Transcript No Slide Title

DSS for Integrated
Water Resources
Management (IWRM)
Terms and definitions
(suggested for self-study)
DDr. Kurt Fedra
[email protected]
ESS GmbH, Austria
http://www.ess.co.at
Environmental Software & Services A-2352 Gumpoldskirchen
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Terminology defined
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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
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• 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
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Terminology defined
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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
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• 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)
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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
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Alternative:
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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.
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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).
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Choice:
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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
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Constraint:
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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
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Constraints:
CONSTRAINTS are minimal
or maximal values of
CRITERIA (target values)
that a feasible alternative
must fulfil.
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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)
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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 ?
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Criterion, criteria:
• Measurable attributes of the
problem and decision
alternatives;
• valued attributes or
components of the system;
• measures of system
performance.
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Criteria examples:
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Supply/Demand ratio, availability
Reliability of Supply (%)
Efficiencies (water, economic)
Sustainability (content change)
Water quality (BOD, FC, NO3, …)
Equity, sustainability
• Costs and benefits: $$$ !
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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)
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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
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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.
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Decision Support System:
A DSS provides
• structured presentation of problem
context (physical, regulatory, political, economic),
• and tools for the
design,
– evaluation,
– selection
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of alternatives
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(for non-trivial problems).
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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.
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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)
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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 !
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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.
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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.
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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
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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.
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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)
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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.
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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.
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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).
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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
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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)
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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 ?)
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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).
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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.
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Optimization:
Given: a transfer function (model)
f : Decision Alternative Response
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").
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Pareto set or frontier:
the set of all non-dominated
alternatives (final selection
requires trade-off between
criteria, explicit or implicit
weights)
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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).
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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.
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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)
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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
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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.
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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.
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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
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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.
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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.
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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.
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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 ?
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Uncertainty:
Uncertainty: inability to measure
or forecast with some
(specified) precision
Measurement uncertainty:
• Principle element (Heisenberg)
• Practical element (methodological,
measurement and sampling error)
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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).
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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
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