© K.Fedra DSS for Integrated Water Resources Management (IWRM) Formal decision making, rational choice Formal decision making, rational choice DDr. Kurt Fedra ESS GmbH, Austria Environmental Software & Services A-2352 Gumpoldskirchen DDr. Kurt Fedra ESS GmbH, Austria Environmental Software & Services A-2352 Gumpoldskirchen

© K.Fedra DSS operational definition A DSS should provide A DSS should provide structured presentation of the problem (physical, regulatory, political, economic ),structured presentation of the problem (physical, regulatory, political, economic ), and integrated tools for theand integrated tools for the – design, – evaluation, – selection of alternatives (for non-trivial problems). A DSS should provide A DSS should provide structured presentation of the problem (physical, regulatory, political, economic ),structured presentation of the problem (physical, regulatory, political, economic ), and integrated tools for theand integrated tools for the – design, – evaluation, – selection of alternatives (for non-trivial problems).

© K.Fedra Decision support paradigms Information systems (menu of feasible options)Information systems (menu of feasible options) Scenario analysis (and comparison)Scenario analysis (and comparison) WHAT IF WHAT IF Rational maximizationRational maximization HOW TO (reach objectives) HOW TO (reach objectives) Information systems (menu of feasible options)Information systems (menu of feasible options) Scenario analysis (and comparison)Scenario analysis (and comparison) WHAT IF WHAT IF Rational maximizationRational maximization HOW TO (reach objectives) HOW TO (reach objectives)

© K.Fedra Decision support paradigms Rational maximizationRational maximization HOW TO (reach objectives) HOW TO (reach objectives) Combines both the DESIGN of alternativesDESIGN of alternatives SELECTION of preferencesSELECTION of preferences Rational maximizationRational maximization HOW TO (reach objectives) HOW TO (reach objectives) Combines both the DESIGN of alternativesDESIGN of alternatives SELECTION of preferencesSELECTION of preferences

© K.Fedra DSS “theory” Simon's Proposition 1: Information stored in computers can increase human rationality if it accessible when it is needed for the making of decisions.Information stored in computers can increase human rationality if it accessible when it is needed for the making of decisions. Simon, Herbert A., Administrative Behavior, A study of decision-making processes in administrative organization (3rd edition). New York: The Free Press, 1945, 1965, Simon's Proposition 1: Information stored in computers can increase human rationality if it accessible when it is needed for the making of decisions.Information stored in computers can increase human rationality if it accessible when it is needed for the making of decisions. Simon, Herbert A., Administrative Behavior, A study of decision-making processes in administrative organization (3rd edition). New York: The Free Press, 1945, 1965, 1976.

© K.Fedra DSS “theory” Simon's Proposition 2: Specialization of decision-making (including computer based DSS) functions is largely dependent upon … channels of communication to and from decision centers (participants in the DM process).Specialization of decision-making (including computer based DSS) functions is largely dependent upon … channels of communication to and from decision centers (participants in the DM process). Simon's Proposition 2: Specialization of decision-making (including computer based DSS) functions is largely dependent upon … channels of communication to and from decision centers (participants in the DM process).Specialization of decision-making (including computer based DSS) functions is largely dependent upon … channels of communication to and from decision centers (participants in the DM process).

© K.Fedra Formal decision making FIRST define the RULES, then apply … Minimax and Bayesian approaches: Decision maker has: a finite number of possible decision alternativesa finite number of possible decision alternatives a finite number of situations (state of nature) which may have a known probabilitya finite number of situations (state of nature) which may have a known probability a cost or benefit for each decision - state-of- nature combinationa cost or benefit for each decision - state-of- nature combination FIRST define the RULES, then apply … Minimax and Bayesian approaches: Decision maker has: a finite number of possible decision alternativesa finite number of possible decision alternatives a finite number of situations (state of nature) which may have a known probabilitya finite number of situations (state of nature) which may have a known probability a cost or benefit for each decision - state-of- nature combinationa cost or benefit for each decision - state-of- nature combination

© K.Fedra Formal decision making Decision Table: State of Nature State of Nature Decisionrain no-rain take an umbrella 0 3 no umbrella 6 0 (0, 3, 6, are the associated costs) What do you do ? Decision Table: State of Nature State of Nature Decisionrain no-rain take an umbrella 0 3 no umbrella 6 0 (0, 3, 6, are the associated costs) What do you do ?

