Decision Support Systems in Water Resoiurces Management ... · Decision Support SystemsDecision Support Systems in Water Resources ManagementWater Resources Management A Keynote Address

Decision Support SystemsDecision Support Systemsin

Water Resources ManagementWater Resources Management

A Keynote Addressyby

Keith W Hipel1Keith W. Hipel ,Liping Fang1, 2, and D. Marc Kilgour1, 3

1Department of Systems Design Engineering University of Waterloo1Department of Systems Design Engineering, University of Waterloo2Department of Mechanical and Industrial Engineering, Ryerson University3Department of Mathematics, Wilfrid Laurier University

M f C dMap of Canada

Great Lakes – St. Lawrence River Basin

World map

ObjectivesObjectives

• Stress the great importance of decision support systems g p pp y(DSSs) in water resources and environmental management.

• Use a DSS for conflict resolution to illustrate how DSSs can be designed, built and implemented.can be designed, built and implemented.

Outline1 Societal and Physical Systems Modeling

2 Decision Support Systems2.1 Basic Design2 2 Decision Support Systems in Water Resources and Environmental Planning and2.2 Decision Support Systems in Water Resources and Environmental Planning and

Management2.3 Decision Support Systems for Negotiation2.4 Application of Negotiation Preparation Systems

3 The Decision Support System GMCR II3 The Decision Support System GMCR II3.1 The Graph Model for Conflict Resolution3.2 Structure of GMCR II

4 Applying GMCR II to an Aquifer Contamination Conflict4 1 Background to the Elmira Conflict4.1 Background to the Elmira Conflict4.2 Decision Makers and Options4.3 Scenario Generation and Reduction4.4 Allowable State Transitions

5 Preference Elicitation5 Preference Elicitation5.1 Relative Preference Information5.2 Option Prioritizing5.3 Comparison of Option Weighting and Option Prioritizing

6 Analyses and Results6 Analyses and Results

7 Guidance in Designing Decision Support Systems

Societal Systems

Physical SystemsSystems

ModelsSystems Models

The duality of systems modeling of a realworld problem

Aquifer ContaminationAquifer Contamination

By-products from a local chemical factory contaminate y p yan aquifer supplying a town with fresh water.

Societal LevelConflict among stakeholders over who is responsible and who should pay for cleansing the aquiferand who should pay for cleansing the aquifer.

Physical RealmyMathematical modeling of the dispersion and elimination of the pollution.

Water Resources and Environmental M tManagement

Problems and Opportunitiespp

• Span the societal and physical systems realms.

• Are interdisciplinary.

• Require a team of practitioners and researchers from a wide spectrum of disciplines to systematically solvewide spectrum of disciplines to systematically solve problems in a creative fashion to arrive at solutions that are equitable, sustainable and cost effective.

AccomplishmentsAccomplishments

Water resources and environmental personnel are pioneers in de eloping and appl ingare pioneers in developing and applying:

• Societal decision analysis models• Societal decision analysis models,

• Physical chemical and biological systems• Physical, chemical and biological systems models, and

• Decision support systems (DSSs) for allowing societal and physical systems models to be

i tl d i ticonveniently used in practice.

Operational ResearchOperational Research

• Started by the British High Command in July 1938 to in estigate research into the operational aspects of radarinvestigate research into the operational aspects of radar systems.

Th B iti h d A i d OR th h t W ld• The British and Americans used OR throughout World war II to assist decision making in large-scale military operations.

• OR is a scientific approach to systematically solving problems.

• After the War, OR was greatly expanded to address a wide variety of challenging systems problems arising in

fi ldmany fields.

Operational Research in Water ResourcesOperational Research in Water Resources

• Maas et al. (1962) wrote the landmark book ( )entitled Design of Water Resource Systems.

• Loucks, Stedinger and Haith (1981) wrote a popular book called Water Resource System Planning and AnalysisPlanning and Analysis.

• Many books journals and conference• Many books, journals and conference proceedings contain articles on OR methods in water resources and environmental management.

OR TechniquesOR Techniques• Constrained optimization including linear, nonlinear, integer and

dynamic programming.

