From: AAAI Technical Report WS-94-04. Compilation copyright © 1994, AAAI (www.aaai.org). All rights reserved.
122
The GenericMetamodel,the Conflict ModellingCycle and Decision Support
M.I.Yolles
Liverpool John MooresUniversity Business School
19 April 1994
Abstract
Metamodelsprovide a mechanismfor guidance in modelling. They offer a structured approach,which is
appropriatefor the modellingof situations and processes. An exampleof a metamodel
is a modellingcycle,
and one is proposedsuitable for conflict processesin groups with critical size. The wayin whichsuch a
modellingcycle can be implementedon a computersystemfor decision support is discussed.
1. Introduction
This paper is concernedwith the modellingprocess
of conflict which arises from change, and the
simulation of that process. The modelling of
modellingprocesses is a metamodellingprocess,
providing guidanceand structured reasoning to
situations that maybe somewhat
messyin the way
in whichthey are defined. Thus, this paper is
concernedwith metamodelling,
andin particular it
concernsthe structuredmodelling
of conflict arising
fromchangefor large groups.It also has interest in
the computerisationof such modelling.
Whenattempting to model, and thus examining
the conflict processesof groups,it is appropriateto
differentiate betweenthose whichare small and
those whichare large: smallgroupstend tO, be illstructured in their communications
and ffecision
processes, that is they do not conform to a
predeterminable
pattern or relate groupentities in a
predeterminable way. Larger groups tend to be
morestructured, havingformalised processes and
entity relationships that are better known
and more
predeterminable. Thus, the structural nature of
groupsunderchangeitself changesaccordingto the
size of the group within whichit occurs. Small
group processes tend to operate differently from
those of large groups. They mostly operate
informally and are unslructured. A group can be
thought of as becominga large group whenit has
acquired a critical massof people. Like so many
examplesin real life that can be described as
having an instantaneous metamorphosis[Thom,
1975],the critical changethat distinguishessmall
group processes from large group ones maywell
appear to be sudden and distinct. However,as
groupsget larger, so groupnormsstart to appear;as
they increasein size, so too doesthe complexityof
their relationships, communications,and other
processes. Withthis formalisedprocessesstart to
develop, and the group thus becomes more
structured andmoreeasily representableby formal
models.
Models can be classified on a hard to soft
continuum.In hard modelsthings tend to dominate
a problem
andits setting, whilein soft modelsit is
peopleandtheir psychologicalneedsthat dominate.
In very soft contexts, the modelmaybecomethe
activity. For example the named conceptual
domains of Systems Engineering, Project
Management,SystemsAnalysis~ and Operational
Research are closer to the hard end of the
continuumwhile ManagementCybernetics, Soft
Systems Methodology, and Organisational
Developmentare closer to the soft end. Harder
approachestend to adoptmoreexternally structured
elementswithin their operational frameworks
than
the softer approaches which tend to be more
unstructured. Domains
that are highly structured
haveelementsthat are explicitly well defined and
can more easily be modelled. Thus, in systems
engineering it is the norm to modeland where
possible test a solution before implementingit,
while in OrganisationalDevelopment,
the only way
to test a modelis to experienceit.
Theapproachto modellingconflict processesis
frequently better undertaken whenguidance in
creating and validating modelsis provided. This
representsa structuredapproach.In particular, it is
important that the approach to a problembeing
modelledis to be well structured. This can often be
accomplished
throughthe use of a modellingcycle,
and modelling cycles are representative of
metamodels. One purpose of this paper is to
consider a modellingcycle whichis directed at
large groupconflicts.
Whenconsidering computeraided application of
a metamodelto a problemdomain,the softer the
problem, the more difficult it is to generate a
computer system able to adequately deal with
general modelling and simulation requirements
becauseof the needfor machineintelligence, level
of knowledge,
and decision makingfacility needed.
123
2. ModellingCycles
Simon [1960] was a major contributor to
ManagementScience. His concern lay in the
development
of a decision science, and in order to
do this he expressed the prevalent ideas on
modelling developmentas a general cycle for
decision makingprocesses under goal seeking
behaviour.Its three phasesare Intelligence,Design,
and Choice which can be iterated through to
progress a problem.The cycle provides guidance
in modellingdecision problemswhichtells us that
problem domains must be properly examined,
options identified, and models generated and
applied to the domains.
The Simoncycle has provided guidanceto the
process of decision making.However,it does not
provide modelbuilders with direction about model
building techniques or approaches,nor provide a
philosophical orientation for so doing. A
development of this cycle was suggested by
Rubensteinand Haberstroh[1965], and a variation
designed for computer software developers was
later produced[Sprague, 1986]in order to tackle
well structured problemdomains.Thelatter hard
approach come out of the stable of Systems
Development
Life Cycle, and this type of approach
has been used in situations in whichthe analyst
does not envisagethe involvementof people while
addressing the problem domain. It therefore
providesfor a highlystructuredmodellingai~proach
that can be represented in terms of well defined
harder modelling techniques. To tackle more
unstructuredprocessesinvolvingpeoplea different
needarises.
