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DECISIONARIUM Aiding Decisions, Negotiating and Collecting Opinions on the Web www. decisionarium. hut. fi DECISIONARIUM Aiding Decisions, Negotiating and Collecting Opinions on the Web www. decisionarium. hut. fi Raimo P. Hämäläinen Systems Analysis Laboratory Aalto University, School of Science www. raimo. hut. fi December, 2010 1

DECISIONARI g l Ub a l s p a c e f o r DECISIONARI g l Ub a l s p a c e f o r d e c i s i o n s o M upport group collaboration group decision making multicriteria decision analysis GDSS, NSS decision making Joint multi-party. Gains negotiation support with the method of improving directions Opinions. Online RICH CSCW platform for global participation, voting, surveys, and group decisions DSS rank inclusion in criteria Decisions hierarchies internet computer support preference programmi ng, PAIRS WINPR E Windows software for decision analysis with imprecise ratio statements PRIME Decisions Web. HIPRE value tree and Smart-Swaps 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. smartswaps. hut. fi www. rich. hut. fi PRIME Decisions and WINPRE downloadable at www. sal. hut. fi/Downloadables Systems Analysis Laboratory Updated 25. 10. 2004 selected publications elimination of criteria and alternatives by even swaps 2 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

Mission of Decisionarium Provide resources for decision and negotiation support and advance the real 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 3

Opinions-Online (www. opinions. hut. fi) • Platform for global participation, voting, surveys, and group Opinions-Online (www. opinions. hut. fi) • Platform for global participation, voting, surveys, and group decisions Web-HIPRE (www. hipre. hut. fi) • Value tree based decision analysis and support WINPRE and PRIME Decisions (for Windows) • Interval AHP, interval SMART/SWING and PRIME methods RICH Decisions (www. rich. hut. fi) • Preference programming in MAVT Smart-Swaps (www. smart-swaps. hut. fi) • Multicriteria decision support with the even swaps method Joint Gains (www. jointgains. hut. fi) • Negotiation support with the method of improving directions 4

New Methodological Features • Possibility to compare different weighting and rating methods • AHP/MAVT 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 5

e. Learning Decision Making www. dm. hut. fi SAL e. Learning sites: Multiple Criteria e. Learning Decision Making www. dm. hut. fi SAL e. Learning sites: Multiple Criteria Decision Analysis www. mcda. hut. fi Decision Making Under Uncertainty Negotiation Analysis www. negotiation. hut. fi 6

Opinions-Online Platform for Global Participation, Voting, Surveys and Group Decisions www. opinions. hut. fi Opinions-Online Platform for Global Participation, Voting, Surveys and Group Decisions www. opinions. hut. fi www. opinions-online. net Design: Raimo P. Hämäläinen Programming: Reijo Kalenius Systems Analysis Laboratory Aalto University, School of Science http: //www. sal. hut. fi

Surveys on the web • • • Fast, easy and cheap Hyperlinks to background 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 8

Creating a new session • Browser-based generation of new sessions • Fast and simple Creating a new session • Browser-based generation of new sessions • Fast and simple • Templates available 9

Possible questions • Survey section Multiple/single choice • Best/worst • Ranking • Rating • Possible questions • Survey section Multiple/single choice • Best/worst • Ranking • Rating • Approval voting • Written comments 10

Viewing the results • In real-time • By selected fields • Questionwise public or Viewing the results • In real-time • By selected fields • Questionwise public or restricted access • Barometer • Direct links to results 11

Approval voting • The user is asked to pick the alternatives that he/she can 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 12

Examples of use • • • Teledemocracy – interactive citizens’ participation Group decision making Examples of use • • • Teledemocracy – interactive citizens’ participation Group decision making Brainstorming Course evaluation in universities and schools Marketing research Organisational surveys and barometers 13

Global Multicriteria Decision Support by Web-HIPRE A Java-applet for Value Tree and AHP Analysis Global Multicriteria Decision Support by Web-HIPRE A Java-applet for Value Tree and AHP Analysis www. hipre. hut. fi Raimo P. Hämäläinen and Jyri Mustajoki Systems Analysis Laboratory Aalto University, School of Science http: //www. sal. hut. fi

Multiattribute value tree analysis • Value tree: • Overall value of alternative x: n Multiattribute value tree analysis • Value tree: • Overall value of alternative x: n = number of attributes wi = weight of attribute i xi = consequence of alternative x with respect to attribute i vi(xi) = rating of xi

