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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

http://www.raimo.hut.fi/

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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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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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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

6


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


Opinions-Online Platform for Global Participation, Voting,

Surveys and Group Decisions

Design: Raimo P. HämäläinenProgramming: 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

9


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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Examples of use

• Teledemocracy – interactive citizens’ participation

• Group decision making• Brainstorming• Course evaluation in universities and schools• Marketing research• Organisational surveys and barometers


Global Multicriteria Decision Support by Web-HIPRE

A Java-applet for Value Tree and AHP Analysis

Raimo P. HämäläinenJyri Mustajoki

www.hipre.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

NormalizationDecision

makers 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 domainsDomain 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)(

35


2

1

2

61

31

21

C

B

C

A

B

A

ww

wwww

Interval statements define a feasible region S for the weights

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Uses of interval modelsNew generalized AHP and SMART/SWING methodsDM can also reply with intervals instead of exact point

estimates – a new way to accommodate uncertaintyInterval sensitivity analysisVariations allowed in several model parameters

simultaneously - worst case analysisGroup decision makingAll 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 – MethodologySalo, 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)


RICH Decisions

www.rich.hut.fi

Design: Ahti Salo and Antti PunkkaProgramming: Juuso Liesiö



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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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LiteratureSalo, 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)


Smart-Swaps Smart Choices with the Even Swaps Method

Design: Raimo P. Hämäläinen and Jyri MustajokiProgramming: Pauli Alanaatu

www.smart-swaps.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 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

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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 support

Information about what can be achieved with each swap

Notification of dominancesRankings indicated by coloursProcess 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

56


Entering trade-offs

57


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


Joint-Gains Negotiation Support in the Internet

Eero Kettunen, Raimo P. Hämäläinenand Harri Ehtamo

www.jointgains.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 participantsbuyer and seller

• Three decision variablesunit price ($): 10..50amount (lb): 1..1000delivery (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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LiteratureEhtamo, 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.


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 Analysis

Module 3

Module 2

74


Learning modules

Theory• HTML pages

• motivation, detailed instructions, 2 to 4 hour sessions

Case• slide shows• video clips

Assignments• online quizzes• software tasks• report templates

Evaluation • Opinions Online

Web software

• Web-HIPRE

• video clips

AssignmentsTheory VideosCases Quizzes

LearningPaths Evaluation

Introduction to Value Tree Analysis

Module 3

Module 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

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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)

http://www.paijanne.hut.fi/

http://www.kallavesi.hut.fi/

http://www.opinion.hut.fi/servlet/tulokset?foldername=syke



http://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

No Slide Titlesal.aalto.fi/publications/ppt-files/Decisionar… · PPT file · Web view · 2010-08-13The table displays the possible ... .hut.fi PRIME Decisions and WINPRE downloadable

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