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S ystemsAnalysis LaboratoryHelsinki University of Technology
Raimo P. HämäläinenSystems Analysis Laboratory
Helsinki University of Technologywww.raimo.hut.fi
JMCDA, Vol. 12 , No. 2-3, 2003, pp. 101-110.
Aiding Decisions, Negotiating and Collecting Opinions on the Web
www.decisionarium.hut.fi
D E C I S I O N A R I U M
v. 3.2006
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selected publications J. Mustajoki, R.P. Hämäläinen and A. Salo: Decision support by interval SMART/SWING – Incorporating
imprecision in the SMART and SWING methods, Decision Sciences, 2005.H. Ehtamo, R.P. Hämäläinen and V. Koskinen: An e-learning module on negotiation analysis, Proc. of HICSS-37, 2004.
J. Mustajoki and R.P. Hämäläinen, Making the even swaps method even easier, Manuscript, 2004. R.P. Hämäläinen, Decisionarium - Aiding decisions, negotiating and collecting opinions on the Web, J. Multi-Crit. Dec. Anal., 2003.
H. Ehtamo, E. Kettunen and R.P. Hämäläinen: Searching for joint gains in multi-party negotiations, Eur. J. Oper. Res., 2001. J. Gustafsson, A. Salo and T. Gustafsson: PRIME Decisions - An interactive tool for value tree
analysis, Lecture Notes in Economics and Mathematical Systems, 2001.J. Mustajoki and R.P. Hämäläinen: Web-HIPRE - Global decision support by value tree and AHP analysis, INFOR, 2000.
D E C I S I O N A R I U M
PRIME DecisionsWINPRE
web-sites www.decisionarium.hut.fi www.dm.hut.fi
www.hipre.hut.fi www.jointgains.hut.fi www.opinions.hut.fi www.smart-swaps.hut.fi www.rich.hut.fiPRIME Decisions and WINPRE downloadable at www.sal.hut.fi/Downloadables
Web-HIPREvalue tree and AHP based decision support
Smart-Swaps
Opinions-Online platform for global participation, voting, surveys, and group decisions
Joint Gains
groupcollaboration decision
making
computer support
CSCW
multicriteriadecision analysis
internet
groupdecision making
GDSS, NSS
DSS
multi-party negotiation support with the method of improving directions
Windows software for decision analysis with imprecise ratio statements
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
elimination of criteria and alternatives by even swaps
preference programming, PAIRS
Updated 25.10.2004
SystemsAnalysis Laboratory
RICH Decisionsrank inclusion in criteria
hierarchies
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Mission of Decisionarium
Provide resources for decision and negotiation support and advance the real and correct use of MCDA
History: HIPRE 3+ in 1992 MAVT/AHP for DOS systems
Today: e-learning modules provide help to learn the methods and global access to the software also for non OR/MS people
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Opinions-Online (www.opinions.hut.fi)• Platform for global participation, voting, surveys, and group
decisions
Web-HIPRE (www.hipre.hut.fi)• Value tree based decision analysis and support
WINPRE and PRIME Decisions (for Windows)
• Interval AHP, interval SMART/SWING and PRIME methods
RICH Decisions (www.rich.hut.fi)• Preference programming in MAVT
Smart-Swaps (www.smart-swaps.hut.fi)• Multicriteria decision support with the even swaps method
Joint Gains (www.jointgains.hut.fi)• Negotiation support with the method of improving directions
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• Possibility to compare different weighting and rating methods
• AHP/MAVT and different scales
• Preference programming in MAVT and in the Even Swaps procedure
• Jointly improving direction method for negotiations
New Methodological Features
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SAL eLearning sites:
Multiple Criteria Decision Analysis www.mcda.hut.fiDecision Making Under UncertaintyNegotiation Analysis www.negotiation.hut.fi