One well knownmetamodel[Checkland, 1981;
Checkland,Scholes, 1990] called Soft Systems
Methodology (SSM) has been used to solve
unstructuredsmall groupdynamicchangeproblems.
Thephilosophicalstand of SSM,however,is strong
is demandingconformityto the wayin whichthe
cycle operates, and suggests that consistent with
soft perceptions, solutions to problemscannot be
modelledbut rather mustbe experienced.
Someauthors perceive that while SSMprovides
a soundapproachtowardsthe solution of problems
of change,it is constrainedin its breadth,since its
philosophyof operationis very specific. Floodand
Jackson[1990] have defined their moreflexible
metamodel which introduces the idea of a
"metaphor"
to representan analogousconceptto the
problem in hand. The metaphor thus helps to
identify the context of a situation. This approach
has beencalled Total SystemsIntervention (TSI),
andconsists of a cycle of three phases:Creativity:
use systemmetaphorsas organisingstructures; e.g.
see an organisation as a machine(closed system),
an organism (open system), a brain (learning
system), culture (norms, values), team (unitary
political system), coalition (pluralist political
system), or prison (coercive political system).
Outcomeis the dominantmetaphor.Choice:select
an interventionstrategy or set of methodologies
as
appropriate.Useanyof the tools available fromthe
hard-soft continuumof techniques. A dominant
technology may be found. Implementation:
employs a particular system methodology to
translate the dominant
visionof the organisation,its
structure, and the general orientation adoptedto
concernsand problemsinto specific proposals for
change.
Common
to the above metamodels,and indeed
others, it is possibleto generatea definitionfor a
generic modellingcycle. Thethree phasesthat are
defined are Analysis, Synthesis, and Choice.
Analysisis the breakingdownof a probleminto its
components,
includingits context,the identification
of its structures, andits orientations.Synthesisis
the building up of a set of componentsinto a
coherent picture, from the integration of ideas
derivedfromthe analysis, to the constructionof the
prerequisites for a model. Finally, Choice is
anythingthat involves the selection of something,
including implementation.
It is quite a simplematter to apply the generic
modelto any modellingcycle from the very hard
SystemsDevelopment
Life Cycle, to the quite soft
Organisational Development cycle.
The
fundamental distinction between the different
metamodels then becomes the philosophical
approach.In principle, one can consider that the
generic metamodel
is held on a slide on the hardsoft continuum on which reside a variety of
modelling/problemstructuring tools, and as it
movesit picks up those modellingtools it requires
and appropriate philosophical approaches. Having
completed
a cycle, it is possible to moveto another
division on the continuum,also perhapsadoptinga
newphilosophical position. This approach must
implicitlyholdflexibility in its philosophical
stance.
3. The Conflict ModellingCycle
In the social sciences,it is frequentlythe case that
modelsare built in a waywhichis not structured.
Theycouldthereforebenefit fromthe applicationof
a metamodel.Modelsfor such processes mayvary
in their position on the hard-soft continuum.
Whilethe TSI approach could be useful here
since it providessomelevel of flexibility, it does
not appearto give guidancein providinga wayof
directly connectingthe examinationof the problem
124
Context
analysis
~
f
Structur, d {/~-~ system _*
N~-~fluence
a~ecstI°rY
Outcome
evaluation
~
analysis
~
C~C-rf
~ ~R/gffON
/
~
’
Mode
©~oic~
se~)eclnon
I
Smbillty
analysis
M delN~~Acflv
pmro "~c~a’~l~ng
Structured
synthesis
Propositional
synthesis
~
Pruning
Alternatives
synthesis
Fig.1
Conflict
Modelling
Synthesis
Cycle
domain with traditional
hard madelling
approachesas might be required in the semistructured problemsof large group conflicts.
In an attempt to address this type of problem
domain, the conflict modelling cycle (CMC)
presented in figure 1 adopts the three phases of the
generic model. It is thus consistent with the Simon
cycle, and broadly SSM and TSI, though
necessarily the terminology and philosophy have
changed.
In CMC,the first phase Analysis involves domain
examination and evaluation. The second phase is
Structured Synthesis, and involves propositional
definition of the unstructured problemdomain, and
the structuring of decision alternatives. Modelling
Choicesis the third phase; it is concernedwith the
selection/implementation of modelling approaches
which may be soft or hard. In the case of soft
modelling the model defines the approach to a
solution. In the case of hard modelsthen this phase
in addition addresses explicit modelselection and
evaluation, and ensures consistency between the
propositional base of each of a possible set of
modelsavailable for selection, and the propositions
defined in phase 2.