• Probabilistic models such as time series analysis and queuing theory.

• Network analysis approaches such as program evaluation and review technique (PERT) and critical path method (CPM).

• Multiple criteria decision analysis.

• Game theory.

• The development of conflict analysis (Fraser and Hipel, 1984) and the graph model for conflict resolution (Fang, Hipel and Kilgour, 1993) was motivated by the desire to resolve water resources and1993) was motivated by the desire to resolve water resources and environmental conflicts.

Classification of decision making models

OBJECTIVES

O T MOne Two or More

DECISION

OneMost OR Models Multiple Objective

Decision MakingDECISIONMAKERS Two or

MoreTeam

TheoryConflictModels

Operational ResearchOperational Research

Focuses on:

• Quantitative methods

• Tactical level of decision making

• Special components of an overall system problem

• Single decision maker situation

Systems EngineeringSystems Engineering

Another systems science field in which decision analysis y ymodels were developed.

Focuses on:

• Quantitative and qualitative methods• Strategic and tactical levels of decision makingg g• Integration of technology, institutional perspectives and

value judgements• Entire system including the components and their y g p

synergistic connections• Holistic viewpoint• Unstructured and complex problemsp p• Single and multiple decision makers

Physical Systems ModelingPhysical Systems Modeling

Significant contributions from water resources and i t l i d i ti tenvironmental engineers and scientists.

Examples:Examples:• To model an aquifer pollution problem, researchers

construct a system of differential equations that can be solved approximately using finite difference or finite element methods.

• Long memory models were first proposed in hydrology to explain the Hurst Phenomenon.

Strategic Unstructured Qualitative SoftLevel Information Systems

InformedDecision Making

Social and Political Analyses

Finance and Economics

EnvironmentalEnvironmentalFactors

ProposedPhysical Designs

AlternativeSolutions

Tactical Highly Quantitative HardLevel Structured Information SystemsSolutions

Need for CreativeProblem Solving

Level Structured Information Systems

Decision making in Engineering

A Tool BoxA Tool Box

• Select appropriate societal and physical systems tools to pp p p y ysolve a given problem.

• Decision Support systems permit these tools to be conveniently used in practice.

2 Decision Support Systems2 Decision Support Systems

2.1 Basic Designg

2.2 Decision Support Systems in Water Resources and Environmental Planning and Management

2 3 Decision Support Systems for Negotiation2.3 Decision Support Systems for Negotiation

2 4 Application of Negotiation Preparation Systems2.4 Application of Negotiation Preparation Systems

ModelsModels

• A model, or set of models, is a description of a system., , p y

• A particular model attempts to capture the key characteristics of the system being studied so that the system can be better understood and hence informed decisions can be made.decisions can be made.

• A model is not the actual system—it is always an approximation to and simplification of reality.

Types of ModelsTypes of Models

• Verbal descriptionp

• Written explanation

• Graphical display

• Formal mathematical model

• Any combination of the above

Decision Support SystemDecision Support System

• An easy-to-use computer package that encodes d li d l ti l biliti fmodeling and analytical capabilities for one or more

formal models.

• A system that permits practitioners and researchers to conveniently employ appropriate societal and physical

t d l t t d h d i isystems models to support and enhance decision making.

• One of the most important components of the field of Information Technology (IT) which includes the d l t f t ft d h ddevelopment of computer software and hardware.

User

Dialogue Generation and Management System

Database Management System

Model Base Management System

M i t f d i i t tMain components of a decision support system as defined by Sage (1991)

Determine key characteristics ofDetermine key

characteristics of

Select models with capabilities that match key characteristics

characteristics of Model

SelectionPhase

realworld problem Model

SelectionPhase

Model calibrationModel calibration

Analysis

Interpretation of results

Analysis

DecisionSupport

Sensitivity analysesSensitivity analyses

SupportSystem

U i d i i t t i ti

Information for guiding decision makers

Using a decision support system in practice

Expert SystemsExpert Systems

• Originated in the IT field of Artificial or Machine gIntelligence.

• Knowledge of experts is computerized.