eLearning Decision Makingwww.dm.hut.fi
S ystemsAnalysis LaboratoryHelsinki University of Technology
Opinions-Online
Platform for Global Participation, Voting, Surveys and Group Decisions
Design: Raimo P. Hämäläinen
Programming: Reijo Kalenius
www.opinions.hut.fiwww.opinions-online.com
Systems Analysis LaboratoryHelsinki University of Technology
http://www.sal.hut.fi
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Surveys on the web
• Fast, easy and cheap
• Hyperlinks to background information
• Easy access to results
• Results can be analyzed on-line
• Access control: registration, e-mail list, domain, password
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Creating a new session
• Browser-based generation of new sessions
• Fast and simple
• Templates available
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Possible questions
• Survey section Multiple/single choice• Best/worst• Ranking• Rating• Approval voting• Written comments
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Viewing the results
• In real-time• By selected fields• Questionwise public
or restricted access• Barometer• Direct links
to results
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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
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Advanced voting ruleswww.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
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Examples of use
• Teledemocracy – interactive citizens’ participation
• Group decision making
• Brainstorming
• Course evaluation in universities and schools
• Marketing research
• Organisational surveys and barometers
S ystemsAnalysis LaboratoryHelsinki University of Technology
Global Multicriteria Decision Support by Web-HIPRE
A Java-applet for Value Tree and AHP Analysis
Raimo P. Hämäläinen
Jyri Mustajoki
www.hipre.hut.fi
Systems Analysis LaboratoryHelsinki University of Technology
http://www.sal.hut.fi
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Web-HIPRE links can refer to any web-pages
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Note: Weights in this example are her personal opinions
Direct Weighting
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• SMARTER uses rankings only
SWING,SMART and SMARTER Methods
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• Continuous scale 1-9
• Numerical, verbal or graphical approach
Pairwise Comparison - AHP
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• Ratings of alternatives shown
• Any shape of the value function allowed
Value Function
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• Bar graphs or numerical values
• Bars divided by the contribution of each criterion
Composite Priorities
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• Group model is the weighted sum of individual decision makers’ composite priorities for the alternatives
Group Decision Support
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• Individual value trees can be different
• Composite priorities of each group member
- obtained from their individual models
- shown in the definition phase
Defining Group Members
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• Contribution of each group member indicated by segments
Aggregate Group Priorities
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• Changes in the relative importance of decision makers can be analyzed
Sensitivity analysis
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Future challenges
Web makes MCDA tools available to everybody - Should everybody use them?
It is the responsibility of the multicriteria decision analysis community to:
• Learn and teach the use different weighting methods
