Transcript Slide 1

DECISIONARIUM
Aiding Decisions, Negotiating and
Collecting Opinions on the Web
www.decisionarium.hut.fi
Raimo P. Hämäläinen
Systems Analysis Laboratory
Aalto University, School of Science
www.raimo.hut.fi
December, 2010
1
DECISIONARIUM
global space for decision support
group
collaboration
group
decision
making
GDSS, NSS
multicriteria
decision
analysis
decision
making
Joint Gains
multi-party negotiation support
with the method of improving
directions
OpinionsOnline
RICH Decisions
CSCW
platform for global
participation, voting, surveys,
and group decisions
rank inclusion in criteria
hierarchies
DSS
Windows software for
decision analysis with
imprecise ratio statements
internet
computer
support
WINPRE
PRIME
Decisions
preference
programming,
PAIRS
Web-HIPRE
Smart-Swaps
value tree and AHP
based decision
support
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
Systems
selected publications
elimination of criteria
and alternatives by
even swaps
Analysis Laboratory
Updated 25.10.2004
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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.
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
4
New Methodological Features
• 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
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eLearning Decision Making
www.dm.hut.fi
SAL eLearning sites:
Multiple Criteria Decision Analysis
www.mcda.hut.fi
Decision Making Under Uncertainty
Negotiation Analysis
www.negotiation.hut.fi
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Opinions-Online
Platform for Global Participation, Voting,
Surveys and Group Decisions
www.opinions.hut.fi
www.opinions-online.net
Design: Raimo P. Hämäläinen
Programming: Reijo Kalenius
Systems Analysis Laboratory
Aalto University, School of Science
http://www.sal.hut.fi
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
9
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
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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 Mustajoki
Systems Analysis Laboratory
Aalto University, School of Science
http://www.sal.hut.fi
Multiattribute value tree analysis
• Value tree:
• Overall value of alternative x:
n
v ( x )   w i v i ( xi )
i 1
n = number of attributes
wi = weight of attribute i
xi = consequence of alternative x with respect to attribute i
vi(xi) = rating of xi
Elements link to
web-pages
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Direct Weighting
Note: Weights in
this example are
her personal
opinions
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SWING,SMART and SMARTER Methods
• SMARTER uses
rankings only
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Pairwise Comparison - AHP
• Continuous scale
1-9
• Numerical,
verbal or graphical
approach
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Value Function
• Ratings of
alternatives shown
• Any shape of the
value function
allowed
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Composite Priorities
• Bar graphs or
numerical values
• Bars divided by
the contribution of
each criterion
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Group Decision Support
• Group model is the
weighted sum of
individual decision
makers’ composite
priorities for the
alternatives
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Defining Group Members
• Individual value
trees can be
different
• Composite
priorities of each
group member
- obtained from
their individual
models
- shown in the
definition phase
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Aggregate Group Priorities
• Contribution of
each group
member indicated
by segments
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Sensitivity analysis
• Changes in the
relative importance
of decision makers
can be analyzed
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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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Sources of biases and problems
Weighting methods
yield different weights
Number-of-attributelevels effect in
conjoint analysis
Hierarchical
weighting leads to
steeper weights
Decision
makers only
give ordinal
information
Normalization
Division of
attributes changes
weights
Range effect
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Averages over a
group yield even
weights
Rank reversal in
AHP
Splitting bias with
weighting methods
based on ranking
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, 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.
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Multiattribute value tree analysis
under uncertainty –
Preference programming
Intervals to describe uncertainty
• Preferential uncertainty
• Incomplete information
• Uncertainty about the consequences of the alternatives
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:
n
v ( x )  min  w i v i ( xi )
i 1
– Upper bound correspondingly
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Interval statements define a feasible
region S for the weights
wA

2
wB
wA
1
1
3 
wC
1
2
wB
 
2
wC
1
6
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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
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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
PRIME Decisions Software
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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
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.
