DECISIONARIUM Aiding Decisions, Negotiating and Collecting Opinions

DECISIONARIUM Aiding Decisions, Negotiating and Collecting Opinions on the Web www.decisionarium.hut.fi S ystems Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology www.raimo.hut.fi JMCDA, Vol. 12 , No. 2-3, 2003, pp. 101-110. Analysis Laboratory Helsinki University of Technology 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 S ystems Systems Analysis Laboratory Analysis Laboratory Updated 25.10.2004 Helsinki University of Technology 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 S ystems Analysis Laboratory Helsinki University of Technology 3

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 S ystems Analysis Laboratory Helsinki University of Technology 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 S ystems Analysis Laboratory Helsinki University of Technology 5

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 S ystems Analysis Laboratory Helsinki University of Technology 6

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 S ystems Analysis Laboratory Helsinki University of Technology Systems Analysis Laboratory Helsinki University of Technology 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 S ystems Analysis Laboratory Helsinki University of Technology 8

Creating a new session Browser-based generation of new sessions Fast and simple Templates available S ystems Analysis Laboratory Helsinki University of Technology 9

Possible questions Survey section Multiple/single choice Best/worst Ranking Rating Approval voting Written comments S ystems Analysis Laboratory Helsinki University of Technology 10

Viewing the results In real-time By selected fields Questionwise public or restricted access Barometer Direct links to results S ystems Analysis Laboratory Helsinki University of Technology 11

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 S ystems Analysis Laboratory Helsinki University of Technology 12

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 S ystems Analysis Laboratory Helsinki University of Technology 13

Examples of use Teledemocracy – interactive citizens’ participation Group decision making Brainstorming Course evaluation in universities and schools Marketing research Organisational surveys and barometers S ystems Analysis Laboratory Helsinki University of Technology 14

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 Analysis Laboratory 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 S ystems Analysis Laboratory Helsinki University of Technology 16

Direct Weighting Note: Weights in this example are her personal opinions S ystems Analysis Laboratory Helsinki University of Technology 17

SWING,SMART and SMARTER Methods SMARTER uses rankings only S ystems Analysis Laboratory Helsinki University of Technology 18

Pairwise Comparison - AHP Continuous scale 1-9 Numerical, verbal or graphical approach S ystems Analysis Laboratory Helsinki University of Technology 19

Value Function Ratings of alternatives shown Any shape of the value function allowed S ystems Analysis Laboratory Helsinki University of Technology 20

Composite Priorities Bar graphs or numerical values Bars divided by the contribution of each criterion S ystems Analysis Laboratory Helsinki University of Technology 21

Group Decision Support Group model is the weighted sum of individual decision makers’ composite priorities for the alternatives S ystems Analysis Laboratory Helsinki University of Technology 22

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 S ystems Analysis Laboratory Helsinki University of Technology 23

Aggregate Group Priorities Contribution of each group member indicated by segments S ystems Analysis Laboratory Helsinki University of Technology 24

Sensitivity analysis Changes in the relative importance of decision makers can be analyzed S ystems Analysis Laboratory Helsinki University of Technology 25

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 S ystems Analysis Laboratory Helsinki University of Technology 26

Sources of biases and problems Weighting methods yield different weights Number-of-attributelevels effect in conjoint analysis Decision makers only give ordinal information Hierarchical weighting leads to steeper weights Range effect 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 S ystems Analysis Laboratory Helsinki University of Technology 27

Visits to Web-HIPRE 16526 13151 14216 9233 5303 6826 1617 1998 1999 2000 2001 2002 2003 2004 S ystems Analysis Laboratory Helsinki University of Technology 28

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) S ystems Analysis Laboratory Helsinki University of Technology 29

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) S ystems Analysis Laboratory Helsinki University of Technology 30

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 31

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. S ystems Analysis Laboratory Helsinki University of Technology 32

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 S ystems Analysis Laboratory Helsinki University of Technology 33

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 n the overall value of x: v ( x ) min w i v i ( x i ) i 1 – Upper bound correspondingly S ystems Analysis Laboratory Helsinki University of Technology 34

Interval statements define a feasible region S for the weights 1 2 1 3 wA 2 wB wA 1 wC 1 6 wB 2 wC S ystems Analysis Laboratory Helsinki University of Technology 35

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 S ystems Analysis Laboratory Helsinki University of Technology 36

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 S ystems Analysis Laboratory Helsinki University of Technology 37

WINPRE Software S ystems Analysis Laboratory Helsinki University of Technology 38

PRIME Decisions Software S ystems Analysis Laboratory Helsinki University of Technology 39

