DSS for Integrated Water Resources Management IWRM Terms

  • Slides: 49
Download presentation
DSS for Integrated Water Resources Management (IWRM) Terms and definitions (suggested for self-study) DDr.

DSS for Integrated Water Resources Management (IWRM) Terms and definitions (suggested for self-study) DDr. Kurt Fedra kurt@ess. co. at ESS Gmb. H, Austria http: //www. ess. co. at Environmental Software & Services A-2352 Gumpoldskirchen 1 © K. Fedra 2007

Terminology defined • • • • • Actor, stakeholder, participant Alternative Attribute Choice Compromise,

Terminology defined • • • • • Actor, stakeholder, participant Alternative Attribute Choice Compromise, trade-off Conflict Constraint Criterion, criteria Decision matrix Decision, to decide Decision Support System (DSS) Decision variable Dominated, non-dominated Feasible, infeasible Multi attribute theory Multi-criteria analysis Objectives, multiple objectives 2 • Optimization • Simulation, modeling • Scenario, scenario analysis • Pareto optimality, set, surface • Referene point • Utopia, nadir • Uncertainty • Risk • Robustness, resilience • Instrument, measure • Conservation laws, mass budget • Valuation, CVM, TCM • Economics NPV, EAC • Game theory • Zero sum games • Cooperative games • Win-win solutions © K. Fedra 2007

Terminology defined • • • • Hobsons’ choice Score Pugh method Elicitation Preference structure

Terminology defined • • • • Hobsons’ choice Score Pugh method Elicitation Preference structure Ranking, order Cardinal (criteria) Ordinal (criteria) Nominal (criteria) Normalization Benefits, non-monetary Compliance Rational choice, maximization Utility, utility function First order logic 3 • Modus ponens • Implementation • Efficiency • Equity • Sustainability • Price elasticity • Value (of water) • Investment (EAC, cost recovery) • Operating costs • Cost-benefit analysis • Monetization • Damages, penalties • Demand, Supply • Reliability (of supply) © K. Fedra 2007

Actor, stakeholder Any legitimate participant in the decision making process, affecting or affected by

Actor, stakeholder Any legitimate participant in the decision making process, affecting or affected by the underlying issues and problem situation: – Major water users, suppliers: e. g. , utilities, communities, irrigation consortia, farmers/associations, industries; – Governmental regulatory and administrative institutions; – Interests groups (commercial, NGOs) – Academic and research institutions, consultants – Media 4 © K. Fedra 2007

Alternative: • • one of several solutions to the problem; a set of actions,

Alternative: • • one of several solutions to the problem; a set of actions, measures, defined by one or more decision variables; Alternative: L, alius, other. Webster’s: • Offering or expressing a choice • A proposition offering choice between two or more things • One of two or more things to be chosen. 5 © K. Fedra 2007

Attribute: Property, variable, parameter, criterion describing a problem or solution (alternative); measurable (scalar or

Attribute: Property, variable, parameter, criterion describing a problem or solution (alternative); measurable (scalar or ordinal) Attribute: L. ad tribuere, to bestow) • An inherent (measurable) characteristic • An object closely related or belonging to a specific thing • To regard as a characteristic of a thing (verb). 6 © K. Fedra 2007

Choice: • • • Option, the power of choosing; Selection, the act of choosing;

Choice: • • • Option, the power of choosing; Selection, the act of choosing; A sufficient number or variety to choose from. Choice, (old G, koisan, to choose) syn: option, alternative, preference, selection, election 7 © K. Fedra 2007

Constraint: • A limitation of possible (acceptable) attribute values for an alternative Constraint: L,

Constraint: • A limitation of possible (acceptable) attribute values for an alternative Constraint: L, constringere, constrict, constrain The act, result of constraining: To force by imposed stricture, restriction, or limitation To restrict … to a particular mode • • • 8 © K. Fedra 2007

Constraints: CONSTRAINTS are minimal or maximal values of CRITERIA (target values) that a feasible

Constraints: CONSTRAINTS are minimal or maximal values of CRITERIA (target values) that a feasible alternative must fulfil. 9 © K. Fedra 2007

Cooperative games: Payoffs are calculated for coalitions (groups) of players that coordinate their strategies,

Cooperative games: Payoffs are calculated for coalitions (groups) of players that coordinate their strategies, assuming: Transferable utilities (sharing of benefits) Aiming at non-zero sum win-win solutions (increase in resource base) 10 © K. Fedra 2007

Cooperative games: Assume water is used competitively by • inefficient irrigation (farmer) • high