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RICH Decisions
www.rich.hut.fi
Design: Ahti Salo and Antti Punkka
Programming: Juuso Liesiö
Systems Analysis Laboratory
Aalto University, School of Science
http://www.sal.hut.fi
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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Weight Elicitation
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.
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Dominance Structure and Decision
Rules
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Literature
Salo, A. and Punkka, A.: Rank Inclusion in Criteria Hierarchies, European
Journal of Operational Research, Vol. 163, No. 2, 2005, pp. 338-356.
Salo, A. and Hämäläinen, R.P.: Preference ratios in multiattribute evaluation
(PRIME) – Elicitation and decision procedures under incomplete information,
IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and
Humans, Vol. 31, No. 6, 2001, pp. 533-545.
Salo A. and Hämäläinen, R.P.: Preference Programming. (manuscript)
Ojanen, O., Makkonen, S. and Salo, A.: A Multi-Criteria Framework for the
Selection of Risk Analysis Methods at Energy Utilities. International Journal of
Risk Assessment and Management, Vol. 5, No. 1, 2005, pp. 16-35.
Punkka, A. and Salo, A.: RICHER: Preference Programming with Incomplete
Ordinal Information. (submitted manuscript)
Salo, A. and Liesiö, J.: A Case Study in Participatory Priority-Setting for a
Scandinavian Research Program, International Journal of Information
Technology & Decision Making, Vol. 5, No. 1, 2006, pp. 65-88.
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Smart-Swaps
Smart Choices with the Even Swaps
Method
www.smart-swaps.hut.fi
Design: Raimo P. Hämäläinen and Jyri Mustajoki
Programming: Pauli Alanaatu
Systems Analysis Laboratory
Aalto University, School of Science
http://www.sal.hut.fi
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)
51
Smart-Swaps software
www.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
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
55
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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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
Decision support
Even Swaps
Problem initialization
Preference
Programming
Initial statements about the attributes
Practical dominance candidates
Eliminate dominated
alternatives
Updating of
Eliminate irrelevant
attributes
No
the model
More than one
remaining alternative
Yes
Even swap suggestions
Make an even swap
Trade-off information
The most preferred
alternative is found
58
Smart-Swaps
• 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
59
Example
• Office selection problem (Hammond et al. 1999)
25
78
Practically
dominated
by
An even swap
Commute time
removed as irrelevant
Montana
(Slightly better in Monthly Cost, but equal or worse in all other attributes)
60
Dominated
by
Lombard
Problem definition
61
Entering trade-offs
62
Process history
63
Literature
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.
64
Joint-Gains
Negotiation Support in the Internet
www.jointgains.hut.fi
Eero Kettunen, Raimo P. Hämäläinen
and Harri Ehtamo
Systems Analysis Laboratory
Aalto University, School of Science
http://www.sal.hut.fi
Method of Improving Directions
Ehtamo, Kettunen, and Hämäläinen (2002)
• Interactive method for reaching
efficient alternatives
Utility of DM 2
Efficient frontier
• Search of joint gains from a given initial
alternative
.
Utility of DM 1
• In the mediation process participants are
given simple comparison tasks:
“Which one of these two alternatives do you prefer,
alternative A or B?”
66
.
.
.
Mediation Process
Tasks in Preference Identification
• Initial alternative considered as “current alternative”
• Task 1 for identifying participants’
series of pairwise
comparisons
most preferred directions
• Joint Gains calculates a jointly improving direction
• Task 2 for identifying participants’ most preferred
alternatives in the jointly improving direction
series of pairwise comparisons
67
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
68
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
69
Case example: Business
• Two participants
30
• Three decision variables
unit price ($): 10..50
amount (lb): 1..1000
delivery (days): 1..30
delivery (days)
buyer and seller
• Delivery constraint (figure):
999*delivery - 29*amount  970
1
1
amount (lb)
• Initial agreement: 30 $, 100 lb, 25 days
70
1000
Creating a case: Criteria to
provide optional decision aiding
71
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
72
Joint Gains - Business
Session 1  efficient point
Session 2
 efficient point
Session 3
..