3.1.
Systems Analysis
Systems analysis requires that the problem domain
is defined, examined, and analysed. Analysis is
concerned with examining the system situation for
actors and conditions which relate to the resultant
states that mustbe defined and selected; data inputs
are obtained, processed, and examinedfor clues that
mayidentify problems or concemedwith gathering
data, identifying objectives, diagnosing problems,
validating data, and structuring problems and
environments. Systems may also be dynamically
and structurally stable. Dynamicstability relates to
the movementof a system in its achievement of
desired goals. Here, a desired goal will be achieved
as time passes if the systemis dynamicallystable.
Necessarily, therefore, dynamicstability is time
related. If a system has reached a goal and adheres
to it, then the system has achieved a condition of
equilibrium. Structural stability is more properly
referred to as structural criticality. A systemwhich
is close to criticality mayreact significantly to a
small change in one of its parameters. Under
change, a system may pass through a critical
condition, whenits structural relationships alter. A
big change caused by a small event shows the
125
systemnot to be stable.
In terms of the conflict modellingcy¢’le, the
phaseelementsare defined in the followingway’P1.1 Context analysis Examinethe nature and
context of the conflict in general terms, and the
environment
in whichit operates. Identify the type
of problem, i.e. hard (quantifiable or single
solution) or soft (unclear solution), bounded
unbounded.Use techniques like mindmaps, spay
diagrams, force field analysis, relationship
diagrams.
P1.2 Structural analysis Define the system at
various levels; use system maptechniques and
define the nature andboundariesof actors andother
entities. Usesystemsmethodology
to identifying
domainstructure andits description(e.g. boundary
parameters, degree of boundaryfuzziness, system
entropy); identify actor problemperspectives.
Identify generalgoals of the actor systems.
P1.3 Trajectory analysis Trajectory analysis is
concerned with problem definition and the
movements
that are madein solving it. Problem
definition: Theproblemdomainis the problemand
its set of actors, parameters, variables, and
constraints.Cleardefinitionof this canbe difficult
whenthere is sufficient complexity.In reducing
complexity one then might: (a) examine the
changes that mayhave invoked the problem, Co)
identify the problemboundaries and associated
parameters, (c) examinehowmanyproblemsexist
and whetherthey can be reducedto discrete subproblems, (d) examinepossible problemsolving
schedules.Trajectorydefinition: Eachparticipant
in the conflict is an actor with a frameworkof
perception, perspectives to the problem, and
decisions and actions taken which constitute a
pathway through the domain. The pathwaywill
havea direction which,if intended, representsthe
aimof the process,andidentifies a set of vectorsof
movement.The trajectory is the set of vectors
takenwiththe resultant goals that maybe achieved.
The difference betweenan intended and an actual
trajectoryis an indicatorof howstable the situation
is. Thus, if measuresof achievableand intended
goals, aimsand objectives can be made,a measure
of stability can be found.
Therationale andfeasibility of actor trajectories
should be evaluated in relation to earlier phase
elements. Techniqueslike objective trees, and
multiple cause diagramscan be used.
P1.4 Influence analysis Establish relationship
between entities within the system and its
environment. Use techniques like influence
diagrams. This phase should reflect on P1.3 by
suggestinginfluencesfor trajectory changes.
Toundertakeanalysis, it is essential that actors
and their influences are adequately understood.
Actors havegoals, objectives, strategies, and an
external environmentwith whichthey interact.
Theyhaveinternal constraints as well as external
ones, variables which include general cultural
attributes. This applies to all classes of actor,
whetherthey are enterprises, cultural groups, or
nationstates.
Culture, defined in terms of its attributes,
determines the value attached to data and
information[Yolles, 1992].
3.2 Alternatives Synthesis
By alternatives synthesis is meant selecting,
inventing, or developing possible options or
decisionscenarios.It is effectivelya designphase,
and the development of decision scenario
alternatives requires an adequateknowledge
of the
problemarea and an ability to generate feasible
alternatives.
In the modellingcycle, alternatives synthesis
includes the followingelements:P2.1 Proposition synthesis Formally define
propositionsrelating to the situation. This can be
important because parties to a conflict operate
within their owndistinct frameworks;whetherone
party acts rationally maydependvery muchupon
the nature of the framework,
andif conflicts are to
reachsatisfactory conclusions,it mustbe possible
to mapfrom one frameworkto another coherently.
This in essencedefines a principle of interactive
relatively for the parties to the conflict.
Propositional definition may usefully use
established terminology (e.g. game theory)
introducingterms like player rationality, closed
games,stationary games.Alternatively in a softer
approach,it can simplylist the set of assumptions
that are madein order to construct a modelling
approach.
