Transcript No Slide Title

DECISIONARIUM
Aiding Decisions, Negotiating and
Collecting Opinions on the Web
www.decisionarium.hut.fi
Raimo P. Hämäläinen
Systems Analysis Laboratory
Helsinki University of Technology
www.raimo.hut.fi
JMCDA, Vol. 12 , No. 2-3, 2003, pp. 101-110.
S ystems
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v. 3.2006 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
Opinions-Online
platform for global participation,
voting, surveys, and group
decisions
RICH Decisions
CSCW
DSS
internet
computer
support
WINPRE
rank inclusion in criteria
hierarchies
Windows software for decision
analysis with imprecise ratio
statements
PRIME Decisions
preference
programming,
PAIRS
Smart-Swaps
Web-HIPRE
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
selected publications
elimination of criteria
and alternatives by even
swaps
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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.
2
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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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.com
Design: Raimo P. Hämäläinen
Programming: Reijo Kalenius
Systems Analysis Laboratory
Helsinki University of Technology
http://www.sal.hut.fi
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Surveys on the web
•
•
•
•
•
Fast, easy and cheap
Hyperlinks to background information
Easy access to results
Results can be analyzed on-line
Access control: registration, e-mail list,
domain, password
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Creating a new session
• Browser-based generation of new
sessions
• Fast and simple
• Templates available
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Possible questions
• Survey section
Multiple/single
choice
• Best/worst
• Ranking
• Rating
• Approval voting
• Written comments
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Viewing the results
• In real-time
• By selected fields
• Questionwise public
or restricted access
• Barometer
• Direct links
to results
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Approval voting
• The user is asked to pick the alternatives
that he/she can approve
• Often better than a simple “choose best”
question when trying to reach a consensus
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Advanced voting rules
www.opinion.vote.hut.fi
• Condorcet criteria
– Copeland’s methods, Dodgson’s method,
Maximin method
• Borda count
– Nanson’s method, University method
• Black’s method
• Plurality voting
– Coombs’ method, Hare system, Bishop method
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Examples of use
• Teledemocracy – interactive citizens’
participation
• Group decision making
• Brainstorming
• Course evaluation in universities and schools
• Marketing research
• Organisational surveys and barometers
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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
Jyri Mustajoki
S ystems
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Helsinki University of Technology
Systems Analysis Laboratory
Helsinki University of Technology
http://www.sal.hut.fi
Web-HIPRE links
can refer to any
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
Number-of-attributelevels effect in
conjoint analysis
Hierarchical
weighting leads to
steeper weights
Range effect
Weighting methods
yield different weights
Decision
makers only
give ordinal
information
Averages over a
group yield even
weights
Normalization
Division of
attributes changes
weights
Rank reversal in
AHP
Splitting bias with
weighting methods
based on ranking
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Visits to Web-HIPRE
16526
13151
14216
9233
6826
5303
1617
1998
1999
2000
2001
2002
2003
2004
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Visitors’ top-level domains
