Multiagent Environments
Brahim Chaib-draa
Computer ScienceDepartment, PavillonPouliot,
LavalUniversity, Ste-Foy, PQ,Canada G1K 7P4.
email: [email protected]
Abstract
Analyticaltechniquesaregenerallyinadequatefordealingwithcausal
interrelationshipsamong asetofindividual andsocialconcepts. Inthis
paper,wepresentasoftwaretoolcalledCM-RELVIEWbasedonrelational
algebrafordealingwithsuchcausalinterrelationships. Then weinvesti-
gatetheissueofusingthistoolinmultiagentenvironments,particularlyin
thecaseof:(1)thequalitativedistributeddecisionmakingand,(2)theor-
ganizationofagentsconsideredasawholisticapproach.Foreachofthese
aspects,wefocusonthecomputationalmechanismsdevelopedwithinCM-
RELVIEWtosupportit.
Index Terms Decision support, cognitive maps, knowledge base manage-
ment,tools andsupports,causalmaps,agentandmultiagentsystems.
1 Introduction
Cognitivemapsfollowpersonalconstruct theory,rstputforwardbyKelly[9].
Thistheory providesa basis forrepresentinganindividual's multiple perspec-
tives. Kellysuggeststhatunderstandinghowindividualsorganizetheirenviron-
mentsrequiresthat subjectsthemselvesdenetherelevantdimensions of that
environment. Heproposedasetoftechniques,knowncollectivelyasarepertory
grid, in order to facilitate empirical research guided by the theory. Personal
construct theory has spawned many elds and has been used as a rst step
in generating cognitive maps. Hu [8] has identied vegeneric families of
cognitivemaps:
1. Mapsthatassessattention,associationandimportanceofconcepts: With
thesemaps,themapmakersearchesforfrequentuseofrelatedconceptsas
ThisworkhasbeensupportedbytheNaturalSciencesandEngineeringResearchCouncil
ofCanada,NSERC.
nization,forexample,andlookfortheassociationoftheseconceptswith
otherstoinfermentalconnectionbetweenimportantstrategicthemes. She
alsomightmakejudgmentsaboutthecomplexityoftheserelationshipsor
dierencesintheuseofconcepts.
2. Maps that show dimension of categories and cognitive taxonomies: The
mapmakerinvestigatesheremorecomplexrelationshipsamongconcepts.
She might dichotomize concepts and construct hierarchical relationships
amongbroadconceptsandmorespecicsubcategories. Mapsofthistype
have been used to dene the competitive environment, and to explore
therangeand natureof choicesperceived by decisionsmakers ina given
setting.
3. Mapsthat show inuence, causalityand systemdynamics(causalmaps):
Thesemapsallowthemapmakertofocusonaction,asforexample,how
therespondentexplainsthecurrentsituationintermsofprevious events,
and what changesshe expects in thefuture. This kindofcognitivemap
iscurrently,hasbeen,andisstill,themostpopular mappingmethod.
4. Maps that show the structure of argument and conclusion: This typeof
map attemptsto showthe logicbehindconclusionsand decisionsto act.
The map maker includes here causal beliefs, but looks more broadly at
the text, as a whole, to show the cumulative impact of varied evidence
andthelinksbetweenlongerchainsofreasoning.
5. Maps that specify frames and perceptual codes: This approach suggests
that cognitionishighlyconditionedbypreviousexperience,andthat ex-
perienceisstoredinmemoryasa setofstructuredexpectations.
In this paper, we extend the causal map by providing a tool called CM-
RELVIEW, which has a precise semantics based on relation algebra [6]. Then
weinvestigatetheissueofusingthistoolinmultiagentenvironments,specially
for(1)thequalitativedistributed decision makingand,(2)theorganizationof
agentsconsideredasa wholisticapproach.