P2.2 SynthesisingAlternative Structures Generate
a range of options: these include player
participation, and possible player choices which
maybe defined for the situation; include possible
player state choicesthat maybe feasible andplayer
coalitions in an n-player system. This involves
modelling interactive player relationships as
definitive scenario possibilities. Decisiontable
techniques are appropriate, as might be other
decision related approaches like Pugh’s matrix
[1984] often associated with the Organisational
Developmentmethodology.
P2.3 Pruning Pruning is the reduction of the
alternatives identified in P2.2. These will be
unstablescenarioswhich,by their elimination,will
reducethe alternatives to a core set of Optional
126
Reality State (ORS)scenarios. ORSare perceived
options or modesof adaptation within the system,
andrepresentthe possible states in a systemswhich
contributeto the definitionof the systemstructure.
Usetechniqueslike expert evaluation or Conflict
Analysistheory.
In general, this phase is concernedwith the
manipulation of data, quantifying objectives,
generatingreports, generatingalternative scenarios,
assigninguncertaintiesor valuesto alternatives. In
very soft applications, it relates to reducingthe
tactical optionsavailable to a core approach.
3.3 Modelling Choices
Modellingchoices involves identifying/selecting
modelscapable of representing feasible decision
scenariosfromthose optionsavailable. It includes
the evaluationof modeloptionsandtheir ability to
represent player environments and decision
scenarios, and examinationof the consequencesof
modelling option in respect of a changing
environment. It is necessary to activate these
modelsas a solution to the problem. This will
generate outcomeswhichmaybe possible problem
outcomes. The modelscan be validated and the
outcomes evaluated in terms of the problem
domain.In termsof the modellingcycle, this phase
includes:P3.1 ModelSelection Identify modellingoptions
and approaches; identify methodsfor modelling
perceivedand hypothetical situations whichmight
also model the future; provide the choice of
selecting modelalternatives; identify the model
demands,constraints andperspectives explicitly;
define modelpropositions, stability mechanisms,
and propositional base including any normative
assumptions.Evaluate models,comparingselected
modelsto the real worldsituation, and identifying
convergencebetweenmodelling options and real
worldsituation - that is convergence
with P2.1. In
very soft applications,this step simplyrelates to
adoptingthe core approachof P2.3.
P3.2 Activate Modelling Process Involve ORS
scenarios from P2.3, and set up a modelling
technique. Identify any additional parameter
estimationor variable estimationrequirementsbased
on historical evidence,assembledata, and prepare
process modellingcomponents.
Generate modelling results. In the case of
stochastic modellingprocesses, approacheslike
MonteCarlo simulation, Markovprocesses, or
Weibullgames(see section 6) can be adopted, and
perhaps compared. The application of models
involving normativemechanisms
must be connected
with P2.1. In soft problemsthis step relates to
starting the experience.
P3.3 Stability Analysis Investigate dynamicand
structural stability of the synthesisedsystem.Use
mechanismsimplicit to modelswhere possible.
Examineconvergencewith P1.3.
P3.4 Outcome Evaluations Validate model:
examinethe selected modeloutput and comparethis
to actual events. In relatively hard situations this
maywell involve a quantitative approach, and in
the case of there being numericaloutputs these
mustbe interpreted qualitatively. Asoft approach
requires checking that the progress of the
experienceis appropriate. A matchbetweenmodel
outputs and acceptable or real world events will
indicate the level of ability of the modelling
approach. Thus, relate modeloutcomesto phase
P1.3. If convergenceoccurs, identify impact of
modellingprocessby scenario adaptationin phases
PI.1, P1.2, P1.4. Otherwisereturn to P3.1.
This phasedistinguishesthe ability of eachmodel
to representthe situation andthe constraints under
which it operates. Validation of a model only
occursif the modellingoption evaluationhas been
successful.
4. ModellingDecision Options
The modelling cycle requires a full systemic
evaluationof the conflict domainsatisfying each
phase of the Conflict Modelling Cycle. In
Analysis, the problemenvironment
is very strictly
viewedas a system whichis formally defined in
termsof the appropriatetheory; thus the constituent
elementsof the problemenvironmentare defined,
includingwhothe participating players are, their
attributes, functions,andrelationships. It defines
the frameworkof the situation being studied for
eachplayer: that is, identificationof the natureof a
player and its boundariesand influences. This is
equivalentto defining the framework
of perception
of players, and very closely relates to the
propositions that determinethe wayin whichthey
operate.
Correctly, data should be collected from all
players within the defined system and
differentiation shouldbe madebetweenobservations
and player perceptionsin order to addressthe next
phase.
In the secondphase, Synthesis, a formalset of
propositions are synthesised whichrelate to the
analysis of the conflictual system. For instance
howfar can one assume player rationality and
within whatcontexts. Thepropositional base may
include, for instance, considerationof underwhat
conditionsplayerswill act against their rationally
establishedpreferences.
