Guideline for Building Policies Scenarios
*1. Define Panorama
for each Main Regional Actor
Use menu Cognitive Maps's Management
Create a Cognitive Map
or Show/Modify a Cognitive Map
in CM TXT Format
or CM XLS Format
Show/Modify the Actors List
Show/Modify the Accointances Table
Show/Modify Evaluated Actions List
end for each Actor
if all Actors Positions have been identified then
Use menu Actors Groups's Management
Create an Actors Positions Matrix
or Show/Modify an Actors Positions Matrix
in APM KBF Format
in APM TXT Format
or APM XLS Format
2. Simulate Actors Decision & Concertation
if all Cognitive Maps have been built then
for each Actor
Show & Clear APMs KBF Format (delete irrelevant data)
Use menu Decisions Analysis
Select an Actor's Cognitive Map
in CM TXT Format
or CM XLS Format
[Use Tools menu and Show/Print the simulated Actor's Logic]
Show/Modify Evaluated Actions List
Define a Story Beginning
Remove Events/Actions
or Add Events/Actions
or Change Links among Concepts
or Merge Actors Logics
Search for Possible Story Continuations,
by simulating
Structural Analysis
Forecasting Analysis
Decision Analysis
Explanation Analysis
Strategic Analysis
Show Results
end for each Actor
if all Actors's Positions have been identified then :
Use menu Policy Evaluation
Select a list of Actors from the a*.MAP/DBF files
(if necessary, make as many empty CMaps as actors)
Select an Actors's Positions Matrix list from gAPMx.KBF files
(if necessary, translate APM TXT Format in APM KBF Format,
by using Select KBF Format of the Strategies Analysis menu)
Define Beginning/Showing/Modifying Actors/Goals weights
Run the multicriteria Evaluation
Use menu Strategies Analysis
Select a Goal-Actors Positions Matrix (Scenario Scope)
in APM KBF Format
in APM TXT Format
or APM XLS Format
Define a Strategy Beginning
Remove Actors
or Remove Actions
or Change Actors's Positions
Search for Possible Strategy Continuations
by computing
Total Positions Distances
Alliances Strategies
Alliances among all Actors
Allies of an Actor
Opposites of an Actor
Common Positions of 2 allies
Differences between 2 Opposites
Optimized Allotment
Show Results
#$+!KDEFINE the regional PANORAMA consists of specifying :
(a) an Actors List, i.e. the names of the main regional Actors Types (public institutions, farmers and others business enterprises, ...) whose you will modelize the "theory" about the impact of the studied actions/strategies ;
(b) the Evaluated Actions List, i.e. the names of the policies, programmes and actions to be evaluated
(c) the Cognitive Map of each Actor Type, i.e. its "theory" and decision/position logic about the using and the impacts of each action to be evaluated
(d) the Accointances Table, i.e. the relationships among the Actors
To do that :
. use Define Panorama.Cognitive Maps Management menu
. select the appropriate submenu
#$+!KHere, A COGNITIVE MAP refers to a conceptual digraph representing the theory of an Actor about the role of some actions (notably public policies) for changing the regional situation, i.e. the relationships and causalities perceived or believed by an Actor to exist among the components of its decision/position-making logic, about the effectiveness of the current actions of the public institutions.
Such a graph may be modelled as a Square Matrix A[N,N], in which the N lines and the N columns represent the N concepts used by an Actor for describing the relationships existing between the components of its decision-making logic (the nods of the graph) ; the cells A[i,j] representing the sense and the magnitude of the direct impacts of the concept-lines i on the concept-columns j (the arrows of the graph), each used value being viewed as a subjective correlation between X and Y, i.e :
'0' for 'no related'
'+1' or '-1' for 'related without multiplier effect'
'+1.1' or '-0.9'for 'related with some multiplier effect'
'+1.3' or '-0.7'for 'related with much multiplier effect'
'+1.5' or '-0.5'for 'related with a lot of multiplier effect'
So as to do that :
.make a list of the N attributes and concepts used by an actor, so as to describe its decision-making logic by gathering them in 4 types of variables :
(a) "p.." concepts, that reflect possible alternatives or options from which the actor select its actions/positions,
(b) "c.." concepts, that denote events, contextual situations and problems that interact (including the points of view of other Actors by using their "ck.." Accronym), and that may be directly related to one or more P concepts,
(c) "a..." concepts, that refer to internal and external actions, and that are directly related to a C-concept or to an other action,
(d) "o..." concepts, that refer to the goals of the modeled actor, and that are directly related to the A-concepts.
Be careful : Actors's Accronym must be built by adding the second and the last letter of the fullname of the corresponding Actor's Cognitive Map file to the prefix ck.
.use the Define Panorama.Cognitive Maps Management.menu
.select the Create Cognitive Map.
as a CM may be built,
either by using a classical editor (use menu Create/Modify CM TXT Format),
or, for bigger matrix, by using Excel (use CM XLS format),
.select the appropriate Format submenu.
.respect the corresponding instructions
.use the Tool Tree menu for displaying the corresponding graph and sub-graphs ...
Note that such a (valency) matrix has the following properties :
a. the number L(i) of non nil cells of the line i of the matrix A represents the number of concepts that are directly influenced by the concept i (the out-degree, or direct motricity indice, of the concept i)
b. the number C(j) of non nil cells of the column j of the matrix A represents the number of concepts that directly influence the concept j (the in-degree, or direct dependance indice, of the concept j)
c. the sum L(n)+C(n) represents the degree of direct cognitive centrality of the concept n
d. the number Lp(i) of non nil cells of the line i of the reachability matrix R associated to the valency matrix A represents the number of concepts that are directly and indirectly influenced by the concept i (the out-degree, or total motricity indice, of the concept i)
e. the number Cp(j) of non nil cells of the column j of the matrix R represents the number of concepts that directly and indirectly influence the concept j (the in-degree, or total dependance indice, of the concept j)
f. the sum Lp(n)+Cp(n) represents the degree of total cognitive centrality of the concept n
g. the indirect effect of a concept i on a concept j is positive if the path i->j has an even number of negative links
h. the indirect effect of a concept i on a concept j is negative if the path i->j has an odd number of negative links
i. the total effect of a concept i on a concept j is the sum of the indirect effects of all the paths from i to j
j. the total effect of a concept i on a concept j is positive if all the paths from i to j are positive, it is negative if all the paths are negative, it is indeterminate if some indirect effects are positive and some other are negative.
rules [a,b,c,d,e,f] will be used by the Strucural Analysis process
rules [g,h,i,j] will be used by the Forecasting, Decision, Explanation and Stategic pulse proceesses
#$+!KCognitive Maps in TeXT Format may be Created/Modified and Saved as *.MAP file using the editor
For creating a Creating/Showing/Modifying a cognitive map in TXT Format
. use the Define Panorama.Cognitive Maps Management menu
. select the appropriate submenu
. open an appropriate A*.MAP file (see Actors List)
. input you data by respecting the following way :
line 1 : the N Accronyms of the N used concepts (5 char maxi),
in the following order : policies, contexts, actions, objectives,
and by using [tab 6 characters] for going from colum J to colum J+1 ;
line i+1 : the N effects of the Concept (i) on Concepts (j),
by using real numbers (0 if no relationship) ;
add the fullname of concept i in colum J+1 (without space)
...
Be carrefull :
the total number of concepts must be smaller than 80 !
don't forget to include the CM File Name in the ACTORS LIST !
#$+!KCognitive Maps in eXceLS Format may be Created/Modified and Saved by using the following way :
For creating a Creating/Showing/Modifying a cognitive map in XLS Format
. Run Excel
. Open an appropriate A*.XLS file (see Actors List)
. input you data by respecting the following way :
line 1 : the N Accronyms of the N used concepts (5 char maxi),
in the following order : policies, contexts, actions, objectives,
add the key-word CONCEPTS in colum J+1 ;
line i+1 : the N effects of the Concept (i) on Concepts (j),
by using real numbers (0 if no relationship) ;
add the fullname of concept i in colum J+1 (an only one string of characters)
...
. Create New file,
. Copy your A*.XLS in New file
. Save this New file as DBaseIV file (A*.DBF)
Be carrefull :
the total number of concepts must be smaller than 80 !
don't forget to include the CM File Name in the ACTORS LIST !
#$+!KThe studied ACTORS'S SOCIETY is defined by the list of the names of the main regional Actors who would have an interest in the actions of public institutions : i.e. public institutions themselves, main farms types and others main business enterprises.
This list must be stored in the ACTORS.TXT File.
For Creating/Showing/Modifying this ACTORS.TXT File :
. use the Panorama.Cognitive Map Management menu
. if you have created Actors's Cognitive Maps
select the Create Actors List menu
select relevant A*.MAP and/or A*.DBF Files
be carreful : the existing ACTORS.TXT File will be deleted !
. if you have not yet created Actors's Cognitive Maps
select the Show/Modify Actors List sub menu
select TXT Format
add/remove an Actor, by respecting the way :
line 1 : structure of the database
Number Accro(ckSL) ActorFullName(UpperCases)
line i+1 : data
1 ckZ1 AZ....C1
2 ckZ2 AZ....C2
... ... A.......
7 ckOY AORKNEY
...
Be carreful :
S&L represent respectively the second and the last letter of the File Name correponding to the Cognitive Map of a given Actor.
S&L must be UpperCases, and File Names must begin with "A"
#$+!KACCOINTANCES TABLE collects the accointances of each Actor, i.e. the relationships existing among the M main regional Actors Types.
These relationships must be stored in the ACCOINTS.TXT File, which must be a square matrix A[M,M].
Fore Creating/Showing/Modifying this file
. use the Panorama.Cognitive Map Management menu
. use the Show/Modify Accointances Table submenu
. input&save the M*M relationships among the M Actors,
by respecting the following way :
line 1 : the M Accronyms of the M Actors's Names, in which :
first and second letters must be respectively "c" and "k",
third and fourth leters must be respectively the second and the last letter of the File Name of the CM corresponding to a given Actor m ;
line i+1 : M values 1/0, A[i,j]=1/0 meaning that Actor i uses/dont uses the point of view of the Actor j for making its decision ;
...
example :
ckZ1 ckZ2 ckZ3 ... ckOY ...
0 1 1 ... 0 ...
0 0 0 ... 0 ...
0 0 0 ... 0 ...
... ... ... ... ... ...
0 0 0 ... 0 ...
... ... ... ... ... ...
#$+!KACTORS POSITIONS are the Actors's points of view about the effectiveness of the public actions on each of their goals.
They are automatically achieved by the Forecasting pulse process, which computes the total effect of each action on each goal of an Actor, by using its Decision-Making Logic, and by taking into account the positions of its accointances.
They are saved in as many gAPMx.KBF matrix as goals in the Cognitive Maps
To do that, after having fully/partly defined the regional Panorama :
. use Build Scenarios.Decision Analysis menu
. select an Actor Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run the Forecasting process
#$+!KACTORS POSITIONS MATRIX collect the Actors's points of view about the effectiveness of the public actions on each of their goals.
