1. Aiding Decisions and Collecting Opinions on the Web
D E C I S I O N A R I U M
a g l o b a l s p a c e f o r d e c i s i o n s u p p o r t
Raimo P. Hämäläinen
Systems Analysis Laboratory
Helsinki University of Technology

2. D E C I S I O N A R I U M
g l o b a l s p a c e f o r d e c i s i o n s u p p o r t
mobile group support facility
group
decision making
group
collaboration
multicriteria
decision analysis
WINPRE
internet
decision
making
GDSS, NSS
notebooks in a wireless LAN
CSCW
PAIRS, interval AHP/SMART
DSS
Joint Gains
multi-party negotiation support with the method of improving directions
internet
tools for decision analysis with imprecise ratio statements
computer support
PRIME Decisions
Opinions-Online
Web-HIPRE
value tree and AHP based decision support
platform for global participation, voting, surveys, and group decisions
web-sites
PRIME Decisions and WINPRE: downloadable at
Smart Swaps
elimination of criteria/alternatives by even swaps
selected publications
J. Mustajoki and R.P. Hämäläinen: Web-HIPRE - Global decision support by value tree and AHP analysis, manuscipt, 2000.
A. Salo and R.P. Hämäläinen: Preference assessment by imprecise ratio statements, Operations Research, 1992.
A. Salo and R.P. Hämäläinen: Preference programming through approximate ratio comparisons, EJOR, 1995.
R.P. Hämäläinen and M. Pöyhönen: On-line group decision support by preference programming in traffic planning, Group Decision and Negotiation, 1996.
A. Salo and R.P. Hämäläinen: PRIME - Preference ratios in multiattribute evaluation, manuscipt , 1999.
H. Ehtamo, E. Kettunen and R.P. Hämäläinen: Searching for Joint Gains in Multi-Party Negotiations, EJOR, 2000.
Updated
Systems
Analysis Laboratory

3. Global Decision Support
Opinions-Online (
Platform for global participation, voting, surveys, and group decisions
Web-HIPRE (
Value tree based decision analysis and support
Smart-Swaps Software (
Multicriteria decision support with the Even Swaps method
Joint Gains (
Multi-party negotiation support with the method of improving directions

4. eLearning Decision Making
SAL eLearning sites:
Multiple Criteria Decision Analysis
Decision Making Under Uncertainty
Negotiation Analysis

5. SAL eLearning sites Material:
Theory sections, interactive computer assignments
Animations and video clips, online quizzes, theory assignments
Decisionarium software:
Web-HIPRE, PRIME Decisions, Opinions-Online.vote,
and Joint Gains, video clips help
eLearning modules:
4 - 6 hours study time
Instructors can create their own modules using the material
and software
Academic non-profit use is free

6. Opinions-Online
Platform for Global Participation, Voting, Surveys and Group Decisions
Design: Raimo P. Hämäläinen
Programming: Reijo Kalenius

7. Surveys on the web Fast, easy and cheap
Hyperlinks to background information
Easy access to results
Results can be analyzed on-line

8. Creating a new session Browser-based generation of new sessions
Fast and simple
Templates available

9. Possible questions Survey section Best/worst Ranking Rating
Approval voting
Written comments

10. Survey section Multiple single choice questions
Unrestricted number of alternatives
Also used to select user groups in results

11. Ranking
Participant ranks all or some of the best alternatives

12. Multiattribute rating
Numerical values for different attributes

13. Approval voting
The user is asked to pick the alternatives that he/she can approve
Often better than a simple “choose best” question when trying to reach a consensus

14. Sessions with access restrictions
By: Registration, domain, list
The participants have a username and password
The system sends passwords by if an -address list is specified
Users previous submission is replaced by the newest one -> Opinion barometer

15. Viewing the results In real-time In selected user groups
Public or restricted access

16. Advanced voting rules www.opinion.vote.hut.fi
Condorcet criteria
Copeland’s methods, Dodgson’s method, Maximin method
Borda count
Nanson’s method, University method
Black’s method
Plurality voting
Coombs’ method, Hare system, Bishop method

17. Advanced voting rules
For example
Borda Count

18. Examples of use Course evaluation (universities,schools,seminars)
Customer satisfaction survey
Gathering marketing opinions
Teledemocracy - citizens’ participation
Organisation’s internal surveys
Website feedback forms
Etc, etc.