• Focus on the praxis and avoidance of behavioural biases
• Develop and identify “best practice” procedures
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Number-of-attribute-levels effect in
conjoint analysis
Splitting bias withweighting methodsbased on ranking
Rank reversal inAHP
Averages over agroup yield even
weights
Normalization
Decisionmakers onlygive ordinalinformation
Division ofattributes changes
weightsRange effect
Hierarchicalweighting leads to
steeper weights
Weighting methodsyield different weights
Sources of biases and problems
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Visits to Web-HIPRE
1617
53036826
9233
1315114216
16526
1998 1999 2000 2001 2002 2003 2004
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Visitors’ top-level domains
Domain Visits Domain Visits Domain Visitsfi 14174 (36.8 %) ch 385 (1.0 %) sg 124 (0.3 %)
com 4758 (12.3 %) hu 348 (0.9 %) be 119 (0.3 %)net 4677 (12.1 %) jp 321 (0.8 %) is 112 (0.3 %)edu 2729 (7.1 %) br 284 (0.7 %) ru 97 (0.3 %)si 1606 (4.2 %) tr 251 (0.7 %) gov 92 (0.2 %)uk 981 (2.5 %) it 198 (0.5 %) mx 87 (0.2 %)ca 894 (2.3 %) pl 187 (0.5 %) il 82 (0.2 %)ee 849 (2.2 %) gr 171 (0.4 %) org 77 (0.2 %)es 834 (2.2 %) fr 165 (0.4 %) no 72 (0.2 %)nl 774 (2.0 %) pt 147 (0.4 %) za 65 (0.2 %)de 744 (1.9 %) mil 144 (0.4 %) ar 63 (0.2 %)au 571 (1.5 %) se 141 (0.4 %)at 449 (1.2 %) tw 132 (0.3 %)
Total 38557 visits (+ 28315 visits with only IP)
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Visitors’ first-level domainsDomain Domain Domainhkkk.fi 4593 (11.9 %) aol.com 343 (0.9 %) bke.hu 181 (0.5 %)hut.fi 4453 (11.5 %) ac.at 324 (0.8 %) comcast.net 181 (0.5 %)uni-mb.si 1220 (3.2 %) edu.au 323 (0.8 %) carleton.ca 172 (0.4 %)duke.edu 1069 (2.8 %) siol.net 290 (0.8 %) te-keskus.fi 172 (0.4 %)ac.uk 776 (2.0 %) arnes.si 287 (0.7 %) uwaterloo.ca 160 (0.4 %)htv.fi 763 (2.0 %) omakaista.fi 262 (0.7 %) net.br 157 (0.4 %)inktomi.com 689 (1.8 %) abo.fi 215 (0.6 %) co.uk 156 (0.4 %)inet.fi 629 (1.6 %) estpak.ee 215 (0.6 %) edu.tr 151 (0.4 %)googlebot.com 608 (1.6 %) asu.edu 206 (0.5 %) rr.com 151 (0.4 %)helsinki.fi 497 (1.3 %) knology.net 205 (0.5 %) sympatico.ca 149 (0.4 %)ja.net 464 (1.2 %) uab.es 205 (0.5 %) ne.jp 142 (0.4 %)kolumbus.fi 417 (1.1 %) verizon.net 203 (0.5 %)t-dialin.net 376 (1.0 %) cesca.es 193 (0.5 %) (+ 28315 visits with only IP)
Total 38557 visits
Visits Visits Visits
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Visits through sites linking to Web-HIPRESite Sitehut.fi 7375 (40.4 %) google.fi 190 (1.0 %)
duke.edu 1480 (8.1 %) man.ac.uk 174 (1.0 %)
google.com 1349 (7.4 %) unige.ch 165 (0.9 %)
dicksmart.net 589 (3.2 %) msn.com 162 (0.9 %)
cmu.edu 568 (3.1 %) colorado.edu 144 (0.8 %)
carleton.ca 527 (2.9 %) helsinki.fi 143 (0.8 %)
uni-mb.si 403 (2.2 %) clarolineserver.com 122 (0.7 %)
ttu.ee 383 (2.1 %) urjc.es 118 (0.6 %)
clarku.edu 324 (1.8 %) univie.ac.at 115 (0.6 %)
yahoo.com 272 (1.5 %) gov.uk 100 (0.5 %)
altavista.com 229 (1.3 %) ecohotelsbolivia.com 97 (0.5 %)
100gen.fi 222 (1.2 %) nus.edu.sg 96 (0.5 %)
informs.org 218 (1.2 %)
Visits Visits
Total 18261 visits
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Literature
Mustajoki, J. and Hämäläinen, R.P.: Web-HIPRE: Global decision support by value tree and AHP analysis, INFOR, Vol. 38, No. 3, 2000, pp. 208-220.
Hämäläinen, R.P.: Reversing the perspective on the applications of decision analysis, Decision Analysis, Vol. 1, No. 1, pp. 26-31.
Mustajoki, J., Hämäläinen, R.P. and Marttunen, M.: Participatory multicriteria decision support with Web-HIPRE: A case of lake regulation policy. Environmental Modelling & Software, Vol. 19, No. 6, 2004, pp. 537-547.
Pöyhönen, M. and Hämäläinen, R.P.: There is hope in attribute weighting, INFOR, Vol. 38, No. 3, 2000, pp. 272-282.