They are automatically computed and saved in gAPMx KBF Format Files by the forecasting pulse process. Consequently, at the end of the running of this pulse process, it exists as many gAPMx KBF Format Files as Goals x in the Cognitive Maps.
These GAPMx.KBF matrix may be translated in gAPMx.TXT Format before using by STRATEGIES ANALYSIS processes. After this translation, each cell gAPMx(i,j) of an gAPMx.APM matrix represents the point of view of an of the I actors-lines A(i) about the effectiveness of the J actions-columns G(j) on the Goal X.
example : Actors Position Matrix GAPMG1.APM
actio1 actio2 actio3 actio4 actio5
2.00 3.00 0.00 0.00 1.00 actor1
-2.00 5.00 3.00 -1.00 -3.00 actor2
-1.00 0.00 -3.00 3.00 -2.00 actor3
0.00 3.00 2.00 0.00 1.00 actor4
-1.00 0.00 -2.00 2.00 -2.00 actor5
0.00 0.00 0.00 0.00 3.00 actor6
Nevertheless, it is possible to input/modify manually the Actors's positions in an GAPMX.APM TXT files (one for each goal). In this case, it may be usefull to use the following scale :
' 0' for 'no opinion'
'+/-1' for 'a little favourable/unfavourable'
'+/-3' for 'favourable/unfavourable'
'+/-5' for 'much favourable/unfavourable'
'+/-7' for 'very much favourable/unfavourable'
'+/-9' for 'very very much favourable/unfavourable'
#$+!KgAPMx TXT Format Files collect the Actors's Positions about the effectiveness of the public actions on their goals, after translating from gAPMx KBF files.
Each cell gAPMx(i,j) of an gAPMx.APM TXT Format matrix represents the point of view of an of the I actors-lines A(i) about the effectiveness of the J actions-columns G(j) on the Goal X.
It's the only one Format that may be used for computing total position distances, allies, ...among the Actors.
So as to do this translating :
.use Build Scenarios.Strategic Analysis menu
.select&load an Actors Positions Matrix KBF Format
example :
content of the gAPMosp.KBF file
apm("ckZ2","ale",-0.25)
apm("ckZ2","acns",-0.76)
apm("ckZ2","ae",-0.18)
apm("ckZ2","abt",-0.045)
apm("ckZ3","ale",-0.25)
apm("ckZ3","acns",-0.22)
apm("ckZ3","ae",-0.81)
apm("ckZ3","abt",-0.081)
apm("ckZ4","ale",-0.45)
apm("ckZ4","acns",-0.07)
apm("ckZ4","ae",-0.04)
apm("ckZ4","abt",-0.04)
apm("ckZ1","ale",-0.5)
apm("ckZ1","acns",0.6076)
apm("ckZ1","ae",-0.0476)
apm("ckZ1","abt",-0.099792)
content of the gAPMosp.APM file (translated from gAPMosp.KBF)
ale acns ae abt
-0.50 0.61 -0.05 -0.10 ckZ1
-0.25 -0.76 -0.18 -0.04 ckZ2
-0.25 -0.22 -0.81 -0.08 ckZ3
-0.45 -0.07 -0.04 -0.04 ckZ4
#$+!KgAPM XLS Format Files may collect the point of view of the user on the Actors's Positions about public actions on the regional situation, or the results of the translating of an gAPM TXT Format File (by using the import function of Excel).
They must only be used so as to make changes easier.
They must be re-translated in APM TXT Format before using by STRATEGIES ANALYSIS processes
#$+!KDECISION making ANALYSIS aims to answer the question "what could be done so as to achieve one or several objective?", by searching for all the policy/actions concepts i and paths ij that have a positive/negative total effect on objective concepts j1, j2, ... under a positive impulse from concept.
So, Decision-making Analysis aims to identify the possible future position of an Actor about each action of each public institution, facing given events and/or other actions, i.e facing possible beginnings of the future regional stories ...
Identify the point of view and the possible strategies of each major Stakeholder or regional Actor who would have an interest in the strategies and actions of public institutions, is very useful for driving the change of a rural area. Because many studies show that, facing to problems and challenges, actors tend to gather and to use their resources for upon the society their points of view and solutions. Consequently, any institutional strategy and action that could support the alliance and negotiation strategies of the "progressive" regional actors must be viewed as ways by which the regional situation could be improved. In other words, ways by which the effectiveness of the current policies could be improved.
To do that, after having fully/partly defined the regional Panorama
. use Build Scenarios.Decision Analysis menu
. select an Actor Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run Decision Analysis
#$+!KDefine the STORY BEGINNINGS consists of definning the possible events, internal and external actions (i.e. public policies), and relationships to be simulated.
So as to do that, use two steps :
1.Make a list of current trends, or predetermined elements, that affect the variables of interest. This trends will be trends hierarchically organized and interrelated, and their pro and con arguments will be identified.
2.Identify key uncertainties, whose resolution will significantly affect the most interesting variables. Then explain why and how these uncertain events matter, and examine how they interrelate. This task may be achieved by using the following way :
. use Build Scenarios.Decision Analysis menu,
. build the cognitive map of the "society", by piling the cognitive maps of all the actors,
. include this CM in the ACTORS LIST
. select this CM
. use Searching for Continuations submenu
. run Structural Analysis of this cognitive map for identifying the most motive and dependent variable ;
3. Remove irrelevant concepts and/or change links among the concepts, by using the following way :
. use Build Scenarios.Decision Analysis,
. select an Actor's Cognitive Map,
. Select the appropriate Define Story Beginnings submenu.
Be carefull :
.changes thus defined only concern the loaded Cognitive Map : they must be defined again for each Actor (sorry ...)!
.changes thus defined will be not taken into account by the strustural analysis process : use the Show/Modify CM submenu for saving changes to be simulated using the structural analysis process (save them in new CMs ...)
#$+!KIdentify POSSIBLE STORY CONTINUATIONS may be achieved by searching for the point of view and the possible strategies of each major Stakeholder or regional Actor on the effectiveness of the strategies and actions of public institutions. Because many studies show that, facing to problems and challenges, actors tend to gather and to use their resources for upon the society their points of view and solutions. Consequently, any institutional strategy and action that could support the alliance and negotiation strategies of the "progressive" regional actors must be viewed as way by which the regional situation could be improved. In other words, ways by which the effectiveness of the current policies could be improved.
To do that, use the following way :
For each possible possible event and/or action (within/without somethings, and with different level of relationships among events, actions and goals)
for each Actor,
. use Build Scenarios.Decision Analysis menu
. select the Actor's Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run Forecasting Analysis ; this pulse process
(a) search for its accointances,
(b) search for the total effect of each action on each goals of the actor by taking account the positions of its accointances
end for Actors,
end for event and action
The results will be automatically saved in an gAPMgoal.KBF matrix
#$+!KSTRUCTURAL ANALYSIS aims to identify the global shape of a Cognitive Map, and especially the changing patterns of its structure that may have different ramifications for the future.
Structural analysis generates a focus of attention on the main dilemma of the studied region by highlighting :
(a) the most relay and motive variables (the ones that cognitically have the most impact on the others (the greatest out-degree (ref. Cognitive Map's properties)),
(b) the most dependant concepts (the ones that cognitically most depend of the others(the greatest in-degree)).
To do that, after having fully/partly defined the regional Panorama
. use Build Scenarios.Decision Analysis menu
. select an Actor Cognitive Map [must be included in the ACTORS LIST !]
. select the Searching for the possible Continuations submenu
. run Structural Analysis
. input the number of policy concepts you wish to exclude of the analysis
. input the minimum value of the active links
example of results :
STUDIED COGNITIVE MAP : AZHANGC1.MAP
names of the used concepts :
ale=ale_law_enforcement
acns=acns_clean_needle_supply
ae=ae_education
abt=abt_blood_test
cacbt=cacbt_aids_contaminated_blood_trans
cusc=cusc_unsafe_sexual_contact
cdns=cdns_drug_needle_sharing
cdd=cdd_drug_dealing
cavs=cavs_aids_virus_spread
cdu=cdu_drug_using
ckZ2=czhangc2
ckZ3=czhangc3
ckZ4=czhangc4
osp=osp_social_problem
Searching for accointances
Accointances Network :
listnoeuds(1,["ckZ1","ckZ2"],1)
listnoeuds(1,["ckZ1","ckZ3"],1)
listnoeuds(1,["ckZ1","ckZ4"],1)
STRUCTURAL ANALYSIS
QUESTION1: WHAT ARE THE MOTIVE/DEPENDANT CONCEPTS...?
with a valency threshold = 0
MOTIVITY/DEPENDANCY OF EACH CONCEPT IN TERMS OF
1.DIRECT EFFECT (graphe1)...
2.INDIRECT EFFECT (graphe2)...
3.DIRECT and INDIRECT MOTIVITY (graphe3)...
4.DIRECT and INDIRECT DEPENDANCY (graphe4)...
d20:------------cavs---:-------------------:
e : : . :
p : : . :
e : : . :
n : : . :
d : : . :
a : : . :
n osp ckZ3 : .ae :
c : : . :
y : : . :
10:-------------------:-------------------:
: . : :
: . : :
: ckZ2t : ckZ4 abts
: . : :
: . : :
: . : :
: . : :
: . : :
: . : :
0:------------ale----:-------------------:
0 10 20 motivity
osp= l.motive(0) & DEPENDANT(13)
ale= l.motive(6.5) & l.dependant(0)
cacbt= l.motive(6.5) & l.dependant(7)
cusc= l.motive(6.5) & l.dependant(7)
cdns= l.motive(6.5) & l.dependant(7)
cdd= l.motive(6.5) & l.dependant(7)
cavs= l.motive(6.5) & DEPENDANT(20)
cdu= l.motive(6.5) & DEPENDANT(13)
ckZ2= l.motive(6.5) & l.dependant(7)
ckZ3= l.motive(6.5) & DEPENDANT(13)
ae= MOTIVE(13.5) & DEPENDANT(13)
ckZ4= MOTIVE(13.5) & l.dependant(7)
acns= MOTIVE(20) & l.dependant(7)
abt= MOTIVE(20) & l.dependant(7)
d20osp-----------------:-------------------:
e : : . :
p : : . :
e : : . :
n : : . :
d : : . :
a : cavs : . :
n : : . :
c : : . :
y : : . :
10:-------------------:-------------------:
: . : :
: . : :
: . : :
: cdu cusc : :
: . : ckZ3 :
: . : :
: . cdnst : :
: . cdd : ckZ2 ckZ4
: . : :
0:----------------ale:-------------------:
0 10 20 motivity
osp= l.motive(0) & DEPENDANT(20)
cavs= l.motive(3) & DEPENDANT(14)
cdu= l.motive(3) & l.dependant(6)
cacbt= l.motive(5.5) & l.dependant(3)
cusc= l.motive(5.5) & l.dependant(6)
cdns= l.motive(5.5) & l.dependant(3)
cdd= l.motive(5.5) & l.dependant(2)
ale= l.motive(8.5) & l.dependant(0)
ae= MOTIVE(11.5) & l.dependant(5)
ckZ3= MOTIVE(11.5) & l.dependant(5)
acns= MOTIVE(14.5) & l.dependant(2)
ckZ2= MOTIVE(14.5) & l.dependant(2)
abt= MOTIVE(20) & l.dependant(2)
ckZ4= MOTIVE(20) & l.dependant(2)
i20:-------------------:------ckZ4---------abt
d : : . :
e : : . :
p : : . :
e : : . :
d : : . :
a : ckZ2 : . acns
n : : . :
c : : . :
y : ckZ3 : . ae :
10:-------------------:-------------------:
: ale . : :
: . : :
: . : :
: .cddst : :
: . : :
: . : :
: . cdus : :
: . : :
: . : :
0osp-----------------:-------------------:
0 10 20 motivity
d20:-------------------:------osp----------:
e : : . :
p : : . :
e : : . :
n : : . :
d : : . :
a : : . cavs
n : : . :
c : : . :
y : : . :
10:-------------------:-------------------:
: . : :
: . : :
: . : :
: .cusc : cdu :
: . : ckZ3 :
: . : :
: . cdnst : :
: . ckZ4 : :
: . : :
0ale-----------------:-------------------:
0 10 20 motivity
STRUCTURAL ANALYSIS
QUESTION2: WHAT ARE THE STABILIZING/UNSTABILIZING CONCEPTS ?
