1. 1 S ystems Analysis Laboratory Helsinki University of Technology Decision and Negotiation Support in Multi-Stakeholder Development of Lake Regulation Policy Raimo P. Hämäläinen 1, Eero Kettunen 1, Mika Marttunen 2, and Harri Ehtamo 1 1 Systems Analysis Laboratory, Helsinki University of Technology 2 Finnish Environment Institute http://www.hut.fi/Units/Systems.Analysis/ Report on the testing phase -

2. 2 S ystems Analysis Laboratory Helsinki University of Technology The Framework 1. Structuring the problem 2. Identifying Pareto-optimal alternatives 3. Seeking group consensus 4. Seeking public acceptance Objective to provide support for the whole decision process

3. 3 S ystems Analysis Laboratory Helsinki University of Technology Information Technology Problem Structuring - comparison of policy alternatives: HIPRE 3+ Web-HIPRE Dynamic policy alternatives: ISMO - Interactive analysis of dynamic water regulation Strategies by Multicriteria Optimization

4. 4 S ystems Analysis Laboratory Helsinki University of Technology Group-consensus: HIPRE Grouplink (Interval AHP model) WINPRE - Workbench for Interval Preference Programming (Interval AHP, SMART/SWING) Opinion Online - Web-based survey and voting Public - acceptance: Pareto-optimal policies: Joint Gains - Generating efficient alternatives (in testing with simplefied goals)

5. 5 S ystems Analysis Laboratory Helsinki University of Technology Illustrative reference case Regulation policy defined by annual water level goals Stakeholders with conflicting objectives –Hydro power producers, fishermen, farmers,... First phase of true testing –Role playing experiments Development of Water Level Management Policy in Lake Päijänne

6. 6 S ystems Analysis Laboratory Helsinki University of Technology LAKE PÄIJÄNNE LAKES RUOTSALAINEN AND KONNIVESI RIVER KYMIJOKI 0 10 20 30 40 50 km LAKE PYHÄJÄRVI

7. 7 S ystems Analysis Laboratory Helsinki University of Technology Need for modeling and decision support Dynamic system No intuitive solutions - impacts are functions of decision variables Interactive analysis of impacts Multiple criteria Many stakeholder groups

8. 8 S ystems Analysis Laboratory Helsinki University of Technology

9. 9 S ystems Analysis Laboratory Helsinki University of Technology Water level Outflow Utopia solutionRealistic solution

10. 10 S ystems Analysis Laboratory Helsinki University of Technology Structuring the Problem Iterative value tree analysis –Hierarchical structuring and prioritization –Decision criteria –Learning the ranges by initial prioritizations with temporary alternatives –Stakeholder grouping Decision variables defining regulation policy – Target water levels at April 1st and September 1st

11. 11 S ystems Analysis Laboratory Helsinki University of Technology Value tree analysis by Web-HIPRE

12. 12 S ystems Analysis Laboratory Helsinki University of Technology Method of Improving Directions Ehtamo, Kettunen and Hämäläinen (1998) Interactive method for identification of efficient alternatives - Joint Gains software Subjects are onlygiven simple comparison tasks: “Which one of these alternatives do you prefer most?” or “Which one of these two alternatives do you prefer, A or B?”

13. 13 S ystems Analysis Laboratory Helsinki University of Technology Pareto-efficiency in group settings Inefficient alternative: Alternatives preferred to x by DM1Alternatives preferred to x by DM2 x Efficient alternative:

14. 14 S ystems Analysis Laboratory Helsinki University of Technology x1x1 x2x2 Most preferred alternative on the circle Approximation at x x Approximating DM’s utility function’s gradient direction

15. 15 S ystems Analysis Laboratory Helsinki University of Technology Required preference information: DMs’ utility functions’ gradient directions Solution of a nonlinear direction finding optimization problem –Special case with two DMs: bisecting direction Calculation of jointly improving direction

16. 16 S ystems Analysis Laboratory Helsinki University of Technology x1x1 x2x2 DM1 Jointly improving direction x DM2 DM1 DM2 Iteration step DMs select most preferred points in this direction New iteration point: nearest