Cooperative games: Assume water is used competitively by • inefficient irrigation (farmer) • high value (agro)industry • Industry provides funds (bank loan) to farmer to improve irrigation efficiency (flooding drip), using the (future) revenues of the additional income from water saved (increased production value) water market ? 11 © K. Fedra 2007

Criterion, criteria: • Measurable attributes of the problem and decision alternatives; • valued attributes

Criterion, criteria: • Measurable attributes of the problem and decision alternatives; • valued attributes or components of the system; • measures of system performance. 12 © K. Fedra 2007

Criteria examples: • • • Supply/Demand ratio, availability Reliability of Supply (%) Efficiencies (water,

Criteria examples: • • • Supply/Demand ratio, availability Reliability of Supply (%) Efficiencies (water, economic) Sustainability (content change) Water quality (BOD, FC, NO 3, …) Equity, sustainability • Costs and benefits: $$$ ! 13 © K. Fedra 2007

Criteria and Preferences • Feasibility (physical/technical, economic, socio-political: acceptability) • Economic efficiency (benefit/cost, net

Criteria and Preferences • Feasibility (physical/technical, economic, socio-political: acceptability) • Economic efficiency (benefit/cost, net benefit, IRR, opportunity costs) • Compliance (water law, international agreements, environmental standards) • Sustainability (long-term effects) • Equity (distribution of costs and benefits) 14 © K. Fedra 2007

Decision, decide: Decision: L, decidere: to cut off • to arrive at a solution

Decision, decide: Decision: L, decidere: to cut off • to arrive at a solution that ends uncertainty or dispute about … • to make a choice or judgement • to come or cause to come to a conclusion 15 © K. Fedra 2007

Decision Support System: A Decision Support System is a • computer based problem solving

Decision Support System: A Decision Support System is a • computer based problem solving system (HW, SW, data, people) that can • assist non-trivial choice • between alternatives in • complex and controversial domains. 16 © K. Fedra 2007

Decision Support System: A DSS provides • structured presentation of problem context (physical, regulatory,

Decision Support System: A DSS provides • structured presentation of problem context (physical, regulatory, political, economic), • and tools for the design, – evaluation, – selection – of alternatives 17 (for non-trivial problems). © K. Fedra 2007

Decision variable: Attributes of a decision (alternative) that can be set or defined by

Decision variable: Attributes of a decision (alternative) that can be set or defined by the decision maker(s); Variables or parameters that define the measures, instruments, technologies, strategies, policies that implement the decision. 18 © K. Fedra 2007

Decision making processes Basic components: • Describe (understand) the problem situation, background, context, genesis,

Decision making processes Basic components: • Describe (understand) the problem situation, background, context, genesis, physiography, resources, stakeholders, rules = awareness) • Identify a preference structure (participation): – Criteria, Objectives/Constraints • Identify or design alternatives, instruments • Evaluate the alternatives, measure their contribution to the objectives • Rank and select an alternative (participation) 19 © K. Fedra 2007

Dominated: DOMINATED alternative: There is at least one alternative that is better in all

Dominated: 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 ! 20 © K. Fedra 2007

Economics, NPV, EAC: Costs and benefits are central criteria of any problem or solution

Economics, NPV, EAC: Costs and benefits are central criteria of any problem or solution (alternative); to compare streams of money over time in projects or components of different life time and a discount rate (cost of capital). NPV: net present value computes the current value of (discounted) future costs and benefits; EAC: equivalent annual cost, combines annualized capital outlays (based on a discounted capital recovery factor) and annual operational costs. 21 © K. Fedra 2007

Efficiency: is a ratio of ouput per unit input. • Economic efficiency: cost per

Efficiency: is a ratio of ouput per unit input. • Economic efficiency: cost per unit output or benefit, benefit cost ratio. • Water efficiency: water use per unit output, e. g. , hydropower or crop production. 22 © K. Fedra 2007

Feasible, infeasible: Alternatives can be • Feasible: they meet a set of requirements or

Feasible, infeasible: Alternatives can be • Feasible: they meet a set of requirements or CONSTRAINTS (specified a priori) • Infeasible: they fail to meet any or all of the CONSTRAINTS 23 © K. Fedra 2007

Game theory Branch of applied mathematics, economics (von Neumann, Morgenstern 1944): • Players, (agents,

Game theory Branch of applied mathematics, economics (von Neumann, Morgenstern 1944): • Players, (agents, actors, stakeholders) choose • Strategies that maximise their • Payoff (return, gain net benefit) • given the strategies of other agents. 24 © K. Fedra 2007

Hobson’s choice Decision problem with only one alternative (take it or leave it) Identification