Can be used within a DSS• Can be used within a DSS.

Knowledge-based Expert SystemsKnowledge based Expert Systems

Rule-Based Reasoning Systemg y• Reflects the logic and experience of an expert.Example: Can be used to select the most appropriate set of

models to investigate a specific type of problem.

Case Based Reasoning SystemCase-Based Reasoning System• Utilizes knowledge gained through the investigation of

similar cases to improve current modeling of a system.p g yExample: Knowledge gained from earlier conflict modeling

studies can suggest a preliminary model design for a fli t (R F d Hi l 2002)new conflict (Ross, Fang and Hipel, 2002).

DSSs in Water Resources and Environmental M tManagement

• Water resources and environmental researchers pioneered the development and application of both societal and physical systems models.

• They are also leaders in the construction of DSSs implementing these models.

DSS PublicationsDSS Publications

• Exist in every field in which formal model are employed.

• Examples in water resources and the environmental management include:g

– Proceedings of the sequence of conferences held in Adelaide (2008) Dresden (2002) Brisbane (1999)Adelaide (2008), Dresden (2002), Brisbane (1999), Kyoto (1996) and Waterloo (1993).

– Hayes and McKee (2001).– Denzer, Swayne, Purris and Shimak (2001).– Loucks and da Costa (1991).

Plus many other conference proceedings journal papers– Plus many other conference proceedings, journal papers and books.

NegotiationNegotiation

• Most water resources and environmental management gproblems and projects involve stakeholders and decision makers with different interests.

• DSSs that can be employed for systematically investigating negotiations and conflict resolution constitute an important class of DSSs for addressing societal systems problems.

A taxonomy of negotiation support systems

(1) E t S t(1) R l t d C i ti

Negotiation PreparationOn-line Negotiation Support

(2) Decision Analysis Systems(2) Process Support

(1) Expert Systems(1) Regulated Communication

(2b) Multiple Participant Models

(2a) Single Participant Models(3) Context Support

Table appearing in Kilgour, Fang and Hipel (1995) based on research by Thiessen and Loucks (1992).

Application of Negotiation Preparation S tSystems

• Intended to provide advice to one party only.p p y y

• A DSS for conflict resolution may be used as:− An analysis or simulation tool for a participant or a

participant’s agent.

− Analysis tool for a third party.

− A communication and analysis tool in mediation.

3 The Decision Support System GMCR II3 The Decision Support System GMCR II

3.1 The Graph Model for Conflict Resolutionp

3.2 Structure of GMCR II

The Graph Model for Conflict ResolutionThe Graph Model for Conflict Resolution

• The DSS called GMCR II is a negotiation preparation g p psystem that allows a flexible methodology called the Graph Model for Conflict Resolution to be conveniently applied to realworld conflictconveniently applied to realworld conflict.

• This conflict resolution methodology and GMCR II wereThis conflict resolution methodology and GMCR II were developed by researchers in the Conflict Analysis Group in the Department of Systems Design Engineering at the University of WaterlooEngineering at the University of Waterloo.

GenealogyGenealogy

• Classical Game Theory(V N d M t 1953)(Von Neumann and Morgenstern, 1953)

• Metagame Analysis (Howard, 1971)

• Conflict Analysis (Fraser and Hipel, 1984)

• Graph Model for Conflict Resolution (Fang, Hipel and Kilgour, 1993)

• GMCR II(Hipel, Kilgour , Fang and Peng, 1997) (Kilgour, Hipel, Fang and Peng, 2002)

Game Theory

Nonquantitative Quantitative Procedures

Cooperative GameMetagame Normal Extensive Form

Nonquantitative Approaches

Q

Cooperative Game Theory

gAnalysis

Drama TheoryConflict

Analysis

Normal Form

Extensive Form . . .

Analysis

Graph Model for Conflict Resolution

Genealogy of Formal Conflict Models

Graph Model for Conflict ResolutionGraph Model for Conflict Resolution

• Theory is founded upon a rigorous mathematical framework, utilizing t f h th t th d l i th th ticoncepts from graph theory, set theory and logic—the mathematics

of relationships.