17. 17 S ystems Analysis Laboratory Helsinki University of Technology Generation of efficient frontier from different initial points Efficient frontier x2x2 x1x1

18. 18 S ystems Analysis Laboratory Helsinki University of Technology Joint Gains - Negotiation Support System Joint Gains Mediator Joint Gains DM interface Subject 5: “power company” Joint Gains DM interface Subject 4: “farmer” Joint Gains DM interface Subject 3: “fisherman” Joint Gains DM interface Subject 2: “summer resident” Joint Gains DM interface Subject 1: “environmentalist” Local area network questions replies

19. 19 S ystems Analysis Laboratory Helsinki University of Technology Interfaces for comparison tasks Scanning alternatives Answer a series of pairwise comparison questions A B A B etc. or

20. 20 S ystems Analysis Laboratory Helsinki University of Technology Proposal for jointly preferred alternative X Y

21. 21 S ystems Analysis Laboratory Helsinki University of Technology Role Playing Experiments Roles (fisherman, environmentalist, summer resident, farmer, power company) and objectives (e.g., high and diverse catch, natural reproduction) given 2 or 3 subjects in 9 test groups Questions of interest: Subjects’ opinion about the tasks Consistency of statements Convergence speed

22. 22 S ystems Analysis Laboratory Helsinki University of Technology Mediation processes for 2 DM groups Fisherman & Environmentalist Fisherman & Summer resident Environmentalist & Farmer initial and intermediate points stopping point Roles:

23. 23 S ystems Analysis Laboratory Helsinki University of Technology Mediation processes for 2 DM groups Fisherman & Environmentalist Fisherman & Power company Power company & Environmentalist initial and intermediate points stopping point Roles:

24. 24 S ystems Analysis Laboratory Helsinki University of Technology Mediation processes for 2 and 3 DM groups Fisherman & Farmer Farmer, Power company & Summer resident Summer resident & Environmentalist Roles: initial and intermediate points stopping point

25. 25 S ystems Analysis Laboratory Helsinki University of Technology Role playing experiments - observations Subjects found the stated questions easy to reply with both elicitation methods Statements and results were consistent with the given role objectives Experiment suggests a high speed of convergence Low degree of conflict (similar objectives)  same nearby points reached from different initial points

26. 26 S ystems Analysis Laboratory Helsinki University of Technology Seeking Group Consensus Select and evaluate a representative set of efficient alternatives by interval value tree analysis Objective to reach consensus Tools for consensus seeking –HIPRE 3+ Group Link –WINPRE - Workbench for Interactive Preference Programming

27. 27 S ystems Analysis Laboratory Helsinki University of Technology Individual AHP prioritizations (HIPRE) Combination of prioritizations (Group Link) Interval preference model (WINPRE) Recreation Landscape Biodiversity DM1 DM2 DM3 View from interval preference model for three DMs: HIPRE Group Link

28. 28 S ystems Analysis Laboratory Helsinki University of Technology WINPRE - Workbench for Interactive Preference Programming (AHP mode) Group priorities embedded in the interval statements

29. 29 S ystems Analysis Laboratory Helsinki University of Technology Conclusion Framework for supporting complex decision processes –An evolutionary learning process Shown to be feasible by role playing experiments –Real application –Testing of methods and tools –Biases related to elicitation procedure tested Important testing phase often neglected –Allows improvements before final process

30. 30 S ystems Analysis Laboratory Helsinki University of Technology WWW-sites Systems Analysis Laboratory Activity Report: http://www.hut.fi/Units/SAL/Research/. WINPRE - Workbench for Interactive Preference Programming v. 1.0, Computer software, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Downloadables/. Web-HIPRE - Java-applet for Value Tree and AHP Analysis, Computer software, Systems Analysis Laboratory, Helsinki University of Technology (http://www.hipre.hut.fi). The Päijänne regulation policy project: (http://leino.hut.fi/päijänne.htm) References Ehtamo, H., R. P. Hämäläinen, P. Heiskanen, J. Teich, M. Verkama, and S. Zionts (1998), “Generating Pareto Solutions in Two-Party Negotiations by Adjusting Artificial Constraints,” Manuscript, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/. Ehtamo, H., E. Kettunen, and R. P. Hämäläinen (1998), “Searching for Joint Gains in Multi-Party Negotiations,” Manuscript, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/. R.P. Hämäläinen, E. Kettunen, M. Marttunen and H. Ehtamo: An approach to decision and negotiation support in multi-stakeholder development of lake regulation policy. Manuscript, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/. References