Hobson’s choice Decision problem with only one alternative (take it or leave it) Identification or design of alternatives is crucial: probability of a good solution increases with the number of alternatives ! Thomas Hobson (1544 -1630), stable owner, offered only the horse nearest to the gate: Where to elect there is but one, Tis’ Hobson’s choice, take that - or none. (Thomas Ward, 1688) 25 © K. Fedra 2007

Instrument, measure: Instruments, measures, strategies, policies, are defined in terms of • Decision variables

Instrument, measure: Instruments, measures, strategies, policies, are defined in terms of • Decision variables which define the specific configuration • Effectiveness (which attributes and criteria will be affected) • Efficiencies (costs and benefits) which together define the alternatives. 26 © K. Fedra 2007

Multi-attribute theory Multi-Attribute (Utility) Theory is an evaluation scheme that combines several attributes (criteria)

Multi-attribute theory Multi-Attribute (Utility) Theory is an evaluation scheme that combines several attributes (criteria) in the evaluation of (the utility of) an object, decision, plan, project, …. by using some weighted sum of the individual attributes to arrive at a global overall summary or total evaluation. 27 © K. Fedra 2007

Multi-criteria analysis Includes a number of methods to arrive at a single evaluation (scoring,

Multi-criteria analysis Includes a number of methods to arrive at a single evaluation (scoring, and subsequent ranking) for objects, decisions, plans, projects that are described by multiple (and noncommensurable) criteria (see also: multi -attribute theory). 28 © K. Fedra 2007

Non-zero sum games: • Some (cooperative) strategies can increase the resource base • Sum

Non-zero sum games: • Some (cooperative) strategies can increase the resource base • Sum of benefits greater zero • Non-zero sum games describe HOW TO MAKE A BIGGER CAKE 29 © K. Fedra 2007

Objectives, multiple objectives • Something towards which effort is directed • An aim or

Objectives, multiple objectives • Something towards which effort is directed • An aim or end of action • Criteria we want to maximize or minimize Multiple objectives refer to more than one such goal addressed simultaneously in a given decision making situation (see also: multiple criteria) 30 © K. Fedra 2007

Objectives: OBJECTIVES are concepts we wish to maximize or minimize, measured by CRITERIA; several

Objectives: OBJECTIVES are concepts we wish to maximize or minimize, measured by CRITERIA; several CRITERIA can contribute to the same OBJECTIVE, (e. g. , to “maximize net benefit”, various costs and benefits contribute); • Criteria can be (hierarchically) structured and thus closely related/correlated (bias ? ) 31 © K. Fedra 2007

Objectives, example: • Criterion: net benefit • Objective: maximize net benefit • Constraint: at

Objectives, example: • Criterion: net benefit • Objective: maximize net benefit • Constraint: at least a net benefit of X DSS output: the values (settings) of the decision variables (instruments applied) to reach some targets; the problem may be feasible (can be solved) or infeasible (no possible solution). 32 © K. Fedra 2007

Optimization: Mathematical procedure to find the MAXIMUM or MINIMUM of an OBJECTIVE FUNCTION that

Optimization: Mathematical procedure to find the MAXIMUM or MINIMUM of an OBJECTIVE FUNCTION that may consist of one or more criteria subject to a set of CONSTRAINTS e. g. : Maximize NET BENEFIT = f(X) subject to meeting maximum investment cost limits where f(X) is a model of the system that yields net benefit. 33 © K. Fedra 2007

Optimization: Given: a transfer function (model) f : Decision Alternative Response from some set

Optimization: Given: a transfer function (model) f : Decision Alternative Response from some set of decision alternatives DA Sought: an element x 0 in DA such that f(x 0) ≤ f(x) for all x in DA ("minimization") or such that f(x 0) ≥ f(x) for all x in A ("maximization"). 34 © K. Fedra 2007

Pareto set or frontier: the set of all non-dominated alternatives (final selection requires trade-off

Pareto set or frontier: the set of all non-dominated alternatives (final selection requires trade-off between criteria, explicit or implicit weights) 35 © K. Fedra 2007

Preference structure: Expresses (one or more) decision makers’ preferences, expectation, aspirations quantitatively. Consists of:

Preference structure: Expresses (one or more) decision makers’ preferences, expectation, aspirations quantitatively. Consists of: 1. A set of Criteria with an indication of the optimization direction (minimize, maximize) 2. Constraints (minimal or maximal acceptable values for some criteria; 3. Objectives, all other (unconstrained) criteria (several criteria could contribute to the same objective). 36 © K. Fedra 2007

Price elasticity: Micro-economic theory, assumes that the consumption (purchase) of a commodity decreases with

