Decisionarium

Aiding Decisions, Negotiating and Collecting Opinions on the Web: 

Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology www.raimo.hut.fi JMCDA, Vol. 12 , No. 2-3, 2003, pp. 101-110. Aiding Decisions, Negotiating and Collecting Opinions on the Web www.decisionarium.hut.fi D E C I S I O N A R I U M v. 3.2006

Slide2: 

selected publications J. Mustajoki, R.P. Hämäläinen and A. Salo: Decision support by interval SMART/SWING – Incorporating imprecision in the SMART and SWING methods, Decision Sciences, 2005. H. Ehtamo, R.P. Hämäläinen and V. Koskinen: An e-learning module on negotiation analysis, Proc. of HICSS-37, 2004. J. Mustajoki and R.P. Hämäläinen, Making the even swaps method even easier, Manuscript, 2004. R.P. Hämäläinen, Decisionarium - Aiding decisions, negotiating and collecting opinions on the Web, J. Multi-Crit. Dec. Anal., 2003. H. Ehtamo, E. Kettunen and R.P. Hämäläinen: Searching for joint gains in multi-party negotiations, Eur. J. Oper. Res., 2001. J. Gustafsson, A. Salo and T. Gustafsson: PRIME Decisions - An interactive tool for value tree analysis, Lecture Notes in Economics and Mathematical Systems, 2001. J. Mustajoki and R.P. Hämäläinen: Web-HIPRE - Global decision support by value tree and AHP analysis, INFOR, 2000. D E C I S I O N A R I U M PRIME Decisions WINPRE 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 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 RICH Decisions rank inclusion in criteria hierarchies

Mission of Decisionarium: 

Mission of Decisionarium Provide resources for decision and negotiation support and advance the real and correct use of MCDA History: HIPRE 3+ in 1992 MAVT/AHP for DOS systems Today: e-learning modules provide help to learn the methods and global access to the software also for non OR/MS people

Slide4: 

Opinions-Online (www.opinions.hut.fi) Platform for global participation, voting, surveys, and group decisions Web-HIPRE (www.hipre.hut.fi) Value tree based decision analysis and support WINPRE 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

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 New Methodological Features

Slide6: 

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

Opinions-Online Platform for Global Participation, Voting, Surveys and Group Decisions: 

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

Surveys on the web: 

Surveys on the web Fast, easy and cheap Hyperlinks to background information Easy access to results Results can be analyzed on-line Access control: registration, e-mail list, domain, password

Creating a new session: 

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

Possible questions: 

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

Viewing the results: 

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

Approval voting: 

Approval voting The user is asked to pick the alternatives that he/she can approve Often better than a simple “choose best” question when trying to reach a consensus

Advanced voting ruleswww.opinion.vote.hut.fi: 

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

Examples of use: 

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

Global Multicriteria Decision Support by Web-HIPRE A Java-applet for Value Tree and AHP Analysis: 

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

Web-HIPRE links can refer to any web-pages: 

Web-HIPRE links can refer to any web-pages

Direct Weighting: 

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

SWING,SMART and SMARTER Methods: 

SMARTER uses rankings only SWING,SMART and SMARTER Methods

Pairwise Comparison - AHP: 

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

Value Function: 

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

Composite Priorities: 

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

Group Decision Support: 

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

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 Defining Group Members

Aggregate Group Priorities: 

Contribution of each group member indicated by segments Aggregate Group Priorities

Sensitivity analysis: 

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

Slide26: 

Future challenges Web makes MCDA tools available to everybody - Should everybody use them? It is the responsibility of the multicriteria decision analysis community to: Learn and teach the use different weighting methods Focus on the praxis and avoidance of behavioural biases Develop and identify “best practice” procedures

Slide27: 

Sources of biases and problems

Visits to Web-HIPRE: 

Visits to Web-HIPRE

Visitors’ top-level domains: 

Visitors’ top-level domains

Visitors’ first-level domains: 

Visitors’ first-level domains

Slide31: 

Visits through sites linking to Web-HIPRE

Slide32: 

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

New Theory: Preference programming: 

New Theory: Preference programming Analysis with 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

Preference Programming – The PAIRS method: 

Preference Programming – The PAIRS method Imprecise statements with intervals on Attribute weight ratios (e.g. 1/2  w1 / w2  3)  Feasible region for the weights Alternatives’ ratings (e.g. 0.6  v1(x1)  0.8)  Intervals for the overall values Lower bound for the overall value of x: Upper bound correspondingly

Interval statements define a feasible region S for the weights: 

Interval statements define a feasible region S for the weights

Uses of interval models: 

Uses of interval models New generalized AHP and SMART/SWING methods DM can also reply with intervals instead of exact point estimates – a new way to accommodate uncertainty Interval sensitivity analysis Variations allowed in several model parameters simultaneously - worst case analysis Group decision making All members´ opinions embedded in intervals = a joint common group model

Interval SMART/SWING: 

Interval SMART/SWING A as reference - A given 10 points Point intervals given to the other attributes: 5-20 points to attribute B 10-30 points to attribute C Weight ratio between B and C not explicitly given by the DM

WINPRE Software : 