Domain Visits
fi
14174
com
4758
net
4677
edu
2729
si
1606
uk
981
ca
894
ee
849
es
834
nl
774
de
744
au
571
at
449
(36.8 %)
(12.3 %)
(12.1 %)
(7.1 %)
(4.2 %)
(2.5 %)
(2.3 %)
(2.2 %)
(2.2 %)
(2.0 %)
(1.9 %)
(1.5 %)
(1.2 %)
Domain Visits
ch
385
hu
348
jp
321
br
284
tr
251
it
198
pl
187
gr
171
fr
165
pt
147
mil
144
se
141
tw
132
(1.0 %)
(0.9 %)
(0.8 %)
(0.7 %)
(0.7 %)
(0.5 %)
(0.5 %)
(0.4 %)
(0.4 %)
(0.4 %)
(0.4 %)
(0.4 %)
(0.3 %)
Domain Visits
sg
124 (0.3 %)
be
119 (0.3 %)
is
112 (0.3 %)
ru
97 (0.3 %)
gov
92 (0.2 %)
mx
87 (0.2 %)
il
82 (0.2 %)
org
77 (0.2 %)
no
72 (0.2 %)
za
65 (0.2 %)
ar
63 (0.2 %)
Total 38557 visits
(+ 28315 visits with only IP)
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Helsinki University of Technology
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Visitors’ first-level domains
Domain
Visits
hkkk.fi
4593 (11.9 %)
hut.fi
4453 (11.5 %)
uni-mb.si
1220 (3.2 %)
duke.edu
1069 (2.8 %)
ac.uk
776 (2.0 %)
htv.fi
763 (2.0 %)
inktomi.com
689 (1.8 %)
inet.fi
629 (1.6 %)
googlebot.com 608 (1.6 %)
helsinki.fi
497 (1.3 %)
ja.net
464 (1.2 %)
kolumbus.fi
417 (1.1 %)
t-dialin.net
376 (1.0 %)
Domain Visits
aol.com
343 (0.9 %)
ac.at
324 (0.8 %)
edu.au
323 (0.8 %)
siol.net
290 (0.8 %)
arnes.si
287 (0.7 %)
omakaista.fi 262 (0.7 %)
abo.fi
215 (0.6 %)
estpak.ee
215 (0.6 %)
asu.edu
206 (0.5 %)
knology.net 205 (0.5 %)
uab.es
205 (0.5 %)
verizon.net 203 (0.5 %)
cesca.es
193 (0.5 %)
Domain
Visits
bke.hu
181 (0.5 %)
comcast.net 181 (0.5 %)
carleton.ca
172 (0.4 %)
te-keskus.fi
172 (0.4 %)
uwaterloo.ca 160 (0.4 %)
net.br
157 (0.4 %)
co.uk
156 (0.4 %)
edu.tr
151 (0.4 %)
rr.com
151 (0.4 %)
sympatico.ca 149 (0.4 %)
ne.jp
142 (0.4 %)
Total 38557 visits
(+ 28315 visits with only IP)
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Helsinki University of Technology
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Visits through sites linking to Web-HIPRE
Site
hut.fi
duke.edu
google.com
dicksmart.net
cmu.edu
carleton.ca
uni-mb.si
ttu.ee
clarku.edu
yahoo.com
altavista.com
100gen.fi
informs.org
S ystems
Analysis Laboratory
Helsinki University of Technology
Visits
7375 (40.4 %)
1480 (8.1 %)
1349 (7.4 %)
589 (3.2 %)
568 (3.1 %)
527 (2.9 %)
403 (2.2 %)
383 (2.1 %)
324 (1.8 %)
272 (1.5 %)
229 (1.3 %)
222 (1.2 %)
218 (1.2 %)
Site
Visits
google.fi
190 (1.0 %)
man.ac.uk
174 (1.0 %)
unige.ch
165 (0.9 %)
msn.com
162 (0.9 %)
colorado.edu
144 (0.8 %)
helsinki.fi
143 (0.8 %)
clarolineserver.com
122 (0.7 %)
urjc.es
118 (0.6 %)
univie.ac.at
115 (0.6 %)
gov.uk
100 (0.5 %)
ecohotelsbolivia.com
97 (0.5 %)
nus.edu.sg
96 (0.5 %)
Total 18261 visits
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Literature
Mustajoki, J. and Hämäläinen, R.P.: Web-HIPRE: Global decision support by
value tree and AHP analysis, INFOR, Vol. 38, No. 3, 2000, pp. 208-220.
Hämäläinen, R.P.: Reversing the perspective on the applications of decision
analysis, Decision Analysis, Vol. 1, No. 1, pp. 26-31.
Mustajoki, J., Hämäläinen, R.P. and Marttunen, M.: Participatory multicriteria
decision support with Web-HIPRE: A case of lake regulation policy.
Environmental Modelling & Software, Vol. 19, No. 6, 2004, pp. 537-547.
Pöyhönen, M. and Hämäläinen, R.P.: There is hope in attribute weighting,
INFOR, Vol. 38, No. 3, 2000, pp. 272-282.
Pöyhönen, M. and Hämäläinen, R.P.: On the Convergence of Multiattribute
Weighting Methods, European Journal of Operational Research, Vol. 129,
No. 3, 2001, pp. 569-585.
Pöyhönen, M., Vrolijk, H.C.J. and Hämäläinen, R.P.: Behavioral and
Procedural Consequences of Structural Variation in Value Trees, European
Journal of Operational Research, Vol. 134, No. 1, 2001, pp. 218-227.
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New Theory: Preference programming
Analysis with incomplete preference
statements (intervals):
”...attribute is at least 2 times as but no
more than 3 times as important as...”