Price elasticity: Micro-economic theory, assumes that the consumption (purchase) of a commodity decreases with increasing price or cost. High elasticity: commercial use; Inelastic: consumption is independent of price, e. g. , water for vital needs. 37 © K. Fedra 2007

Problem structure: Inputs (initial and boundary conditions) • Driving conditions (uncontrollable) • Decision variables

Problem structure: Inputs (initial and boundary conditions) • Driving conditions (uncontrollable) • Decision variables (controlled) Outputs (measures of performance): • Objectives (minimize or maximize, continuous, distance measure) • Constraints (minimal or maximal levels, binary: feasible or not) 38 © K. Fedra 2007

Pugh method: MCA method, syn. for Decision Matrix: • A matrix is used to

Pugh method: MCA method, syn. for Decision Matrix: • A matrix is used to summarize alternatives and (multiple) criteria; • Scoring is based on subjective weights defined for (normalized) criteria • Ranking and selection is based on maximum or minimum score 39 © K. Fedra 2007

Ranking, order: Establishing a sequence of alternatives; Ranking or order requires cardinal or ordinal

Ranking, order: Establishing a sequence of alternatives; Ranking or order requires cardinal or ordinal criteria. Complete order requires a single, common criterion (most frequently: monetary cost) Multiple criteria or attributes only allow a partial order (ranking) that separates dominated from non-dominated (pareto optimal) alternatives. 40 © K. Fedra 2007

Rational choice Is a theory, hypothesis, paradigm, model … based on micro-economics: The DM

Rational choice Is a theory, hypothesis, paradigm, model … based on micro-economics: The DM is assumed to choose a set of actions (decisions) that MAXIMIZE his/her UTILITY given the DM preferences and expected outcome of the actions. 41 © K. Fedra 2007

Rational choice Assumes that (rational) individuals maximize welfare (individual and collective utility) as they

Rational choice Assumes that (rational) individuals maximize welfare (individual and collective utility) as they conceive it, forward looking and consistently. G. Becker, 1993 Rational: based on reason Ratio (L. ): computation, reason Reason: sufficient ground, explanation, logical defense; something (principle, law) that supports a conclusion; drawing of (logical) inferences 42 © K. Fedra 2007

Reference point: A point in N dimensional decision space (one value for each of

Reference point: A point in N dimensional decision space (one value for each of the criteria/dimensions), defined by the DM’s preference structure (default: UTOPIA) that defines scaling and measures of distance for individual (feasible) alternatives. The feasible alternative closest to the reference point (by some measure of distance) is the optimal (efficient) solution. 43 © K. Fedra 2007

Robustness, resilience: Robustness: low sensitivity of a system (or decision) to changes (uncertainty) in

Robustness, resilience: Robustness: low sensitivity of a system (or decision) to changes (uncertainty) in the inputs; implies stabilizing or buffer capacity. Resilience: the ability of a system to return to “normal” function after a (major) disturbance; implies self repair mechanisms. 44 © K. Fedra 2007

Simulation, modeling: The imitative representation of the functioning of one system by process by

Simulation, modeling: The imitative representation of the functioning of one system by process by another. Mathemtical modeling: representation of a physical system by systems of equations to describe the system’s evolution in time and space. 45 © K. Fedra 2007

Scenario analysis explores the reaction of a system to changes in the boundary conditions

Scenario analysis explores the reaction of a system to changes in the boundary conditions (uncontrolled inputs and control or decision variables) on the performance variables (criteria) in terms of the objectives and constraints of the decision problem: WHAT … IF ? 46 © K. Fedra 2007

Uncertainty: inability to measure or forecast with some (specified) precision Measurement uncertainty: • Principle

Uncertainty: inability to measure or forecast with some (specified) precision Measurement uncertainty: • Principle element (Heisenberg) • Practical element (methodological, measurement and sampling error) 47 © K. Fedra 2007

Valuation: Provides an economic or monetary value for criteria (e. g. , as the

Valuation: Provides an economic or monetary value for criteria (e. g. , as the basis for cost-benefit analysis). Economic assessment or monetization of the costs and benefits of the supply and use of water, water quality, and all instruments and measures. Can be based on: • Market prices (may include subsidies) • Indirect estimates (contingent valuation, travel cost method). 48 © K. Fedra 2007

Zero-sum games: • Assumes finite resources independent of strategies • Game only allocates resources

Zero-sum games: • Assumes finite resources independent of strategies • Game only allocates resources between players • Sum of all players gains is zero • Zero sum games describe HOW TO DIVIDE THE CAKE 49 © K. Fedra 2007