1.1 Causal Maps: Motivations Through an Example
Muchattentionhasbeenrecentlydirected towardtheproblemofstrategicde-
cisionmakingin dynamic and open environments. Theissues ofthis problem
tendtobecomemore complicated,unstructured, andnotalwaysreadilyquan-
tiable. Theyparticularlyinvolveinteractingvariablesthatmakethemdicult
todealwith. Analyticaltechniqueshavebeenusedtohandlewell-denedprob-
lems,but theyare inadequateforthis typeofproblem. A causalmap maybe
employedtocopewithcomplicatedproblemsbecauseitoerstomodelinterre-
lationshipsamongavarietyofconcepts. Causalmaps(CMs)makethefollowing
threeassumptionsaboutcognitionin thecontext ofdecision[8]:
1. Causal associations are a major way in which decision problems can be
describedandunderstood;
comes;and
3. Choiceamongalternativedecisionactionsinvolvescausalrelations.
Causal maps are usually based on human communications collected by
interviewingor found in documents such as corporate reports or memos. We
arelookingthereforexpressionshavingthegeneraltype:
Entity/phenomenon/conceptAleads-to/causes/inuences/etc.
/Entity/phenomenon/conceptB or,
B iscaused/eected/inuenced/etc./ byA.
Thesearecausal assertions,which aretakento indicatethat theconcerned
subjects: (1) use concepts (A,B) to refer to some phenomena or concepts in
theirdomains,and(2)think(believe,assume,know,argue,etc.) thatthereare
certainrelationships betweentheseconcepts.
Wegenerallyusecausalmapsfordealingwithsuchcause-eectrelationsem-
bedded in deciders' thinking. Thesesmaps are representedas directed graphs
where the basic elementsare simple. The conceptsan individual (a decision-
maker or a group of decision-makers) uses are represented as points and the
causal links between these concepts are represented as arrows between these
points. This representation givesagraph ofpointsandarrows,called a causal
map(CM). Thestrategicalternatives,allofthevariouscausesandeects,goals,
andtheultimateutility 1
ofthedecision-makercanallbeconsideredasconcept
variablesand representedas pointsin the CM. Causalrelationships can take
ondierent values basedon themost basic values + (positive), (negative),
and0 (neutral). Logical combinationsof these three basic values give thefol-
lowing: neutral ornegative ( ), neutral orpositive (), non-neutral (),
ambivalent (a) and, nally, positive, neutral, or negative (i.e.,universal)
(?) [1,5,13].
The real powerof this approach appears when a CM is pictured in graph
form. Itisthenrelativelyeasytoseehowconceptsandcausalrelationshipsare
relatedtoeachotherand tosee theoverall causalrelationshipsof oneconcept
withanother,particularlyifthese conceptsaretheconceptsofseveralagents.
Forinstance,theCM ofFig. 1,takenfrom[11], explainshowtheJapanese
made the decision to attack Pearl Harbor. Indeed, this CM states that re-
maining idle promotes the attrition of Japanese strength while enhancingthe
defensivepreparedness of the United States, bothof which decrease Japanese
prospectsforsuccessinwar. Thus,aCM isasetofconceptsasJapanremains
idle, Japaneseattrition, andsoforth, anda setof signededgesrepresenting
causalrelationslikepromote(s), decrease(s),andso forth.
Notethattheconcepts'domainsarenotnecessarilydenedpreciselybecause
therearenoobviousscalesformeasuring US preparedness, successinwar,,
andsoforth. Nevertheless,itseems easyto catchtheintendedmeaningofthe
1
Utilitymeanstheunspeciedbestinterestsofadecisionmaker.
Japanese attrition Japan
remains idle
preparedness
Japanese success
in war US
+
+ _
_
Figure1: Anexampleofcausalmap(from[11]).
signedrelationships in thismodel [19 ]. As any causal map, theCM ofFig. 1
canbetransformedin amatrixcalledanadjacencyor valencymatrixwhichis
asquare matrix,withonerowand onecolumn foreach concept. Thus,ifa, b,
canddrepresentsJapanremainsidle, Japaneseattrition,Japanesesuccess
inwar, andUSpreparedness, respectively,weobtainthefollowingadjacency
orvalencymatrix:
0
B
B
@
a b c d
a 0 + 0 +
b 0 0 0
c 0 0 0 0
d 0 0 0
1
C
C
A
Inferencesthat wecan drawfrom a CM arebasedona qualitativereason-
ingsimilartofriend'senemyisenemy, enemy'senemyisfriend,andso forth.
Thus,inthecaseofFig.1,remainingidledecreasestheprospectsforJapanese
successin a war along two causal paths. Noticethat therelationship between
idlenessandwar prospectsisnegativebecausebothpathsagree. Inthese con-
ditions,Japanhasaninterestin startingwarassoonas possible ifshebelieves
thatwar isinevitable.