Windows software
• WINPRE – Workbench for Interactive Preference Programming
Interval AHP, interval SMART/SWING and PAIRS
• PRIME-Preference Ratios in Multiattribute Evaluation Method
Incomplete preference statements
Web software
• RICH Decisions – Rank Inclusion in Criteria Hierarchies
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Preference Programming –
The PAIRS method
• Imprecise statements with intervals on
– Attribute weight ratios (e.g. 1/2  w1 / w2  3)
 Feasible region for the weights
– Alternatives’ ratings (e.g. 0.6  v1(x1)  0.8)
 Intervals for the overall values
– Lower bound for
the
overall
value
of
x:
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
1
2
1
3
wA

2
wB
wA

1
wC
wB
 
2
wC
1
6
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Uses of interval models
New generalized AHP and SMART/SWING methods
DM can also reply with intervals instead of exact point
estimates – a new way to accommodate uncertainty
Interval sensitivity analysis
Variations allowed in several model parameters
simultaneously - worst case analysis
Group decision making
All members´ opinions embedded in intervals =
a joint common group model
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Interval SMART/SWING
• A as reference - A given 10 points
• Point intervals given to the other attributes:
– 5-20 points to attribute B
– 10-30 points to attribute C
• Weight ratio between B and C not explicitly
given by the DM
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WINPRE Software
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PRIME Decisions Software
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Literature – Methodology
Salo, A. and Hämäläinen, R.P.: Preference assessment by imprecise ratio
statements, Operations Research, Vol. 40, No. 6, 1992, pp. 1053-1061.
Salo, A. and Hämäläinen, R.P.: Preference programming through approximate
ratio comparisons, European Journal of Operational Research, Vol. 82, No. 3,
1995, pp. 458-475.
Salo, A. and Hämäläinen, R.P.: Preference ratios in multiattribute evaluation
(PRIME) – Elicitation and decision procedures under incomplete information,
IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and
Humans, Vol. 31, No. 6, 2001, pp. 533-545.
Salo, A. and Hämäläinen, R.P.: Preference Programming. (Manuscript)
Downloadable at http://www.sal.hut.fi/Publications/pdf-files/msal03b.pdf
Mustajoki, J., Hämäläinen, R.P. and Salo, A.: Decision Support by Interval
SMART/SWING - Incorporating Imprecision in the SMART and SWING
Methods, Decision Sciences, Vol. 36, No.2, 2005, pp. 317-339.
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Literature – Tools and 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
(to appear)
Mustajoki, J., Hämäläinen, R.P. and Lindstedt, M.R.K.: Using intervals for
Global Sensitivity and Worst Case Analyses in Multiattribute Value Trees,
European Journal of Operational Research. (to appear)
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RICH Decisions
www.rich.hut.fi
Design: Ahti Salo and Antti Punkka
Programming: Juuso Liesiö
Systems Analysis Laboratory
Helsinki University of Technology
http://www.sal.hut.fi
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The RICH Method
Based on:
Incomplete ordinal information about the
relative importance of attributes
• ”environmental aspects belongs to the
three most important attributes” or
• ”either cost or environmental aspects is
the most important attribute”
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Score Elicitation
• Upper and lower
bounds for the
scores
• Type or use the
scroll bar
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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. (to appear)
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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
Helsinki University of Technology
http://www.sal.hut.fi
S ystems
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Smart Choices
• An iterative process to support
multicriteria decision making
• Uses the even swaps method
to make trade-offs
(Harvard Business
School Press,
Boston, MA, 1999)
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Even Swaps
• Carry out even swaps that make
Alternatives dominated (attribute-wise)
• There is another alternative, which is equal or better
than this in every attribute, and better at least in one
attribute
Attributes irrelevant
• Each alternative has the same value on this attribute
 These can be eliminated
• Process continues until one alternative, i.e.
the best one, remains
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Supporting Even Swaps with
Preference Programming
• Even Swaps process carried out as usual
• The DM’s preferences simultaneously modeled
with Preference Programming
– Intervals allow us to deal with incomplete
information
– Trade-off information given in the even swaps can
be used to update the model
 Suggestions for the Even Swaps process
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Decision support
Even Swaps
Problem initialization
Eliminate dominated
alternatives
Preference
Programming
Initial statements about the attributes
Practical dominance candidates
Updating of
Eliminate irrelevant
attributes
No
the model
More than one
remaining alternative
Yes
Make an even swap
Even swap suggestions
Trade-off information
The most preferred
alternative is found
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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
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Example
• Office selection problem (Hammond et al. 1999)
25
78
Practically
An even swap
dominated
Commute time removed
by
as irrelevant
Montana
(Slightly better in Monthly Cost, but equal or worse in all other attributes)
Dominated
by
Lombard
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Problem definition
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Entering trade-offs
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Process history
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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.
Salo, A., Hämäläinen, R.P., 1992. Preference assessment by imprecise ratio
statements, Operations Research, 40(6), 1053-1061.