Pöyhönen, M. and Hämäläinen, R.P.: On the Convergence of Multiattribute Weighting Methods, European Journal of Operational Research, Vol. 129, No. 3, 2001, pp. 569-585.
Pöyhönen, M., Vrolijk, H.C.J. and Hämäläinen, R.P.: Behavioral and Procedural Consequences of Structural Variation in Value Trees, European Journal of Operational Research, Vol. 134, No. 1, 2001, pp. 218-227.
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New Theory: Preference programming
Analysis with incomplete preference statements (intervals):
”...attribute is at least 2 times as but no more than 3 times as important as...”
Windows software• WINPRE – Workbench for Interactive Preference Programming Interval AHP, interval SMART/SWING and PAIRS• PRIME-Preference Ratios in Multiattribute Evaluation Method Incomplete preference statements Web software• RICH Decisions – Rank Inclusion in Criteria Hierarchies
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Preference Programming – The PAIRS method
• Imprecise statements with intervals on– Attribute weight ratios (e.g. 1/2 w1 / w2 3)
Feasible region for the weights
– Alternatives’ ratings (e.g. 0.6 v1(x1) 0.8)
Intervals for the overall values– Lower bound for the overall value of x:
– Upper bound correspondingly
n
iiii xvwxv
1
)(min)(
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2
1
2
61
31
21
C
B
C
A
B
A
w
w
w
ww
w
Interval statements define a feasible region S for the weights
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Uses of interval models
New generalized AHP and SMART/SWING methods
DM can also reply with intervals instead of exact point estimates – a new way to accommodate uncertainty
Interval sensitivity analysis
Variations allowed in several model parameters simultaneously - worst case analysis
Group decision making
All members´ opinions embedded in intervals = a joint common group model
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Interval SMART/SWING• A as reference - A given 10 points
• Point intervals given to the other attributes:– 5-20 points to attribute B– 10-30 points to attribute C
• Weight ratio between B and C not explicitly given by the DM
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WINPRE Software
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PRIME Decisions Software
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Literature – Methodology
Salo, A. and Hämäläinen, R.P.: Preference assessment by imprecise ratio statements, Operations Research, Vol. 40, No. 6, 1992, pp. 1053-1061.
Salo, A. and Hämäläinen, R.P.: Preference programming through approximate ratio comparisons, European Journal of Operational Research, Vol. 82, No. 3, 1995, pp. 458-475.
Salo, A. and Hämäläinen, R.P.: Preference ratios in multiattribute evaluation (PRIME) – Elicitation and decision procedures under incomplete information, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 31, No. 6, 2001, pp. 533-545.
Salo, A. and Hämäläinen, R.P.: Preference Programming. (Manuscript) Downloadable at http://www.sal.hut.fi/Publications/pdf-files/msal03b.pdf
Mustajoki, J., Hämäläinen, R.P. and Salo, A.: Decision Support by Interval SMART/SWING - Incorporating Imprecision in the SMART and SWING Methods, Decision Sciences, Vol. 36, No.2, 2005, pp. 317-339.
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Literature – Tools and applicationsGustafsson, J., Salo, A. and Gustafsson, T.: PRIME Decisions - An Interactive
Tool for Value Tree Analysis, Lecture Notes in Economics and Mathematical Systems, M. Köksalan and S. Zionts (eds.), 507, 2001, pp. 165-176.
Hämäläinen, R.P., Salo, A. and Pöysti, K.: Observations about consensus seeking in a multiple criteria environment, Proc. of the Twenty-Fifth Hawaii International Conference on Systems Sciences, Hawaii, Vol. IV, January 1992, pp. 190-198.
Hämäläinen, R.P. and Pöyhönen, M.: On-line group decision support by preference programming in traffic planning, Group Decision and Negotiation, Vol. 5, 1996, pp. 485-500.