WINPRE Software

PRIME Decisions Software: 

PRIME Decisions Software

Slide40: 

Literature – Methodology Salo, A. and Hämäläinen, R.P.: Preference assessment by imprecise ratio statements, Operations Research, Vol. 40, No. 6, 1992, pp. 1053-1061. Salo, A. and Hämäläinen, R.P.: Preference programming through approximate ratio comparisons, European Journal of Operational Research, Vol. 82, No. 3, 1995, pp. 458-475. Salo, A. and Hämäläinen, R.P.: Preference ratios in multiattribute evaluation (PRIME) – Elicitation and decision procedures under incomplete information, IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol. 31, No. 6, 2001, pp. 533-545. Salo, A. and Hämäläinen, R.P.: Preference Programming. (Manuscript) Downloadable at http://www.sal.hut.fi/Publications/pdf-files/msal03b.pdf Mustajoki, J., Hämäläinen, R.P. and Salo, A.: Decision Support by Interval SMART/SWING - Incorporating Imprecision in the SMART and SWING Methods, Decision Sciences, Vol. 36, No.2, 2005, pp. 317-339.

Slide41: 

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

RICH Decisions : 

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

The RICH Method: 

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

Slide44: 

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

Slide45: 

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

Slide46: 

Dominance Structure and Decision Rules

Slide47: 

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

Smart-Swaps Smart Choices with the Even Swaps Method: 

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

Smart Choices: 

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

Even Swaps: 

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

Supporting Even Swaps with Preference Programming: 

Supporting Even Swaps with Preference Programming Even Swaps process carried out as usual The DM’s preferences simultaneously modeled with Preference Programming Intervals allow us to deal with incomplete information Trade-off information given in the even swaps can be used to update the model  Suggestions for the Even Swaps process

Decision support: 

Decision support

Slide53: 

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

Example: 

Example Office selection problem (Hammond et al. 1999) An even swap

Problem definition: 

Problem definition

Entering trade-offs: 

Entering trade-offs

Process history: 

Process history

Slide58: 

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. Literature

Joint-Gains Negotiation Support in the Internet: 

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

Method of Improving DirectionsEhtamo, Kettunen, and Hämäläinen (2002): 

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?”

Mediation Process  Tasks in Preference Identification: 

Mediation Process Tasks in Preference Identification Initial alternative considered as “current alternative” Task 1 for identifying participants’ most preferred directions Joint Gains calculates a jointly improving direction Task 2 for identifying participants’ most preferred alternatives in the jointly improving direction series of pairwise comparisons series of pairwise comparisons

Joint Gains Negotiation: 

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

DM’s Utility Functions: 

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

Case example: Business: 

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

Creating a case: Criteria to provide optional decision aiding: 

Creating a case: Criteria to provide optional decision aiding

Sessions: 

Sessions 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 . . . Participants take part in sessions within the case ® efficient point ® efficient point ® efficient point ® efficient point

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

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

Session view - joint gains after two steps: 

Session view - joint gains after two steps

Slide69: 

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

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

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

eLearning sites: 

eLearning sites Material: Theory sections, interactive computer assignments Animations and video clips, online quizzes, theory assignments Decisionarium software: Web-HIPRE, PRIME Decisions, Opinions-Online.vote, and Joint Gains, video clips help the use eLearning modules: 4 - 6 hours study time Instructors can create their own modules using the material and software Academic non-profit use is free

Learning paths and modules: 

Learning paths and modules Learning path: guided route through the learning material Learning module: represents 2-4 h of traditional lectures and exercises

Learning modules: 

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

Cases: 

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

Video clips: 

Video clips Recorded software use with voice explanations (1-4 min) Screen capturing with Camtasia AVI format for video players e.g. Windows Media Player, RealPlayer GIF format for common browsers - no sound

Slide77: 

testing the knowledge on the subject, learning by doing, individual and group reports Software use value tree analysis and group decisions with Web-HIPRE ﴀ

Academic Test Use is Free !: 

Academic Test Use is Free ! Opinions-Online (www.opinions.hut.fi) Commercial site and pricing: www.opinions-online.com Web-HIPRE (www.hipre.hut.fi) WINPRE and PRIME Decisions (Windows) RICH Decisions (www.rich.hut.fi) Joint Gains (www.jointgains.hut.fi) Smart-Swaps (www.smart-swaps.hut.fi) Please, let us know your experiences.

Slide79: 

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

Public participation project sites: 

Public participation project sites PÄIJÄNNE - Lake Regulation (www.paijanne.hut.fi) PRIMEREG / Kallavesi - Lake Regulation (www.kallavesi.hut.fi, www.opinion.hut.fi/servlet/tulokset?foldername=syke) STUK / Milk Conference - Radiation Emergency (www.riihi.hut.fi/stuk)

SAL eLearning sites: 

SAL eLearning sites www.dm.hut.fi Decision making resources at Systems Analysis Laboratory www.mcda.hut.fi eLearning in Multiple Criteria Decision Analysis www.negotiation.hut.fi eLearning in Negotiation Analysis www.decisionarium.hut.fi Decision support tools and resources at Systems Analysis Laboratory www.or-world.com OR-World project site