Applications of Even Swaps:
Gregory, R., Wellman, K., 2001. Bringing stakeholder values into environmental
policy choices: a community-based estuary case study, Ecological Economics, 39,
37-52.
Kajanus, M., Ahola, J., Kurttila, M., Pesonen, M., 2001. Application of even swaps
for strategy selection in a rural enterprise, Management Decision, 39(5), 394-402.
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Joint-Gains
Negotiation Support in the Internet
www.jointgains.hut.fi
Eero Kettunen, Raimo P. Hämäläinen
and Harri Ehtamo
Systems Analysis Laboratory
Helsinki University of Technology
http://www.sal.hut.fi
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Method of Improving Directions
• Interactive method for reaching
efficient alternatives
Efficient frontier
Utility of DM 2
Ehtamo, Kettunen, and
Hämäläinen (2002)
.
.
.
.
Utility of DM 1
• 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?”
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Mediation Process
Tasks in Preference Identification
• Initial alternative considered as “current alternative”
• Task 1 for identifying participants’
series of pairwise
most preferred directions
comparisons
• Joint Gains calculates a jointly improving direction
• Task 2 for identifying participants’ most preferred
alternatives in the jointly improving direction
series 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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Case example: Business
delivery (days)
• Two participants
30
buyer and seller
• Three decision variables
unit price ($): 10..50
amount (lb): 1..1000
1
delivery (days): 1..30
1
amount (lb)
• Delivery constraint (figure):
999*delivery - 29*amount  970
• Initial agreement: 30 $, 100 lb, 25 days
1000
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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
Joint Gains - Business
Session 1  efficient point
Session 2  efficient point
Session 3  efficient point
..
.
Session n  efficient point
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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
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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
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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.
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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
Helsinki University of Technology
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
Learning
Paths
Theory
Cases
Quizzes
Videos Assignments
Evaluation
Introduction to Value Tree Analysis
Module 2
Module 3
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Learning
Theory
Paths
Learning modules
Quizz VideosAssignments
Evaluation
es
Introduction to Value Tree Analysis
Module 2
Cases
Module 3
• motivation, detailed instructions, 2 to 4 hour sessions
Theory
• HTML
pages
Case
• slide shows
• video clips
Web
software
• Web-HIPRE
• video clips
Assignments
• online quizzes
• software tasks
• report templates
Evaluation
• Opinions
Online
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Cases
Family selecting a car
Job selection case
• basics of value tree analysis
Theory
Assignments
• how to use Web-HIPRE
Intro
Car selection case
Problem
structuring
• imprecise preference statements,
interval value trees
• basics of Prime Decisions software
Preference
elicitation
Family selecting a car
Theoretical
foundations
Evaluation
• group decision-making with Web-HIPRE
• weighted arithmetic mean method
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Video clips
• 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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Learning Theory
Paths
Cases Quizzes
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
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Learning Theory
Paths
Cases Quizzes
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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Contributions of colleagues and
students at SAL
• HIPRE 3 +: Hannu Lauri
• Web-HIPRE: Jyri Mustajoki, Ville Likitalo, Sami Nousiainen
• Joint Gains: Eero Kettunen, Harri Jäälinoja, Tero Karttunen, Sampo
Vuorinen
• Opinions-Online: Reijo Kalenius, Ville Koskinen Janne Pöllönen
• Smart-Swaps: Pauli Alanaatu, Ville Karttunen, Arttu Arstila, Juuso
Nissinen
• WINPRE: Jyri Helenius
• PRIME Decisions: Janne Gustafsson, Tommi Gustafsson
• RICH Decisions: Juuso Liesiö, Antti Punkka
• e-learning MCDA: Ville Koskinen, Jaakko Dietrich, Markus Porthin
Thank you!
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Public participation project sites
• PÄIJÄNNE - Lake Regulation
(www.paijanne.hut.fi)
• PRIMEREG / Kallavesi - Lake Regulation
(www.kallavesi.hut.fi,
www.opinion.hut.fi/servlet/tulokset?foldername=syke)
• STUK / Milk Conference - Radiation Emergency
(www.riihi.hut.fi/stuk)
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SAL eLearning sites
• www.dm.hut.fi
Decision making resources at Systems Analysis Laboratory
• www.mcda.hut.fi
eLearning in Multiple Criteria Decision Analysis
• www.negotiation.hut.fi
eLearning in Negotiation Analysis
• www.decisionarium.hut.fi
Decision support tools and resources at Systems Analysis
Laboratory
• www.or-world.com
OR-World project site
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