Thus,causalmapsandthequalitativereasoningthatitsustainsservegener-
allyas themodelinglanguage forproblemresolutionthrough decisionmaking,
particularlyinmultiagentsystemswheredecisionemergesgenerallyfrominter-
relationshipsamongagents'concepts. Suchisthecaseforthepreviousexample
that reects a multiagent system in the sense where Japan and USA are
individualagents.
1.2 Related Work
As previously stated, causal maps appear to be the most widely used cogni-
tive maps in practice. Causal maps have been used for decision analysis in
theeldsofinternational relations[1,5],administrativesciences[15],manage-
mentsciences [7, 17], and distributed group decision support [21]. Inthe lat-
ter context, Zhangand his colleagues provided thenotions of NPN (negative-
positive-neutral) logic, NPN relations, neurons, and neural networks. Zhang
[20 ]extendedhisapproachbyanNPNfuzzysettheoryandanNPNqualitative
of CM, based on particular techniques for associating intervals with edges of
directedgraphs. Intheirmodel,quantitiescombinebypropagationalongpaths,
butthere is nootherconnectionto theoriginalspirit ofCMs, (i.e.,thecausal
reasoning).
Exceptfor Zhang'swork [21,20], all other approachesto CMs were based
onsimple inference mechanisms about the consequences of a CM. Thus, the
denition of a precise semantic interpretation of qualitative causality has re-
ceivedverylittleattention. Theonlywork thatweareawareofinthiscontext
is Wellman's [19 ] and Nadkarni's [12] approaches. The rst author used an
approach based on graphical dependency models, for probabilistic reasoning,
andsignalgebras, forqualitativereasoning. Thesecond authorused Bayesian
network approachtomakinginferencesincausalmaps. Thus, bothusedprob-
ability theory as is the case usually in Articial Intelligence. However, their
approachesareapplicableonlyintheacycliccase,becausecircularrelationsor
causal loops, common in causal maps, violate the acyclic graphical structure
requiredin aBayesian network.
In this paper, wepresent an implementation of a formal model which has
been implemented in a system used as a computational tool supporting the
relational manipulations. This tool is called CM-RELVIEW and is built over
the RELVIEW software 2
, a freeware package developed by Berghammer and
Schmidt[2].
2 CM-RELVIEW: An Implementation of a Relation
Model of CMs
Withthis tool, alldata are representedas binaryrelations, which the system
visualizesin two dierentways. For homogeneous relations, CM-RELVIEW of-
fersa representation as CMs, includingseveraldierentalgorithmsforpretty-
printing. Asanalternative,anarbitraryrelationmaybedisplayedonthescreen
asaBooleanmatrix. Withmatrixrepresentation,wecanvisuallyeditandalso
discover various structural properties that are not evident from the CM rep-
resentation. CM-RELVIEW system can manage as many graphs and matrices
simultaneously as memory allows and the user may manipulate and analyze
the relations behind these objects by combining them with the operators of
relational algebra. Theelementary operationscan be accessed through simple
mouse-click,buttheycanalsobecombinedintorelationalexpressions,mapping,
andimperativeprograms. CM-RELVIEWallowsalsouserstostorerelationsand
CMs.
CM-RELVIEWoersamenuwindow(aswecanshowinFigure2)thatcanbe
dividedintothreeparts: therstpart,thatistherstthreebuttonrows,deals
withsystemadministrationtaskslike(a)FILES:opensthele-chooserwindow;
2
This software can be obtained by anonymous ftp from http://www.informatik.uni-
kiel.de/progsys/relview.html.
editor; (d) GRAPH: Pops thewindow of CMs editor; (e) XRV/PROG: display
thedirectorywindowshowingthestateoftheworkspaceandthereasoningon
cognitivemaps; (f)LABEL: Opensthelabeldirectorylistinglabelset whichis
inourcaseC:=fa;+; ;0;; ;;?g.