Elements link to web-pages 16 Elements link to web-pages 16

Direct Weighting Note: Weights in this example are her personal opinions 17 Direct Weighting Note: Weights in this example are her personal opinions 17

SWING, SMART and SMARTER Methods • SMARTER uses rankings only 18 SWING, SMART and SMARTER Methods • SMARTER uses rankings only 18

Pairwise Comparison - AHP • Continuous scale 1 -9 • Numerical, verbal or graphical Pairwise Comparison - AHP • Continuous scale 1 -9 • Numerical, verbal or graphical approach 19

Value Function • Ratings of alternatives shown • Any shape of the value function Value Function • Ratings of alternatives shown • Any shape of the value function allowed 20

Composite Priorities • Bar graphs or numerical values • Bars divided by the contribution Composite Priorities • Bar graphs or numerical values • Bars divided by the contribution of each criterion 21

Group Decision Support • Group model is the weighted sum of individual decision makers’ Group Decision Support • Group model is the weighted sum of individual decision makers’ composite priorities for the alternatives 22

Defining Group Members • Individual value trees can be different • Composite priorities of 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 23

Aggregate Group Priorities • Contribution of each group member indicated by segments 24 Aggregate Group Priorities • Contribution of each group member indicated by segments 24

Sensitivity analysis • Changes in the relative importance of decision makers can be analyzed Sensitivity analysis • Changes in the relative importance of decision makers can be analyzed 25

Future challenges Web makes MCDA tools available to everybody Should everybody use them? It 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 26

Sources of biases and problems 27 Sources of biases and problems 27

Literature Mustajoki, J. and Hämäläinen, R. P. : Web-HIPRE: Global decision support by value Literature Mustajoki, J. and Hämäläinen, R. P. : Web-HIPRE: Global decision support by value tree and AHP analysis, INFOR, Vol. 38, No. 3, 2000, pp. 208 -220. Hämäläinen, R. P. : Reversing the perspective on the applications of decision analysis, Decision Analysis, Vol. 1, No. 1, 2004, pp. 26 -31. Mustajoki, J. , Hämäläinen, R. P. and Marttunen, M. : Participatory multicriteria decision support with Web-HIPRE: A case of lake regulation policy. Environmental Modelling & Software, Vol. 19, No. 6, 2004, pp. 537 -547. Pöyhönen, M. and Hämäläinen, R. P. : There is hope in attribute weighting, INFOR, Vol. 38, No. 3, 2000, pp. 272 -282. Pöyhönen, M. and Hämäläinen, R. P. : On the Convergence of Multiattribute Weighting Methods, European Journal of Operational Research, Vol. 129, No. 3, 2001, pp. 569 -585. Pöyhönen, M. , Vrolijk, H. C. J. and Hämäläinen, R. P. : Behavioral and Procedural Consequences of Structural Variation in Value Trees, European Journal of Operational Research, Vol. 134, No. 1, 2001, pp. 218 -227. Hämäläinen, R. P. and Alaja, S. : The Threat of Weighting Biases in Environmental Decision Analysis, Ecological Economics, Vol. 68, 2008, pp. 556 -569. 28

Multiattribute value tree analysis under uncertainty – Preference programming Intervals to describe uncertainty • Multiattribute value tree analysis under uncertainty – Preference programming Intervals to describe uncertainty • Preferential uncertainty • Incomplete information • Uncertainty about the consequences of the alternatives

Theory Analysis with incomplete preference statements (intervals): ”. . . attribute is at least Theory Analysis with incomplete preference statements (intervals): ”. . . attribute is at least 2 times as but no more than 3 times as important as. . . ” Windows software • WINPRE – Workbench for Interactive Preference Programming Interval AHP, interval SMART/SWING and PAIRS • PRIME-Preference Ratios in Multiattribute Evaluation Method Ordinal score rankings decision rules Web software • RICH Decisions – Rank Inclusion in Criteria Hierarchies 30

Preference Programming – The PAIRS method • Imprecise statements with intervals on – Attribute Preference Programming – The PAIRS method • Imprecise statements with intervals on – Attribute weight ratios (e. g. 1/2 w 1 / w 2 3) Feasible region for the weights – Alternatives’ ratings (e. g. 0. 6 v 1(x 1) 0. 8) Intervals for the overall values – Lower bound for the overall value of x: – Upper bound correspondingly 31

