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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 S ystems

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aiding decisions negotiating and collecting opinions on the web
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

slide2

Systems

Analysis Laboratory

Updated 25.10.2004

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

mission of decisionarium
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 fornon OR/MS people

slide4
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 andPRIME 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
new methodological features
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
slide6

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

opinions online platform for global participation voting surveys and group decisions

Opinions-OnlinePlatform 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

surveys on the web
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
creating a new session
Creating a new session
  • Browser-based generation of new sessions
  • Fast and simple
  • Templates available
possible questions
Possible questions
  • Survey section

Multiple/single

choice

  • Best/worst
  • Ranking
  • Rating
  • Approval voting
  • Written comments
viewing the results
Viewing the results
  • In real-time
  • By selected fields
  • Questionwise public or restricted access
  • Barometer
  • Direct links to results
approval voting
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
advanced voting rules www opinion vote hut fi
Advanced voting ruleswww.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
examples of use
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

Global Multicriteria Decision Support by Web-HIPREA 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

direct weighting
Direct Weighting

Note: Weights in this example are her personal opinions

swing smart and smarter methods
SWING,SMART and SMARTER Methods
  • SMARTER uses rankings only
pairwise comparison ahp
Pairwise Comparison - AHP
  • Continuous scale 1-9
  • Numerical, verbal or graphical approach
value function
Value Function
  • Ratings of alternatives shown
  • Any shape of the value function allowed
composite priorities
Composite Priorities
  • Bar graphs or numerical values
  • Bars divided by the contribution of each criterion
group decision support
Group Decision Support
  • Group model is the weighted sum of individual decision makers’ composite priorities for the alternatives
defining group members
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
aggregate group priorities
Aggregate Group Priorities
  • Contribution of each group member indicated by segments
sensitivity analysis
Sensitivity analysis
  • Changes in the relative importance of decision makers can be analyzed
slide26

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
slide32

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.

new theory preference programming
New Theory: Preference programming

Analysis with incompletepreference 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
preference programming the pairs method
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
uses of interval models
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

interval smart swing
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
slide40

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.

slide41

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)

rich decisions

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

the rich method
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”
slide44

Score Elicitation

  • Upper and lower bounds for the scores
  • Type or use the scroll bar
slide45

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.

slide47

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)

smart swaps smart choices with the even swaps method

Smart-SwapsSmart 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

smart choices
Smart Choices
  • An iterative process to support multicriteria decision making
  • Uses theeven swaps method to make trade-offs

(Harvard Business School Press, Boston, MA, 1999)

even swaps
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
supporting even swaps with preference programming
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

decision support

Preference

Programming

Even Swaps

Updating of the model

Problem initialization

Initial statements about the attributes

Practical dominance candidates

Eliminate dominated alternatives

Eliminate irrelevant attributes

No

More than one

remaining alternative

Yes

Even swap suggestions

Make an even swap

Trade-off information

The most preferred alternative is found

Decision support
slide53

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

example

25

78

Practically

dominated

by

Montana

Dominated

by

Lombard

Commute time removed as irrelevant

(Slightly better in Monthly Cost, but equal or worse in all other attributes)

Example
  • Office selection problem (Hammond et al. 1999)

An even swap

slide58

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.

joint gains negotiation support in the internet

Joint-GainsNegotiation 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

method of improving directions ehtamo kettunen and h m l inen 2002

Efficient frontier

.

.

.

Utility of DM 2

.

Utility of DM 1

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?”

mediation process tasks in preference identification
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

joint gains negotiation
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
dm s utility functions
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
case example business
Case example: Business

30

  • Two participants

buyer and seller

  • Three decision variables

unit price ($): 10..50

amount (lb): 1..1000

delivery (days): 1..30

  • Delivery constraint (figure):

999*delivery - 29*amount ³ 970

  • Initial agreement: 30 $, 100 lb, 25 days

delivery (days)

1

1000

1

amount (lb)

sessions
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

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

New comparison task is given after all participants have completed the first one
slide69

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.

slide70

eLearning Decision Makingwww.mcda.hut.fieLearning sites on:Multiple Criteria Decision AnalysisDecision Making Under Uncertainty Negotiation Analysis

Prof. Raimo P. Hämäläinen

Systems Analysis Laboratory

Helsinki University of Technology

http://www.sal.hut.fi

elearning sites
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
learning paths and modules

Learning

Paths

Introduction to Value Tree Analysis

Quizzes

Videos

Assignments

Theory

Cases

Evaluation

Module 2

Module 3

Learning paths and modules

Learning path: guided route through the learning material

Learning module: represents 2-4 h of traditional lectures and exercises

learning modules

Quizzes

Videos

Assignments

Cases

Theory

Introduction to Value Tree Analysis

Module 2

Module 3

Learning

Paths

Evaluation

Learning modules
  • 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
cases
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

video clips

Learning

Paths

Assignments

Quizzes

Videos

Cases

Theory

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

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
slide77

Assignments

Learning

Paths

Quizzes

Videos

Cases

Theory

  • 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
academic test use is free
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.

slide79
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!

public participation project sites
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)

sal elearning sites
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