The buttons in the second part of the menu window cover actions which
are mostlyneeded while working with the systemlike (g) EVAL: Pops upthe
evaluation window for entering a relational term (a relational term can be a
relation,afunction,orarelationalprogram);(h)TESTS:Popsupawindowfor
invokingtests. Withthelatterwindowwecanperformthefollowingactions(1)
TEST-1-R:toexecutevariouskinds oftestsona relation(isitempty,injective,
symmetric? etc.); (2) TEST-2-R: to execute tests on two relations (are they
equal, included? etc.); (3) SUBJECTIVE VIEWS: to do some tests on CMs
in the case of the reasoning on subjective views (comparison, prediction and
explanation) discussed in details in [6]; (4) WHOLISTIC-CM to execute some
strategies of changes on the particular CMs representing an organization of
agentsasdiscussedin Section4.
Inthethirdandlastpartofthemenuwindow,anumberofrelationalopera-
tionsaredirectlyaccessibleviapushbuttons. Thereisalsoadirectorywindow
thatpresentstheactualstateoftheworkspaceto theuser.
FILES INFO QUIT
RELATION GRAPH
LABEL XRV/PROG
DEFINE EVAL ITER
| & - * ^
S/R R\S SYQ
TRANS REFL SYMM
DEF 1st 2nd ORD TESTS
Figure2: ThemenuwindowofCM-RELVIEW.
TherelationeditorcanbeopenedbyclickingontothebuttonRELATIONin
themenu window (seeFigure 2). Selecting a relation in the rstscroll list of
thedirectorywindowloadsthisrelationintotheeditor. Evaluationofrelational
termsperformsa load commandimplicitly, i.e., theresultof theevaluation, a
relation,is automatically displayedin therelation editor window. There isno
explicitsave commandbecauseanymodicationoftherelationshowninthe
relationeditordirectlychangestherelationstoredin theworkspace. Typically,
the window of the relation editor looks as a grid network in which a single
is represented byone ofthe set C :=fa;+; ;0;; ;;?g. An exampleof a
relationisshowninFigure3.
+
+ +
- -
-
+
- Name : R1 Dim : 8 x 8
Figure3: Therelationeditor.
Ifthemousepointer islocatedonanitemof arelation,themousebuttons
invoke thefollowing dierent actions: (1) theleft mouse button sets theitem
ifit was cleared, or clearsitif itwas set; (2) themiddle mousebutton allows
tochooseone relation (which isused bythe leftmouse toset) ofthe set C:=
fa;+; ;0;; ;;?g;nally(3) therightmousebuttonpopsupamenu that
appearsinFigure 4.
RELATION-MENU
RENAME DELETE NEW
CLEAR RANDOM FILL RELATION -> GRAPH PRINT
LABEL DRAW_MODE
Figure4: Thepop-upmenuoftherelationeditor.
Themostinterestingmenusofthepop-upmenuare: NEW:Itcreateanew
relation,DELETE:Itdeletestherelationdisplayedintherelationeditorwindow
from the workspace (the cognitive map associated with the deleted relation
is also deleted), RELATION ! GRAPH: It creates a CM from homogeneous
relationwiththesamenameastherelation(suchCM isdisplayedinthegraph
GRAPH-MENU
DELETE NEW
COPY
GRAPH -> RELATION PRINT
MARK NODES MARK EDGES UNMARK GRAPH FIT IN WINDOW TOGGLE GRID GRAPH-DRAWING
Figure5: Thepop-upmenuofthecognitivemapeditor.
Finallythewindowofthe grapheditor (i.e.,CM editor) canbe openedby
pressing the button GRAPH in the menu window. Similarly to relations, all
actions within this menu are selected with the same right mouse button. By
pressingsuchbutton,wereachthegraph-menuthatappearsinFigure5. Within
thismenu,wecanparticularlyinvokethefollowingactions: DELETE:Itdeletes
all nodes of a CM; NEW: It opens a dialog window which allows to enter a
name for a cognitive map; GRAPH !RELATION: It creates a relation from
a cognitivemap, GRAPH-DRAWING:It opens a submenu from which dierent
graph algorithms can be chosen, particularly: LAYER which places the edges
vertically;FORESTwhichdrawsadirectedforestandWHOLISTIC-APPROACH
whichdrawsa particularCM that wewilldetaillaterin section4.
3 CMs as a Tool for Qualitative Distributed De-
cision Making
CMs canalsohelpanagentoragroupofagentsconsideredasawholetomake
a decision. Given a cognitive map with one or more decision variables and a
utilityvariable, which decision should be taken andwhichshould be rejected?