Interval statements define a feasible region S for the weights 32 Interval statements define a feasible region S for the weights 32

Uses of interval models New generalized AHP and SMART/SWING methods Interval sensitivity analysis Variations Uses of interval models New generalized AHP and SMART/SWING methods Interval sensitivity analysis Variations allowed in several model parameters simultaneously - worst case analysis Group decision making All members´ opinions embedded in intervals = a joint common group model 33

WINPRE Software 34 WINPRE Software 34

Interval SMART/SWING • A as reference - A given 10 points • Point intervals 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 35

Imprecise rating of the alternatives Imprecise rating of the alternatives

Interval SMART/SWING weighting Interval SMART/SWING weighting

Value intervals and dominances • Jobs C and E dominated Can be eliminated • Value intervals and dominances • Jobs C and E dominated Can be eliminated • One can continue the process by narrowing the weight ratio intervals – Easier as Jobs C and E already eliminated

Benefits of interval SMART/SWING • SMART and SWING are simple and relatively well known Benefits of interval SMART/SWING • SMART and SWING are simple and relatively well known methods • Intervals provide an easy way to model uncertainty • Interval SMART/SWING preserves the cognitive simplicity of the original methods Behaviorally Interval SMART/SWING is likely to be easily adapted

PRIME Decisions Software 40 PRIME Decisions Software 40

Interval methods in group decision support • The individual DMs can use either point Interval methods in group decision support • The individual DMs can use either point estimates or intervals in their preference elicitation • Embed all models into a group interval model • Interval model includes the range of preferences of all the different DMs • The group process is to negotiate and tighten the intervals by interpersonal trade-offs

Literature – Methodology Salo, A. and Hämäläinen, R. P. : Preference assessment by imprecise Literature – Methodology Salo, A. and Hämäläinen, R. P. : Preference assessment by imprecise ratio statements, Operations Research, Vol. 40, No. 6, 1992, pp. 1053 -1061. Salo, A. and Hämäläinen, R. P. : Preference programming through approximate ratio comparisons, European Journal of Operational Research, Vol. 82, No. 3, 1995, pp. 458 -475. Salo, A. and Hämäläinen, R. P. : Preference ratios in multiattribute evaluation (PRIME) – Elicitation and decision procedures under incomplete information, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 31, No. 6, 2001, pp. 533 -545. Mustajoki, J. , Hämäläinen, R. P. and Salo, A. : Decision Support by Interval SMART/SWING - Incorporating Imprecision in the SMART and SWING Methods, Decision Sciences, Vol. 36, No. 2, 2005, pp. 317 -339. Mustajoki, J. , Hämäläinen, R. P. And Lindstedt, M. R. K. : Using Intervals for Global Sensitivity and Worst Case Analyses in Multiattribute Value Trees, European Journal of Operational Research, Vol. 174, No. 1, 2006, pp. 278 -292. 42

Literature – Tools and applications Gustafsson, J. , Salo, A. and Gustafsson, T. : Literature – Tools and applications Gustafsson, J. , Salo, A. and Gustafsson, T. : PRIME Decisions - An Interactive Tool for Value Tree Analysis, Lecture Notes in Economics and Mathematical Systems, M. Köksalan and S. Zionts (eds. ), 507, 2001, pp. 165 -176. Hämäläinen, R. P. , Salo, A. and Pöysti, K. : Observations about consensus seeking in a multiple criteria environment, Proc. of the Twenty-Fifth Hawaii International Conference on Systems Sciences, Hawaii, Vol. IV, January 1992, pp. 190 -198. Hämäläinen, R. P. and Pöyhönen, M. : On-line group decision support by preference programming in traffic planning, Group Decision and Negotiation, Vol. 5, 1996, pp. 485 -500. Liesiö, J. , Mild, P. and Salo, A. : Preference Programming for Robust Portfolio Modeling and Project Selection, European Journal of Operational Research, Vol. 181, Issue 3, pp. 1488 -1505. 43

RICH Decisions www. rich. hut. fi Design: Ahti Salo and Antti Punkka Programming: Juuso RICH Decisions www. rich. hut. fi Design: Ahti Salo and Antti Punkka Programming: Juuso Liesiö Systems Analysis Laboratory Aalto University, School of Science http: //www. sal. hut. fi