path
1.0acns>1.0ckZ2>1.0acns;
->magnitude= 1.000
it exists 1 path(s) between acns and acns
total effect of acns on acns
is positive( 1.000)
path with the greatest effect :
1.0acns>1.0ckZ2>1.0acns;
->magnitude= 1.000
path with the smallest effect :
1.0acns>1.0ckZ2>1.0acns;
->magnitude= 1.000
path with the greatest positive effect :
1.0acns>1.0ckZ2>1.0acns;
->magnitude= 1.000
path
1.0ae>1.0ckZ3>1.0ae;
->magnitude= 1.000
it exists 1 path(s) between ae and ae
total effect of ae on ae
is positive( 1.000)
path with the greatest effect :
1.0ae>1.0ckZ3>1.0ae;
->magnitude= 1.000
path with the smallest effect :
1.0ae>1.0ckZ3>1.0ae;
->magnitude= 1.000
path with the greatest positive effect :
1.0ae>1.0ckZ3>1.0ae;
->magnitude= 1.000
path
1.0abt>1.0ckZ4>1.0abt;
->magnitude= 1.000
it exists 1 path(s) between abt and abt
total effect of abt on abt
is positive( 1.000)
path with the greatest effect :
1.0abt>1.0ckZ4>1.0abt;
->magnitude= 1.000
path with the smallest effect :
1.0abt>1.0ckZ4>1.0abt;
->magnitude= 1.000
path with the greatest positive effect :
1.0abt>1.0ckZ4>1.0abt;
->magnitude= 1.000
path
1.0ckZ2>1.0acns>1.0ckZ2;
->magnitude= 1.000
it exists 1 path(s) between ckZ2 and ckZ2
total effect of ckZ2 on ckZ2
is positive( 1.000)
path with the greatest effect :
1.0ckZ2>1.0acns>1.0ckZ2;
->magnitude= 1.000
path with the smallest effect :
1.0ckZ2>1.0acns>1.0ckZ2;
->magnitude= 1.000
path with the greatest positive effect :
1.0ckZ2>1.0acns>1.0ckZ2;
->magnitude= 1.000
path
1.0ckZ3>1.0ae>1.0ckZ3;
->magnitude= 1.000
it exists 1 path(s) between ckZ3 and ckZ3
total effect of ckZ3 on ckZ3
is positive( 1.000)
path with the greatest effect :
1.0ckZ3>1.0ae>1.0ckZ3;
->magnitude= 1.000
path with the smallest effect :
1.0ckZ3>1.0ae>1.0ckZ3;
->magnitude= 1.000
path with the greatest positive effect :
1.0ckZ3>1.0ae>1.0ckZ3;
->magnitude= 1.000
path
1.0ckZ4>1.0abt>1.0ckZ4;
->magnitude= 1.000
it exists 1 path(s) between ckZ4 and ckZ4
total effect of ckZ4 on ckZ4
is positive( 1.000)
path with the greatest effect :
1.0ckZ4>1.0abt>1.0ckZ4;
->magnitude= 1.000
path with the smallest effect :
1.0ckZ4>1.0abt>1.0ckZ4;
->magnitude= 1.000
path with the greatest positive effect :
1.0ckZ4>1.0abt>1.0ckZ4;
->magnitude= 1.000
#$+!KFORECASTING ANALYSIS aims to answer the question " what could happen if a variable V were increase/decrease ? ", by identifying all the consequences of a positive/negative impulse from an event/action concept i.
To do that :
. use Build Scenarios.Decision Analysis menu
. select the Actor's Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run Forecasting Analysis ;
this pulse process :
(a) search for its accointances,
(b) search for the total effect of each action on each goals of the actor by taking account the positions of its accointances
It should be observed that the goals affected on by this events/actions are most often those that have been identified as most dependant by the structural analysis.
But, firstly, the forecasting pulse process will show that several other gaols could be affected on by these events/actions.
Secondly, this pulse process will say, why (in what circumstances)), how (in what sense), and with what magnitude, the goals could be affected on.
Be carefull : when the Forecasting process is running with an action to be evaluated, it autmatically generates Actors postions about the effectiveness of this action about each of this goals.
#$+!KDefine the STRATEGY BEGINNINGS consists of modifying the studied regional PANORAMA, i.e. main possible regional Actors's Types, the possible public actions, as well as the positions taken by each Actor about the effectiveness of the public actions.
So as to do that :
. use Build Scenarios.Strategies Analysis menu,
. select a gAPMx KBF/TXT Format Matrix,
. Select the appropriate Define Strategy Beginnings submenu.
Be carefull :
the changes thus defined must be saved in a new gAPMx file !
#$+!KIdentify the POSSIBLE STRATEGY CONTINUATIONS may be achieved by seeing Scenarios as communication devices for identifying which events and actions could be the cause of some inconsistencies in the current policies, which could be the cause of alliance and negotiation strategies among the progressive actors, as well as which could change the point of view of the opposite actors into desirable one. And thus identify some ways that could improve the total effect of the current policies, now and/or in the future. Because many studies show that, facing to problems and challenges, actors trend to gather and to use their resources for upon the society their points of view and solutions.
Consequently, any institutional strategy and action that could support the alliance and negotiation strategies of the "progressive" regional actors may be viewed as way by which the effectiveness of the current policies and the regional situation could be improved.
In other words, after having identified "what could happen if a variable V were increase/decrease ?", "why and how something could happen ?" and "what could be done so as to achieve an objective O ?", you must identify "what strategies could collaboratively enlarge the desired effects, as well as reduce major side effects of a policy ?".
So as to do that :
1.Make the structural analysis of the Actors Positions Matrix built from the positions taken by each actor about the effectiveness of each actions/policies on their goals :
.use Build Scenarios.Strategic Analysis menu
.select an Actors Positions Matrix
[Show/Modify the Story Beginings]
[use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
.select the Searching for the possible Continuations submenu
.run Total Position Distances computing
.use Possible Alliance Strategies menu
This process will allow you :
(a) to identify the main actors groups "for"/"against" each action ... and thus the main progressive, defeatist, conservative, ... actors groups,
(b) to identify actions/policies that most gather/divides the actors,
(c) to identify possible allies/opposites of each actor
2.Search for the causes of the positions taken by the opposites actors:
.use Build Scenarios.Decision Analysis menu
.select an Actor Cognitive Map
.select the Searching for the possible Continuations submenu
.run Strategic/Explanation Analysis
#$+!KIdentify the POSITIONS DISTANCES among the actors about the effectiveness of the current policies, allows the searcher to highlight the possible alliance and negotiation strategies among the regional actors. And thus identify strategies and actions that public institutions could promote so as to support the "progressive" regional actors. In other words, some first ways by which the effectiveness of the current policies could be improved.
To do that :
.use Build Scenarios.Strategic Analysis menu
.select an Actors Positions Matrix
[Show/Modify the Story Beginings]
[use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
.select the Searching for the possible Continuations submenu
.run Total Position Distances computing
Example of the resultst produced by this strategic analysis :
STUDIED Actors Position Matrix : GROUP1.APM
actio1 actio2 actio3 actio4 actio5
2.00 3.00 0.00 0.00 1.00 actor1
-2.00 5.00 3.00 -1.00 -3.00 actor2
-1.00 0.00 -3.00 3.00 -2.00 actor3
0.00 3.00 2.00 0.00 1.00 actor4
-1.00 0.00 -2.00 2.00 -2.00 actor5
0.00 0.00 0.00 0.00 3.00 actor6
[SIMACTOR 9-5-1996 … 15:21:43]
analysis of the positions matrix GROUP1.APM...