To achieve this, the concerned agent should calculate the total eect of each
decisionontheutilityvariable. Thosedecisionsthathavea+ortotaleect
onutilityshouldbechosen,anddecisionsthathavea or totaleectshould
berejected. Generally,noadvicecanbegivenaboutdecisionswitha,thatisan
ambivalent,total eectonutility,whereasthat a or ? totaleectonutility
should not be rejected because it raises the undetermined decision problem.
To solve such undeterminated decision,wepropose here anoriginal algorithm
whichisbasedontheprincipleofsuperpositionadoptedforCMs. Thisprinciple
stipulates that the result of applying together two concepts C
1 and C
2 is the
sameasapplyingC
1 andC
2
in sequence.
1. Foranyconcept C thathas anundeterminedresult onthe utility U,calculate all
theindirecteectsbetweenCandU;thenseparatethoseindirecteectsinpositive
andnegativepaths;i.e.,pathswith+ and totalindirecteect respectively;
2. Cutoallthenegativepathsandevaluate theeectofpositivepathsonU,then
noteP
1
thisevaluation;
3. RepeatthepreviousstepfortheeectofnegativepathsonU (withouttakinginto
accountthepositivepaths)andnoteP2 thisevaluation;
4. CompareP
1 andP
2
(a) ifP
1
ismorevaluablethanP
2
thenthesignbetweenCandU is+;
(b) elseif P
1
islessvaluablethanP
2
thenthesignbetweenCandU is ;
(c) elseif P
1
iaasvaluableasP
2
thenthesignbetweenC andU is0.
We will showbelow how thisalgorithm operates with a concrete example.
Before that, we now illustrate the decision-making process in the context of
multiagent environments using CMs. To achieve this, consider for example
the causal map of a professor P
1
(considered as an agent) shown in Figure 6
whosupervisesaresearchgroupcalledG
12
)andwhohastochoosebetweentwo
coursesD
1 andD
2 (D
1 andD
2
aredecisionsvariables). Thequestionnowishow
P
1
canchoosebetweenD
1 andD
2
knowingthefactsreectedbythecausalmap,
showninFigure6. ThiscausalmapincludesthefollowingP
1
beliefs. D
1 favors
thetheoreticalknowledgeofG
12
'sstudents. Greatertheoreticalknowledgegives
agreatermotivationto students. Greatermotivationofstudentsgivesa better
qualityofresearchforgroup G
12
,which givesagreaterutilityofG
12
which, in
turn,hasapositiveresultonutilityofP
1
. Finally,theseconddecisionvariable
D
2
isan easycoursethat decreasestheworkloadofP
1
. Obviously, decreasing
P
1
'sworkloadincreasesherutility.
Inthiscase,howcanP
1
makeherchoicebetweenthetwocoursesD
1 andD
2
?
Noticethatinthecontextofourexample,P
1
shouldreasonaboutanotheragent
which isthe groupG
12
to makeherdecision. Inothercontexts, and forother
decisions, she can also collaborate with her group to develop herdecision. In
thissense,thedecision-makingprocessconsideredhereisamultiagentprocess.
Torunthisprocess,itmightbeusefultoconvertthecausalmapbeinganalyzed
totheform ofa valency matrixV. Withthevalencymatrix, P
1
can calculate
indirectpathsoflength2(i.e.V 2
),3(i.e.V 3
),etc.,andthetotaleectmatrix
V
t
. Infact,V
t tellsP
1
howthedecision variables D
1 and D
2
aect herutility
and G
12
's utility. This gives the following matrix of size 22 (keeping only
therelevantentries)involvingtwodecisionconcepts(DC),D
1 andD
2
,andtwo
utilitiesconsideredasvalueconcepts(VC),namely, UtilitiesofG
12 andP
1 .
Theoreticalknowledge
ofstudents
Student
motivation Researchquality
P
1
workload
Utility
ofP
1
+
+ +
+
ofG
12
ofG
12
+ D1
D2
Figure6: Anillustrativeexamplefordecision-makingin amultiagentEnviron-
ment.
DCnVC UtilityofG
12
UtilityofP
1
D
1
+ ?
