1

Raimo P. HämäläinenSystems Analysis Laboratory

Aalto University, School of Sciencewww.raimo.hut.fi

December, 2010

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

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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.fi

PRIME 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

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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

Opinions-Online Platform for Global Participation, Voting,

Surveys and Group Decisions

Design: Raimo P. Hämäläinen

Programming: Reijo Kalenius

www.opinions.hut.fiwww.opinions-online.net

Systems Analysis LaboratoryAalto University, School of Science

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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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

www.hipre.hut.fi

Raimo P. Hämäläinen and Jyri MustajokiSystems Analysis Laboratory

Aalto University, School of Sciencehttp://www.sal.hut.fi

Multiattribute value tree analysis• Value tree:

• Overall value of alternative x:

n = number of attributeswi = weight of attribute i

xi = consequence of alternative x with respect to attribute i

vi(xi) = rating of xi

n

iiii xvwxv

1

)()(

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Elements link to 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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LiteratureMustajoki, 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, 2004, 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.

Hämäläinen, R.P. and Alaja, S.: The Threat of Weighting Biases in Environmental Decision Analysis, Ecological Economics, Vol. 68, 2008, pp. 556-569.

Multiattribute value tree analysis under uncertainty –

Preference programming

Intervals to describe uncertainty

• Preferential uncertainty• Incomplete information• Uncertainty about the consequences of the alternatives

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Theory 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 Ordinal score rankings decision rules 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)(

32

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

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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WINPRE Software

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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

Imprecise rating of the alternatives

Interval SMART/SWING weighting

Value intervals and dominances

• Jobs C and E

dominated Can be eliminated

• One can continue the process by narrowing the weight ratio intervals– Easier as Jobs C and E already eliminated

Benefits of interval SMART/SWING

• SMART and SWING are simple and relatively well known methods

• Intervals provide an easy way to model uncertainty• Interval SMART/SWING preserves the cognitive

simplicity of the original methods

Behaviorally Interval SMART/SWING is likely to be easily adapted

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PRIME Decisions Software

Interval methods in group decision support

• The individual DMs can use either point estimates or intervals in their preference elicitation

• Embed all models into a group interval model• Interval model includes the range of preferences of all

the different DMs• The group process is to negotiate and tighten the

intervals by interpersonal trade-offs

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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.

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.

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, Vol. 174, No. 1, 2006, pp. 278-292.

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Literature – Tools and applications

Gustafsson, 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, Vol. 181, Issue 3, pp. 1488-1505.

RICH Decisions

www.rich.hut.fi

Design: Ahti Salo and Antti Punkka

Programming: Juuso Liesiö

Systems Analysis LaboratoryAalto University, School of Science

http://www.sal.hut.fi

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The RICH Method

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, Vol. 5, No. 1, 2006, pp. 65-88.

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 LaboratoryAalto University, School of Science

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)

Smart-Swaps softwarewww.smart-swaps.hut.fi

• Support for the PrOACT process (Hammond et al., 1999)– Problem– Objectives– Alternatives– Consequences– Trade-offs

• Trade-offs carried out with the Even Swaps method

Problem / Objectives / Alternatives

Even Swaps

• Multicriteria method to find the best alternative• An even swap:

– A value trade-off, where a consequence change in one attribute is compensated with a comparable change in some other attribute

– A new alternative with these revised consequences is equally preferred to the initial one

The new alternative can be used instead

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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

Use of trade-off information

• With each even swap the user reveals new information about her preferences

• This trade-off information can be utilized in the process

Tighter bounds for the weight ratios obtained from the given even swaps

Better estimates for the values of the alternatives

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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 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)

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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.

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.

Luo, C.-M., Cheng, B.W., 2006. Applying Even-Swap Method to Structurally Enhance the Process of Intuition Decision-Making, Systemic Practice and Action Research, 19(1), 45-59.