1.favourable positions
o1 o2 o3 o4 o5
1 1 0 0 1 a1
0 1 1 0 0 a2
0 0 0 1 0 a3
0 1 1 0 1 a4
0 0 0 1 0 a5
0 0 0 0 1 a6
2.unfavourable positions
o1 o2 o3 o4 o5
0 0 0 0 0 a1
-1 0 0 -1 -1 a2
-1 0 -1 0 -1 a3
0 0 0 0 0 a4
-1 0 -1 0 -1 a5
0 0 0 0 0 a6
3.number of similar positions
a1 a2 a3 a4 a5 a6
0 1 0 2 0 1 a1
1 0 2 2 2 0 a2
0 2 0 0 4 0 a3
2 2 0 0 0 1 a4
0 2 4 0 0 0 a5
1 0 0 1 0 0 a6
4.number of opposit positions
a1 a2 a3 a4 a5 a6
0 -2 -2 0 -2 0 a1
-2 0 -2 -1 -2 -1 a2
-2 -2 0 -2 0 -1 a3
0 -1 -2 0 -2 0 a4
-2 -2 0 -2 0 -1 a5
0 -1 -1 0 -1 0 a6
5.relative salience of the favourable positions
o1 o2 o3 o4 o5
2.00 3.00 0.00 0.00 1.00 a1
0.00 5.00 3.00 0.00 0.00 a2
0.00 0.00 0.00 3.00 0.00 a3
0.00 3.00 2.00 0.00 1.00 a4
0.00 0.00 0.00 2.00 0.00 a5
0.00 0.00 0.00 0.00 3.00 a6
6.relative salience of the unfavourable positions
o1 o2 o3 o4 o5
0.00 0.00 0.00 0.00 0.00 a1
-2.00 0.00 0.00 -1.00 -3.00 a2
-1.00 0.00 -3.00 0.00 -2.00 a3
0.00 0.00 0.00 0.00 0.00 a4
-1.00 0.00 -2.00 0.00 -2.00 a5
0.00 0.00 0.00 0.00 0.00 a6
7.degree of similarity of the actors
a1 a2 a3 a4 a5 a6
0.00 15.00 0.00 10.00 0.00 3.00 a1
15.00 0.00 8.00 21.00 8.00 0.00 a2
0.00 8.00 0.00 0.00 17.00 0.00 a3
10.00 21.00 0.00 0.00 0.00 3.00 a4
0.00 8.00 17.00 0.00 0.00 0.00 a5
3.00 0.00 0.00 3.00 0.00 0.00 a6
8.distance between the actors
a1 a2 a3 a4 a5 a6
0.00 -7.00 -4.00 0.00 -4.00 0.00 a1
-7.00 0.00 -12.00 -3.00 -8.00 -9.00 a2
-4.00 -12.00 0.00 -8.00 0.00 -6.00 a3
0.00 -3.00 -8.00 0.00 -6.00 0.00 a4
-4.00 -8.00 0.00 -6.00 0.00 -6.00 a5
0.00 -9.00 -6.00 0.00 -6.00 0.00 a6
LES POIDS DES COLONNES SONT MULTIPLIES PAR 10 ** 2
-------------------------------------------
NOMJ(J)! V1 V2 V3 V4 V5
-------------------------------------------
PJ(J) ! -200 1100 0 400 -200 6
-------------------------------------------
1MATRICE DES CORRELATIONS
------------------------
(TOUS LES COEFFICIENTS SONT MULTIPLIES PAR 1000)
V1 V2 V3 V4 V5
----------------------------------------------------------------------------
V1 1000
V2 -23 1000
V3 -64 862 1000
V4 -162 -767 -932 1000
V5 710 -213 113 -322 1000
1
LES VALEURS PROPRES VAL(1)= 2.72671
------------------------------------------------------------------------------
!NUM ! VAL PROPRE ! POURC.! CUMUL !VARIAT.!*! HISTOGRAMME DES VALEURS PROPRES
------------------------------------------------------------------------------
! 1 ! 2.72671 ! 54.534! 54.534!*******!*!***************!***************!
! 2 ! 1.77772 ! 35.554! 90.089! 18.980!*!***************!*****
! 3 ! .44019 ! 8.804! 98.892! 26.751!*!*****
! 4 ! .04881 ! .976! 99.868! 7.828!*!*
! 5 ! .00658 ! .132!100.000! .845!*!
1AXE HORIZONTAL( 1)--AXE VERTICAL( 2)--TITRE:ANCOMP
NOMBRE DE POINTS : 6
==ECHELLE : 4 CARACTERE(S) = .260 1 LIGNE = .108
A2 ----------------------------+---------------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! A5 A3 ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
+-------------------------------+---------------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! A4 ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! A6 ! 0 01
! A1 ! ! 0 01
+-------------------------------+---------------------------------------+ 0 01
1AXE HORIZONTAL( 1)--AXE VERTICAL( 2)--TITRE:ANCOMP
NOMBRE DE POINTS : 5
==ECHELLE : 4 CARACTERE(S) = .108 1 LIGNE = .045
V2 --------------------------------+-----------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
V3 ! V4 ! 0 01
! ! ! 0 01
! ! ! 0 01
+-----------------------------------+-----------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! V1 ! ! 0 01
! V5 ! ! 0 01
+-----------------------------------+-----------------------------------+ 0 01
1 FIN NORMALE DU PROGRAMME ANCOMP
PLACE MEMOIRE RESERVEE : 10000
PLACE MEMOIRE UTILISEE : 60
#$+!KALLIANCES among Actors answer three questions :
(a)how the Actors could be gathered/splited, by taking into account positions they have taken about the impact of all the actions to be evaluated (on a given goal) ?
(b)what are the Actors which have taken the most simular position that a given Actor ?
(c)what are the actions that gather two Allies ?
To do that :
.use Build Scenarios.Strategic Analysis menu
.select an Actors Positions Matrix
[Show/Modify the Story Beginings]
[use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
.select the Searching for the possible Continuations submenu
.run Total Position Distances computing
.run Possible Alliance Strategies.Alliances among Actors
examples :
STUDIED Actors Position Matrix : C:_4TMP.APM
actio1 actio2 actio3 actio4 actio5
2.00 3.00 0.00 0.00 1.00 a1
-2.00 5.00 3.00 -1.00 -3.00 a2
-1.00 0.00 -3.00 3.00 -2.00 a3
0.00 3.00 2.00 0.00 1.00 a4
-1.00 0.00 -2.00 2.00 -2.00 a5
0.00 0.00 0.00 0.00 3.00 a6
[SIMACTOR 9-5-1996 … 15:21:43]
analysis of the positions matrix GROUP1.APM...
...
7.degree of similarity of the actors
a1 a2 a3 a4 a5 a6
0.00 15.00 0.00 10.00 0.00 3.00 a1
15.00 0.00 8.00 21.00 8.00 0.00 a2
0.00 8.00 0.00 0.00 17.00 0.00 a3
10.00 21.00 0.00 0.00 0.00 3.00 a4
0.00 8.00 17.00 0.00 0.00 0.00 a5
3.00 0.00 0.00 3.00 0.00 0.00 a6
8.distance between the actors
a1 a2 a3 a4 a5 a6
0.00 -7.00 -4.00 0.00 -4.00 0.00 a1
-7.00 0.00 -12.00 -3.00 -8.00 -9.00 a2
-4.00 -12.00 0.00 -8.00 0.00 -6.00 a3
0.00 -3.00 -8.00 0.00 -6.00 0.00 a4
-4.00 -8.00 0.00 -6.00 0.00 -6.00 a5
0.00 -9.00 -6.00 0.00 -6.00 0.00 a6
STRATEGIC ANALYSIS
Searching for ALLIANCE STRATEGIES among the Actors
possible allies (nbr of common objectives) :
a1 & a4; (2)
a2; (0)
a3 & a5; (4)
a4 & a1; (2)
a5 & a3; (4)
a6 & a4 & a1; (2)
In summary : these actors may be gathered in two groups : [A1, A4 and A6], and the other actors ;
but it exists some divergences between A2, and [A3 and A5]
#$+!KPossible ALLIES of a given Actor can be identified by using the following way :
.use Build Scenarios.Strategic Analysis menu
.select an Actors Positions Matrix
[Show/Modify the Story Beginings]
[use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
.select the Searching for the possible Continuations submenu
.run Total Position Distances computing
.run Possible Alliance Strategies.Alliances among Actors
.select an Actor
example :
STUDIED Actors Position Matrix : GAPMP1.APM
actio1 actio2 actio3 actio4 actio5
2.00 3.00 0.00 0.00 1.00 a1
-2.00 5.00 3.00 -1.00 -3.00 a2
-1.00 0.00 -3.00 3.00 -2.00 a3
0.00 3.00 2.00 0.00 1.00 a4
-1.00 0.00 -2.00 2.00 -2.00 a5
0.00 0.00 0.00 0.00 3.00 a6
[SIMACTOR 9-5-1996 … 15:21:43]
analysis of the positions matrix GROUP1.APM...
...
7.degree of similarity of the actors
a1 a2 a3 a4 a5 a6
0.00 15.00 0.00 10.00 0.00 3.00 a1
15.00 0.00 8.00 21.00 8.00 0.00 a2
0.00 8.00 0.00 0.00 17.00 0.00 a3
10.00 21.00 0.00 0.00 0.00 3.00 a4
0.00 8.00 17.00 0.00 0.00 0.00 a5
3.00 0.00 0.00 3.00 0.00 0.00 a6
8.distance between the actors
a1 a2 a3 a4 a5 a6
0.00 -7.00 -4.00 0.00 -4.00 0.00 a1
-7.00 0.00 -12.00 -3.00 -8.00 -9.00 a2
-4.00 -12.00 0.00 -8.00 0.00 -6.00 a3
0.00 -3.00 -8.00 0.00 -6.00 0.00 a4
-4.00 -8.00 0.00 -6.00 0.00 -6.00 a5
0.00 -9.00 -6.00 0.00 -6.00 0.00 a6
STRATEGIC ANALYSIS
Searching for ALLIANCE STRATEGIES among the Actors
i.e. possible allies (and nbr common objectives) :
a1 & a4; (2)
a2; (0)
a3 & a5; (4)
a4 & a1; (2)
a5 & a3; (4)
a6 & a4 & a1; (2)
STRATEGIC ANALYSIS
Possibles Allies of a2 ?
possible allies :
nbr common views : (0)
STRATEGIC ANALYSIS
Possibles Allies of a1 ?
possible allies : a6 & a4;
nbr common views : 1 & 2;(3)
STRATEGIC ANALYSIS
Searching for causes of Possible Alliances
between a1 and a2
nbr common views : (1)
common views : o2;
a1 positions : 1;
a1 saliences : 3;
a2 positions : 1;
a2 saliences : 5;
#$+!KSearching for the OPPOSITES OF AN ACTOR allows the searcher to identify the ways in which contentious public actions and strategies could be changed into desirable ones. In other words, some other ways by which the effectiveness of the current policies could be improved.
That is made by answering two questions :
taking into account the positions taken by each I actors on each J concepts,
(a) what are the sources of oppositions between an actor A and an actor B ?
(b) what are the positions distances of these two actors for each source of oppositions ?