D
2
+
Thus, P
1
perceives (1) decision D
1
as having a positive eect on Utility
ofG
12
andan undeterminedeect onher utility; (2) decision D
2
as having a
negative eecton Utility of G
12
and a positive eect onher utility. In these
conditions,itisimportanttoremovetheundeterminedresultofD
1
decisionon
P
1
utility. Toachievethis,weapply theprevious algorithmasfollows:
1. ToseetheimpactofgivingthecourseD
1
onutilityofG
12
wecutothe
negativepathproduced by Studentmotivation (+)>Workloadof
P
1
( )>UtilityofP
1
. Practically,thismeansthatP
1
evaluatesthe
followinghypotheticsituation: ifthecourseD
1
willbe givenbyanother
colleaguewhatwillbetheimpact(I
1 )ofD
1
onmyutilitywithouttaking
intoaccounttheworkloadinducedbyD
1
?
2. Similarly, wecuto thepositivepathproducedby Studentmotivation
(+)>ResearchqualityofG
12
(+)>UtilityofG
12
(+)>
UtilityofP
1
. Bydoingso,wecanseetheimpact(I
2
)ofgivingthecourse
D
1
ontheworkload(W2) ofP
1
without thepositiveimpactinducedby
the group G
12
. Practically, this means that P
1
evaluates the following
hypotheticsituation: Whatwillbetheimpact(I
2
)onmyutilityifIgive
thecourseD
1
toanothergroupthathasnoconnectionwithme?.
3. Finally,If theimpactI
1
compensatesI
2
then D
1
(0)>utilityof P
1
;
(b) is more valuable thanI
2
then D
1
(+)> utility of P
1
; (c) is less
valuable thanI
2
thenD
1
( )>utilityofP
1
1 1
eectsonher utility, viahergroup ofresearch, whichare more valuablesthan
whatthiscoursegivesheras workload. Intheseconditions,wehave
DCnVC UtilityofG
12
UtilityofP
1
D
1
+ +
D
2
0 +
Itis clearhere thatdecision D
1
wouldbepreferredondecision D
2
because
thisdecisionhasapositiveimpactonP
1
'sutilityandonG
12
utility. Conversely,
D
2
hasonly limitedimpact becauseit only positively inuences theutility of
P
1 .
How the CM-RELVIEW tool can be used by Decision Makers
(DMs) for their QDM
Decision makers (DMs) can elicit causal knowledge about their decision and
utilityvariablesfrom dierentsources,includingdocuments(suchascorporate
reportsor memos),questionnaires, interviews,grids,and interactionand com-
munication between other agents. After that, they use the relation editor of
CM-RELVIEWforlling matricesrelativeto thiscausalknowledge. Then, they
use the GRAPH button for transforming those matrices, into graphs (causal
maps). Finally,theyanalyzethosecausalmapsusingtheTRANSbutton.
Here,howa decisionmaker(DM)canusethistool. Bypressing thebutton
TRANSin the menu window(Fig. 2), CM-RELVIEW, a decision maker (DM)
cancalculatethetransitiveclosure,(i.e.,thetotaleectthat adecision hason
the utility variable). Inthe case where there is an undeterminedresult, CM-
RELVIEWappliesthealgorithmintroducedpreviouslyandaskstheDMtogive
it some guidance to solve the undetermined result. In particular, the DM is
askedtosupply(1)theimpactofpositiveandnegativepathsand,(2)themost
valuableimpact. Afullyautomatedprocessforsolvingtheundeterminedresult
problemisscheduledintheagendaofourfuturework.
4 CMs as a Tool For Studying Changes in Orga-
nization of Agents
Inmultiagentsystems,thestudyof anorganizationofagentshasgenerallyfo-
cusedonsome structuralmodels such: (1)centralized andhierarchicalorgani-
zations,(2)organizationsas authoritystructure,(3) market-likeorganizations,
(4) organizations as communities with rules of behavior. All these structures
misseddynamicaspectsandinuencesthat existin anorganizationofagents.
Weick[18]suggestedtochangetheprevalentstaticviewofanorganizationof
agentstoa dynamicviewwhichissustainedbychange. Precisely, heproposed
that organization and change were two sides of the same social phenomena.
His reasoning was that an organizationis a process of co-evolution of agents'
organizationandchangebasedonthegraphsofloopsinevolvingsocialsystems.
Inthelast decade,additionalinvestigationguidedbythisapproach [3 ,4]tried
to articulate how CMs provide a way to identify theloops that produce and
controlanorganization.
