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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
2
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
3
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
4
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. 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
)()(
16
Elements link to web-pages
17
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
25
• 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
28
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
31
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
33
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
34
WINPRE Software
35
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.
43
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
45
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”
46
Score Elicitation
• Upper and lower bounds for the scores
• Type or use the scroll bar
47
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
48
Dominance Structure and Decision Rules
49
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
51
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
55
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)
78
25
An even swap
Commute time removed as irrelevant
61
Problem definition
62
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
66
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
67
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
83
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
86
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
87
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
90
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