19. Global Multicriteria Decision Support by Web-HIPRE
A Java-applet for Value Tree and AHP Analysis
Raimo P. Hämäläinen
Jyri Mustajoki
Systems Analysis Laboratory
Helsinki University of Technology

20. Web-HIPRE links can refer to any web-pages

21. Direct Weighting
Note: Weights in this example are her personal opinions

22. SWING,SMART and SMARTER Methods
SMARTER uses rankings only

23. Pairwise Comparison - AHP
Continuous scale 1-9
Numerical, verbal or graphical approach

24. Value Function Ratings of alternatives shown
Any shape of the value function allowed

25. Composite Priorities Bar graphs or numerical values
Bars divided by the contribution of each criterion

26. Group Decision Support
Group model is the weighted sum of individual decision makers’ composite priorities for the alternatives

27. Defining Group Members
Individual value trees can be different
Composite priorities of each group member
- obtained from their individual models
- shown in the definition phase

28. Aggregate Group Priorities
Contribution of each group member indicated by segments

29. Sensitivity analysis
Changes in the relative importance of decision makers can be analyzed

30. Now that we have the global Web-HIPRE
Is there finally hope in real life attribute weighting ?
NO: If
procedural and behavioural aspects are ignored
YES: If
risks of biases are acknowledged and avoided
by instruction

31. The sources of biases and problems

32. Future challenges Web makes MCDA tools available to everybody -
Should everybody use them ?
It is the resbonsibility of the multicriteria decision
analysis community to:
learn to use different weighting methods
focus on the praxis and avoidance of behavioural biases
develop and identify “best practice” procedures

33. Visits to Web-HIPRE

34. Visitors’ top-level domains

35. Visitors’ first-level domains

36. Visits through sites linking to Web-HIPRE

37. Literature
M. Pöyhönen and R.P. Hämäläinen: There is hope in attribute weighting,
INFOR, Vol. 38, no. 3, Aug. 2000, pp
M. Pöyhönen and R.P. Hämäläinen: On the Convergence of
Multiattribute Weighting Methods, European Journal of Operational
Research, Vol. 129, No. 3, March 2001, pp
M. Pöyhönen, H.C.J. Vrolijk and R.P. Hämäläinen: Behavioral and
Procedural Consequences of Structural Variation in Value Trees,
European Journal of Operational Research, Vol. 134/1, 2001, pp
J. Mustajoki and R.P.Hämäläinen: Web-HIPRE: Global decision support by
value tree and AHP analysis, INFOR, Vol. 38, no. 3, Aug. 2000, pp
J. Mustajoki, R.P. Hämäläinen and M. Marttunen: Participatory multicriteria
decision support with Web-HIPRE: A case of lake regulation policy.
Environmental Modelling & Software, Vol. 19, No. 6, 2004, pp

38. Smart-Swaps Software Smart Choices with the Even Swaps Method
Design: Raimo P. Hämäläinen, Jyri Mustajoki
Programming: Pauli Alanaatu
Systems Analysis Laboratory
Helsinki University of Technology 1

39. Smart Choices
An iterative process to support multicriteria decision making
Uses the even swaps method to make trade-offs
two alternatives are made equal in some attribute and the change is compensated in some other attribute
(Harvard Business School Press, Boston, MA, 1999)

40. Even Swaps Carry out even swaps that make
Alternatives dominated (attribute-wise)
There is another alternative, which is equal or better than this in every attribute, and better at least in one attribute
Attributes irrelevant
Each alternative has the same value on this attribute
 These can be eliminated
Process continues until one alternative, i.e. the best one, remains

41. Supporting Even Swaps with Preference Programming
Even Swaps process carried out as usual
The DM’s preferences simultaneously modeled with Preference Programming
Intervals allow us to deal with incomplete information about the DM’s preferences
Trade-off information given in the even swaps can be used to update the model
 Suggestions for the Even Swaps process

42. Smart-Swaps software Preference Programming approach
Identification of practical dominances
Suggestions for the next even swap to be made
Additional support
Information about what can be achieved with each swap
Notification of dominances
Ranks indicated by colours
Process history allows backtracking

43. Problem definition

44. Entering trade-offs

45. Process history

46. Joint Gains Negotiation Support in the Internet
Eero Kettunen, Raimo P. Hämäläinen, and Harri Ehtamo
Systems Analysis Laboratory
Helsinki University of Technology 1