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. S ystems Analysis Laboratory Helsinki University of Technology 40

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) S ystems Analysis Laboratory Helsinki University of Technology 41

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 S ystems Analysis Laboratory Helsinki University of Technology

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” S ystems Analysis Laboratory Helsinki University of Technology 43

Score Elicitation Upper and lower bounds for the scores Type or use the scroll bar S ystems Analysis Laboratory Helsinki University of Technology 44

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. S ystems Analysis Laboratory Helsinki University of Technology 45

Dominance Structure and Decision Rules S ystems Analysis Laboratory Helsinki University of Technology 46

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) S ystems Analysis Laboratory Helsinki University of Technology 47

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 Analysis Laboratory Helsinki University of Technology

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) S ystems Analysis Laboratory Helsinki University of Technology 49

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 S ystems Analysis Laboratory Helsinki University of Technology 50

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 S ystems Analysis Laboratory Helsinki University of Technology 51

Decision support Even Swaps Problem initialization Eliminate dominated alternatives Initial statements about the attributes Practical dominance candidates Updating of Eliminate irrelevant attributes No Preference Programming the model More than one remaining alternative Yes Make an even swap Even swap suggestions Trade-off information The most preferred alternative is found S ystems Analysis Laboratory Helsinki University of Technology 52

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 S ystems Analysis Laboratory Helsinki University of Technology 53

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 S ystems Analysis Laboratory Helsinki University of Technology 54

Problem definition S ystems Analysis Laboratory Helsinki University of Technology 55

Entering trade-offs S ystems Analysis Laboratory Helsinki University of Technology 56

Process history S ystems Analysis Laboratory Helsinki University of Technology 57

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. S ystems Analysis Laboratory Helsinki University of Technology 58

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 S ystems Analysis Laboratory Helsinki University of Technology

Ehtamo, Kettunen, and Hämäläinen (2002) Interactive method for reaching alternatives Utility of DM 2 Method of Improving Directions Efficient frontier efficient. . . . 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?” S ystems Analysis Laboratory Helsinki University of Technology 60

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 S ystems Analysis Laboratory Helsinki University of Technology 61

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 S ystems Analysis Laboratory Helsinki University of Technology 62

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 S ystems Analysis Laboratory Helsinki University of Technology 63

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 S ystems Analysis Laboratory Helsinki University of Technology 64

Creating a case: Criteria to provide optional decision aiding S ystems Analysis Laboratory Helsinki University of Technology 65

Sessions Participants take part in sessions within the case Sessions produce efficient alternatives Joint Gains - Business Case administrator can start new Session 1 efficient sessions on-line and define new point Session 2 efficient initial starting points point Session 3 efficient Sessions can be parallel . point . Each session has an independent Session n efficient mediation process point S ystems Analysis Laboratory Helsinki University of Technology 66

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 S ystems Analysis Laboratory Helsinki University of Technology 67

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 S ystems Analysis Laboratory Helsinki University of Technology 68

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. S ystems Analysis Laboratory Helsinki University of Technology 69

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 S ystems Analysis Laboratory Helsinki University of Technology

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 S ystems Analysis Laboratory Helsinki University of Technology 71

S ystems Analysis Laboratory Helsinki University of Technology 72

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 S ystems Analysis Laboratory Helsinki University of Technology 73

Learning modules Learning Theory Paths Cases Quizz VideosAssignments Evaluation es Introduction to Value Tree Analysis Module 2 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 S ystems Analysis Laboratory Helsinki University of Technology 74

Cases Family selecting a car Job Job selection selection case case basics of value tree analysis basics of value tree analysis Theory Assignments how howto touse useWeb-HIPRE Web-HIPRE Intro Theoretical foundations Problem structuring Preference elicitation Evaluation Car Car selection selection case case imprecise imprecisepreference preferencestatements, statements, interval intervalvalue valuetrees trees basics basicsof ofPrime PrimeDecisions Decisionssoftware software Family Family selecting selecting aa car car group groupdecision-making decision-makingwith withWeb-HIPRE Web-HIPRE weighted weightedarithmetic arithmeticmean meanmethod method S ystems Analysis Laboratory Helsinki University of Technology 75

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 S ystems Analysis Laboratory Helsinki University of Technology Learning Theory Cases Paths 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 76

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 S ystems Analysis Laboratory Helsinki University of Technology 77

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. S ystems Analysis Laboratory Helsinki University of Technology 78

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! S ystems Analysis Laboratory Helsinki University of Technology 79

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) S ystems Analysis Laboratory Helsinki University of Technology 80

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 S ystems Analysis Laboratory Helsinki University of Technology 81