The RICH Method Incomplete ordinal information about the relative importance of attributes • ”environmental The RICH Method Incomplete ordinal information about the relative importance of attributes • ”environmental aspects belongs to the three most important attributes” or • ”either cost or environmental aspects is the most important attribute” 45

Score Elicitation • Upper and lower bounds for the scores • Type or use Score Elicitation • Upper and lower bounds for the scores • Type or use the scroll bar 46

Weight Elicitation The user specifies sets of attributes and corresponding sets of rankings. Here 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. 47

Dominance Structure and Decision Rules 48 Dominance Structure and Decision Rules 48

Literature Salo, A. and Punkka, A. : Rank Inclusion in Criteria Hierarchies, European Journal Literature Salo, A. and Punkka, A. : Rank Inclusion in Criteria Hierarchies, European Journal of Operational Research, Vol. 163, No. 2, 2005, pp. 338 -356. Salo, A. and Hämäläinen, R. P. : Preference ratios in multiattribute evaluation (PRIME) – Elicitation and decision procedures under incomplete information, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 31, No. 6, 2001, pp. 533 -545. Salo A. and Hämäläinen, R. P. : Preference Programming. (manuscript) Ojanen, O. , Makkonen, S. and Salo, A. : A Multi-Criteria Framework for the Selection of Risk Analysis Methods at Energy Utilities. International Journal of Risk Assessment and Management, Vol. 5, No. 1, 2005, pp. 16 -35. Punkka, A. and Salo, A. : RICHER: Preference Programming with Incomplete Ordinal Information. (submitted manuscript) Salo, A. and Liesiö, J. : A Case Study in Participatory Priority-Setting for a Scandinavian Research Program, International Journal of Information Technology & Decision Making, Vol. 5, No. 1, 2006, pp. 65 -88. 49

Smart-Swaps Smart Choices with the Even Swaps Method www. smart-swaps. hut. fi Design: Raimo Smart-Swaps Smart Choices with the Even Swaps Method www. smart-swaps. hut. fi Design: Raimo P. Hämäläinen and Jyri Mustajoki Programming: Pauli Alanaatu Systems Analysis Laboratory Aalto University, School of Science http: //www. sal. hut. fi

Smart Choices • An iterative process to support multicriteria decision making • Uses the Smart Choices • An iterative process to support multicriteria decision making • Uses the even swaps method to make trade-offs (Harvard Business School Press, Boston, MA, 1999) 51

Smart-Swaps software www. smart-swaps. hut. fi • Support for the Pr. OACT process (Hammond Smart-Swaps software www. smart-swaps. hut. fi • Support for the Pr. OACT process (Hammond et al. , 1999) – – – Problem Objectives Alternatives Consequences Trade-offs • Trade-offs carried out with the Even Swaps method

Problem / Objectives / Alternatives Problem / Objectives / Alternatives

Even Swaps • Multicriteria method to find the best alternative • An even swap: Even Swaps • Multicriteria method to find the best alternative • An even swap: – A value trade-off, where a consequence change in one attribute is compensated with a comparable change in some other attribute – A new alternative with these revised consequences is equally preferred to the initial one The new alternative can be used instead

Even Swaps • Carry out even swaps that make Alternatives dominated (attribute-wise) • There Even Swaps • Carry out even swaps that make Alternatives dominated (attribute-wise) • There is another alternative, which is equal or better than this in every attribute, and better at least in one attribute Attributes irrelevant • Each alternative has the same value on this attribute These can be eliminated • Process continues until one alternative, i. e. the best one, remains 55

Supporting Even Swaps with Preference Programming • Even Swaps process carried out as usual 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 56

Use of trade-off information • With each even swap the user reveals new information Use of trade-off information • With each even swap the user reveals new information about her preferences • This trade-off information can be utilized in the process Tighter bounds for the weight ratios obtained from the given even swaps Better estimates for the values of the alternatives

Decision support Even Swaps Problem initialization Preference Programming Initial statements about the attributes Practical Decision support Even Swaps Problem initialization Preference Programming Initial statements about the attributes Practical dominance candidates Eliminate dominated alternatives Updating of Eliminate irrelevant attributes No the model More than one remaining alternative Yes Even swap suggestions Make an even swap Trade-off information The most preferred alternative is found 58