Liesiö, J., Mild, P. and Salo, A.: Preference Programming for Robust Portfolio Modeling and Project Selection, European Journal of Operational Research (to appear)
Mustajoki, J., Hämäläinen, R.P. and Lindstedt, M.R.K.: Using intervals for Global Sensitivity and Worst Case Analyses in Multiattribute Value Trees, European Journal of Operational Research. (to appear)
S ystemsAnalysis LaboratoryHelsinki University of Technology
RICH Decisions
www.rich.hut.fi
Design: Ahti Salo and Antti Punkka
Programming: Juuso Liesiö
Systems Analysis LaboratoryHelsinki University of Technology
http://www.sal.hut.fi
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The RICH Method
Based on:
Incomplete ordinal information about the relative importance of attributes
• ”environmental aspects belongs to the three most important attributes” or
• ”either cost or environmental aspects is the most important attribute”
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Score Elicitation
• Upper and lower bounds for the scores
• Type or use the scroll bar
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The user specifies sets of attributes and corresponding sets of rankings.
Here attributes distance to harbour and distance to office are the two most important ones.
The table displays the possible rankings.
Weight Elicitation
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Dominance Structure and Decision Rules
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Literature
Salo, A. and Punkka, A.: Rank Inclusion in Criteria Hierarchies, European Journal of Operational Research, Vol. 163, No. 2, 2005, pp. 338-356.
Salo, A. and Hämäläinen, R.P.: Preference ratios in multiattribute evaluation (PRIME) – Elicitation and decision procedures under incomplete information, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 31, No. 6, 2001, pp. 533-545.
Salo A. and Hämäläinen, R.P.: Preference Programming. (manuscript)
Ojanen, O., Makkonen, S. and Salo, A.: A Multi-Criteria Framework for the Selection of Risk Analysis Methods at Energy Utilities. International Journal of Risk Assessment and Management, Vol. 5, No. 1, 2005, pp. 16-35.
Punkka, A. and Salo, A.: RICHER: Preference Programming with Incomplete Ordinal Information. (submitted manuscript)
Salo, A. and Liesiö, J.: A Case Study in Participatory Priority-Setting for a Scandinavian Research Program, International Journal of Information Technology & Decision Making. (to appear)
S ystemsAnalysis LaboratoryHelsinki University of Technology
Smart-Swaps
Smart Choices with the Even Swaps Method
Design: Raimo P. Hämäläinen and Jyri MustajokiProgramming: Pauli Alanaatu
www.smart-swaps.hut.fi
Systems Analysis LaboratoryHelsinki University of Technology
http://www.sal.hut.fi
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Smart Choices
• An iterative process to support multicriteria decision making
• Uses the even swaps method to make trade-offs
(Harvard Business School Press, Boston, MA, 1999)
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Even Swaps
• Carry out even swaps that makeAlternatives 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
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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 – Trade-off information given in the even swaps can
be used to update the model
Suggestions for the Even Swaps process
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Decision support
Problem initialization
Updating of
the model
Make an even swap
Even Swaps Preference Programming
Practical dominance candidates
Initial statements about the attributes
Eliminate irrelevant attributes
Eliminate dominated alternatives
Even swap suggestions
More than oneremaining alternative
Yes
The most preferred alternative is found
No
Trade-off information
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• Identification of practical dominances
• Suggestions for the next even swap to be made
• Additional supportInformation about what can be achieved with each
swap
Notification of dominances
Rankings indicated by colours
Process history allows backtracking
Smart-Swaps
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Example• Office selection problem (Hammond et al. 1999)
Dominatedby
Lombard
Practicallydominated
byMontana
(Slightly better in Monthly Cost, but equal or worse in all other attributes)
78
25
An even swap
Commute time removed as irrelevant
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Problem definition
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Entering trade-offs
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Process history
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Hammond, J.S., Keeney, R.L., Raiffa, H., 1998. Even swaps: A rational method for making trade-offs, Harvard Business Review, 76(2), 137-149.
Hammond, J.S., Keeney, R.L., Raiffa, H., 1999. Smart choices. A practical guide to making better decisions, Harvard Business School Press, Boston.