31. 31 S ystems Analysis Laboratory Helsinki University of Technology Ehtamo, H., M. Verkama, and R. P. Hämäläinen (1992), “On Contracting under Incomplete Information Using Linear Proposals,” Preprints of the Fifth International Symposium on Dynamic Games and Applications, Grimentz, Switzerland, 128-133. Ehtamo, H., M. Verkama, and R. P. Hämäläinen (1998), “How to Select Fair Improving Directions in a Negotiation Model over Continuous Issues,” IEEE Transactions on Systems, Man, and Cybernetics, to appear. A shortened version in Proceedings of the Decision Science Institute 1995 Annual Meeting, November 20-22, 1995, Boston, Massachusetts, 2, 549-551. Hämäläinen, R. P. (1988), “Computer Assisted Energy Policy Analysis in the Parliament of Finland,” Interfaces, 18(4), 12-23. Hämäläinen, R. P., A. A. Salo, and K. Pöysti (1991), “Observations about Consensus Seeking in a Multiple Criteria Environment,” Proceedings of the 25th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, 4, 190-198. Hämäläinen, R. P. and E. Kettunen (1994), “On-Line Group Decision Support by HIPRE 3+ Group Link,” Proceedings of the Third International Conference on the Analytic Hierarchy Process, July 11-13, 1994, George Washington University, Washington D.C., 547-557. Hämäläinen, R. P. and H. Lauri (1998), HIPRE 3+ Decision Support Software v. 3.15b, Computer software, Systems Analysis Laboratory, Helsinki University of Technology. Hämäläinen, R. P., and O. Leikola (1995), “Spontaneous Decision Conferencing in Parliamentary Negotiations,” Proceedings of the 28th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, 4, 290-299.

32. 32 S ystems Analysis Laboratory Helsinki University of Technology Hämäläinen, R. P., K. Sinkko, M. Lindstedt, M. Ammann, and A. Salo (1998), RODOS and Decision Conferencing on Early Stage Protective Actions in Finland, RODOS Report (WG7) EU Research Project on Decision Support for Nuclear Emergencies. Hämäläinen, R. P. and J. Mäntysaari (1998), “Interactive Spreadsheet Modelling of Regulation Strategies for a Lake-River System,” Proceedings of the 17th IASTED International Conference on Modelling, Identification and Control, February 18-20, 1998, IASTED - Acta Press, Anaheim, Grindelwald, Switzerland, 181-184. Hämäläinen, R. P. and M. Pöyhönen (1996), “On-Line Group Decision Support by Preference Programming in Traffic Planning,” Group Decision and Negotiation, 5, 485-500. Marttunen, M. and R. P. Hämäläinen (1995), “Decision Analysis Interviews in Environmental Impact Assessment,” European Journal of Operational Research, 87, 551-563. Pöyhönen, M., H. C. Vrolijk, and R. P. Hämäläinen (1997), Behavioral and Procedural Consequences of Structural Variation in Value Trees, Research Report A69, Systems Analysis Laboratory, Helsinki University of Technology. Downloadable at http://www.hut.fi/Units/SAL/Publications/. Salo, A. A. and R. P. Hämäläinen (1992), “Preference Assessment by Imprecise Ratio Statements,” Operations Research, 40, 1053-1061. Salo, A. A. and R. P. Hämäläinen (1995), “Preference Programming through Approximative Ratio Comparisons,” European Journal of Operational Research, 82, 458-475. Salo A. (1995), “Interactive decision aiding for group decision support,” European Journal of Operational Research, 84, 134-149.