Smart-Swaps • Identification of practical dominances • Suggestions for the next even swap to Smart-Swaps • Identification of practical dominances • Suggestions for the next even swap to be made • Additional support Information about what can be achieved with each swap Notification of dominances Rankings indicated by colours Process history allows backtracking 59

Example • Office selection problem (Hammond et al. 1999) 25 78 Practically An even Example • Office selection problem (Hammond et al. 1999) 25 78 Practically An even swap Dominated dominated by Commute time by Lombard removed as irrelevant Montana (Slightly better in Monthly Cost, but equal or worse in all other attributes) 60

Problem definition 61 Problem definition 61

Entering trade-offs 62 Entering trade-offs 62

Process history 63 Process history 63

Literature Hammond, J. S. , Keeney, R. L. , Raiffa, H. , 1998. Even Literature Hammond, J. S. , Keeney, R. L. , Raiffa, H. , 1998. Even swaps: A rational method for making trade-offs, Harvard Business Review, 76(2), 137 -149. Hammond, J. S. , Keeney, R. L. , Raiffa, H. , 1999. Smart choices. A practical guide to making better decisions, Harvard Business School Press, Boston. Mustajoki, J. Hämäläinen, R. P. , 2005. A Preference Programming Approach to Make the Even Swaps Method Even Easier, Decision Analysis, 2(2), 110 -123. Applications of Even Swaps: Gregory, R. , Wellman, K. , 2001. Bringing stakeholder values into environmental policy choices: a community-based estuary case study, Ecological Economics, 39, 37 -52. Kajanus, M. , Ahola, J. , Kurttila, M. , Pesonen, M. , 2001. Application of even swaps for strategy selection in a rural enterprise, Management Decision, 39(5), 394 -402. Luo, C. -M. , Cheng, B. W. , 2006. Applying Even-Swap Method to Structurally Enhance the Process of Intuition Decision-Making, Systemic Practice and Action Research, 19(1), 45 -59. 64

Joint-Gains Negotiation Support in the Internet www. jointgains. hut. fi Eero Kettunen, Raimo P. Joint-Gains Negotiation Support in the Internet www. jointgains. hut. fi Eero Kettunen, Raimo P. Hämäläinen and Harri Ehtamo Systems Analysis Laboratory Aalto University, School of Science http: //www. sal. hut. fi

Method of Improving Directions Ehtamo, Kettunen, and Hämäläinen (2002) Utility of DM 2 • Method of Improving Directions Ehtamo, Kettunen, and Hämäläinen (2002) Utility of DM 2 • Interactive method for reaching efficient alternatives Efficient frontier • Search of joint gains from a given initial alternative . Utility of DM 1 • In the mediation process participants are given simple comparison tasks: “Which one of these two alternatives do you prefer, alternative A or B? ” 66 . . .

Mediation Process Tasks in Preference Identification • Initial alternative considered as “current alternative” • Mediation Process Tasks in Preference Identification • Initial alternative considered as “current alternative” • Task 1 for identifying participants’ most preferred directions series of pairwise 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 67

Joint Gains Negotiation • User can create his own case • 2 to N Joint Gains Negotiation • User can create his own case • 2 to N participants (negotiating parties, DM’s) • 2 to M continuous decision variables • Linear inequality constraints • Participants distributed in the web 68

DM’s Utility Functions • DM’s reply holistically • No explicit assessment of utility functions DM’s Utility Functions • DM’s reply holistically • No explicit assessment of utility functions • Joint Gains only calls for local preference information • Post-settlement setting in the neighbourhood of the current alternative • Joint Gains allows learning and change of preferences during the process 69

Case example: Business buyer and seller • Three decision variables unit price ($): 10. Case example: Business buyer and seller • Three decision variables unit price ($): 10. . 50 amount (lb): 1. . 1000 delivery (days): 1. . 30 • Delivery constraint (figure): 999*delivery - 29*amount ³ 970 30 delivery (days) • Two participants 1 1 amount (lb) 1000 • Initial agreement: 30 $, 100 lb, 25 days 70

Creating a case: Criteria to provide optional decision aiding 71 Creating a case: Criteria to provide optional decision aiding 71

Sessions • Participants take part in sessions within the case • Sessions produce efficient Sessions • Participants take part in sessions within the case • Sessions produce efficient alternatives • Case administrator can start new sessions on-line and define new initial starting points • Sessions can be parallel • Each session has an independent mediation process 72 Joint Gains - Business Session efficient point 1 Session efficient 2 point Session efficient 3. point. . Session efficient point n

