1 Raimo P. Hämäläinen Systems Analysis Laboratory Aalto University, School of Science December, 2010 Aiding Decisions, Negotiating and Collecting Opinions on the Web D E C I S I O N A R I U M

2 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, H. Ehtamo, R.P. Hämäläinen and V. Koskinen: An e-learning module on negotiation analysis, Proc. of HICSS-37, J. Mustajoki and R.P. Hämäläinen, Making the even swaps method even easier, Manuscript, R.P. Hämäläinen, Decisionarium - Aiding decisions, negotiating and collecting opinions on the Web, J. Multi-Crit. Dec. Anal., H. Ehtamo, E. Kettunen and R.P. Hämäläinen: Searching for joint gains in multi-party negotiations, Eur. J. Oper. Res., J. Gustafsson, A. Salo and T. Gustafsson: PRIME Decisions - An interactive tool for value tree analysis, Lecture Notes in Economics and Mathematical Systems, J. Mustajoki and R.P. Hämäläinen: Web-HIPRE - Global decision support by value tree and AHP analysis, INFOR, D E C I S I O N A R I U M PRIME Decisions WINPRE web-sites PRIME Decisions and WINPRE downloadable at Web-HIPRE value tree and AHP based decision support Smart-Swaps Opinions- Online platform for global participation, voting, surveys, and group decisions Joint Gains group collaboration decision making computer support CSCW multicriteria decision analysis internet group decision making GDSS, NSS DSS multi-party negotiation support with the method of improving directions Windows software for decision analysis with imprecise ratio statements 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 elimination of criteria and alternatives by even swaps preference programming, PAIRS Updated S ystems Analysis Laboratory RICH Decisions rank inclusion in criteria hierarchies

3 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

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

5 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 New Methodological Features

6 SAL eLearning sites: Multiple Criteria Decision Analysis Decision Making Under Uncertainty Negotiation Analysis eLearning Decision Making

Opinions-Online Platform for Global Participation, Voting, Surveys and Group Decisions Design: Raimo P. Hämäläinen Programming: Reijo Kalenius Systems Analysis Laboratory Aalto University, School of Science

8 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, list, domain, password

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

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

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

12 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

13 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 Raimo P. Hämäläinen and Jyri Mustajoki Systems Analysis Laboratory Aalto University, School of Science

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

16 Elements link to web-pages

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

18 SMARTER uses rankings only SWING,SMART and SMARTER Methods

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

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

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

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

23 Individual value trees can be different Composite priorities of each group member - obtained from their individual models - shown in the definition phase Defining Group Members

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

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

26 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

27 Sources of biases and problems

28 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 Hämäläinen, R.P.: Reversing the perspective on the applications of decision analysis, Decision Analysis, Vol. 1, No. 1, 2004, pp 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 Pöyhönen, M. and Hämäläinen, R.P.: There is hope in attribute weighting, INFOR, Vol. 38, No. 3, 2000, pp Pöyhönen, M. and Hämäläinen, R.P.: On the Convergence of Multiattribute Weighting Methods, European Journal of Operational Research, Vol. 129, No. 3, 2001, pp 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 Hämäläinen, R.P. and Alaja, S.: The Threat of Weighting Biases in Environmental Decision Analysis, Ecological Economics, Vol. 68, 2008, pp

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

30 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

31 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

32 Interval statements define a feasible region S for the weights

33 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

34 WINPRE Software

35 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

Imprecise rating of the alternatives

Interval SMART/SWING weighting

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 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

40 PRIME Decisions Software

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

42 Literature – Methodology Salo, A. and Hämäläinen, R.P.: Preference assessment by imprecise ratio statements, Operations Research, Vol. 40, No. 6, 1992, pp Salo, A. and Hämäläinen, R.P.: Preference programming through approximate ratio comparisons, European Journal of Operational Research, Vol. 82, No. 3, 1995, pp 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 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 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