127
This can be followedby the creation of a set of
decision tables. Eachdecisiontable will consist of
three connectedsub-tables: .(a) properties, (b)
objectives, and (c) goals [Yolles, 1992]. In the
context of social conflicts, properties formthe
currentcharacteristicsof a player, andrelate to its
power base over each of the social, economic,
political, andcultural domains.Goalsrepresentthat
whicheach player intends to achieve in the long
term. Objectives are the set of decision 9ptions
available to the players whichnormallyrelate to
their rationally establishedpreferences.Asa result
of Decisiontable analysis, feasible solution can be
found for the problemunderconsideration.
Feasiblesolutions are those whichare logically
consistent. Feasible ORSare those ORSstates
whichcan logically exist, and are determinedfrom
the set Of systemproperties. Theset of feasible
ORSsin an n-player system composepossible
scenariosof interaction for examinationwithin a
decision makingframework.Thus, each scenario
becomesa feasible decision makingsolution of the
problemenvironment. A decision table would be
created for each of 4 dimensionsof concernin the
aboveexample,that is political, social, economic
and cultural [Kemp,Yolles, 1992]. Scenarios are
established within these tables. Ascenario maybe
a oneplayer identity of ORSthat is feasible under
definedconditions. Theseconditionsare definedin
the first part of the table. A scenario mayalso
representinteractionwhenat least twoplayers(e.g.
an ethnic groups and a host player, or two
coincidentcultural minoritygroups)defininga set
of states in contrapositionto eachother acrosseach
of the 4 domains.In this case the scenario also
represents a set of mutualpositions taken by each
playeron eachstate in the interaction.
5. Developinga Conflict Tableau
In consultation with a colleague G.Kemp,it was
found that the decision table approach can be
developedfurther as a modellingtool in its own
right within the conflict modellingcycle. Here,
objectives fromeach player havebeenassembledin
order to form a conflict tableau whichcan be
formulated into a set of feasible interactive
scenarios(see fig. 2 and3).
WemaychooserSp to represent the rth decision
table of player p, and’s~ to representthe combined
objectivestables for all of the playersat the rth
iteration at the mstable decision options. Wecan
nowdefine a futures trajectory whichmayoccur
alonganybranchof the futures tree.
Thefuturescenarioset’sj for the rth iteration for
the jth scenario is generatedby inspectionthrough
the initial use of the methodology
describedbelow.
Initially the ~Spdecision tables for r=0 are
generatedwithin the modellingcycle, anda tableau
’s i with j scenario possibilities is created. This
tableau enables an interactive evaluation of the
conflict domainto be attempted. Oncethis has
beenpruned,an investigation of howselection can
effect the 1Sp decision tables will be examined
within the modellingcycle. It maybe that there is
no difference between °Sp and ISp, whena new
futures set ls~ is generateddirectly.
Consider each objective table as a possible
outcome of a decision option. A number of
different outcomescan developwhichare presented
within a conflict tableau. Eachof these possible
outcomesis as a scenario. For an exampleof this
see [Yolles, 1992a].
6.
Choosing a Model
Thethird phase of the modellingcycle is that of
Choice,wherethe modellingapproachis chosento
solve the perceivedproblem.Twoof the features
of the approachadoptedwithin this proposalare:
1) use the goals table for examining dynamic
stability duringiteration withinthe modelling
cycle;
2) use the objectivestable withina conflict tableau
to examinethe structural stability of the system
under examination.
Theidentification of a stable set of ORSreduces
the size of the conflict tableau. This reducedset
can be usedto evaluatethe impactof each optional
scenarioonthe original propertiestable to create a
set of possible futures as shownin fig. 2. This in
turn enablesa dynamicstability evaluationto occur
on possiblefutures.
The need to examinethe conflict tableau to
investigate the structural stability of the conflict
environment
requires the use of a methodology,
the
propositional basis of whichconforms to that
determined for the system overall. Examplesof
some of the methodologies that might be
appropriate are in particular Conflict Analysis
[Fraser, Hipel, 1984], or a variation on Saaty’s
multivariate decision analysis approach(called
Analytic Hierarchy Process [Zahedi, 1986]), or
simplyexpert evaluation.
After identifying the propositionalrequirements
of the methodologiesbeing considered, it maybe
appropriateto re-examinethe overall systemwithin
the Analysis phase, reconsidering the player
frameworks; this can enable the problem to be
differently defined, i.e. whether a difference
betweenexpressedand perceivedobjectives should
be identified, or whetherthe very nature of a given
player or player set should be redefmed. New
128
propositionsmaythus develop, anddecision tables
already determined may in consequence be
redefined; thus newobjective.s tables and a new
conflict tableau maydevelop. In terms of game
theorythis relates to a redefinitionof the gamethat
each player is playing, and evenwhetherit is the
same perceived game.
