Environmental decision analysis and Decision Support Systems

1. Decision Support Systems introduction Environmental decision analysis and Decision Support Systems

2. MAIN ISSUES FROM PREVIOUS LECTURE • There are complex, ill structured problems, particularly related to environmental resources management • Humans usually follow a specific decision process, but they may be biased by shortcuts or framing of the problem • Decision making is the process of identifying and choosing alternatives to solve a problem, based on decision maker(s) values and preferences • Many aspects are concerned with environmental decision making process: uncertainty, public participation and different stakeholders involvement, sustainability and multi-disciplinarity • Support is needed, in different forms (structuring of the problem, definition and evaluation of alternatives, communication, involvement of stakeholders…)

3. FROM PREVIOUS LECTURE… DECISION PROCESS (Shim et al., 2002)

4. DECISION MAKING PROCESS (French and Geldermann, 2005)

5. DECISION MAKING PROCESS (CLIMATE CHANGE) Tier 1. Risk screening Tier 2. Generic quantitative risk assessment Tier 3. Detailed quantitative risk assessment UKCIP, 2003

6. MAIN ISSUES TO DISCUSS • What is a decision support system? • Which are the main components? • Which are the included methodologies? • Technical elements and architecture/structure • Advantages/Disadvantages • DSS design

7. WHAT ARE WE TALKING ABOUT? Problem People Decision Providing answers Aiding not deciding Support Complexity Computer assisted Systems

8. DECISION MAKING APPROACHES/METHODOLOGIES • Environmental methods: tools derived from the scientific disciplines of ecology, environmental sciences, toxicology and similar • Economic methods: pricing techniques, such as cost- benefit analysis or opportunity costs analysis, and valuation techniques, based on revealed preferences (adopted and modified from Hill and Zammit, 2000; Kontoleon et al., 2001 and Pollard et al., 2004)

9. DECISION MAKING APPROACHES/METHODOLOGIES • Participatory or deliberative methods: methods developed within the social sciences for the active participation of stakeholders in the decision making process • Expert-based methods: where experts judgements are gained through the use of mathematical approaches, such as Multi-criteria Analysis (MCA) (adopted and modified from Hill and Zammit, 2000; Kontoleon et al., 2001 and Pollard et al., 2004)

10. DECISION MAKING APPROACHES/METHODOLOGIES (adopted and modified from Hill and Zammit, 2000; Kontoleon et al., 2001 and Pollard et al., 2004)

11. DECISION SUPPORT METHODOLOGIES • MONADIC: pattern recognition with the use of simple or complex sets of procedure. It cannot deal with ambiguos inputs nor resolve conflicts. E.g. query, text retrieval, graphs… • STRUCTURAL: analysis of specific aspects of information input and decisions as to its further processing. It allows resolution of some ambiguities. E.g. formal models… • CONTEXTUAL: dealing with the ambiguities not resolved by the structural analysis. Inputs and their attributes are analyzed in a broader context outside their domain. E.g. cognitive maps, sensitivity analysis…. (De May, 1992)

12. DECISION SUPPORT Support methodologies Monadic Structural Contextual Information management Presentation Choice Process modeling Analysis & reasoning Judgment refinement Spatial: maps, multimedia Verbal: text, hypertext, speech recognition, natural language processing Database, data warehouse, knowledge base, model base. Organization, access, update, monitoring Simulation, stochastic models, qualitative modeling, optimization, knowledge-based models Preference modeling, MAUT, value functions, multiple criteria methods, decision rules Quantitative analysis, sensitivity & what-if analysis, goal seeking, qualitative analysis, symbolic processing Decision structuring, decision trees, Bayesian updating, statistical analysis, adaptive modeling (Zachary, 1986)

13. DECISION SUPPORT TYPES Generic framework for any type of decision Framework for a specific decision modeling Specificity Framework for a specific component of the decision Specific for a component of a specific decision

14. WHAT IS A DECISION SUPPORT SYSTEM? QUESTIONNAIRE

15. DSSs DEFINITIONS DSS is an interactive computer-based information provider (Loucks, 1995) DSS is an integrated, interactive computer system, consisting of analytical tools and information management capabilities, designed to aid decision makers in solving relatively large, unstructured problems. ( Watkins & McKinney, 2001) DSS can be defined as a computer-based tool used to support complex decision-making and problem solving (Shim et al., 2002)

16. WHAT IS A DECISION SUPPORT SYSTEM? Decision Support Systems are computer-based systems used to assist and aid decision makers in their decision making processes They AID and ASSIST decision makers, but they DO NOT REPLACE them