• Design is mathematically based but completely non-quantitative in nature.

• Can handle any finite number of decision makers and options.

• Utilizes relative preferences.

C h dl i ibl d• Can handle irreversible and common moves.

Key characteristics of the realworld phenomenon

Design theoretical model based on these assumptions

Develop theoretical properties andanalysis procedures

Testing and refining model based on applications

Implementation algorithmsp g

Decision Support System (DSS)

Developing a systems model

Use of DSS by researchers and practitioners


Applying the Graph Model for Conflict Resolution

Competition and Cooperation in Conflict Resolution

Conflict Resolution

CompetitionCooperation

(Coalitions, Group Decision Making)


Multiple Criteria Decision Analysis

Fair Resource Allocation

Noncooperative Behaviour

Coalition Analysis

Implementation Algorithms

Decision Support Systems

4 Applying GMCR II to an Aquifer C t i ti C fli tContamination Conflict

4.1 Background to the Elmira Conflict

4 2 D i i M k d O ti4.2 Decision Makers and Options

4 3 Scenarios Generation and Reduction4.3 Scenarios Generation and Reduction

4.4 Allowable State Transitionso ab e S a e a s o s

Background to the Elmira ConflictBackground to the Elmira Conflict

• Elmira is a town of 7,500 people located in a rich , p pagricultural region of Southwestern Ontario.

• In late 1989, the Ontario Ministry of the Environment (MoE) discovered that the aquifer was contaminated by a carcinogen called N-nitro demethylamine (NDMA).a carcinogen called N nitro demethylamine (NDMA).

• Blame fell on Uniroyal Chemical Ltd. (Uniroyal) located in Elmira.

Elmira ConflictElmira Conflict

• MoE issued a Control Order under the Environmental Protection Act of Ontario to require Uniroyal to cleanProtection Act of Ontario to require Uniroyal to clean up the contaminant and properly treat future discharges.

• Uniroyal appealed the Control Order.

• Negotiation among MoE, Uniroyal and Local Government commenced in mid 1991.

• Conflict is employed to illustrate key design features and capabilities of GMCR II and its convenient application in practice.

Decision makers and options in the graph model of the Uniroyal dispute

Decision Makers InterpretationsAnd Options

MoE Ontario Ministry of the Environment and Energy

1. Modify MoE modifies the Control Order to make it acceptable to y pUniroyal

Uniroyal Uniroyal Chemicals Ltd.2. Delay Uniroyal lengthens the appeal process3. Accept Uniroyal accepts responsibility4. Abandon Uniroyal abandons its Elmira operation

Local Government Regional Municipality of Waterloo and Township of Woolwich

5. Support Local Government supports the original Control Order

StatusQuo

Non-cooperative Equilibrium

CooperativeEquilibrium

MoE

1. Modify N N Y

Uniroyal

2 Dela Y Y N2. Delay Y Y N

3. Accept N N Y

4. Abandon N N N

Local Government

5. Insist N Y Y

State Number 1 5 8

Evolution of the Elmira conflict from the status quo (state 1) to the non-cooperative equilibrium (state 5) and on to the cooperative

equilibrium (state 8)

Scenario Generation and ReductionScenario Generation and Reduction

The user identifies the options and then GMCR II generates the resulting states. Because each option can be taken or not, a conflict model having m p , goptions processes 2m states.

Number of Options Number of States

2 42 43 84 165 32..

.

.. .

20 More than one million

GMCR II has procedures to reduce the number of states before generating the remaining feasible states.