7. Finding Stable ORS
Oncea conflict tableau has beengenerated, it is
appropriateto reducethe modelto a set of feasible
ORSsthat are slructurally stable. This mayoccur
throughhumaninspection, or throughexpert system
inspection,or by the use of oneof the multivariate
decision analysis methods(e.g. Zahedi, 1986).
could also, for instance, be accomplished
usingthe
methodknownas Conflict Analysis[Frase~ Hipel,
1984]. This approachprovidesa useful introduction
to the logical qualitative aspects whichmaybe
associated with scenario formulation within
environmentsunder changeand in whichthere are
potentially confrontationalcomponents.
A tableau maybe generated to represent the
objectiveset of feasible scenariospossibleto each
conflict situation. Players normallyhavedistinct
preferencesand biases, and under this condition
there will be a distinct and different preference
ordering of scenarios whichshould be considered
whenundertakinga stability analysis.
The modelling approach can be extended by
makinga secondcircuit of the Choicesphase; this
can occur by makingthe conflict tableau the
determinantfor establishinga set of futures, since
feasible objectives within the ORSwill impactthe
properties and goals of each player to a degree
which mayor maynot be discemable (fig. 3).
Other circuits of the Choices phase might also
occurperhapsprior to this.
Throughoutthe outcomeevaluation and systems
analysis, the futures can also be examined
against
the goals table which can contribute to an
investigation of possible divergence, and thus
dynamicstability. It should be realised that the
selectionof scenarioswithinoneiteration represents
the investigation of structural stability. The
outcome
of the iteration will be a redefinitionof the
propositional synthesis, and the creation bf new
decisiontables.
Themethodology
is intended to be sufficiently
robust to enable the changingenvironmentwithin
the domain of conflict to be satisfactorily
representedthroughexaminingpossible futures. A
futures tree of newdecision tables modelsis then
created with as manybranchesas there are possible
futures. Thesefutures are then re-analysedin a
continuediteration of the full cyclecreatinga set of
possiblefuture trajectories whichwill best suite the
players both individuallyandinteractively. Asreal
events progress within the domainof conflict, the
inappropriatebranchesare shed. Since a branchis
a discrete component
of a trajectory, inappropriate
trajectories are thus also shed.
The evaluation of whether certain scenarios
represent stable as well as feasible outcomesis
determined by the use of the decimal values
generatedby each scenario as already explainedin
the previoussection. Theseare flags whichenable
a logical investigationof the stable situation to be
madeaccording to an algorithmic process, rather
than by forced logic alone. Certain scenarios are
termed UI’s (Unilateral Improvement)enabling
the state conditions of the environmentfor that
player. A computer program is available to
generate solutions to certain classes of problem.
Thewholeapproachis particularly suitable for
computersimulation, since futures can be modelled
examined,and evaluated moreeasily.
8. Simulationand the ModellingCycle
This section of the paper is concernedwith someof
architectural needsthat shouldbe consideredwhen
designinga simulation systemcapable of helping
modellers use the conflict modelling cycle to
structure a modellingprocess.
In order to establish the modellingcycle within
a computersystem it is essential to take into
accounta numberof aspects. Theseinclude the use
of a decision aid to assist in determining the
suitability of a modellingapproachor technique;
applying the concepts of knowledgebased systems
to guide the modellingprocess; andmonitoringthe
systemto identify wherethe modellingprocessis in
the modelling cycle, its general progress and
conceptual suitability, and howthe modelling
processescompareswith reality.
8.1
ModellingDecision Support
In order to apply computer techniques to the
conflict modelling
cycle, it is appropriateto discuss
the needsof a computersystemto enable it to be
able to determinewhatmodelto select, and howto
do so. A Modelling Decision Support System
(MDSS)can be thought of to consist of three
subsystems, the Information Base subsystem,
Databasesubsystem,and the Modellingsubsystem.
Thethree subsystemswouldbe linked together by
an interface block composedof: the DGMS
- is a
Dialogue Generation and ManagementSystem
whichenables the user to use the systemin a user
129
PLAYERS
I
I
Goals
Properties
Objectives
]]
I"
Possible4
Futures
Dynamic
Stability
Power
Conflict Tableau for
Decision Making/
Bargaining
Processes
Structural
stability
Domains
fig. 2
Relationship
between Goals, Objectives,
and Properties
in the Decision Table Model
OAnalysis
~Formal
I
OSynthesis
definition
System
of system
propositions
Decision tables
(Sr,
r=l,p)
I
player
frameworks
of perception
I
Players
Goals
I
Properti es
Obj ec t i ves
I
stable
Scenario set
of size k at
the 0th pass
in Conflict
Tableau
OChoices
Ist cycle
0J
scenarios
0! .....