17. WHAT IS A DECISION SUPPORT SYSTEM? Decision Support Systems couple the intellectual resources of individuals with the capabilities of computers to improve the quality of decisions. It is a computer-based support for management decision makers who deal with semi-structured problems Keen and Scott-Morton, 1978

18. DSS PROPERTIES AND CHARACTERISTICS Decision Support Systems should focus on effective support and not on automatic selection They require direct use involvement in the analysis of the decision problem, evaluation of decision outcomes and preference specification

19. DECISION MAKING AND DSS Decision Support Systems can aid and assist decision makers in one or more phases of the decision making process Functionalities, components and other characteristics are tuned to the goals of the considered assessment/management phase(s)

20. WHAT IS A DECISION SUPPORT SYSTEM? Decision Support Systems have evolved significantly since their early development in 1970s. Over the past decades, DSS have taken on both narrower or broader definition, while other systems have emerged to assist specific types of decision-makers faced with specific kinds of problems. Improve the EFFICIENCY with which a user makes a decision and the EFFECTIVENESS of that decision (Shim et al., 2002)

21. DECISION PROCESS Where communications technology facilitates interchange among people, the transaction with expert systems is between person and computer. And where conventional applications respond with information from user requests, expert systems not only provide information, but also suggest or make recommendations They create an intensive human-computer interaction that evolves through a series of stages - a usage paradigm - to perform complex analyses and derive computer-generated solutions to problem (Workman, 2005)

22. DEFINITIONS Communication-driven DSS is a type of DSS that emphasizes communications, collaboration and shared decision-making support Technologies include: LANs, WANs, Internet, ISDN, and Virtual Private Networks. Tools used include: groupware, videoconferencing, and Bulletin Boards. Communication DSSs may support synchronous (same time) and asynchronous (different times) meetings/communication, as well as face-to-face (same place) and distributed (different places). Some features: agenda creation, document sharing, meeting scheduling, chat, video/voice interaction, whiteboard

23. DEFINITIONS Information-based DSS is a type of DSS that provides access to information content, as opposed to data The first level involves no numeric information or data and hence involves non explicit quantitative analysis. Some features: textual information, static tables, pictures, graphics, web-searching. A second level includes numerical data in addition to information, used to provide ad hoc decision support and not for the purpose of inference, prediction or decision analysis. In any case the decision component is qualitative.

24. DEFINITIONS Knowledge-driven DSS can suggest or recommend actions to managers. These DSS are person-computer systems with specialized problem-solving expertise. They store and apple knowledge for a variety of problems/tasks that would otherwise be resolved by human expert. The systems may be rules-based, statistics-based, object-based. Some features: asks questions, explain how/why, initiate actions, resume analysis, train users, store inputs/results/user actions Decision made using knowledge-driven DSS can be biased by the responses provided by the user

25. DEFINITIONS Data-driven DSS is a type of DSS that emphasizes access to and manipulation of a time-series or real time operational data The key element is the easy and rapid access to a large amount of accurate, well organized and multidimensional data Some features: data filtering and retrieval, alerts, data summarization, metadata creation and retrieval, report design and generation, view data/reports

26. DEFINITIONS Model-driven/based DSS emphasize access to and manipulation of a model, for example, statistical, financial, optimization and/or simulation models. Simple statistical and analytical tools provide the most elementary level of functionality, allowing numerical solutions. Some features: change model parameter, what if analysis, create and manage scenarios, extract specific data values, specify and seek goals, generate sensitivity analysis, value elicitation and data input. The decision component is qualitative and quantitative.

27. DEFINITIONS MODEL-DRIVEN/BASED DSS Costs and values judgments not included Inference and prediction Costs and values judgments included Decision analysis

28. DEFINITIONS   Web-based DSS deliver decision support information or decision support tools to a manager using a "thin-client" Web browser Web-based means the entire application is implemented using Web technologies

29. EVOLVEMENT OF DSSs IN RELATION TO TECHNOLOGY

30. FRAMEWORK AND STRUCTURE The decisional or conceptual FRAMEWORK allows to identify and frame the problem of interest by defining main functionalities and methodologies of the system The STRUCTUREis intended as the description of the technical characteristics and links of the components of the DSS (eg. databases, models, user interfaces, GIS)