To do that :
.use Build Scenarios.Strategic Analysis menu
.select an Actors Positions Matrix
[Show/Modify the Story Beginings]
[use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
.select the Searching for the possible Continuations submenu
.run Total Position Distances computing
.run Possible Alliance Strategies.Opposites of an Actor
.select an Actor
example :
STUDIED Actors Position Matrix : GAPMP1.APM
actio1 actio2 actio3 actio4 actio5
2.00 3.00 0.00 0.00 1.00 a1
-2.00 5.00 3.00 -1.00 -3.00 a2
-1.00 0.00 -3.00 3.00 -2.00 a3
0.00 3.00 2.00 0.00 1.00 a4
-1.00 0.00 -2.00 2.00 -2.00 a5
0.00 0.00 0.00 0.00 3.00 a6
STRATEGIC ANALYSIS
Possible Opposites of a1 ?
a5 & a3 & a2;
nbr opposing views : 2 & 2 & 2;(6)
distances : -4 & -4 & -7;
STRATEGIC ANALYSIS
Possible Opposites of a2 ?
a6 & a5 & a4 & a3 & a1;
nbr opposing views : 1 & 2 & 1 & 2 & 2;(8)
distances : -9 & -8 & -3 & -12 & -7;
STRATEGIC ANALYSIS
Searching for causes of Possible Oppositions
between a2 and a4
nbr opposite views : (1)
opposits views : o5;
a2 positions : -1;
a2 saliences : -3;
a4 positions : 1;
a4 saliences : 1;
STRATEGIC ANALYSIS
Searching for causes of Possible Oppositions
between a1 and a2
nbr opposite views : (2)
opposits views : o5 & o1;
a1 positions : 1 & 1;
a1 saliences : 1 & 2;
a2 positions : -1 & -1;
a2 saliences : -3 & -2;
example :
QUESTION4: WHAT COULD BE THE CAUSES OF OPPOSITION BETWEEN a1 and a3 ?
nbr opposite views : (2)
opposite views : o5 & o1;
a1 positions : 1 & 1;
a1 saliences : 1 & 2;
a3 positions : -1 & -1;
a3 saliences : -2 & -1;
QUESTION4: WHAT COULD BE THE CAUSES OF OPPOSITION BETWEEN a1 and a5 ?
nbr opposite views : (2)
opposite views : o5 & o1;
a1 positions : 1 & 1;
a1 saliences : 1 & 2;
a5 positions : -1 & -1;
a5 saliences : -2 & -1;
QUESTION4: WHAT COULD BE THE CAUSES OF OPPOSITION BETWEEN a1 and a2 ?
nbr opposite views : (2)
opposite views : o5 & o1;
a1 positions : 1 & 1;
a1 saliences : 1 & 2;
a2 positions : -1 & -1;
a2 saliences : -3 & -2;
... that shows that
.a good way for improving the effectiveness of the studied policies could be to change the point of view
of A2, A3 and A5 about the actions O1 and O5,
.and, taking into account the saliences of the points of view, these changes should be more easy in the
case of A3 and A5 than in the case of A2
#$+!KCOMMON POSITIONS OF 2 ALLIES
example :
STUDIED Actors Position Matrix : GAPMP1.APM
o1 o2 o3 o4 o5
2.00 3.00 0.00 0.00 1.00 a1
-2.00 5.00 3.00 -1.00 -3.00 a2
-1.00 0.00 -3.00 3.00 -2.00 a3
0.00 3.00 2.00 0.00 1.00 a4
-1.00 0.00 -2.00 2.00 -2.00 a5
0.00 0.00 0.00 0.00 3.00 a6
[SIMACTOR 9-5-1996 … 15:21:43]
analysis of the positions matrix GROUP1.APM...
1.favourable positions
o1 o2 o3 o4 o5
1 1 0 0 1 a1
0 1 1 0 0 a2
0 0 0 1 0 a3
0 1 1 0 1 a4
0 0 0 1 0 a5
0 0 0 0 1 a6
2.unfavourable positions
o1 o2 o3 o4 o5
0 0 0 0 0 a1
-1 0 0 -1 -1 a2
-1 0 -1 0 -1 a3
0 0 0 0 0 a4
-1 0 -1 0 -1 a5
0 0 0 0 0 a6
3.number of similar positions
a1 a2 a3 a4 a5 a6
0 1 0 2 0 1 a1
1 0 2 2 2 0 a2
0 2 0 0 4 0 a3
2 2 0 0 0 1 a4
0 2 4 0 0 0 a5
1 0 0 1 0 0 a6
4.number of opposit positions
a1 a2 a3 a4 a5 a6
0 -2 -2 0 -2 0 a1
-2 0 -2 -1 -2 -1 a2
-2 -2 0 -2 0 -1 a3
0 -1 -2 0 -2 0 a4
-2 -2 0 -2 0 -1 a5
0 -1 -1 0 -1 0 a6
5.relative salience of the favourable positions
o1 o2 o3 o4 o5
2.00 3.00 0.00 0.00 1.00 a1
0.00 5.00 3.00 0.00 0.00 a2
0.00 0.00 0.00 3.00 0.00 a3
0.00 3.00 2.00 0.00 1.00 a4
0.00 0.00 0.00 2.00 0.00 a5
0.00 0.00 0.00 0.00 3.00 a6
6.relative salience of the unfavourable positions
o1 o2 o3 o4 o5
0.00 0.00 0.00 0.00 0.00 a1
-2.00 0.00 0.00 -1.00 -3.00 a2
-1.00 0.00 -3.00 0.00 -2.00 a3
0.00 0.00 0.00 0.00 0.00 a4
-1.00 0.00 -2.00 0.00 -2.00 a5
0.00 0.00 0.00 0.00 0.00 a6
7.degree of similarity of the actors
a1 a2 a3 a4 a5 a6
0.00 15.00 0.00 10.00 0.00 3.00 a1
15.00 0.00 8.00 21.00 8.00 0.00 a2
0.00 8.00 0.00 0.00 17.00 0.00 a3
10.00 21.00 0.00 0.00 0.00 3.00 a4
0.00 8.00 17.00 0.00 0.00 0.00 a5
3.00 0.00 0.00 3.00 0.00 0.00 a6
8.distance between the actors
a1 a2 a3 a4 a5 a6
0.00 -7.00 -4.00 0.00 -4.00 0.00 a1
-7.00 0.00 -12.00 -3.00 -8.00 -9.00 a2
-4.00 -12.00 0.00 -8.00 0.00 -6.00 a3
0.00 -3.00 -8.00 0.00 -6.00 0.00 a4
-4.00 -8.00 0.00 -6.00 0.00 -6.00 a5
0.00 -9.00 -6.00 0.00 -6.00 0.00 a6
LES POIDS DES COLONNES SONT MULTIPLIES PAR 10 ** 2
-------------------------------------------
NOMJ(J)! V1 V2 V3 V4 V5
-------------------------------------------
PJ(J) ! -200 1100 0 400 -200 6
-------------------------------------------
1MATRICE DES CORRELATIONS
------------------------
(TOUS LES COEFFICIENTS SONT MULTIPLIES PAR 1000)
V1 V2 V3 V4 V5
----------------------------------------------------------------------------
V1 1000
V2 -23 1000
V3 -64 862 1000
V4 -162 -767 -932 1000
V5 710 -213 113 -322 1000
1
LES VALEURS PROPRES VAL(1)= 2.72671
------------------------------------------------------------------------------
!NUM ! VAL PROPRE ! POURC.! CUMUL !VARIAT.!*! HISTOGRAMME DES VALEURS PROPRES
------------------------------------------------------------------------------
! 1 ! 2.72671 ! 54.534! 54.534!*******!*!***************!***************!
! 2 ! 1.77772 ! 35.554! 90.089! 18.980!*!***************!*****
! 3 ! .44019 ! 8.804! 98.892! 26.751!*!*****
! 4 ! .04881 ! .976! 99.868! 7.828!*!*
! 5 ! .00658 ! .132!100.000! .845!*!
1AXE HORIZONTAL( 1)--AXE VERTICAL( 2)--TITRE:ANCOMP
NOMBRE DE POINTS : 6
==ECHELLE : 4 CARACTERE(S) = .260 1 LIGNE = .108
A2 ----------------------------+---------------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! A5 A3 ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
+-------------------------------+---------------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! A4 ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! A6 ! 0 01
! A1 ! ! 0 01
+-------------------------------+---------------------------------------+ 0 01
1AXE HORIZONTAL( 1)--AXE VERTICAL( 2)--TITRE:ANCOMP
NOMBRE DE POINTS : 5
==ECHELLE : 4 CARACTERE(S) = .108 1 LIGNE = .045
V2 --------------------------------+-----------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
V3 ! V4 ! 0 01
! ! ! 0 01
! ! ! 0 01
+-----------------------------------+-----------------------------------+ 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! ! ! 0 01
! V1 ! ! 0 01
! V5 ! ! 0 01
+-----------------------------------+-----------------------------------+ 0 01
1 FIN NORMALE DU PROGRAMME ANCOMP
PLACE MEMOIRE RESERVEE : 10000
PLACE MEMOIRE UTILISEE : 60
STRATEGIC ANALYSIS
Searching for ALLIANCE STRATEGIES among the Actors
i.e. possible allies (and nbr common objectives) :
a1 & a4;(2)
a2;(0)
a3 & a5;(4)
a4 & a1;(2)
a5 & a3;(4)
a6 & a4 & a1;(2)
STRATEGIC ANALYSIS
Possibles Allies of a1 ?
possible allies : a6 & a4;
nbr common views : 1 & 2;(3)
STRATEGIC ANALYSIS
Searching for causes of Possible Alliances
between a1 and a2
nbr common views : (1)
common views : o2;
a1 positions : 1;
a1 saliences : 3;
a2 positions : 1;
a2 saliences : 5;
#$+!KDIFFERENCES BETWEEN 2 OPPOSITES
examples :
#$+!KOPTIMIZED ALLOTMENT process allows to identify which actors could be the best supporters of each action, by taking into account the position of each Actor for each action (ref. Egervary and Konig, Ford-Fulkerson algorithm)
To do that :
.use Build Scenarios.Strategic Analysis menu
.select an Actors Positions Matrix
[Show/Modify the Story Beginings]
[use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
.select the Searching for the possible Continuations submenu
.run Total Position Distances computing
.run Possible Alliance Strategies.Optimized Allotment
.select an Actor
example :
STRATEGIC ANALYSIS
Optimizing Actions/Means Allotment
preferences (from positions)
1:2:1:4:4:3:
2:6:1:3:6:6:
3:4:4:4:1:4:
4:4:1:2:4:3:
5:3:3:3:1:3:
6:4:4:4:4:1:
solutions
l:c:pos
1:1:2:
2:2:1:
3:4:1:
4:3:2:
6:5:1:
#$!KgAPMx KBF Format Files collect the Actors's Positions about the effectiveness of the public actions on their goals.
They are automatically achieved by the Forecasting pulse process, which computes the total effect of each action on each goal of an Actor, by using its Decision-Making Logic, and by taking into account the positions of its accointances.
They are saved in as many gAPMx.KBF matrix as goals in the Cognitive Maps
To do that, after having fully/partly defined the regional Panorama :
. use Build Scenarios.Decision Analysis menu
. select an Actor Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run the Forecasting process
example :
apm("ckZ2","ale",-0.25)
apm("ckZ2","acns",-0.76)
apm("ckZ2","ae",-0.18)
apm("ckZ2","abt",-0.045)
apm("ckZ3","ale",-0.25)
apm("ckZ3","acns",-0.22)
apm("ckZ3","ae",-0.81)
apm("ckZ3","abt",-0.081)
apm("ckZ4","ale",-0.45)
apm("ckZ4","acns",-0.07)
apm("ckZ4","ae",-0.04)
apm("ckZ4","abt",-0.04)
apm("ckZ1","ale",-0.5)
apm("ckZ1","acns",0.6076)
apm("ckZ1","ae",-0.0476)
apm("ckZ1","abt",-0.099792)
Be carefull : they must be modifyed/customized and translated in gAPMx TXT format files before using STRATEGIES ANALYSIS processes (i.e. total positions distances, allies, opposites ... among the studied Actors"s Society).