State ReductionState Reduction

1. Identification and removal of infeasible states.

2. Combination of indistinguishable states into a single scenario.

E l GMCR II d d 20 ti fli t d lExample: GMCR II reduced a 20-option conflict model on bargaining over trade in services from more than one million states to 185,000 states .,

Trade in services conflictDecision Makers and Options Representative State

US1. Push Liberalization2. Retaliation3. Bilateral Demonstration4. Set GATT Agenda

YYYY

USStrategy

5. Issue Linkages Y

EC6. Internal Liberalization7. Follow U.S.8. Resistance

YNY

ECStrategy

9. Separate Deals Y

Japan10. More Support11. Separate Deals12. Stress Agenda

YNY

JapaneseStrategy

g

Asian NICs13. Strong Support14. Separate Deals

YN

Asian NICStrategy

India, Brazil and LDCs15 St R i t Y LDC15. Strong Resistance16. Separate Deals17. National Development

YYY

LDCStrategy

Middle Countries18. Support U.S. Y Middle Countries19. Broker20. Separate Deals

YY

Strategy

*Acronyms: US (United States), EC (European Community), NICs (Newly Industrialized Countries), LDCs (Less Developed Countries) and GATT (General Agreement on Tariffs and Trade)

Specifying infeasibilities using GMCR II

Dialog box for the entry of mutually exclusive options in the Elmira conflictElmira conflict

Dialog box for the entry of at least one option in the Elmira conflict

State combination

Feasible states in the Elmira conflict

Design FeaturesDesign Features

• GMCR II is programmed in C++, possesses a carefully p g , p ydesigned data structure, and can handle small, medium and large models.

• A 32-bit doubleword represents a specific selection of options wherein each digit or bit equals 1 or 0 to indicateoptions wherein each digit or bit equals 1 or 0 to indicate whether or not the option it represents is taken or not.

• This design can accommodate up to 32 options, which is more than enough for all realworld applications considered to date.considered to date.

Allowable State TransitionsAllowable State Transitions

• Theoretically, the graph model for conflict resolution has a finite directed graph for each decision makerdirected graph for each decision maker.

• The vertices represent the feasible states and the state transitions are the arcs on the graph connecting the vertices.g p g

• Allowable state transitions in both directions between two states are indicated by two arrowheads pointing in opposite directions.

• An irreversible move is marked using a single arrowhead.

• GMCR II uses a reachable list to keep track of the set of allowable• GMCR II uses a reachable list to keep track of the set of allowable state transitions for a given feasible state and decision maker.

• GMCR II assumes all moves are reversible unless irreversibility is yspecified.

StatusQuo



MoE

1 Modif N N Y1. Modify N N Y

Uniroyal

2. Delay Y Y N

3. Accept N N Y

4. Abandon N N N

Local GovernmentLocal Government

5. Insist N Y Y

State Number 1 5 8



Integrated graph and preference orderings for the Elmira conflict

Dialog box to specify irreversible options in the Elmira contaminated aquifer conflict

5 Preference Elicitation5 Preference Elicitation

5.1 Relative Preference Information

5.2 Option Prioritizing

5.3 Comparison of Option Weighting and Option Prioritizing

PreferencesPreferences

• GMCR II requires only relative preference information to q y prank states from most to least preferred for each decision maker where ties are allowed.

• One can carry out “quick and dirty” conflict analyses using sparse preference information and then refine theusing sparse preference information and then refine the preferences as more information becomes available.

PreferencesPreferences

• Would you like to have tea or coffee to drink.Q tit ti I h tilit l f 1 9673 f– Quantitative response: I have a utility value of 1.9673 for coffee and 1.0000 for tea.

– Human response: I would prefer to drink tea. Thank you.p p y

• Quantitative preferences:C di l b i i d t h t t bj t (– Cardinal number is assigned to each state or object (ex. Dollars or utility value).

• Non-quantitative or relative preferences:– One state is either more preferred, less preferred or

equally preferred to anotherequally preferred to another.

Types of PreferencesTypes of Preferences

• Cardinal

• Ordinal

• Strict Ordinal

• Transitive

• Intransitive

Types of Relative PreferencesTypes of Relative Preferences

• OrdinalT iti

• Strictly ordinalTransitive

• Intransitive

U k• Unknown

• UncertainUncertain

• Strength of Preference

Preference ElicitationPreference Elicitation

• Preference statements reflect the way in which a person naturally expresses his or her preferences in a specific conflictexpresses his or her preferences in a specific conflict.

• Initially, one can simply consider key preference statements such as the obviously most preferred and least preferred situations.

Later, one can refine the conflict model as more preference information becomes knownmore preference information becomes known.