31. EXAMPLES OF FRAMEWORKS PRELIMINARY ASSESSMENT Existing data evaluation PRELIMINARY STATUS EVALUATION Risk index PROBLEM FORMULATION Risk index PRELIMINARY ASSESSMENT Prioritization index Basin characterization INTEGRATED ASSESSMENT Hot spots MANAGEMENT MONITORING Risk index MODELKEY DSS CONCEPTUAL FRAMEWORK INTEGRATED ASSESSMENT SCREENING AT BASIN SCALE Data evaluation and integration monitoring ENVIRONMENTAL ASSESSMENT SOCIO-ECONOMIC CHARACTERIZATION Socio-economic indices HOT SPOTS GIS-based SELECTION DEFINITIVE AT HOT SPOT SCALE Hot spot data evaluation monitoring HOT SPOT-SPECIFIC ASSESSMENT MODELKEY project, 2006 MANAGEMENT

32. EXAMPLES OF FRAMEWORKS Regional Risk Assessment Socio-economic Assessment Identification of regional sources by type of industrial activity Identification of relevant target Selection of relevant pathways Identification of administrative units Hazard Analysis Vulnerability Analysis Selection of socio-economic indicators Preliminary ranking of hazardous sources Ranking of sensitive targets Socio-economic Analysis Relative Risk Analysis (including physical risk) Administrative units ranking for recovery potential Sources (sites) ranking for potential regional risk Integrated Management Integrated Analysis Ranking for priority of investigation and Integrated management areas SYRIADE DSS FRAMEWORK JRC, 2007

33. EXAMPLES OF FRAMEWORK SMARTe DSS FRAMEWORK/STRUCTURE SMARTe team, 2008

34. EXAMPLES OF FRAMEWORK DECERNS DSS FRAMEWORK Yatsalo et al., 2008

35. DSS COMPONENTS Conventional DSSs consist of components for database management, powerful modeling functions and powerful (but simple) user interface designs (Shim et al, 2002; Ascough et al., 2002)

36. DSS COMPONENTS (Salewicza and Nakayama, 2004)

37. DSS COMPONENTS Database management system, which allows organization of basic spatial and thematic data and facilitate their efficient use Model management system, which includes quantitative and qualitative models to support the resource analysis Process/behavioral model, which describes the existing functional and structural relationships among the different elements

38. DSS COMPONENTS Planning model, which integrates the different information with the goals and stakeholders views in order to simulate scenarios Evaluation model, which allows evaluation of impacts of various options/solutions in order to support the selection of the most acceptable one Knowledge base, which provides information on data and models User-friendly interface, which allows communication with the system and visualization of results

39. DSS COMPONENTS: SOME ELEMENTS Flat or relational database(s) Search engines/ key word identification Expert elicitation Inference engine Checklists

40. MODELS/ANALYSIS METHODOLOGIES GIS SDSS ESRI property MCDA – Fuzzy Logic Simulation models Uncertainty/sensitivity analyses

41. EXAMPLES OF STRUCTURE MULINO DSS Jeunesse et al., 2003

42. EXAMPLES OF STRUCTURE WERRA RIVER DSS Hirschfeld et al., 2005

43. EXAMPLES OF STRUCTURE Socio-Economic Assessment Module Characterisation Module Risk Assessment Module (pre) experts phase Technological Assessment Module Risk Assessment Module (post) Definition of different remediation scenarios stakeholders phase Decision Module DESYRE DSS Carlon et al., 2007

44. OUTPUTS Current, timely information that is accurate, relevant and complete: • quantitative results from models (es. projections and forecasts, "what if?" results) • analyses and displays of historical data • displays of facts in various formats (es. trend analysis, performance monitoring) • recommendations • retrieved relevant documents • shared content and interaction a map a chart a tabular data summary a printed report a data file

45. INTERFACES Interface between the computer application and the user often determines whether a specific DSS will be used and whether it will be used effectively • Strive for consistency • Reduce information load • Create an aesthetic and minimalist interface design • Provide informative feedback • Design interactions to create closure (beginning, middle, end) • Anticipate and avoid errors • Allow easy reversal of user actions • Provide accelerators for frequent users • Provide help capabilities and documentation

46. INTERFACES

47. INTERFACES Dialog boxes Visualization maps Results by graphs Textual guidelines Detailed results Bottoms for navigation

48. INTERFACES Menu for navigation Textual guidelines SMARTe DSS SMARTe team, 2008

49. INTERFACES BASINS: Better Assessment Science Integrating point and Nonpoint Sources EPA, 2008

50. DSS PROPERTIES AND CHARACTERISTICS • To be effective in user involvement, the DSS should be: • Flexible • Adaptable to changes in the decision making process and user requirements • User friendly • Cooperative • Providing quantitative and qualitative analyses