.
 efficient point
Session n
 efficient point
New comparison task is given after all
participants have completed the first
one
Not started
Preference identification task 1
Preference identification task 2
JOINT GAIN?
Stopped
73
Session view - joint gains after two
steps
unit_price
30
20
10
1
2
3
2
3
2
3
amount
100
80
60
40
1
delivery
30
20
10
1
74
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.
75
RPM Decisions
Professor Ahti Salo
Dr. Juuso Liesiö
Lic.Sc. Pekka Mild
Lic.Sc. Antti Punkka
Dr. Ville Brummer
M.Sc. Eeva Vilkkumaa
M.Sc. Jussi Kangaspunta
M.Sc. Antti Toppila
http://www.rpm.tkk.fi/
Robust Portfolio Modeling (RPM)
• 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
A
10
4
10
5
5
3
• Project-oriented analysis
C
3
1
2
1 2
6
7
6
7
– Accept core projects that belong to all ND portfolios
8
– Discard exterior projects that do not belong to any of the ND portfolios
– Select between the borderline projects that belong to some ND portfolios
9
B
4
9
8
RPM Framework
• Wide score
intervals
• Loose
weight
statements
Borderline
projects
“uncertain zone”
 Focus
Exterior projects
“Robust zone”
 Discard
Approach to promote robustness
through incomplete information
(integrated sensitivity analysis).
Accounts for group statements
Gradual selection:
Core
•Narrower
intervals
•Stricter weights
Border
Exterior
Negotiation.
Manual
iteration.
Heuristic rules.
Transparency w.r.t. individual projects
Tentative conclusions at any stage of the process
Not selected
Large number
of project
proposals.
Evaluated
w.r.t. multiple
criteria.
Core (exterior) projects stay
core (exterior) projects even, if
additional preference information
is imposed
Selected
Core projects
“Robust zone”
 Choose
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
Literature
Methodology
Liesiö, 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
Applications
Könnölä, 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 Making
www.mcda.hut.fi
eLearning sites on:
Multiple Criteria Decision Analysis
Decision Making Under Uncertainty Negotiation
Analysis
Prof. Raimo P. Hämäläinen
Systems Analysis Laboratory
Aalto University, School of Science
http://www.sal.hut.fi
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
Learning
Paths
Theory
Assignments
Cases Quizzes Videos
Evaluation
Introduction to Value Tree Analysis
Module 2
Module 3
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Learning modules
Learning
Theory Cases Quizz
Paths
VideosAssignments
Evaluation
es
Introduction to Value Tree Analysis
Module 2
Module 3
• motivation, detailed instructions, 2 to 4 hour sessions
Theory
Case
Assignments
Evaluation
• online quizzes
• Opinions
Online
• HTML
• slide shows
Web
software
pages
• video clips
• Web-HIPRE
• software tasks
• video clips
• report templates
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Cases
Family selecting a car
Job selection case
Theory
• basics of value tree analysis
• how to use Web-HIPRE
Car selection case
Intro
Theoretical
foundations
Assignments
• imprecise preference statements,
interval value trees
• basics of Prime Decisions software
Problem
structuring
Family selecting a car
Preference
elicitation
• group decision-making with Web-HIPRE
• weighted arithmetic mean method
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Evaluation
Video clips
Learning Theory Cases Quizze
s
Paths
• 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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Videos Assignments
Videos
Working with Web-HIPRE
Structuring a value tree
Entering consequences of ...
Assessing the form of value...
Direct rating
SMART
SMART
SWING
AHP
Viewing the results
Sensitivity analysis
Group decision making
PRIME method
Learning Theory Cases Quizze
s
Paths
Videos Assignments
testing the knowledge on the subject, learning by doing,
individual and group reports
Software use
• value tree analysis and
group decisions with Web-HIPRE
Report templates
• detailed instructions in a word document
• to be returned in printed format
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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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Programming 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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