1 S ystems Analysis Laboratory Helsinki University of Technology Decision and Negotiation Support in...

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S ystemsAnalysis LaboratoryHelsinki University of Technology

Decision and Negotiation Support in Multi-Stakeholder Development of Lake

Regulation Policy

Raimo P. Hämäläinen1, Eero Kettunen1,Mika Marttunen2, and Harri Ehtamo1

1 Systems Analysis Laboratory, Helsinki University of Technology2 Finnish Environment Institute

http://www.hut.fi/Units/Systems.Analysis/

Report on the testing phase-

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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

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Information Technology

Problem Structuring - comparison of policy alternatives:HIPRE 3+Web-HIPRE

Dynamic policy alternatives:

ISMO - Interactive analysis of dynamic waterregulation Strategies by Multicriteria Optimization

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Group-consensus:HIPRE Grouplink (Interval AHP model)WINPRE - Workbench for Interval PreferenceProgramming (Interval AHP, SMART/SWING)

Opinion Online - Web-based survey and votingPublic - acceptance:

Pareto-optimal policies:Joint Gains - Generating efficientalternatives (in testing with simplefied goals)

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• 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

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LakePyhäjärvi

Lake Päijänne

Inflow

xp(t)A p(t)

9 1011

2

8

7

6

4

3

12

q(t) = Control

qL1 (t)

LakesRuotsalainen and Konnivesi

1

qp(t)

q2(t)

q21 (t)

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Gulf of Finland

q in(t)

x(t)

A(t)

q23 (t)q22 (t)

q212 (t)q211 (t)

x(t)

A(t)

Inflow

dam

lakewater flowpower plant

qL2 (t)Inflow

LAKEPÄIJÄNNE

LAKES RUOTSALAINEN AND KONNIVESI

RIVERKYMIJOKI

0 10 20 30 40 50km

LAKEPYHÄJÄRVI

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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

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Average Water Levels of Lake Päijänne

77.80

77.90

78.00

78.10

78.20

78.30

78.40

78.50

78.60

1 2 3 4 5 6 7 8 9 10 11 12

Month

NN

+mRegulated Unregulated

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76.50

77.00

77.50

78.00

78.50

79.00

79.50

80.00

1.1 25.5 16.10 9.3 31.7 22.12 15.5 6.10 27.2 20.7 11.12

Goal levelMin levelMax levelwater level

Water level

76.50

77.00

77.50

78.00

78.50

79.00

79.50

80.00

1.1 25.5 16.10 9.3 31.7 22.12 15.5 6.10 27.2 20.7 11.12

Goal levelMin levelMax levelwater level

Water level

0

100

200

300

400

500

600

700

30.12 23.5 14.10 7.3 29.7 20.12 13.5 4.10 25.2 18.7 9.12

Min flow

Max flow

Outflow

Outflow

0

100

200

300

400

500

600

700

30.12 23.5 14.10 7.3 29.7 20.12 13.5 4.10 25.2 18.7 9.12

Min flow

Max flow

Outflow

Outflow

Utopia solution Realistic solution

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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

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Value tree analysis by Web-HIPRE

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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?”

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Pareto-efficiency in group settings

Inefficient alternative:

Alternatives preferred to x

by DM1 Alternatives preferred to x

by DM2

x

Efficient alternative:

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x1

x2

Most preferred alternative on the circle

Approximation at x

x

Approximating DM’s utility function’s gradient direction

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• 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

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x1

x2

DM1

Jointly improvingdirectionx

DM2

DM1

DM2

Iteration step

• DMs select most preferred points in this direction

• New iteration point: nearest

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Generation of efficient frontier from different initial points

Efficient frontier

x2

x1

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Joint Gains -Negotiation Support System

Joint Gains Mediator

Joint GainsDM interface

Subject 5:“power company”


Subject 4:“farmer”


Subject 3:“fisherman”


Subject 2:“summer resident”


Subject 1:“environmentalist”

Local area network

questions

replies

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Interfaces for comparison tasks

Expected flood damage costs

0

1

2

3

A B

Mill

ion

s o

f F

IM

Width of potential vegetation zone

0

10

20

30

A B

cm

. . .

I prefer A to B.

I prefer B to A.

Which alternative do you prefer?


0

1

2

3

Mill

ion

s o

f F

IM


0

10

20

30

cm . . .

Choose the best alternative by scrolling the bar.

Scanning alternatives

Answer a series of pairwisecomparison questions

A

B

A B

etc.

or

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Proposal for jointly preferred alternative

X

Y


0

1

2

3

X Y

Mill

ion

s o

f F

IM


0

10

20

30

X Y

cm

. . .

Do you prefer alternative Y to X?

Yes No

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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

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Mediation processes for 2 DM groups

Fisherman &Environmentalist

Fisherman &Summer resident

Environmentalist &Farmer

initial and intermediate pointsstopping point

Roles:

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Mediation processes for 2 DM groups

Fisherman &Environmentalist

Fisherman &Power company

Power company &Environmentalist


Roles:

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Mediation processes for 2 and 3 DM groups

Fisherman &Farmer

Farmer, Power company& Summer resident

Summer resident &Environmentalist

Roles:


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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

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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

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Individual AHP prioritizations (HIPRE)

Combination of prioritizations (Group Link)

Interval preference model (WINPRE)

RecreationRecreationLandscape

LandscapeBiodiversityBiodiversity

DM1

DM1

DM1

DM2

DM2

DM2

DM3

DM3

DM3

View from interval preference model for three DMs:

HIPRE Group Link

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WINPRE - Workbench for Interactive Preference Programming (AHP mode)

Group priorities embedded in the interval statements

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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

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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

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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.

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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.

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