Mustajoki, J. Hämäläinen, R.P., 2005. A Preference Programming Approach to Make the Even Swaps Method Even Easier, Decision Analysis, 2(2), 110-123.
Salo, A., Hämäläinen, R.P., 1992. Preference assessment by imprecise ratio statements, Operations Research, 40(6), 1053-1061.
Applications of Even Swaps:
Gregory, R., Wellman, K., 2001. Bringing stakeholder values into environmental policy choices: a community-based estuary case study, Ecological Economics, 39, 37-52.
Kajanus, M., Ahola, J., Kurttila, M., Pesonen, M., 2001. Application of even swaps for strategy selection in a rural enterprise, Management Decision, 39(5), 394-402.
Literature
S ystemsAnalysis LaboratoryHelsinki University of Technology
Joint-Gains
Negotiation Support in the Internet
Eero Kettunen, Raimo P. Hämäläinen
and Harri Ehtamo
www.jointgains.hut.fi
Systems Analysis LaboratoryHelsinki University of Technology
http://www.sal.hut.fi
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Method of Improving DirectionsEhtamo, Kettunen, and
Hämäläinen (2002)
• 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?”
Efficient frontier
..
..
Utility of DM 1
Utilit
y of
DM
2
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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 series of pairwise comparisonscomparisons
series of pairwise comparisonsseries of pairwise comparisons
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Joint Gains Negotiation
• 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
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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 neighbourhood
of the current alternative
• Joint Gains allows learning and change of
preferences during the process
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• Two participants
buyer and seller• Three decision variables
unit price ($): 10..50
amount (lb): 1..1000
delivery (days): 1..30• Delivery constraint (figure):
999*delivery - 29*amount 970• Initial agreement: 30 $, 100 lb, 25 days
amount (lb)1 1000
1
deliv
ery
(day
s)
30
Case example: Business
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Creating a case: Criteria to provide optional decision aiding
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Sessions
• 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
Session 1
Session 2
Session 3
Joint Gains - Business
Session n
...
• Participants take part in sessions within the case
efficient point
efficient point
efficient point
efficient point
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Preference identification task 2
Not started
Preference identification task 1
JOINT GAIN?
Stopped
New comparison task is given after all participants have completed the first one
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Session view - joint gains after two steps
unit_price
10
20
30
1 2 3
amount
406080
100
1 2 3
delivery
10
20
30
1 2 3
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Literature
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. – Part C, Vol. 29, No. 1, pp. 26-33.
Ehtamo, H., E. Kettunen, and R. P. Hämäläinen (2001). Searching for Joint Gains in Multi-Party Negotiations, European Journal of Operational Research, Vol. 130, No. 1, pp. 54-69.
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. 331-353.
Ehtamo, H., R.P. Hämäläinen, and V. Koskinen (2004). An E-learning Module on Negotiation Analysis, Proc. of the Hawaii International Conference on System Sciences, IEEE Computer Society Press, Hawaii, January 5-8.