© K.Fedra Formal decision making Decision Table: State of Nature State of Nature Decisionrain no-rain take an umbrella 0 3(3) no umbrella 6 0(6) Minimax Solution (conservative, risk averse): take an umbrella ! Decision Table: State of Nature State of Nature Decisionrain no-rain take an umbrella 0 3(3) no umbrella 6 0(6) Minimax Solution (conservative, risk averse): take an umbrella !

© K.Fedra Formal decision making Decision Table: (with added probabilities) State of Nature State of Nature Decisionrain (0.1) no-rain (0.9) take an umbrella 0 (0) 3(2.7)(2.7) no umbrella 6 (0.6) 0 (0)(0.6) Bayesian Solution: don’t take an umbrella ! Decision Table: (with added probabilities) State of Nature State of Nature Decisionrain (0.1) no-rain (0.9) take an umbrella 0 (0) 3(2.7)(2.7) no umbrella 6 (0.6) 0 (0)(0.6) Bayesian Solution: don’t take an umbrella !

© K.Fedra Formal decision making Decision Table: States of Nature States of Nature Decision rain (0.1) little (0.5) none (0.4) take an umbrella 0 (0.0) 0 (0.0) 3 (1.2) no umbrella 6 (0.6) 1 (0.5) 0 (0.0) And now ? Decision Table: States of Nature States of Nature Decision rain (0.1) little (0.5) none (0.4) take an umbrella 0 (0.0) 0 (0.0) 3 (1.2) no umbrella 6 (0.6) 1 (0.5) 0 (0.0) And now ?

© K.Fedra Formal Decision making Decision Table: States of Nature States of Nature Decision rain (0.1) little (0.5) none (0.4) take an umbrella 0 (0.0) 0 (0) 3 (1.2) 1.2 no umbrella 6 (0.6) 1 (0.5) 0 (0) 1.1 MiniMax: take an umbrella Bayesian:take no umbrella Decision Table: States of Nature States of Nature Decision rain (0.1) little (0.5) none (0.4) take an umbrella 0 (0.0) 0 (0) 3 (1.2) 1.2 no umbrella 6 (0.6) 1 (0.5) 0 (0) 1.1 MiniMax: take an umbrella Bayesian:take no umbrella

© K.Fedra Formal decision making MiniMax: take an umbrella Bayesian:take no umbrella The lesson: Solutions also depend on the METHOD CHOSEN ! Methods include bias (implicit !) (it needs a DSS to select a DSS method ?) MiniMax: take an umbrella Bayesian:take no umbrella The lesson: Solutions also depend on the METHOD CHOSEN ! Methods include bias (implicit !) (it needs a DSS to select a DSS method ?)

© K.Fedra DM: concepts and language ALTERNATIVES are decision to take, “things to do”: courses of action, policy, project, measure, instrument, strategy ….. and their outcomes (we can choose) CRITERIA describe alternatives Generic: Cost, availability, legality, effectiveness, efficiency, reliability, sustainability, acceptability, Specific: investment, NPV, IRR, ORM, time, demand reduction, increase in supply, quality effects, m3/s, ….. ANYTHING MEASURABLE and MEANINGFUL ALTERNATIVES are decision to take, “things to do”: courses of action, policy, project, measure, instrument, strategy ….. and their outcomes (we can choose) CRITERIA describe alternatives Generic: Cost, availability, legality, effectiveness, efficiency, reliability, sustainability, acceptability, Specific: investment, NPV, IRR, ORM, time, demand reduction, increase in supply, quality effects, m3/s, ….. ANYTHING MEASURABLE and MEANINGFUL

© K.Fedra DM: concepts and language CRITERIA describe alternatives Alternatives can be Feasible :meet a set of requirements or CONSTRAINTS specified a prioriFeasible :meet a set of requirements or CONSTRAINTS specified a priori Infeasible : fail to meet any or all of the CONSTRAINTSInfeasible : fail to meet any or all of the CONSTRAINTS CRITERIA describe alternatives Alternatives can be Feasible :meet a set of requirements or CONSTRAINTS specified a prioriFeasible :meet a set of requirements or CONSTRAINTS specified a priori Infeasible : fail to meet any or all of the CONSTRAINTSInfeasible : fail to meet any or all of the CONSTRAINTS

© K.Fedra DM: concepts and language CONSTRAINTS are minimal or maximal values of CRITERIA that a feasible alternative must not exceed. OBJECTIVES are CRITERIA that we want to maximize or minimize. CONSTRAINTS are minimal or maximal values of CRITERIA that a feasible alternative must not exceed. OBJECTIVES are CRITERIA that we want to maximize or minimize.