1
0!
2
k
I
...
I
properties
regeneration
I
Futures decision
tables S for each
of p players
OChoices
2nd cycle
P
li3
Outcome
evaluation
II
II
Systems Analysis
II
II
iteration
¯
Redefinition of
propositional
synthesis
and new decision tables
Continued
Note: si= s± (iS0, ISI, ..., ~Sp)
Futures
Fig. 3
Decision Table
Analysis
i
Iiteration
130
oriented way; the SBMSa Strategy Base
ManagementSystem connected to a model base
subsystem,whichenablemodeJlingstrategies to be
collected, offered to a modeller, and after a
modellingselection has beenmade,applied; in an
intelligent system, it will be able to assess the
suitability of strategic modelsaccording to the
characteristics of the modellingdomainand the
modeller; the MIMS- a Modelling Information
Management System part of which is an
Information Base ManagementSystem 0BMS),
connectedto the navigation process and generates
an audit trail or history of the modellingprocess
(through the database subsystem). Part of the
MIMS
is a monitoringsystem (MS)whichenables
implicit and explicit evaluationof the modelling
processto occur.
Suitable modelling DSSenvironmentsmaynot
only be able to chart a navigationprocessthrough
its associatedMIMS,
it will also be able to guide
the modellerthroughdistinct levels of modelling
process. In terms of managementcontrol, the
MIMS
can be described as havingstrategic models
that will providealternativestrategies andprocesses
of modelling,including mediaselection, tactical
modelswill help the modeller navigate though a
modelling domain,operational models will help
modeUers
solve a current problem,for instance by
directing them from a failed test result to a
particular area of test. Thesubsystemcan operate
with data fromthe data subsystemto generate real
time problemorientated modelsthroughthe system
interface. Anextension of this is the explicit
provision of a full monitoringand performance
evaluationsystemwithin-built advisorthat activates
the appropriateassistancerequired. This is system
is shownin figure 4.
8.2
Other Consideration
Otherconsiderations
for the systemrelate to the use
of deep reasoning methodsfor the evaluation of
qualitative aspects of the system.
The
implementation of a modelling approach as
identified within a metamodelhas an implicit
requirementto undertake qualitative reasoning.
This requiresa high level of intelligence whichfew
systemshaveyet beenable to introduce.Thenature
of deepmodelling
processesis basicallyqualitative.
Someapproachesare logically based, identifying
appropriate requirements independent of local
contexts. Othersare rule based,usingmeta-rulesto
determinewhichsurfacerules of a set (with perhaps
contradictory or competingelements) to select.
Other approachesuse mathematicalmethods,or a
combination of all three. One appr~ch in
determiningqualitative evaluations, accordingto
Kuipers, has a history whichgoes backto the mid
1970’s.Theinvestigatorsof this area of studytend
to focus on descriptions of the deep mechanism,
capable of representing incompleteknowledge
of a
structure andbehaviourwithina process.
Afinal considerationthat shall be madehere is
that of monitoring.Themonitoringof a modelling
progress througha particular modellingprocesses
mustbe an implicit feature of a modellingsystem.
Thedecision rules are determinedby identifyingthe
criteria necessary for makinga decision. In a
metamodellingenvironment they are typically
determined from the experience of recurrent
processes. Whenmonitoring suggests that a
modeller has not been successful is modelling a
process, then feedbackto the systemdecision maker
is requiredin order that a decisionrule relating to
the current modellercan be adjusted or a newone
introduced.Amonitoringsystemis depictedin fig.
5.
9. Conclusion
This paper beganby discussingmetamodelling,
thus
the provisionof a structured modellingapproach.
It examinesconflict processesin a systematicway
by initially defininga genericmetamodel,
andthus
offering a modellingcycle involvingthe phasesof
analysis, synthesis, and choice, and relating model
outcomes to the system under change. It is
essential that a structuredapproachis adoptedwhen
modelling processes. In particular, individual
approachesand mathematicalformulations become
moremeaningfulif they are presented in logical
association withina modellingcycle.
One approach in modelling conflict was to
definedecisiontables relating to a conflict that had
beenproperly examinedand defined accordingto a
set of criteria. Afterdefiningthe propositionalbase
tor the conflict, decision tables are created
involvingplayer goals, properties, and objectives.
Theobjectives becomethe interactive component
of
the decision tables whencombinedtogether in a
Conflict tableau. Thetableau is prunedto a set of
stable scenarios.
In the next phase of the cycle, the stable
.scenarioslead to possiblefutures for the conflict
whichmaybe constructedby a variety of methods,
applyingeither soft or hard methods.Soft methods
in the context of social conflicts mayrequire
discussion with participants combinedwith table
inspection, or expert system approaches can be
adopted. Alternatively,harder approachescan be
used, for example through the estimation of
probabilities in Markov
Processes.