S ystemsAnalysis LaboratoryHelsinki University of Technology
eLearning Decision Makingwww.mcda.hut.fi
eLearning sites on:Multiple Criteria Decision Analysis
Decision Making Under Uncertainty Negotiation Analysis
Prof. Raimo P. HämäläinenSystems Analysis Laboratory
Helsinki University of Technologyhttp://www.sal.hut.fi
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eLearning sitesMaterial:• 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 the use
eLearning modules: • 4 - 6 hours study time• Instructors can create their own modules using the material and software• Academic non-profit use is free
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Learning paths and modules
Learning path: guided route through the learning material Learning module: represents 2-4 h of traditional lectures and exercises
AssignmentsTheory VideosCases QuizzesLearningPaths Evaluation
Introduction to Value Tree AnalysisIntroduction to Value Tree Analysis
Module 3Module 3
Module 2Module 2
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Learning modules
Theory• HTML pages
Theory• HTML pages
• motivation, detailed instructions, 2 to 4 hour sessions
Case• slide shows
• video clips
Case• slide shows
• video clips
Assignments• online quizzes
• software tasks
• report templates
Assignments• online quizzes
• software tasks
• report templates
Evaluation • Opinions Online
Evaluation • Opinions Online
Web software
• Web-HIPRE
• video clips
Web software
• Web-HIPRE
• video clips
AssignmentsTheory VideosCases Quizzes
LearningPaths Evaluation
Introduction to Value Tree Analysis
Module 3
Module 2Module 2
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Evaluation
Cases
AssignmentsTheory
Intro
Theoreticalfoundations
Problemstructuring
Preferenceelicitation
Family selecting a car
Job selection case• basics of value tree analysis• how to use Web-HIPRE
Car selection case• imprecise preference statements, interval value trees• basics of Prime Decisions software
Family selecting a car• group decision-making with Web-HIPRE• weighted arithmetic mean method
Job selection case• basics of value tree analysis• how to use Web-HIPRE
Car selection case• imprecise preference statements, interval value trees• basics of Prime Decisions software
Family selecting a car• group decision-making with Web-HIPRE• weighted arithmetic mean method
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Video clips
VideosWorking with Web-HIPREStructuring a value tree Entering consequences of ...Assessing the form of value...Direct rating SMARTSMARTSWINGAHPViewing the resultsSensitivity analysisGroup decision makingPRIME method
AssignmentsTheory Cases QuizzesLearningPaths
Videos
• Recorded software use with voice explanations (1-4 min)
• Screen capturing with Camtasia
• AVI format for video players– e.g. Windows Media Player,
RealPlayer
• GIF format for common browsers - no sound
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Theory VideosCases QuizzesLearningPaths
Assignments
Report templates• detailed instructions in a word document• to be returned in printed format
testing the knowledge on the subject, learning by doing, individual and group reports
Software use• value tree analysis and group decisions with Web-HIPRE
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Academic Test Use is Free !
Opinions-Online (www.opinions.hut.fi)
Commercial site and pricing: www.opinions-online.com
Web-HIPRE (www.hipre.hut.fi)
WINPRE and PRIME Decisions (Windows)
RICH Decisions (www.rich.hut.fi)
Joint Gains (www.jointgains.hut.fi)
Smart-Swaps (www.smart-swaps.hut.fi)
Please, let us know your experiences.
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Contributions of colleagues andstudents at SAL
• HIPRE 3 +: Hannu Lauri• Web-HIPRE: Jyri Mustajoki, Ville Likitalo, Sami Nousiainen• Joint Gains: Eero Kettunen, Harri Jäälinoja, Tero Karttunen, Sampo
Vuorinen• Opinions-Online: Reijo Kalenius, Ville Koskinen Janne Pöllönen• Smart-Swaps: Pauli Alanaatu, Ville Karttunen, Arttu Arstila, Juuso
Nissinen• WINPRE: Jyri Helenius• PRIME Decisions: Janne Gustafsson, Tommi Gustafsson• RICH Decisions: Juuso Liesiö, Antti Punkka• e-learning MCDA: Ville Koskinen, Jaakko Dietrich, Markus Porthin
Thank you!
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Public participation project sites
• PÄIJÄNNE - Lake Regulation(www.paijanne.hut.fi)
• PRIMEREG / Kallavesi - Lake Regulation(www.kallavesi.hut.fi, www.opinion.hut.fi/servlet/tulokset?foldername=syke)
• STUK / Milk Conference - Radiation Emergency(www.riihi.hut.fi/stuk)
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SAL eLearning sites
• www.dm.hut.fiDecision making resources at Systems Analysis Laboratory
• www.mcda.hut.fieLearning in Multiple Criteria Decision Analysis
• www.negotiation.hut.fieLearning in Negotiation Analysis
• www.decisionarium.hut.fiDecision support tools and resources at Systems Analysis Laboratory
• www.or-world.comOR-World project site