New comparison task is given after all participants have completed the first one Not New comparison task is given after all participants have completed the first one Not started Preference identification task 1 Preference identification task 2 JOINT GAIN? Stopped 73

Session view - joint gains after two steps 74 Session view - joint gains after two steps 74

Literature Ehtamo, H. , M. Verkama, and R. P. Hämäläinen (1999). How to select Literature Ehtamo, H. , M. Verkama, and R. P. Hämäläinen (1999). How to select Fair Improving Directions in a negotiation Model over Continuous Issues, IEEE Trans. On Syst. , Man, and Cybern. – Part C, Vol. 29, No. 1, pp. 26 -33. Ehtamo, H. , E. Kettunen, and R. P. Hämäläinen (2001). Searching for Joint Gains in Multi-Party Negotiations, European Journal of Operational Research, Vol. 130, No. 1, pp. 54 -69. Hämäläinen, H. , E. Kettunen, M. Marttunen, and H. Ehtamo (2001). Evaluating a Framework for Multi-Stakeholder Decision Support in Water Resources Management, Group Decision and Negotiation, Vol. 10, No. 4, pp. 331 -353. Ehtamo, H. , R. P. Hämäläinen, and V. Koskinen (2004). An E-learning Module on Negotiation Analysis, Proc. of the Hawaii International Conference on System Sciences, IEEE Computer Society Press, Hawaii, January 5 -8. 75

RPM Decisions Professor Ahti Salo Dr. Juuso Liesiö Lic. Sc. Pekka Mild Lic. Sc. RPM Decisions Professor Ahti Salo Dr. Juuso Liesiö Lic. Sc. Pekka Mild Lic. Sc. Antti Punkka Dr. Ville Brummer M. Sc. Eeva Vilkkumaa M. Sc. Jussi Kangaspunta M. Sc. Antti Toppila http: //www. rpm. tkk. fi/

Robust Portfolio Modeling (RPM) • Supports project portfolio selection w. r. t. multiple criteria Robust Portfolio Modeling (RPM) • Supports project portfolio selection w. r. t. multiple criteria – – – Portfolio = a set of projects Feasible portfolios fulfill resource and possible other constraints Project value additive over criteria Portfolio value = sum of its constituent projects’ values Incomplete preference information (Preference Programming) • Decision recommendations: non-dominated (ND) portfolios – Additional preference information does not make the set of ND portfolios bigger A 10 4 5 3 • Project-oriented analysis 10 4 5 C 3 1 2 6 7 – Accept core projects that belong to all ND portfolios 8 – Discard exterior projects that do not belong to any of the ND portfolios – Select between the borderline projects that belong to some ND portfolios 9 B 9 8

RPM Framework • Wide score intervals • Loose weight statements Borderline projects “uncertain zone” RPM Framework • Wide score intervals • Loose weight statements Borderline projects “uncertain zone” Focus Exterior projects “Robust zone” Discard Approach to promote robustness through incomplete information (integrated sensitivity analysis). Accounts for group statements Gradual selection: Core • Narrower intervals • Stricter weights Border Exterior Negotiation. Manual iteration. Heuristic rules. Transparency w. r. t. individual projects Tentative conclusions at any stage of the process Not selected Large number of project proposals. Evaluated w. r. t. multiple criteria. Selected Core projects “Robust zone” Choose Core (exterior) projects stay core (exterior) projects even, if additional preference information is imposed

RPM Decisions software: data input and value tree construction, elicitation of preference information RPM Decisions software: data input and value tree construction, elicitation of preference information

Analysis phase – elicitation of additional preference information, illustration of core indices, portfolios’ properties Analysis phase – elicitation of additional preference information, illustration of core indices, portfolios’ properties and support to gradual selection of projects