131
MDSS
!:!!
:!:!:i:i:!:!
:!:!
:!:i
:i:i:!:!
:!:i:!i
:i:!:i:i
:i:i
:!:!
:!:!
:!:!
:i~:
i:!
:i:i
:!:!
:i:!
:i:!
:i:i
:i:i
:!:i
:ii
:i:i
:i:i
:i:i
:i:i
:i:i:
?
ii~::
Model base i::ii
subsystemi::i::
!!!! Database
:::: subsystem
I
:
Navigation
& audit ]
:~:~i:!{i~ii~ii~ii~ii~iiii~iiii’{i’~i’{i’iiiii
trail in modelling
’::’~’~~’"’
........
¯
::::ii
I cycle strategy
I Information
iilbase
subsystemii
~~
User
iii!
i::i::
i::
fill
]
!
iii~odellin~ information
~
iiii
Relationship
Fig.
4
between MDSS Subsystems
Player
data input
Modelling cycle
provision
--~
of decision
rule options
,I
Scenario
knowledge
opt ion
selected
yes
Feedback
modelling
manager
Monitoring
I
¯ I
Successful
solution
encountered
no ¯
undetermined
Modelling
strategy
problem
Intelligent
aid in the
selection of
yes
alternative
--modelling
strategy
I
Select & I
execute
modelling
strategy
Fig. 5
Modelling system monitor for determining if
a selection of another modelling strategy is required
The result of this choices modelling approach
then feeds back into the analysis phase before
iteration occurs again, in order to generate a further
phase or set of phases within the process. By
establishing the linkage between the methodologies
the modelling cycle has been shown to have a
continuity in the way that these models can be
applied to real situations.
Finally, some consideration
has been made on
how such a modelling process might be established
within a computer based system. This rests upon
the development of a decision support system with
an implicit intelligent knowledge based system able
to monitor and examine the modelling process as
represented within the modelling cycle.
10. References
Burch Jr,J.G., Slater,F.R.,
Grudnitski, G., 1979.
Information Systems: theory and practice, 3rd Ed.
John Wiley.
Checkland, P., 1981, Systems Thinking Systems
Practice. John Wiley & Son, Chichester.
Checkland,
Scholes,
1990,
Soft
Systems
132
Methodology in Action.
Chichester.
John Wiley & Son,
Fraser,N.M., Hipel,K.W., 1984, Conflict Analysis:
Models and Resolutions. North Holland. ~
Klir, G.,J, Folger,T.A., 1988. Fuzzy Sets,
Uncertainty, and Information. Prentice-Hall
International.
Kuipers,B., 1970. Qualitative Simulation. Artificial
Intelligence. 29,289-338.
Parzen, E., 1962, Stochastic
Francisco: Holden Day.
Processes.
San
Petersen,I.D.,
1980. The Dynamic Laws of
International Political Games1823-1973. Inst.
Political Studies, University of Copenhagen,
CopenhagenPolitical Studies.
Pugh,D.S., 1984, Organisational Theory: Selected
Readings. Penguin.
Rubenstein,A.A., Haberstroh,C.l.,
1965, Some
Theories of Organisation. Richard D. Irwin,
Homewood,I1.
Saaty,T.L., 1989, Conflict Resolution, Praeger, New
York
Simon,H.A.,
1960, The New Science
Management Decision. Harper & Brothers,
York.
of
New
Spague Jr., R.H, Watson, H.J.,1986, Decision
Support Systems. Prentice Hall.
Thom, R., 1975, Structural
Morphogenesis, Benjamin.
Stability
and
Yolles,M.I., 1985. Simulating Conflict Using
Weibull Games. Simulation in Research and
Development. Ed. Javor,A. Elsevier Science Pub.
(North Holland).
Yolles,M.I., 1987. ModellingConflict with Weibull
Games.In Analysing Conflict and its Resolution.
Ed Bennet,P.G. Clarendon Press, Oxford.
Yolles,M., Price,A, 1989. Generic and Contextual
Modelling of Conflict in Knowledge Based
Decision Support System. Unpublished
Yolles, M.I., 1992a. The Conflict Modelling Cycle.
J. Conflict Processes, 1,1.
Yolles, M., 1992, "Towards Simulation of the
Conflict Modelling Cycle". Chinese Association of
Modelling and Simulation conference in Beijing,
October 20-23.
Yolles, M.I., Kemp, G., 1992. The Dynamics of
Culturalism. J. Conflict Processes, 1,1.
Zahedi, F., 1986, The Analytic Hierarchy Process.
Interfaces 16,pp96-108. Institute of Management
Science.