#$!KThere are many methodologies for simulate an ACTOR'S DECISION-MAKING PROCESS from a Cognitive Map.
Notably, if the asserted links among the used concepts are signed and weighted, then it is possible to view a cognitive map as a causal network, and to infer forward and backward
chaining pulse processes.
These pulse processes seek all the heuristic paths related to a nod-event E, and propagate an impulse [1] through this partial causal network by using the following rules :
.if a concept i changes by x units, and if R(i,j1) and R(j1,j2) are the causal links between the concept i and the concept j1, and between the concept j1 and the concept j2, then j2 will change by x*R(i,j1)*R(j1,j2) ;
.the total effect of a concept i on a concept j is the sum of the indirect effects of all the paths from i to j ;
.the total effect of a concept i on a concept j is positive if all the paths from i to j are positive, it is negative if all the paths are negative, it is indeterminate if some indirect effects are positive and some other are negative.
So, FUTURHIS has been designed for deriving whole or partial heuristic paths and closures from any single or a sequence of stimuli/impulse, so as to make 4 kinds of analysis :
(a) Forecasting Analysis ("what could happen if a variable V were increase/decrease ?")
(b) Decision Analysis ("what could be done so as to achieve an objective O ?")
(c) Strategic Analysis ("how to achieve an objective Y from an action X ?")
(d) Explanation Analysi ("why and how a context Z could happen ?")
As FUTURHIS is not a Santa Claus Software, it is not thus intended to generate the best final action/policy of tomorrow. Nevertheless, firstly, it is possible to make more transparent both positive and negative ripple effects of any events or actions. Secondly, using the closure values as thresholds, it is possible to identify corresponding heuristic paths :
the most effective ones with least side effects,
the least effective ones with least side effects,
the least effective ones with the most side effects,
the ones with both maximum effective effects and side effects.
And thus generate some focuses of attention which may have cognitive implications in the building of the future policies.
To do that, after having fully/partly defined the regional Panorama
. use Build Scenarios.Decision Analysis menu
. select an Actor Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run one of the pulse processes of FUTURHIS
#$!KThe fourth stage consists, for each possible beginning of the future regional histories, of identifying the position distances among the actors about the effectiveness of the current policies, so as to highlight the possible alliance and negotiation strategies of the regional actors. And thus identify STRATEGIES and actions that public institutions could promote so as to support the "progressive" regional actors. In other words, some first ways by which the effectiveness of the current policies could be improved.
So as to do that,
1. Firstly, we will make the structural analysis of the Actors-Actions Positions matrix built from the positions taken by each actor for or against each actions/policies (ref. results of Stage C1(i) in the file "effetot.tmp"). This analysis will allow us :
(a) to identify the policies that most gather/divides the actors, (b) to identify the total position-distances among the actors, (c) to search for possible allies/opposings of each actor
And thus identify the main actor types "for" the current policies, the main actor types "against", the main "progressive" actors, the main "conservative" and/or "defeatist" actors, ...
2. secondly, we will identify the ways in which contentious public actions and strategies could be changed into desirable ones. In other words, some other ways by which the effectiveness of the current policies could be improved.So as to do that, we will compute the potential alignments of the actors, so as to identify the political interest of given changes in the positions taken by the actors about the contentious actions.
#$!KACTIONS LIST collects the names of the public policies, programmes and actions which have to be evaluated.
It is automatically created when you select/load a Cognitive Map.
But, you can create it by using
. the Panorama.Cognitive Map Management menu
. and the Create Evaluated Actions List submenu
Next, you may/must Show/Modify it by using the corresponding submenu so as to remove non-actions before use Decision-making pulse processes
Its structure is the following
line 1 : structure of the database
Number Accro(a..) ActionFullname
line i+1 : data
1 ale ale_law_enforcement
2 acns acns_clean_needle_supply
3 ae ae_education
4 abt abt_blood_test
...
Be carrefull :
Accronyms must begin with "a"
each Full Name must be an only one string of characters (e.g. "ale_law_enforecement")
#$!KThere are many methodologies for simulate an ACTOR DECISION-MAKING PROCESS from a Cognitive Map.
Notably, if the asserted links among the used concepts are signed and weighted, then it is possible to view a cognitive map as a causal network, and to infer forward and backward
chaining pulse processes.
These pulse processes seek all the heuristic paths related to a nod-event E, and propagate an impulse [1] through this partial causal network by using the following rules :
.if a concept i changes by x units, and if R(i,j1) and R(j1,j2) are the causal links between the concept i and the concept j1, and between the concept j1 and the concept j2, then j2 will change by x*R(i,j1)*R(j1,j2) ;
.the total effect of a concept i on a concept j is the sum of the indirect effects of all the paths from i to j ;
.the total effect of a concept i on a concept j is positive if all the paths from i to j are positive, it is negative if all the paths are negative, it is indeterminate if some indirect effects are positive and some other are negative.
So, FUTURHIS has been designed for deriving whole or partial heuristic paths and closures from any single or a sequence of stimuli/impulse, so as to make 4 kinds of analysis :
(a) Forecasting Analysis ("what could happen if a variable V were increase/decrease ?")
(b) Decision Analysis ("what could be done so as to achieve an objective O ?")
(c) Strategic Analysis ("how to achieve an objective Y from an action X ?")
(d) Explanation Analysi ("why and how a context Z could happen ?")
As FUTURHIS is not a Santa Claus Software, it is not thus intended to generate the best final action/policy of tomorrow. Nevertheless, firstly, it is possible to make more transparent both positive and negative ripple effects of any events or actions. Secondly, using the closure values as thresholds, it is possible to identify corresponding heuristic paths :
the most effective ones with least side effects,
the least effective ones with least side effects,
the least effective ones with the most side effects,
the ones with both maximum effective effects and side effects.
And thus generate some focuses of attention which may have cognitive implications in the building of the future policies.
To do that, after having fully/partly defined the regional Panorama
. use Build Scenarios.Decision Analysis menu
. select an Actor Cognitive Map [must be included in the ACTORS LIST !]
[ use Def Pano.CM Mana.Show/Modify the List of the Actions to be evaluated]
[ Show/Modify the Begining of the Story]
[ use Def Pano.APM Mana.Show/Modify the previously created gAPMx.KBF files]
. select the Searching for the possible Continuations submenu
. run one of the pulse processes of FUTURHIS
#$!KFUTURHIS Software has been built in the framwork of the "Task C of the AIR3-CT94-1545 project", for helping searchers to assess different Policies Scenarios, by simulating the decision-making logics of an Actors's group under a variety of conditions (events, public actions (1), ...).
What is the PROBLEM ?
Firstly, Tasks A and B of the research project have shown all the rural Actors are not similarly concerned by the institutional actions, and which may be not similarly concerned by the possible future events : their points of view about the effectiveness of these actions depends on the relationships perceived or believed by them among their future situation (and/or the one of their region) and the actions they may use to achieve their goals. In other words, a rural area is plural.
Secondly, a rural area is complex, because the regional Actors are inter-dependent : e.g., the point of view/strategy of an Actor A about an action "a1" may be related to the positions/strategies of the Actors B, C, ...(i.e. the accointances of the Actor A) about the actions "a2", "a3", ...
Thirdly, future is plural. Notably, because time does not show probabilities of events and actions that could make up the future alternatives. In other words, we have no experience with events happening according to various probabilities : they either happen or they do not.
What is the PROPOSED METHOD ?
To cope with these problems, in accordance with Schwartz P. and Godet M., I propose (2) to bring the future down to a manageable scale by simulating the functioning of an Actors's system within/without some possible events and actions, and with different levels of relationships among the events, actions, and actors's goals. In other words, I propose (a) to see a policy as a theory about the role of public actions for changing a Society, and (b) to build possible future policies by "rolling the dice of life". These are possible future histories that we will call policies "scenarios".
As such, scenarios will be not predictions. The focus of the building of these possible future histories is not on forecasting the future, or fully characterizing its uncertainty, but rather on bounding the uncertainty. Scenarios will be thinking communication devices for identifying which events and actions could be the cause of some inconsistencies in the current policies, which ones could be the cause of alliance and negotiation strategies among the progressive actors, as well as which ones could change the point of view of the opposing actors into desirable one. And thus identify some ways that could improve the effect of the current policies, now and/or in the future.
By offering a basis for discussing and structuring possible future regional problems and solutions, scenarios can be viewed as continuations of decision analysis. Instead of striving for supreme rationality within a necessarily simplified view of the world, they permit to use a semi-rationality in which intuition and analysis combine to manage highly complex tasks. They do this by providing a sense of general direction, without being a precision compass.
A similar distinction is relevant when comparing scenarios and traditional forecasting : scenarios try to highlight the reasoning underlying a forecast, with explicit attention to sources of uncertainty. In other words, scenarios complement traditional forecast and cost-benefit approach, either by presenting best and worse cases, or by making forecasts scenario dependent in an explicit manner.
How to BUILD SCENARIOS ?
STAGE A : Define the current Regional Panorama, i.e :
(a) identify the public actions in terms of decision variables (goals, means, actions, ...),
(b) identify the main regional Actors and their accointances,
(c) modelize the decision-making logics of these Actors.
The modelling of the Actors's Decision-Making Logic consists of formalising their theories about the roles of some actions (and notably, of public policies) for changing their situation and the one of their region.
To do that, Futurhis allows you to build as many cross-impact matrix (so called "Cognitive Map(3&4)") as Actors, in which the lines and columns represent the concepts used for describing the economics environment, the Actors's situation and their goals, as well as the actions used/could be used by the Actors for achieving their goals. And where the cells represent the Actors's beliefs about the sense and the magnitude (5) of the direct impacts of each concept-line on each concept-column.
So as to make the management of these relationships easier, the used concepts and attributes must be gathered in 4 types of variables :
.P-concepts, that reflect possible alternatives or options from which the actor select its actions,
.C-concepts, that denote events, contextual situations and problems that interact (including the points of view of other Actors(6)), and that may be directly related to one or more P concepts,
.A-concepts, that refer to internal and external actions(7), and that are directly related to a C-concept or to an other action,
.O-concepts, that refer to the goals of the modeled actor, and that are directly related to the A-concepts.
STAGE B : Define events, actions, and relationships to be simulated, i.e. possible beginnings of the future history....
The second stage of the scenarios building consists of defining possible beginnings of the future history, i.e relevant changes in current events, actions, and relationships among concepts to be simulated.
To do that, Futurhis allows you to identify key uncertainties whose resolution will significantly affect the most interesting variables by making the structural analysis of each cognitive map, and/or of the cognitive map of the studied Actors's Group (i.e the CM built by "piling" all the cognitive maps), and by searching for the causes of the motivity/dependence of each key variable (use the Decision Analysis.Search for ... menu (8).
STAGE C : Simulate the Decision-Making Process of each Actor facing each possible beginning of the future history, for identifying its possible future positions and strategies about the actions to be evaluated.