© K.Fedra Formal decision making Design or select alternatives to Maximize the benefits Minimize the costs Rationally evaluating and comparing alternatives, using quantitative methods, measurable criteria, fixed (predefined) rules: openness, logic, objectivity, reproducibility (= scientific method) Design or select alternatives to Maximize the benefits Minimize the costs Rationally evaluating and comparing alternatives, using quantitative methods, measurable criteria, fixed (predefined) rules: openness, logic, objectivity, reproducibility (= scientific method)

© K.Fedra Rational maximization The individual as rational maximizer chooses a commodity bundle chooses a commodity bundle c = (c 1,...,c i,...,c n ) maximizing the utility u(c) c = (c 1,...,c i,...,c n ) maximizing the utility u(c) over different groups ( i ) over different groups ( i ) over space (x,y,z) over space (x,y,z) over time ( t ) over time ( t ) The social welfare function as the sum of individual or group utility functions The social welfare function as the sum of individual or group utility functions. The individual as rational maximizer chooses a commodity bundle chooses a commodity bundle c = (c 1,...,c i,...,c n ) maximizing the utility u(c) c = (c 1,...,c i,...,c n ) maximizing the utility u(c) over different groups ( i ) over different groups ( i ) over space (x,y,z) over space (x,y,z) over time ( t ) over time ( t ) The social welfare function as the sum of individual or group utility functions The social welfare function as the sum of individual or group utility functions.

© K.Fedra Rational maximization The rational maximizer chooses a commodity bundle ? chooses a commodity bundle ? Think of a shopping cart in the supermarket: Think of a shopping cart in the supermarket: Of all the many things offered, a rational shopper selects what is “most useful” or needed, subject to the available budget and carrying capacity (CONSTRAINTS !) The rational maximizer chooses a commodity bundle ? chooses a commodity bundle ? Think of a shopping cart in the supermarket: Think of a shopping cart in the supermarket: Of all the many things offered, a rational shopper selects what is “most useful” or needed, subject to the available budget and carrying capacity (CONSTRAINTS !)

© K.Fedra Optimization:Optimization: Given: a transfer function (model) f : D ecision A lternative  R esponse from some set of decision alternatives DA Sought: an element x0 in DA such that f(x0) ≤ f(x) for all x in DA ("minimization") or such that f(x0) ≥ f(x) for all x in A ("maximization"). Given: a transfer function (model) f : D ecision A lternative  R esponse from some set of decision alternatives DA Sought: an element x0 in DA such that f(x0) ≤ f(x) for all x in DA ("minimization") or such that f(x0) ≥ f(x) for all x in A ("maximization").

© K.Fedra Optimization:Optimization: f(x0) ≤ f(x) Implies a total order: 5 < 10 ✔ (3,4) < (2,5) ? (a,5) < (,€) ? f(x0) ≤ f(x) Implies a total order: 5 < 10 ✔ (3,4) < (2,5) ? (a,5) < (,€) ?

© K.Fedra Decision support paradigms In the real world: Multiple attributes multiple criteria, objectives: NO OBVIOUS RANKING ORDER valuation, trade-off, compromise, satisfaction, acceptance, agreement (gaming, bargaining) valuation, trade-off, compromise, satisfaction, acceptance, agreement (gaming, bargaining)  participatory decision making, group decisions

© K.Fedra Multiple attributes Acceptability, satisficing: Easier to define several fixed targets in natural units as constraints rather than multiple objectives and pairwise comparisons, trade offs, weights, preferences, etc. Acceptability, satisficing: Easier to define several fixed targets in natural units as constraints rather than multiple objectives and pairwise comparisons, trade offs, weights, preferences, etc.