Literature

Joint-Gains

Negotiation Support in the Internet

Eero Kettunen, Raimo P. Hämäläinen

and Harri Ehtamo

www.jointgains.hut.fi

Systems Analysis LaboratoryAalto University, School of Science

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 participantsbuyer and seller

• Three decision variablesunit 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 daysamount (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• 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

Session 1

Session 2

Session 3

Joint Gains - Business

Session n

...

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.

RPM DecisionsProfessor Ahti Salo

Dr. Juuso LiesiöLic.Sc. Pekka MildLic.Sc. Antti PunkkaDr. Ville BrummerM.Sc. Eeva VilkkumaaM.Sc. Jussi KangaspuntaM.Sc. Antti Toppila

http://www.rpm.tkk.fi/

• Supports project portfolio selection w.r.t. multiple criteria– Portfolio = a set of projects– Feasible portfolios fulfill resource and possible other constraints– Project value additive over criteria– Portfolio value = sum of its constituent projects’ values– Incomplete preference information (Preference Programming)

• Decision recommendations: non-dominated (ND) portfolios– Additional preference information does not make

the set of ND portfolios bigger

• Project-oriented analysis– Accept core projects that belong to all ND portfolios – Discard exterior projects that do not belong to any of the ND portfolios– Select between the borderline projects that belong to some ND portfolios

Robust Portfolio Modeling (RPM)

6

3

10

21

7

45

89

6

3

10

21

7

45

89

AB

C

Core (exterior) projects stay core (exterior) projects even, if additional preference information is imposed

RPM Framework

Approach to promote robustness through incomplete information (integrated sensitivity analysis).Accounts for group statements

•Narrower intervals•Stricter weights

• Wide score intervals• Loose weight

statements

Large number

of project

proposals.

Evaluated

w.r.t. multiple

criteria.

Borderline

projects

“uncertain zone” Focus

Exterior projects

“Robust zone” Discard

Core projects“Robust zone”

Choose

Core

Border

Exterior

Negotiation.Manual iteration.Heuristic rules.

Se

lecte

dN

ot se

lecte

d

Gradual selection: Transparency w.r.t. individual projectsTentative conclusions at any stage of the process

RPM Decisions software: data input and value tree construction, elicitation of preference information

Analysis phase – elicitation of additional preference information, illustration of core indices, portfolios’ properties and support to gradual

selection of projects

LiteratureMethodologyLiesiö, J., Mild, P., Salo, A. (2007). Preference Programming for Robust Portfolio Modeling and

Project Selection, EJOR 181, 1488-1505

Liesiö, J., Mild, P., Salo, A. (2008). Robust Portfolio Modeling with Incomplete Cost Information and Project Interdependencies, EJOR 190, 679-695

ApplicationsKönnölä, T., Brummer, V., Salo, A. (2007). Diversity in Foresight: Insights from the Fostering of

Innovation Ideas, Technological Forecasting and Social Change 74, 608-626

Brummer, V., Könnölä, T., Salo, A. (2008). Foresight within ERA-NETs: Experiences from the Preparation of an International Research Program, Technological Forecasting and Social Change 75, 483-495

Lindstedt, M., Liesiö, J., Salo, A. (2008). Participatory Development of a Strategic Product Portfolio in a Telecommunication Company, International Journal of Technology Management 42, 250-266

Brummer, V., Salo, A., Nissinen, J., Liesiö, J. A Methodology for the Identification of Prospective Collaboration Networks in International R&D Programs, International Journal of Technology Management, Special issue on technology foresight, to appear.

eLearning Decision Makingwww.mcda.hut.fi

eLearning sites on:Multiple Criteria Decision Analysis

Decision Making Under Uncertainty Negotiation Analysis

Prof. Raimo P. Hämäläinen

Systems Analysis LaboratoryAalto University, School of Science

http://www.sal.hut.fi

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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 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 Quizzes

LearningPaths

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 Quizzes

LearningPaths

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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• 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!

Programming at SAL