This tends to eliminate blocks of equally preferred states.

• Preference statements adhere to all rules of first order logic.• An algorithm in GMCR II uses the preference statements to rank

states from most to least preferred for each decision maker wherestates from most to least preferred for each decision maker where ties are allowed.

Game Theory

Nonquantitative Quantitative Procedures

Cooperative GameMetagame Normal Extensive Form

Nonquantitative Approaches

Q

Cooperative Game Theory

gAnalysis

Drama TheoryConflict

Analysis

Normal Form

Extensive Form . . .

Analysis


Genealogy of Formal Conflict Models

Preference Elicitation Using GMCR IIPreference Elicitation Using GMCR II

• Option Weightingp g gAssign weights to each option choice and then GMCR II orders states in accordance with their total weights.

• Option PrioritizingEnter lexicographic statements about option choices and then GMCR II ranks the states accordingly.

• Fine Tuning or Direct Ranking• Fine Tuning or Direct RankingAdjust the initial ranking using fine tuning after option weighting or option prioritizing. One can also use direct

ki itranking on its own.

NN

Y

Dialog box for option prioritizing entry of MoE’s preferencesDialog box for option prioritizing entry of MoE s preferences

Option Prioritizing for MoEOption Prioritizing for MoEPreference Statements Explanation

-4 MoE most prefers that Uniroyal not abandon its Elmira plant.N t M E ld lik U i l t t th3 Next, MoE would like Uniroyal to accept the current Control Order.

2 MoE then prefers that Uniroyal not delay the -2 p y y

appeal process.

-1 MoE would not like to modify the control orderorder.

5 iff -1

MoE prefers that Local Government insists that the original Control Order be applied (5), 5 iff 1 if and only if (iff) it does not modify the Control Order (-1) itself.

State ranking for MoE resulting from option prioritizingState ranking for MoE resulting from option prioritizing

Option PrioritizingOption Prioritizing

• List preference statements in order of importance from most preferred at the top of the list to least preferred atmost preferred at the top of the list to least preferred at the bottom.

E h t t t i d i fi t d l i• Each statement is expressed using first –order logic.

• Preference statements mimic the way people express th i ltheir values.

• Option prioritizing can be drawn as a preference tree in hi h th l f th t d ki f t twhich the leaves of the tree produce a ranking of states.

• GMCR II uses a scoring scheme which assumes ordinal fpreferences.

State ranking for MoE in the Elmira contaminated aquifer conflict

State ranking for Uniroyal in the Elmira contaminated aquiferState ranking for Uniroyal in the Elmira contaminated aquifer conflict

State ranking for Local Government in the Elmira contaminated aquifer conflict

Findings of the Preference Questionnaire

Completed by 79 Groups

Criteria OptionWeighting

OptionPrioritizing

More preferred method 26 53pMore realistic 22 57

More informative 35 44More convenient to use 42 37

Best for obtaining a preliminary ranking before using direct ranking 37 42

6 Analysis and Results6 Analysis and Results

• Individual stabilityy

• Equilibria

• Sensitivity analyses

• Interpretation and insights

• Decision support

INPUT DATA SUBSYSTEM

Decision MakersOptions

INPUT DATA SUBSYSTEM

Decision MakersOptions

Feasible StatesFeasible StatesState Transitions

Preferences

Feasible StatesState Transitions

Preferences

USER INTERFACE

ANALYSIS ENGINE

Coalition AnalysisUSER INTERFACE GMCR II

OUTPUT DATA SUBSYSTEMOUTPUT DATA SUBSYSTEM

I di id l S bili iIndividual StabilitiesEquilibria

Coalition Stability

Individual StabilitiesEquilibria

Coalition Stability

Applying the Graph Model for Conflict Resolution

Stability AnalysisStability Analysis

• Decision makers may behave in different ways under y yconflict.

• A range of solution concepts or stability definitions are incorporated into GMCR II to reflect different kinds of behavior under conflict.behavior under conflict.