Many studies showing that, facing to problems and challenges, actors tend to gather and to use their resources for upon the society their points of view and solutions, the third stage consists of identifying the point of view and the possible strategy (i.e. interest and role) of all the actors about each action of each public institution (i.e. each studied programme), by simulating their decision-making logics within/without given contexts, actions, and with different level of relationships among concepts.
To do that, you must use the following way :
.for each possible event to be simulated
(i.e. within/without some "A" and/or "C" concepts,
and/or with different level of relationships among events, actions and goals)
.for each Actor (and its accointances),
.for each public action to be evaluated
.search for the Actor's positions (9)), by using the forecasting analysis pulse process
Results of these simulations will be gathered in as many (KBF) Actors-Actions Positions matrix (10) as goals
STAGE D : Identify the most contentious actions, by simulating a Global Evaluation Process
Because the public actions may have an impact on several goals, and all the actors may have not the same position about their effectivenness for achieving these goals, the highlighting of the actions that have to be changed (i.e. the 'what' of the possible future policies) has been achieved by running a discrete multicriteria analysis (DMA).
DMA consists : (a) of making a pairwise comparison among the ActorsÆs positions about the effectiveness of each action for achieving each goal, next (b), of ranking and clustering the actions from the frequency of their preference and their 'reject' (i.e. from their over and sub efficiency).
Normally, entry data of the DMA are in the Actors Positions Matrix (i.e. GAPMx.KBF files ; one for each goal X), which have been automatically generated by Futurhis when you have simulated the forecasting analysis of each Actor about the impact of each action on its goals (ref. STAGE C).
Nevertheless, it is possible to run the DMA without modelling the Position-Making Logics of the Actors (i.e. if you have a lack of time for building the ActorsÆs Cognitive Maps ...), by inputing manually the positions taken by the actors in an GAPMX.APM file. Although, in this case, it is not easy to identify "in what context" and "why" actions could be changed, this way could be usefull for computing the global point of view of a focus group about the effectiveness of current and future public actions (and next, for simulating a discuss among its members ...).
Beside, note you can test the robustness of the final ranking by using the Evaluation.Define Story Beginning menu for modifying the actors's and goals's weights, as well as actions's preference and reject thressholds.
STAGE E : Identify the ways in which the most contentious actions could be changed into desirable ones, as well as which under-new actions could improve the regional situation, by simulating a concertation process
The fifth stage consists of searching for : (a) the actors that could be the best supporters of each action, (b) the actors that have to be better convinced of the effectiveness of a given action, and (c) the perceptions and beliefs that have to be changed. In other words, the 'who' and the 'how' of the possible future policies.
To do that, Futurhis allows you :
1.to classify the actors from the frequency of their preference/reject for each action, and to identify the possible alliance's strategies among them (12) and the political interest of a negociation (or the negociation progress) about the content of the contentious actions, by analysing their position distances (ref. Kendall Coefficient of Concordance W, HAC, and results of appropriate Strategies Analysis Processes).
2.to identify perceptions and beliefs that are the causes of the sub-efficiency/reject of the most inconsistent actions (i.e. those having to be changed), by analysing the results of the Forecasting Analaysis of the actors that have most "rejected" these actions (i.e. by scanning the a*.RES files corresponding to the actors having to be better convinced of the effectiveness of these actions (13)).
3.to test the political interest of the changes in these perception and believes, by modifying the appropriate Cognitive Maps, running again the Forecasting Analysis of the coresponding actors as well as the Global Evaluation Process, ... until you are satisfyied ...
How to DISCUSS SCENARIOS ?
These are procedures that would probably not occur to anyone without exposure to the use of cognitive maps. It is precisely this potential for generating accurate descriptions, rational explanations, and new (and even counterintuitive) propositions about persuasion and consensus-building in situations with actors of highly divergent political perspectives that recommends the cognitive map mining technics for the prospective studies.
It is not thus intended to generate the best final action/policy of tomorrow. But by making more transparent both positive and negative ripple effects of any events or actions, this approach may generate some focus of attention that have cognitive implications in the building of the future policies. And By taking into account the diversity of the beliefs of the actors, as well as the possible conflicts that could appear among them, this methodology takes place in the process and comprehensive evaluation methods (14) : it is near to the Judgment Impact Matrix (JIM (15)).
So, the most interesting stage of the scenario approach consists of the comparison of a large number of more or less "risked" scenarios, rather than in the searching for the "best plausible" scenario.
To do that, for each pair of scenarios x-y, compare :
.the utility rank-order of each public action,
.the possible position of each actor about each institutional action,
.the possible alliance strategies among the actors,
.the possible evolution paths of the perception and believes of the actors having to be better convinced of rhe effectiveness of the actions, as well as the politico-economics challenges of these evolutions,
How to LINK SCENARIOS to POLICY FORMULATION ?
1. Firstly, see scenarios as futures against which you intend to evaluate current strategy. Where each ambiguous path constitutes a possible negotiation point, that may be the starting point of possible coalitions around the "progressive" actors, for solving some current and/or future problems, and/or for avoiding some future ones. And where institutional actions that support the alliance strategies of the "progressive" regional constitutes ways by which the effectiveness of the policies could be improved.
2. Secondly, integrate the scenarios in the strategic planning processes of the public institutions by using the two following steps :
.build a Key-Success-Factor matrix, where each line i and column j respectively represents one of the studied institutional actions/strategies and one of the possible beginnings of the future regional history, and where each cell KSF(i,j) represents the point of view of the searcher about the rank-order of the effectiveness of the action i within the context j;
.then, ask the Actors to rank-order the key success factors that are crucial for achieving their goals (ref. Delphi approach).
Once the KSF matrix has been completed, it can be checked if some KSF occur more than once in a given row (implying robustness) or in a given column (implying synergies). Such multiple occurrences of KSF might in turn provide a basis for a sound strategic vision, which focus on doing a few key things very well.
Nevertheless, the translation of each scenario from the "history" level to the "political" level has to be partly intuitive, through the mind of the searchers ...
(1) i.e. programmes
(2) ref. unpublished working papers : "Un système Expert pour réfléchir sur l'améliorations des politiques actuelles des institutions publiques", Bousset J.-P., Caen, Septembre 1994 ; "Prospective Stage : Needs of the Expert System", Bousset J.-P., Heathrow, December 1994 ;"The Orkney's future histories : an example of cognitive map mining using Futuris", Bousset J.-P., Kikwall, June 1995; "About the Prospective Model", Bousset J.-P., Galway, November 1995; "Scenario thinking", Bousset J.-P., Morella, March 1996.
(3) R.Axelrod, " Structure of decision, cognitive maps of policy elites ", Princeton Univesity Press, 1976
(4) W.R..Zhang, S.Chen, W.Wang and R.S.King, "A cognitive-map based approach to the coordination of distributed cooperative agents", IEEE, vol. 22, no1 Jan/Feb 1992
(5) by using Negative Positive Neutral fuzzi values (e.g. 0, +0.5, -1.5, ...)
(6) the acronym of the concept position must be organized as "ckxy", where xy represents respectively the second and the last letters of the CMap File k.
(7) e.g. each action of the studied public institutions (i.e. studied policies)
(8) ref."The Orkney's future histories : an example of cognitive map mining using Futuris",Bousset J.-P., Kikwall, June 1995;
(9) i.e. the strategies that could allow to build one of the possible continuation of the future regional history
(10) M.Godet in "De l'anticipation à l'action, manuel de prospective et de stratégie " Paris, Dunod ; and J.Hart in " Structure of decision : Comparative cognition", R.Axelrod Ed.,
Princeton Univesity Press
(11) ref. Roy, B., "Des critères multiples en recherches opérationnelle", Paris, Economica (1985) ; Saaty, T.L., The Analytical Hierarchy Process", Pittsburgh, University Press (1988).
(12) "About the Prospective Model", Bousset J.-P., Galway, November 1995
(13) ref."The Orkney's future histories : an example of cognitive map mining using Futuris", Bousset J.-P., Kikwall, June 1995;
(14) E. Monnier, CEOPS-Europôle, Vaulx en Velin ; Hill, B., Young, A.A. and Brookes, G., 1989 : Alternative Support Systems for Rural Areas. Report to the DoE and MAFF. Wye College.
(15) ref. McAllister D.M., "Evaluation in Environmental PLanning", Ed Th MIT Press, Cambridge, Massachussetts, 1980
#$!KThe CONCERTATION process (i.e. STRATEGIC ANALYSIS) consists of searching for the conditions and the consequences (political interest) of possible changes in Actors's positions about the effectiveness of public actions, i.e. the conditions and the consequences of possible alliance strategies among an Actors's group. And thus identify actions that public institutions could promote so as to support the most "progressive" regional actors, as well as the ways by which some contentious actions could become more desirable ones.
So as to do that, Futurhis allows you to use the following method :
1. Analysis the results of the Policy Evalution Process (ref. Stage C(2)), for identifying :
. the degree of consensus of an Actors's group about the effectiveness of a list of actions (ref. Kendall Coefficient of Concordance in results of Policy Evaluation Process), and thus the potential interest/fifficulties of a negociation process,
. the actions that have the most interest to be changed (ref. Global Utility in results of the Policy Evaluation Process),
. the Actors that have taken the most unfavourable position about these actions,
. and the possible alliance strategies among the Actors (ref. Policy Evaluation Process and Strategies Analysis Process).
2. Scanning the results of the Forecasting Process of these Actors (ref. Stage C(1)), for identifying the paths (and thus perceptions and beliefs) that are the causes of their unfavourable positions.
3. Modify their Cognitive Maps and run their Forecasting Process, as well as Policy Evaluation Process with various assumption about the actors and goals weights (use Policy Evaluation.Define Story Beginnings menu), for testing the political interest of your changes.
4. Go back to step 1, and so on until you are satisfyied ...
#$!KSHOW RESULTS allows you to display the results of the simulated Decision-making and Concertation processes, by using a Windows "NotePad" Editor.
That means that a File of Results cannot store more than 64 Kb. Consequently, you must look at the end of the results's file at the end of each simulated process. And when the number of lines of your results file is near to 1.500, you must cut and paste a part of the results from this Editor to an other Edit Window (use Tools Editor Menu or Write/WinWord).
Be carefull :
.FUTURHIS will hang your computer if it cannot write its results in the results's file !
.the new saving File must be open before pasting data.
#$!KSTRATEGIC ANALYSIS consists of searching How such an activated concept C1 could increase/decrease a targeted concept C2
#$!KGlobal Evaluation Process aims to identify :
(a) the most inconsistent/ambigous actions, by taking into account the point of view of all the members of the studied Actors's Society, about the effectiveness of each action for achieving their goals ;
(b) the degree of concordance among the Actors's points of view ;
(c) the possible alliance strategies among the Actors.
