Alexander V. Lotov Lomonosov Moscow State University, Russia; and

1. Visualization-based Reasonable Goals Method and its Web Application for Supporting e-Participation in Environmental Decision Making Alexander V. Lotov Lomonosov Moscow State University, Russia; and Dorodnicyn Computing Center of Russian Academy of Sciences, Russia

2. Plan of the talk 1. Pareto frontier methods 2. Reasonable Goals Method for databases 3. Interactive Decision Maps (IDM) technique and visualization of Pareto frontier 4. Environmental applications of RGM/IDM 5. Two main tasks of e-participation: a) informing lay stakeholders; and b) negotiation support 6. RGM/IDM in Web for informing lay stakeholders 7. RGM/IDM inParticipatory Decision Support for River Basin Planning (Werra River, Germany) 8. Using Web for informing lay stakeholders on environmental decision problems related to risk

3. Classification of MCDA methods according to the role of the Decision Maker MCDA no-preference methods a posteriori preference (Pareto frontier) methods a priori preference methods interactive methods

4. Examples of Web application • A priori preference methods -- Web-HIPRE J.Mustajoki and R.P.Hämäläinen • Interactive Methods -- WWW-NIMBUS K.Miettinen and M.M.Mäkelä • Pareto frontier methods – RGDB for Web (Reasonable Goals for DataBases) A.Lotov, A.Kistanov andA.Zaitsev

5. Pareto frontier methods Pareto frontier methods are devoted to approximation of the Pareto set and informing DM concerning it. In contrast to preference-oriented methods, Pareto frontier methods inform the users and help them in forming their preferences. First method: parametric LP method for generating the efficiency frontier for linear bi-criterion problem (S.Gass and T.Saaty, 1955)

6. Parametric LP is used: changes from 0 to 1. Result: Pareto frontier is displayed

7. Two main problems must be solved in the framework of the Pareto frontier methods • How to approximate the Pareto frontier • How to inform the stakeholders about the Pareto frontier

8. Two basic ways for informing a stakeholder about the Pareto frontier • By providing a list of the criterion points that belong to the Pareto frontier • By visualization of the Pareto frontier

9. We apply VISUALIZATION — why it is needed? Visualizationis a transformation of symbolic data into geometric information that must aid in the formation of mental picture of the symbolic data.

10. Structure of a mental model (result of psychological studies)

11. Visualizationcan influence all levels of human thinking!

12. Application of Pareto frontier visualization in finite multi-attribute choice problems The Reasonable Goals Method

13. A decision matrix is considered, i.e., table of N decision alternatives given by a finite number of attributes

14. We use visualization aimed at selecting a small number of «interesting» alternatives.It can be considered as a special form of data mining.

15. Example: real estate on sale

16. For illustrative purposes, let m=2(criterion points are displayed on the plane)

17. Enveloping the criterion points

18. Approximating the Edgeworth-Pareto hull of YC (the so-called CEPH)

19. Pareto frontier is analyzed by user and a preferred combination of criterion values (reasonable goal) is identified

20. The alternatives that are close to the goal are selected

21. General case (m from 3 to 8) Visualization of the Pareto frontier is based on approximation of the CEPH and application of the Interactive Decision Maps (IDM) technique.

22. The IDM technique consists in interactive and animated visualization of collections of bi-criterion slices of the CEPH (decision maps).

25. Approximation Before the start of interactive visualization, approximation of the CEPH is carried out. The main problem that was solved by us: optimal algorithms for polyhedral approximation of convex multi-objective bodies were developed.

26. Several applications of the IDM/RGM technique

27. Example application of the RGM/IDMfor decision screening in local water quality planning(Kolomna city region at the Oka River) About 400 000 decision alternatives were considered

28. Decision map that describes properties of 400 000 decision alternatives

29. Decision map with the goal

30. Selected alternatives that are close to the goal

31. Exploration of pollution abatement cost in the Electricity Sector – Israeli case study(jointly with D. Soloveitchik and others fromMinistry of National Infrastructures, Israel) Several hundreds of pollution reduction alternatives for the Israel electricity sector were developed for the period 2003 – 2013 by application of a complicated non-linear mathematical model. Then, the IDM-based screening was applied.

32. Web application of the RGM/IDM technique(Web RGDM software)for supporting e-participationhttp://www.ccas.ru/mmes/mmeda/rgdb/index.htm

33. Scheme of the Web RGDB application server

34. Two main tasks to be solved in the framework of e-participation in environmental decision problems • a) informing lay stakeholders on environmental decision problems (especially on possible strategies for solving the problems); and • b) supporting negotiations (or aggregating stakeholders’ preferences).

35. Informing lay stakeholders on environmental problems Lay stakeholders (non-experts) usually have minimal knowledge on environmental problems and on the ways how to solve the problems. Nevertheless, they want and often are involved into actions related to such problems. It is clear that the gap between knowledge and actions of lay stakeholders can be misused by irresponsible politicians.

36. Web tools based on the RGM/IDM technique (RGDB) can help lay stakeholders better understand the feasibility frontiers and express preferences by selecting one or several strategies that best fit their concerns. It important that it can be done independently of mass media that can help thrusting the strategies selected by an expert on lay stakeholders.

37. Then, lay stakeholders can base their problem-related legal and political actions (including e-participation) on such knowledge. Web applications of the RGM/IDM technique are aimed, first of all, at supporting the first, pre-negotiation phase. However, they can be used for supporting negotiations, too.

38. The Web RGDB application server

39. Data input

40. Example of the RGDB display

41. Selected alternatives

42. ParticipatoryDecision Support for Integrated River Basin Planning(Funding: German Federal Ministry of Education and Research) The Web RGDB was used as a part of DSS developed by Jörg Dietrich and Andreas H. Schumann, Ruhr University Bochum, Institute for Hydrology, Water Management and Environmental Engineering

43. DSS was calibrated for the Werra River Basin Ems Elbe Weser Werra Rhein

44. For the ParticipatoryDecision Support System, a special form of the Web RGDB was developed. It can support negotiations. It applies selecting several goals and related small groups of alternatives.

46. Architecture of the Web-based DSS

47. Applications of the IDM/RGM technique in decision problems with stochastic models (finite number of alternatives)

48. ModelLet us consider N alternatives, while the i-th alternative is given by its cumulative distribution function Fi(x)=P{v<x}, i=1,..,N, where v is a value to be maximized (or minimized).

49. Approachproposed by Y. Haimes (University of Virginia) Criteria are selected on the basis of the function F(x), i.e., yk=F(x)=P{v<vk}, k=1,..,m, where the values vk are specified by the DM. Then, any multi-criteria method can be applied. We use the IDM/RGM technique.

50. Example of the IDM/RGM application in decision making under risk In the example problem (variants of a dam), three criteria are used: • expectation of losses (including known annual cost); • probability of high losses denoted by P_h; and • probability of catastrophic losses denoted by P_c. We are interested to minimize the values of all three criteria.