Methodology Literature Liesiö, J. , Mild, P. , Salo, A. (2007). Preference Programming for Methodology Literature Liesiö, J. , Mild, P. , Salo, A. (2007). Preference Programming for Robust Portfolio Modeling and Project Selection, EJOR 181, 1488 -1505 Liesiö, J. , Mild, P. , Salo, A. (2008). Robust Portfolio Modeling with Incomplete Cost Information and Project Interdependencies, EJOR 190, 679 -695 Applications Könnölä, T. , Brummer, V. , Salo, A. (2007). Diversity in Foresight: Insights from the Fostering of Innovation Ideas, Technological Forecasting and Social Change 74, 608 -626 Brummer, V. , Könnölä, T. , Salo, A. (2008). Foresight within ERA-NETs: Experiences from the Preparation of an International Research Program, Technological Forecasting and Social Change 75, 483 -495 Lindstedt, M. , Liesiö, J. , Salo, A. (2008). Participatory Development of a Strategic Product Portfolio in a Telecommunication Company, International Journal of Technology Management 42, 250 -266 Brummer, V. , Salo, A. , Nissinen, J. , Liesiö, J. A Methodology for the Identification of Prospective Collaboration Networks in International R&D Programs, International Journal of Technology Management, Special issue on technology foresight, to appear.

e. Learning Decision Making www. mcda. hut. fi e. Learning sites on: Multiple Criteria e. Learning Decision Making www. mcda. hut. fi e. Learning sites on: Multiple Criteria Decision Analysis Decision Making Under Uncertainty Negotiation Analysis Prof. Raimo P. Hämäläinen Systems Analysis Laboratory Aalto University, School of Science http: //www. sal. hut. fi

e. Learning sites Material: • Theory sections, interactive computer assignments • Animations and video e. Learning 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 e. Learning modules: • 4 - 6 hours study time • Instructors can create their own modules using the material and software • Academic non-profit use is free 83

84 84

Learning paths and modules Learning path: guided route through the learning material Learning module: Learning paths and modules Learning path: guided route through the learning material Learning module: represents 2 -4 h of traditional lectures and exercises Learning Theory Paths Cases Quizzes Videos Assignments Evaluation Introduction to Value Tree Analysis Module 2 Module 3 85

Learning modules Learning Theory Cases Quizz Paths Videos Assignments Evaluation es Introduction to Value Learning modules Learning Theory Cases Quizz Paths Videos Assignments 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 86 Assignments Evaluation • online quizzes • Opinions • software tasks • report templates Online

Cases Family selecting a car Job selection case • basics of value tree analysis Cases Family selecting a car Job selection case • basics of value tree analysis • how to use Web-HIPRE Theory Intro Theoretical foundations Car selection case Assignments • imprecise preference statements, interval value trees • basics of Prime Decisions software Problem structuring Family selecting a car Preference elicitation • group decision-making with Web-HIPRE • weighted arithmetic mean method 87 Evaluation

Video clips Learning Theory Cases Quizze s Paths • Recorded software use with voice Video clips Learning Theory Cases Quizze s Paths • Recorded software use with voice explanations (1 -4 min) • Screen capturing with Camtasia • AVI format for video players – e. g. Windows Media Player, Real. Player • GIF format for common browsers - no sound 88 Videos Assignments Videos Working with Web-HIPRE Structuring a value tree Entering consequences of. . . Assessing the form of value. . . Direct rating SMART SWING AHP Viewing the results Sensitivity analysis Group decision making PRIME method

Learning Theory Cases Quizze s Paths Videos Assignments testing the knowledge on the subject, Learning Theory Cases Quizze s Paths Videos Assignments testing the knowledge on the subject, learning by doing, individual and group reports Software use • value tree analysis and group decisions with Web-HIPRE Report templates • detailed instructions in a word document • to be returned in printed format 89

Academic Test Use is Free ! Opinions-Online (www. opinions. hut. fi) Commercial site and 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. 90

Programming at SAL • HIPRE 3 +: Hannu Lauri • Web-HIPRE: Jyri Mustajoki, Ville Programming at SAL • HIPRE 3 +: Hannu Lauri • Web-HIPRE: Jyri Mustajoki, Ville Likitalo, Sami Nousiainen • Joint Gains: Eero Kettunen, Harri Jäälinoja, Tero Karttunen, Sampo Vuorinen • Opinions-Online: Reijo Kalenius, Ville Koskinen Janne Pöllönen • Smart-Swaps: Pauli Alanaatu, Ville Karttunen, Arttu Arstila, Juuso Nissinen • WINPRE: Jyri Helenius • PRIME Decisions: Janne Gustafsson, Tommi Gustafsson • RICH Decisions: Juuso Liesiö, Antti Punkka • e-learning MCDA: Ville Koskinen, Jaakko Dietrich, Markus Porthin Thank you! 91