© K.Fedra 2007 Decision Support (multi-attribute) Reference point approach: nadirnadir utopiautopia A1 A2 A3 A4 better efficientpoint criterion 1 criterion 2 A5 dominated A6

© K.Fedra DM: concepts and language Dominated solution: worse than at least one alternative in every criterion; Non-dominated solution: better in at least one, or equivalent in all (NOT worse) of the criteria; better in at least one, or equivalent in all (NOT worse) of the criteria; Dominated solution: worse than at least one alternative in every criterion; Non-dominated solution: better in at least one, or equivalent in all (NOT worse) of the criteria; better in at least one, or equivalent in all (NOT worse) of the criteria;

© K.Fedra Formal decision making Simple examples: Problem: select and buy a new car Criteria: cost only Alternative 1: Volkswagen, 25,000 Alternative 2: Mercedes, 65,000 Alternative 3: used Jaguar 68,900 What should we choose ? Simple examples: Problem: select and buy a new car Criteria: cost only Alternative 1: Volkswagen, 25,000 Alternative 2: Mercedes, 65,000 Alternative 3: used Jaguar 68,900 What should we choose ?

© K.Fedra Formal decision making Simple examples: Problem: select and buy a new car Criteria: cost, operational life Alternative 1: Volkswagen, 25,000, 10y Alternative 2: Mercedes, 65,000, 20y Alternative 3: used Jaguar68,900, 8y What should we choose ? Simple examples: Problem: select and buy a new car Criteria: cost, operational life Alternative 1: Volkswagen, 25,000, 10y Alternative 2: Mercedes, 65,000, 20y Alternative 3: used Jaguar68,900, 8y What should we choose ?

© K.Fedra Formal decision making Criteria: cost, operational life Alternative 1: Volkswagen, 25,000, 10y Alternative 2: Mercedes, 65,000, 20y Alternative 3: used Jaguar68,900, 8y Alternative 3 is DOMINATED, can be discarded ….. Criteria: cost, operational life Alternative 1: Volkswagen, 25,000, 10y Alternative 2: Mercedes, 65,000, 20y Alternative 3: used Jaguar68,900, 8y Alternative 3 is DOMINATED, can be discarded …..

© K.Fedra Formal decision making DOMINATED alternative: There is at least one alternative that is better in all criteria (or better in at least one and equal in all other) and thus to be preferred ! DOMINATED alternative: There is at least one alternative that is better in all criteria (or better in at least one and equal in all other) and thus to be preferred !

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© K.Fedra

© K.Fedra

© K.Fedra Formal decision making Irrigation system, criteria: total cost Alternative 1: 50,000, 3y, OMR: 13,000 Alternative 2: 150,000, 8y, OMR: 7,500 Which one do you chose ? Hint: at 5% discount rate … Irrigation system, criteria: total cost Alternative 1: 50,000, 3y, OMR: 13,000 Alternative 2: 150,000, 8y, OMR: 7,500 Which one do you chose ? Hint: at 5% discount rate …

© K.Fedra Formal decision making Irrigation system, criteria: total cost Alternative 1:EAC = 31,360 Alternative 2: EAC = 30,780 EAC: Equivalent Annual Cost (discounted investment, annualized plus annual operations) very close ! Irrigation system, criteria: total cost Alternative 1:EAC = 31,360 Alternative 2: EAC = 30,780 EAC: Equivalent Annual Cost (discounted investment, annualized plus annual operations) very close !

© K.Fedra Formal decision making Consider more Criteria: –Investment (capital cost, financing) –Operation and maintenance –Local support, training –Country of origin ? Evaluation strategies: 1.Monetize and combine into “cost, TCO” 2.Treat independently (trade off) Consider more Criteria: –Investment (capital cost, financing) –Operation and maintenance –Local support, training –Country of origin ? Evaluation strategies: 1.Monetize and combine into “cost, TCO” 2.Treat independently (trade off)

© K.Fedra Formal decision making Provides only guidanceProvides only guidance Helps to structure the problem:Helps to structure the problem: A well structured problem contains the elements of the answer Serves as a checklistServes as a checklist Coordinates the DM processCoordinates the DM process Provides only guidanceProvides only guidance Helps to structure the problem:Helps to structure the problem: A well structured problem contains the elements of the answer Serves as a checklistServes as a checklist Coordinates the DM processCoordinates the DM process