Decision Support Systems

Decision Support Systems an introduction to DSS with environmental application examples K.Fedra ‘97

What is a DSS ? • Attempts at definition • Decision making processes • A general DSS architecture • Decision Support Paradigms • Application examples K.Fedra ‘97

DSS Definition A DSS is a computer based problem solving system that assists choice between alternatives in complex and controversial domains. K.Fedra ‘97

DSS Definition A DSS provides • structured presentation • problem context, • and tools for the • design, • evaluation, • selection of alternatives K.Fedra ‘97

Decision making processes Handbook of OR(B.E.Gillet, 1976): • Formulation of the problem • Construction of a mathematical model • Derive solution from model • Testing model and solution • Establish control over the solution • Put it to work (implementation) K.Fedra ‘97

Decision making processes Heuristics(How to solve it, G.Polya) • understand the problem • make a plan (algorithm) • implement step by step • check each step • check the solution (looking back) K.Fedra ‘97

Decision making processes (in the real world) are characterized by: • multiple actors • conflicting objectives • multiple criteria • plural rationalities • hidden agenda K.Fedra ‘97

Decision making processes are characterized by: • multiple actors • conflicting objectives • multiple criteria • plural rationalities • hidden agenda K.Fedra ‘97

Decision making processes multiple actors: • researchers and analysts • planners and managers • policy and decision makers • general public: • consumers (market) • concerned citizen (voters) K.Fedra ‘97

Decision making processes conflicting objectives: • maximize economic benefits • minimize environmental costs • maximize environmental benefits • minimize economic costs • maintain equity: • between social groups • between regions and countries • between generations K.Fedra ‘97

Decision making processes multiple criteria: • economic criteria (costs) • environmental criteria • standards (measurements) • perceptions (believes, fears) • political criteria (equity) • regulatory criteria (constraints) • technological criteria (constraints) K.Fedra ‘97

Decision making processes plural rationalities rational: relating to, based on, agreeable to reason. reason: the power of inferring, comprehending, or thinking in an orderly, rational way. K.Fedra ‘97

Decision making processes plural rationalities rational: L. ratio (reor, reri, ratus) computation, advantage, interest, behavior, procedure, ways and means, motivation, argument, proof, opinion, (scientific) theory. K.Fedra ‘97

Decision making processes plural rationalities reaching different (contradictory) conclusions from the same set of premises in an internally consistent logical way. K.Fedra ‘97

A general DSS architecture • Information resources • The analytical engine • The user interface K.Fedra ‘97

A general DSS architecture data acquisition layer analytical engine models expert system DBMS GIS graphical user interface K.Fedra ‘97

Information Resources • information on the status-quo (monitoring) • background for the identification or design of decision alternatives K.Fedra ‘97

The analytical engine • Data base management system • Geographic Information System • Simulation and optimization models • Expert systems (rules) • Decision Support tools proper K.Fedra ‘97

User interface characteristics • Integration • Interaction • Visualization • Intelligence • Customization K.Fedra ‘97

The User Interface • provides integration of functions • interactive, dialogue oriented, menu driven • intuitive, graphical, symbolic • consistent syntax and semantics, layout and symbolism • intelligent, context aware • customized K.Fedra ‘97

The User Interface • provides integration of functions should provide access to ALLsystems functions and resources.: seamless integration. For the non-technical user, the user interface IS the system. K.Fedra ‘97

The User Interface • interactive dialogue, menu driven modeled after the dialogue with a human expert immediate response incremental problem definitions incremental detail of answers paraphrasing, explanation, analogies K.Fedra ‘97

The User Interface • intuitive, graphical, symbolic intuitively understandable formats graphical representation of complex and large data volumes symbolic representation of abstract concepts K.Fedra ‘97

The User Interface • consistent syntax and semantics, layout and symbolism ease-of-use through familiarity easy orientation fast learning K.Fedra ‘97

The User Interface • intelligent, context aware built-in information about the application domain information about data availability information about the user adaptive behaviour, learning K.Fedra ‘97

The User Interface • customized uses the language, jargon, style, etc. of the end user simulates familiar structures and formats of information It is easier to change software than institutions and habits. K.Fedra ‘97

Decision support paradigms • Information systems • Scenario analysis WHAT IF • Rational maximization HOW TO • Multiple attributes K.Fedra ‘97

Information systems • provide problem context • describe available alternatives • offer a common language and shared information basis for the participants in the decision making process K.Fedra ‘97

Information systems typical application example: State-of-the-Environment Reporting decision process usually diffuse, multi-stage and lengthy without clear technical objectives. Public information, awareness building, assists argumentation. K.Fedra ‘97

Decision support paradigms Scenario analysis explores the reaction of a system to changes in the control or decision variables on the performance variables (criteria) in terms of the objectives and constraints of the decision problem. K.Fedra ‘97

Decision support paradigms Scenario from L. scaenarium, the stage an account or synopsis of a projected course of action or events; a set of assumptions. K.Fedra ‘97

Decision support paradigms typical application example: Environmental Impact Assessment, that evaluates and compares project alternatives. Exploratory (policy) assessment, design of alternatives. K.Fedra ‘97

Rational maximization The individual as rational maximizer chooses a commodity bundle c = (c1,...,ci,...,cn ) that maximizes the utility u(c) K.Fedra ‘97

Rational maximization maximize the utility u(c) • over different groups ( i ) • over space (x,y,z) • over time ( t ) K.Fedra ‘97

Rational maximization The social welfare function u*(c) = f [u1(c),u2(c),...,un(c) ] as the sum S liui(c) of individual or group utility functions ui(c) K.Fedra ‘97

Rational choice context dependence and bias: certainty versus probability gain versus loss absolute versus relative change K.Fedra ‘97

Decision Support Systems