Asanexample,considertheorganizationthatbindsresearchers,grantagen-
ciesand qualiedpersonnel inany(scienceandengineering)department. The
causalmap representingthis organization isshown in Figure7. Themeaning
ofthisCM isclearandweshallexplainitnomore.
Personnal
Qualication satisfaction
Retentionof
bestresearchers
Quantityof Research
quality
Resourcesfor
research Salaries
Grantagencies
satisfaction
qualied
personnel
+
+
+
+
+ +
+
+
+
+ +
(2)
(3) (4)
(5)
(6)and(7)
(1)
Researcher
Figure7: Anorganizationofagentsasloops.
Inthiscausalmap,conceptslinktogetherto formloops,someofwhichare
numbered(1)to(7). Loops(1),(4)(7)aredeviation-amplifyingloops. Change
in theorganizationis theresult ofsuch loops,becauseany initial increase(or
decrease)in any conceptloops back to that concept as anadditional increase
(ordecrease)which, inturn,leads tomoreincrease(ordecrease).
Loops (2) and (3) are deviation-countering loops [4]. The stability of the
organizationis the result of such loops. In thecase of loop (2), for instance,
anincrease ofresources for research can leadto an increaseof salaries which,
in turn, reduces the resources allowed to research. If this reduction is not
enoughtocompensatetheinitialincreaseofresources,thena residualincrease
ofsalaries takesplacewhich,in turn, reduces theresources, andso on,until a
balancebetweentheinitialincreaseofresources andsalaries isreached. Thus,
organization.
Noticethat ina wholistic approachthewhole constraintstheconceptsand
therelationshipsbetweenthem. Withanorganizationofagentsrepresentedasa
wholisticapproach,weobtainadynamicsysteminwhichdeviation-amplifying
loopsareresponsibleforchangeanddeviation-counteringloopsareresponsible
forstabilityoftheorganization. Usingtheseloops,anindividualstrategistcan
direct strategic change in the desired directions. This can be achieved by 1)
choosingandchangingaloopor 2)choosing andchanginga setofloops.
How the CM-RELVIEW Tool can be Used by DMs for the
Reasoning on Organization Changes
Herealso,DMselicitcausalknowledgeabouttheirorganizationsfromdierent
sourcesasreports,memos,questionnaires,interviews,etc.. Afterthat,theyuse
theCM-RELVIEWforconstructingcausalmapsreectingthiscausalknowledge.
Finally,theyusetheCM-RELVIEWtoolforanalyzingthosecausalmaps.
AsstatedinSection2,thesubmenuofthegraphmenucalledWHOLISTIC-
APPROACH allows DMs to draw a wholistic causalmap, whereasthe menu
WHOLISTIC-CM of TEST allows them to test it by choosing and changing a
loop. Obviously,thelooptobechangedshould beaweaklooplooselycoupled
to the system. CM-RELVIEW oers DMs thefollowingactions for changing a
loop(from deviationamplifying to deviationcountering, or vice versa): ADD-
NODE:adding a node;REM-NODE: removinga node; REP-NODE:replacing a
node;CHG-LABEL:changing thelabelofalink.
5 Conclusion and Future Work
We have rstly proposed a tool for qualitative reasoning based on cognitive
mapsrepresentingrelationships betweenagents'beliefs. Thistoolallowsusers
todeterminecertainquantitativeandqualitativefeaturesofanycognitivemap.
Then, we have argued for the use of this tool in the context of multiagent
systems, particularly for the reasoning on interrelationships among a set of
individualandsocialconcepts.
Therearemanydirectionsinwhichtheproposalmadeherecanbeextended.
Thefullpossibilitiesofrelationalgebrahaveyettobeexploited. Another
option is to study fuzzy relations between agents' concepts [20]. Our
approach mightbe extendedin this direction to takeinto accountmany
degreesandvaguedegrees ofinuencebetweenagentssuchas none,very
little,sometimes,a lot,usually,more orless,andsoforth[10,14].
Applications such as the following ones must be investigated in greater
depth:
1) negotiation and mediationbetweenagents in the caseof reasoning
aboutsubjectiveviews;
causalmaps;
3) reasoningaboutthewholisticapproach; and
4) reasoningonsociallaws,particularlyforqualitativedecisionmaking.
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