect critical events. Application areas –Technology Foresight. –Strategic Management. –Policy Analysis. –Emergency Response. –Etc…

37. Conclusions Strong points –A strong theoretical background of the techniques on which the authors proposal in based. –The possibility of working with large sets of events. –Tools for analyzing the key drivers of the scenarios. –Specific software is not needed for making the calculations. –A graphic output that gives a clear representation about the forecast. –It is strongly compatible with other techniques such as the Delphi or multicriteria methods.

38. Conclusions Limitations –We cannot kwon the probability of occurrence of a specific scenario if it is not an output of the model. –The estimation of the occurrence or non-occurrence estimation of the scenarios needs the interpretation of the key drivers and sometimes it would be difficult if there is a probability of occurrence close to 0.5.

39. Scenario Construction Via Cross Impact Prof. Victor A. Bañuls Management Department Pablo de Olavide University Seville, Spain Email: vabansil@upo.esvabansil@upo.es Web: http://webdee.upo.es/vabansil Distinguished Prof. Murray Turoff Information Systems Department New Jersey Institute of Technology Newark NJ, USA Email: turoff@njit.eduturoff@njit.edu Web: http://web.njit.edu/~turoff/ Thank you for your attention!