47. Joint Gains Negotiation Support in the Internet
user can create his own case
2 to N participants (negotiating parties, DM’s)
2 to M continuous decision variables
linear inequality constraints
participants distributed in the web

48. Method of Improving Directions Ehtamo, Kettunen, and Hämäläinen (2002)
Efficient frontier .
Utility of DM 1
Utility of DM 2
Interactive method for reaching efficient alternatives
Search of joint gains from a given initial alternative
In the mediation process participants are given simple comparison tasks:
“Which one of these two alternatives do you prefer, alternative A or B?” 6

49. Mediation Process Tasks in Preference Identification
Initial alternative considered as “current alternative”
Task 1 for identifying participants’ most preferred directions
Joint Gains calculates a jointly improving direction
Task 2 for identifying participants’ most preferred alternatives in the jointly improving direction
series of pairwise comparisons
series of pairwise comparisons

50. Mediation Process Reaching Joint Gain - Acceptance of the New Alternative
Joint Gains finds potential candidate for a jointly improving alternative (compared to the current one)
Joint Gains asks the participants if they prefer the candidate to the current one
If a jointly improving alternative is found, it becomes the next “current alternative” and the process is repeated ?
one pairwise comparison

51. DM’s Utility Functions?
DM’s reply holistically
No explicit assessment of utility functions
Joint Gains only calls for local preference information
Post-settlement setting in the neighborhood of the current alternative
Joint Gains allows learning and change of preferences during the process

52. Case example: Business
Two participants
buyer and seller
Three decision variables
unit price ($):
amount (lb):
delivery (days): 1..30
Delivery constraint (figure):
999*delivery - 29*amount ³ 970
Initial agreement: 30 $, 100 lb, 25 days 30
delivery (days) 1 1
1000
amount (lb)

53. Creating a case: Criteria to provide optional decision aiding

54. Creating a case: Constraints

55. Sessions Participants take part in sessions within the case
Sessions produce efficient alternatives
Case administrator can start new sessions on-line and define new initial starting points
Sessions can be parallel
Each session has an independent mediation process
Joint Gains - Business
Session 1
® efficient point
Session 2
® efficient point
Session 3
® efficient point .
Session n
® efficient point

56. Participant can access the case anywhere in the world with a Java enabled browser
I am case admistrator

57. New comparison task is given after all participants have completed the first one
Preference identification task 2
Not started
Preference identification task 1
JOINT GAIN?
Stopped

58. Pairwise Comparison in a Comparison Task

59. Session view - joint gains after two steps

60. Literature
Kettunen, E., R. P. Hämäläinen, and H. Ehtamo. (1999). “Joint Gains - Negotiation Support in the Internet,” Computer Software. Systems Analysis Laboratory, HUT, Finland.
Ehtamo, H., M. Verkama, and R.P. Hämäläinen. (1999). “How to select Fair Improving Directions in a negotiation Model over Continuous Issues,” IEEE Trans. On Syst., Man, and Cybern. - C. 29(1), 26-33
Ehtamo, H., E. Kettunen, and R. P. Hämäläinen. (2000) “Searching for Joint Gains in Multi-Party Negotiations”. European Journal of Operational Research, Vol. 130, No. 1, February 2001, pp
Hämäläinen, H., E. Kettunen, M. Marttunen, and H. Ehtamo. (2001) Evaluating a Framework for Multi-Stakeholder Decision Support in Water Resources Management, Group Decision and Negotiation, Vol. 10, No. 4, pp , 2001.

61. Academic Use is Free ! Opinions-Online (www.opinions.hut.fi)
Commercial site and pricing:
Web-HIPRE (
Joint Gains (
Smart Swaps (
WINPRE and PRIME for interval models available at:
Please, let us know your experiences.
Is there interest in testing global group processes ?

62. Public participatory projects
PÄIJÄNNE - Lake Regulation (
PRIMEREG / Kallavesi - Lake Regulation (
STUK / Milk Conference - Radiation Emergency (

63. SAL eLearning sites www.dm.hut.fi
Decision making resources at Systems Analysis Laboratory
eLearning in Multiple Criteria Decision Analysis
eLearning in Negotiation Analysis
Decision support tools and resources at Systems Analysis Laboratory
OR-World project site