43 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 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 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 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

RICH Decisions Design: Ahti Salo and Antti Punkka Programming: Juuso Liesiö Systems Analysis Laboratory Aalto University, School of Science

45 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”

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

47 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. Weight Elicitation

48 Dominance Structure and Decision Rules

49 Literature Salo, A. and Punkka, A.: Rank Inclusion in Criteria Hierarchies, European Journal of Operational Research, Vol. 163, No. 2, 2005, pp 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 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 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

Smart-Swaps Smart Choices with the Even Swaps Method Design: Raimo P. Hämäläinen and Jyri Mustajoki Programming: Pauli Alanaatu Systems Analysis Laboratory Aalto University, School of Science

51 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)

Smart-Swaps software Support for the PrOACT process (Hammond et al., 1999) –Problem –Objectives –Alternatives –Consequences –Trade-offs Trade-offs carried out with the Even Swaps method

Problem / Objectives / Alternatives

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

55 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

56 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

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

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

59 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 Smart-Swaps

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

61 Problem definition

62 Entering trade-offs

63 Process history

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

Joint-Gains Negotiation Support in the Internet Eero Kettunen, Raimo P. Hämäläinen and Harri Ehtamo Systems Analysis Laboratory Aalto University, School of Science

66 Method of Improving Directions Ehtamo, 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?” Efficient frontier.... Utility of DM 1 Utility of DM 2

67 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

68 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

69 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

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

71 Creating a case: Criteria to provide optional decision aiding

72 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 Session 1 Session 2 Session 3 Joint Gains - Business Session n  efficient point

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

74 Session view - joint gains after two steps

75 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 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 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 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.

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

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 Project-oriented analysis –Accept core projects that belong to all ND portfolios –Discard exterior projects that do not belong to any of the ND portfolios –Select between the borderline projects that belong to some ND portfolios Robust Portfolio Modeling (RPM) A B C

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

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 and support to gradual selection of projects

Literature Methodology Liesiö, J., Mild, P., Salo, A. (2007). Preference Programming for Robust Portfolio Modeling and Project Selection, EJOR 181, Liesiö, J., Mild, P., Salo, A. (2008). Robust Portfolio Modeling with Incomplete Cost Information and Project Interdependencies, EJOR 190, Applications Könnölä, T., Brummer, V., Salo, A. (2007). Diversity in Foresight: Insights from the Fostering of Innovation Ideas, Technological Forecasting and Social Change 74, 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, Lindstedt, M., Liesiö, J., Salo, A. (2008). Participatory Development of a Strategic Product Portfolio in a Telecommunication Company, International Journal of Technology Management 42, 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.

eLearning Decision Making eLearning sites on: Multiple Criteria Decision Analysis Decision Making Under Uncertainty Negotiation Analysis Prof. Raimo P. Hämäläinen Systems Analysis Laboratory Aalto University, School of Science

83 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: hours study time Instructors can create their own modules using the material and software Academic non-profit use is free

84

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

86 Learning modules Theory HTML pages motivation, detailed instructions, 2 to 4 hour sessions Case slide shows video clips Assignments online quizzes software tasks report templates Evaluation Opinions Online Web software Web-HIPRE video clips Assignments Theory Videos Cases Quizz es Learning Paths Evaluation Introduction to Value Tree Analysis Module 3 Module 2

87 Evaluation Cases Assignments Theory Intro Theoretical foundations Problem structuring Preference elicitation Family selecting a car 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 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

88 Video clips 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 Assignments Theory Cases Quizze s Learning Paths Videos 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

89 Theory Videos Cases Quizze s Learning Paths Assignments 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

90 Academic Test Use is Free ! Opinions-Online ( Commercial site and pricing: Web-HIPRE ( WINPRE and PRIME Decisions (Windows) RICH Decisions ( Joint Gains ( Smart-Swaps ( Please, let us know your experiences.

91 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! Programming at SAL