Aiding Decisions, Negotiating and Collecting Opinions on the Web

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Aiding Decisions, Negotiating and Collecting
Opinions on the Web
D E C I S I O N A R I U M
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.
v. 3.2006
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D E C I S I O N A R I U M
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
group decision making
multicriteria decision analysis
group collaboration
decision making
GDSS, NSS
Joint Gains
multi-party negotiation support with the method
of improving directions
RICH Decisions
rank inclusion in criteria hierarchies
CSCW
DSS
Opinions-Online
Windows software for decision analysis with
imprecise ratio statements
platform for global participation, voting,
surveys, and group decisions
internet
PRIME Decisions
computer support
WINPRE
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
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.
elimination of criteria and alternatives by even
swaps
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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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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 ruleswww.opinion.vote.hut.fi

• Condorcet criteria
• Copelands methods, Dodgsons method, Maximin
  method
• Borda count
• Nansons method, University method
• Blacks 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

Systems Analysis Laboratory Helsinki University
of Technology http//www.sal.hut.fi
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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
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Visits to Web-HIPRE
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Visitors top-level domains
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Visitors first-level domains
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Visits through sites linking to Web-HIPRE
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Literature
Mustajoki, J. and Hämäläinen, R.P. Web-HIPRE
Global decision support by value tree and AHP
analysis, INFOR, Vol. 38, No. 3, 2000, pp.
208-220. Hämäläinen, R.P. Reversing the
perspective on the applications of decision
analysis, Decision Analysis, Vol. 1, No. 1, pp.
26-31. Mustajoki, J., Hämäläinen, R.P. and
Marttunen, M. Participatory multicriteria
decision support with Web-HIPRE A case of lake
regulation policy. Environmental Modelling
Software, Vol. 19, No. 6, 2004, pp.
537-547. Pöyhönen, M. and Hämäläinen, R.P. There
is hope in attribute weighting, INFOR, Vol. 38,
No. 3, 2000, pp. 272-282. Pöyhönen, M. and
Hämäläinen, R.P. On the Convergence of
Multiattribute Weighting Methods, European
Journal of Operational Research, Vol. 129, No. 3,
2001, pp. 569-585. Pöyhönen, M., Vrolijk, H.C.J.
and Hämäläinen, R.P. Behavioral and Procedural
Consequences of Structural Variation in Value
Trees, European Journal of Operational Research,
Vol. 134, No. 1, 2001, pp. 218-227.
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New Theory Preference programming

• Analysis with incomplete preference statements
  (intervals)
• ...attribute is at least 2 times as but no more
  than 3 times as important as...
• Windows software
• WINPRE Workbench for Interactive Preference
  Programming
• Interval AHP, interval SMART/SWING and PAIRS
• PRIME-Preference Ratios in Multiattribute
  Evaluation Method
• Incomplete preference statements
• Web software
• RICH Decisions Rank Inclusion in Criteria
  Hierarchies

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Preference Programming The PAIRS method

• Imprecise statements with intervals on
• Attribute weight ratios (e.g. 1/2 ? w1 / w2 ? 3)
• ? Feasible region for the weights
• Alternatives ratings (e.g. 0.6 ? v1(x1) ? 0.8)
• ? Intervals for the overall values
• Lower bound for the overall value of x
• Upper bound correspondingly

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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
• 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/ms
al03b.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
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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 DMs 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
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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)

An even swap
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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 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?

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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 comparisons
series of pairwise comparisons
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Joint Gains Negotiation

• User can create his own case
• 2 to N participants (negotiating parties, DMs)
• 2 to M continuous decision variables
• Linear inequality constraints
• Participants distributed in the web

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DMs Utility Functions

• DMs 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

• Two participants
• buyer and seller
• Three decision variables
• unit price () 10..50
• amount (lb) 1..1000
• delivery (days) 1..30
• Delivery constraint (figure)
• 999delivery - 29amount ³ 970
• Initial agreement 30 , 100 lb, 25 days

30
delivery (days)
1
1000
1
amount (lb)
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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
efficient point
Session 1
efficient point
Session 2
efficient point
Session 3
. . .
efficient point
Session n
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New comparison task is given after all
participants have completed the first one
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Session view - joint gains after two steps
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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 Makingwww.mcda.hut.fieLearni
ng sites onMultiple Criteria Decision
AnalysisDecision 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
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Learning modules

• motivation, detailed instructions, 2 to 4 hour
  sessions

• Theory
• HTML
• pages

• Case
• slide shows
• video clips

• Assignments
• online quizzes
• software tasks
• report templates

• Evaluation
• Opinions
• Online

• Web software
• Web-HIPRE
• video clips

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Cases

• 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

Family selecting a car
Theory
Evaluation
Assignments
Intro
Theoreticalfoundations
Problemstructuring
Preferenceelicitation
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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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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.c
  om
• 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?foldernamesyke)
• 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