Solution Concepts and Human Behavior

SolutionConcepts

Stability Descriptions

N h S bili F l DM (d i i k ) il llNash Stability(R)

Focal DM (decision maker) cannot move unilaterally to a more preferred state.

GeneralMetarationality

All focal DM’s unilateral improvements are sanctioned by subsequent unilateral moves by others.y

(GMR)q y

SymmetricMetarationality

(SMR)

All focal DM’s unilateral improvements are sanctioned, even after response by the focal DM.

(SMR)

SequentialStability(SEQ)

All focal DM’s unilateral improvements are sanctioned by subsequent unilateral improvements by others.

Limited-moveStability

(Lh)

All DMs are assumed to act optimally and the maximum number of state transitions (h) is specified.

Non myopic Limiting case of limited move stability as the maximum number ofNon-myopic(NM)

Limiting case of limited move stability as the maximum number of state transitions increases to infinity.

Solution Concepts and Behavioral Characteristics

Solution Stability Descriptions ForesightKnowledge of Disimprovement Strategic

Concepts Stability Descriptions Foresight of Preferences

Disimprovement Risk

Nash Stability (R)

Focal DM (decision maker) cannot move unilaterally to a more preferred state.

Low Own Never Ignores risk

General Metarational (GMR)

All focal DM’s unilateral improvements are sanctioned by subsequent unilateral moves by others.

Medium Own By opponents

Avoids risk; ticonservative

Symmetric Metarational (SMR)

All focal DM’s unilateral improvements are sanctioned, even after response by the focal DM.

Medium Own By opponents

All focal DM’s unilateralSequential Stability (SEQ)

All focal DM s unilateral improvements are sanctioned by subsequent unilateral improvements by others.

Medium All NeverTakes some risks; satisfies

Limited All DMs are assumed to act Limited-move Stability (Ln)

optimally and the maximum number of state transitions (h) is specified.

Variable All StrategicAccepts Risk; strategizesNon-myopic Limiting case of limited move

stability as the maximumyStability (NM)

stability as the maximum number of state transitions increases to infinity.

High All Strategic

Stability AnalysisStability Analysis

• A state is stable for a given decision maker if it is not gadvantageous for him or her to depart from the state according to a given solution concept.

• GMCR II assesses each state for stability for every solution concept according to each decision maker’ssolution concept according to each decision maker s viewpoint.

• An equilibrium is stable for all decision makers with respect to a specific solution concept.

Equilibria in the Elmira contaminated aquifer conflict

St t N ti C tiStatusQuo



MoE

1. Modify N N Yy Uniroyal

2. Delay Y Y N

3. Accept N N Y

4. Abandon N N N

Local Government

5. Insist N Y Y

State Number 1 5 8



Coalition AnalysisCoalition Analysis

• GMCR II first determines how well a player can do on his p yor her own, noncooperatively.

• Next, GMCR II indicates if a coalition of two or more decision makers can cooperatively move from one equilibrium to another that is more preferred by allequilibrium to another that is more preferred by all coalition members.

• This is called an equilibrium jump.

Elmira ConflictElmira Conflict

• GMCR II indicates that state 5 is vulnerable to an equilibriun jump from state 5 to state 8.

• In reality, MoE and Uniroyal negotiated a secret deal which caused the conflict to more from state 5 to 8.

StatusQuo



MoE

1. Modify N N Y1. Modify N N Y

Uniroyal

2. Delay Y Y N

3. Accept N N Y

4. Abandon N N N

Local Government

5. Insist N Y Y

State Number 1 5 8


equilibrium (state 8)equilibrium (state 8)

Sensitivity AnalysesSensitivity Analyses

• Answer “what if “ questions.q

• Detection of coalitions and potential equilibrium jumps is a type of sensitivity analysis.

“What” happens to the equilibria “if “ the preferences are• What happens to the equilibria if the preferences are altered?

Abandonment of Uniroyal’s Elmira OperationAbandonment of Uniroyal s Elmira Operation

• Uniroyal alone controls the movement to state 9 where it yabandons its Elmira plant.

• The risk of closure (state 9) is a severe threat to MoE and Local Government, both of whom least prefer this state.state.

• Sensitivity analyses confirm that Uniroyal’s ranking of state 9 is crucial to the final equilibrium.

7 Guidance in Designing Decision Support S tSystems

• DSSs will have an increasing role in improving decision making relating to water and environmental problems.

• Societal and physical systems models underlying DSSs must be l d i dproperly designed.

• DSSs must take into account the kind of information available to calibrate these modelscalibrate these models.

• Allow stakeholders to build, review and test models, using a shared vision approachvision approach.

• Output from DSSs can support decision making within anintegrated water resources and environmental management g gframework.

Societal Systems

Physical SystemsSystems

ModelsSystems Models

The duality of systems modeling of a realworld problem

Key characteristics of the realworld phenomenon

Design theoretical model based on these assumptions

Develop theoretical properties andanalysis procedures

Testing and refining model based on applications

Implementation algorithmsp g

Decision Support System (DSS)

Use of DSS by researchers and practitioners


Experience in Designing, Developing d A l i GMCR IIand Applying GMCR II

• Keep the underlying system models as simple as possible.

• Recognize up-front that all models are approximations to reality.

• Use practical applications and realworld experience to guide the development of system models and DSSs.

• In societal systems modeling, leave scope for the user’s judgment.

• Make sure that a system model can be realistically calibrated.

• Produce analytical results expeditiously.y p y

References• Hipel, K.W. and Fang, L., “Multiple Participant Decision Making in

Societal and Technological Systems”, in Arai, T., Yamamoto, S. and Makino, K. (Editors), Systems and Human Science – For Safety, Security, and Dependability: Selected Papers of the 1st International Symposium, SSR2003, Osaka, Japan, published by Elsevier, Amsterdam, The Netherlands, Chapter 1, pp. 3-31, 2005.

• Hipel, K.W., Fang, L., and Kilgour, D.M., “Decision Support Systems in Water Resources and Environmental Management”, Journal of Hydrologic Engineering, Vol. 13, No. 9, pp. 761-770, 2008.

• Hipel, K.W., Jamshidi, M.M., Tien, J.J., and White III, C.C., “The p , , , , , , , ,Future of Systems, Man and Cybernetics: Application Domains and Research Methods”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 37, No. 5, pp. 726-743, 2007.

94

ReferencesReferences• Hipel, K.W., Kilgour, D.M., Rajabi, S., and Chen, Y., “Chapter 27 -

Operations Research and Refinement of Courses of Action”. In H db k f S t E i i d M t dit d b A PHandbook of Systems Engineering and Management, edited by A.P. Sage and W.B. Rouse, Wiley, New York, Second edition, pp. 1171-1222, 2009.

• Hipel, K.W., Obeidi, A., Fang, L., and Kilgour, D.M., “Adaptive Systems Thinking in Integrated Water Resources Management with I i ht i t C fli t W t E t ” INFOR V l 46 N 1Insights into Conflicts over Water Exports”, INFOR, Vol. 46, No. 1, pp. 51-69, 2008.

• Hipel, K.W., Obeidi, A., Fang, L., and Kilgour, D.M., “Sustainable Environmental Management from a System of Systems Perspective”, In System of Systems Engineering: Principles and Applications, dit d b M J hidi Wil N Y k Ch t 18 443 481edited by M. Jamshidi, Wiley, New York, Chapter 18, pp. 443-481,

2009.95

Competition and Cooperation in Conflict Resolution

Conflict Resolution

CompetitionCooperation

(Coalitions, Group Decision Making)


Multiple Criteria Decision Analysis

Fair Resource Allocation

Noncooperative Behaviour

Coalition Analysis

Implementation Algorithms

Decision Support Systems

Locations of ICWRER ConferencesInternational Conference on Water Resources and Environment Research

Koblenz 2013Koblenz 2013Quebec City 2010Adelaide 2008de a de 008Dresden 2002Brisbane 1999Kyoto 1996Waterloo 1993

www.Water2010.org

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Decision Support Systems in Water Resoiurces Management ... · Decision Support SystemsDecision Support Systems in Water Resources ManagementWater Resources Management A Keynote Address