To do that, before running the Policy Evaluation Process, you must :
.Create a List of Actors
(use Cognitive Maps Management menu)
.Create a List of Actors's Positions Matrix
(use Global Evaluation menu)
Task (a) consists of running a discrete multicriteria judment method (DMJM)
Based on the utility theory, DMJMs are extensions of the cost-benefit method (ref. Roy B., "Des critères multiples en recherche opérationnelle (Electre)", Paris, Economica, 1985 ; Brans J.P. and Vincke Ph., "A preference ranking organisation method ; the Promethee method for multiple criteria decision making", Management Science, 1985 ; Pastijn H. and Leysen J., "Constructing an outranking relation with Oreste", Mathematical and computer modelling, 1989 ; Saaty, T.L., "The Analytical Hierarchy Process", Pittsburg, University Press, 1988 ; ...)
DMJM consists of making a pairwise comparison among the positions taken by the Actors about the effectiveness of each action for achieving their goals.
Normally, the entry data are collected in the Actors Positions Matrix (i.e. GAPMx.KBF files ; one for each goal X), which are automatically generated by Futurhis when you simulate the forecasting process of an Actor. Consequently the Global Evaluation Process doesnÆt need specific data or knowledge. It only needs you simulate the forecasting analysis of each Actor about the impact of each action on its goals (ref. "Scenario Thinking", Stage C).
Nevertheless, it is possible to run the Global Evaluation Process without modelling the Position-Making Logics of the Actors (i.e. if you have a lack of time for building the ActorsÆs Cognitive Maps ...). Because it is possible to input/modify manually the Actors's positions in the GAPMX.APM files (as many as Goals). Although, in this case, it will be not easy to identify "in what context an why actions could be changed", this way could be usefull for computing the global point of view of a focus group about the effectiveness of current and future public actions (and next, for simulating a discuss among its members ...).
In this case, before running the Policy Evaluation Process, you must :
.Create as many empty CMs as Actors and an Actors's List
(use Cognitive Maps Management menu)
.Create the Goals's List
(use Global Evaluation menu)
.Create and fillful as many GAPMX.APM files as Goals
(use Actors Positions Matrix Management menu).
The final DMJM's outputs are the ranking and the clustering of the actions from the frequency of their preference (i.e. their over-efficiency) and from the frequency of their reject (i.e. their sub-efficiency). That allows you to focus Concertation and Searching Stages on the most inconsistent/ambigous actions (ref. "Scenario Thinking", Stages D and E, as well as Link between Scenarios and Policy Evaluation), i.e. on the actions which should be changed for improving the global efficiency of the current policies.
Task (b) consists of computing the Kendall coefficient of concordance W
Futurhis makes this claculation from an internal RANK[I,J] matrix built during the task (a), where I is the number of Actors/experts, J is the number of actions, and the cell RANK[i,j] is the global utility rank-order of the action j for the Actor i, i.e. the frequency of the preference/reject of the Actor i for the action j (ref. Siegel, S., "Nonparametric Statistics for Behavioral Sciences", International Student Edition).
Kendall coefficient of concordance W is usefull for assessing the political interest of a negociation (as well as the progress made in course of a negociation) concerning the content of an action, i.e. after some actors have changed their points of view about the effectiveness of some actions on some goals, or after goals's weights or actors's weights have changed (ref. "Scenario Thinking", Stages D and E, as well as Link between Scenarios and Policy Evaluation).
Task (c) consists of clustering the Actors from their total position distances about the effectiveness of the actions
This task allows you to identify the best supporters/opposites of the current policies, i.e. the possible leaders of each policy, as well as the Actors which should change their points of view for improving the global effectiveness of the current/future policies (ref. "Scenario Thinking", Stages D and E, as well as Link between Scenarios and Policy Evaluation).
example :
MULTI-ACTORS EVALUATION
by 10-9-1996 … 10:18:53
on the Goals
["OCOU","ODEU","OGOU","OPRE"]
... using the following
Goals's weights,preference,reject thresshols
gweights(1,0,100,"OCOU")
gweights(1,0,0,"ODEU")
gweights(1,0,50,"OGOU")
gweights(1,0,50,"OPRE")
Actors's weights
aweights(1,"ckA1")
aweights(1,"ckA2")
aweights(1,"ckA3")
aweights(1,"ckA4")
aweights(1,"ckA5")
actions classified from ...
... the frequency of their over efficiency
a5>a8>a6>a1>a11>a7>a10>a9>a3>a2>a4;
177>176>156>150>136>128>124>104>80>79>26;
... frequency of their sub efficiency
a4>a2>a3>a9>a10>a11>a7>a1>a6>a8>a5;
187>162>159>145>135>124>120>106>94>85>82;
s20a4------------------:-------------------:
u : : . :
b : : . :
: : . :
e : : . :
f : a3 : . :
f : : . :
i : : . :
c : :a9 . :
i : : . :
e10:-------------------:-----a10-----------:
c : . : :
y : . : a11 :
: . : a7 :
: . : :
: . : a1 :
: . : :
: . : :
: . : a6 :
: . : a8
0:-------------------:-------------------a5
0 10 20 over-efficiency
policies/actions's global efficiency ...
actors classified from ...
... the frequency of their favourable positions (i.e.actions's over efficiency)
ckA3>ckA1>ckA4>ckA2>ckA5;
181>181>169>169>164;
... frequency of their unfavourable positions (i.e.actions's sub efficiency)
ckA4>ckA5>ckA3>ckA1>ckA2;
298>286>277>277>261;
u20:-----------ckA4----:-------------------:
n : : . :
f : : . :
a : : . :
v : : . :
o : : . :
u ckA5 : . :
r : : . :
a : : . :
b : : . :
l10:-------------------:-------------------:
e : . : ckA1
: . : :
p : . : :
o : . : :
s : . : :
. : . : :
: . : :
: . : :
: . : :
0:-----------ckA2----:-------------------:
0 10 20 favourable pos.
global actors's positions ...
Actors classified from their Positions about the Actions's efficiency
(see file cahvor1.dat for more details)
1
SOMME DES INDICES DE NIVEAU .22000E+02
--------------------------------------------------------------------------------
! J ! I(J) ! A(J)! B(J)!T(J)!T(Q)! HISTOGRAMME DES INDICES DE NIVEAU
--------------------------------------------------------------------------------
! 9! 8471! 3! 8! 385! 385!****************************************
! 8! 6222! 7! 1! 283! 668!*****************************
! 7! 3905! 6! 4! 177! 845!******************
! 6! 3402! 5! 2! 155!1000!****************
1
-------------------------------------------------------------------------------
! J ! I(J) ! A(J)! B(J)! P(J)! DESCRIPTION DES CLASSES DE LA HIERARCHIE
-------------------------------------------------------------------------------
! 9! 8471! 3! 8! 5!
-------------------------------------------------------------------------------
! 8! 6222! 7! 1! 4! kA5 kA2 kA4 kA1
-------------------------------------------------------------------------------
! 7! 3905! 6! 4! 3! kA5 kA2 kA4
-------------------------------------------------------------------------------
! 6! 3402! 5! 2! 2! kA5 kA2
-------------------------------------------------------------------------------
1REPRESENTATION DE LA CLASSIFICATION HIERARCHIQUE
+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
kA3 ----------------------------------------------------------*-
!
kA5 -----------------------*--*----------------*---------------
! ! !
kA2 ------------------------ ! !
! !
kA4 --------------------------- !
!
kA1 --------------------------------------------
1FIN NORMALE DU PROGRAMME CAHVOR
PLACE MEMOIRE RESERVEE : 10000
PLACE MEMOIRE UTILISEE : 237
Global Actors's Positions about the effectiveness of each action ...
(i.e. frequency of the over/sub efficiency of each action)
a1 30.00 27.00 16.00 19.00 14.00 16.00 14.00 26.00 26.00 24.00
a2 13.00 6.00 14.00 8.00 3.00 29.00 35.00 29.00 32.00 37.00
a3 4.00 11.00 2.00 6.00 9.00 36.00 24.00 35.00 32.00 32.00
a5 33.00 25.00 34.00 31.00 17.00 14.00 16.00 11.00 19.00 22.00
a6 10.00 21.00 33.00 23.00 22.00 26.00 17.00 10.00 21.00 20.00
a7 7.00 22.00 8.00 20.00 28.00 29.00 19.00 32.00 23.00 17.00
a8 22.00 23.00 40.00 28.00 20.00 19.00 17.00 8.00 20.00 21.00
a9 18.00 12.00 10.00 4.00 12.00 25.00 25.00 29.00 33.00 33.00
a10 22.00 6.00 8.00 19.00 16.00 20.00 34.00 33.00 26.00 22.00
a11 22.00 16.00 14.00 11.00 23.00 23.00 22.00 29.00 31.00 19.00
a4 0.00 0.00 2.00 0.00 0.00 40.00 38.00 35.00 35.00 39.00
Colums's names (Actors) of the 5 first columns
(i.e. frequency of over efficiency)
1>2>3>4>5;
ckA1>ckA2>ckA3>ckA4>ckA5;
Colums's names (Actors) of the 5 last columns
(i.e. frequency of sub efficiency)
1>2>3>4>5;
ckA1>ckA2>ckA3>ckA4>ckA5;
Actions classified from their global efficiency
1
SOMME DES INDICES DE NIVEAU .10000E+02
--------------------------------------------------------------------------------
! J ! I(J) ! A(J)! B(J)!T(J)!T(Q)! HISTOGRAMME DES INDICES DE NIVEAU
--------------------------------------------------------------------------------
! 21! 5324! 18! 20! 532! 532!****************************************
! 20! 1772! 16! 19! 177! 710!*************
! 19! 596! 17! 1! 60! 769!****
! 18! 570! 11! 14! 57! 826!****
! 17! 529! 6! 15! 53! 879!****
! 16! 335! 5! 12! 33! 913!***
! 15! 298! 9! 10! 30! 942!**
! 14! 273! 13! 3! 27! 970!**
! 13! 199! 2! 8! 20! 990!*
! 12! 104! 7! 4! 10!1000!*
1
-------------------------------------------------------------------------------
! J ! I(J) ! A(J)! B(J)! P(J)! DESCRIPTION DES CLASSES DE LA HIERARCHIE
-------------------------------------------------------------------------------
! 21! 5324! 18! 20! 11!
-------------------------------------------------------------------------------
! 20! 1772! 16! 19! 7! a6 a8 a5 a7 a10 a11 a1
-------------------------------------------------------------------------------
! 19! 596! 17! 1! 4! a7 a10 a11 a1
-------------------------------------------------------------------------------
! 18! 570! 11! 14! 4! a4 a2 a9 a3
-------------------------------------------------------------------------------
! 17! 529! 6! 15! 3! a7 a10 a11
-------------------------------------------------------------------------------
! 16! 335! 5! 12! 3! a6 a8 a5
-------------------------------------------------------------------------------
! 15! 298! 9! 10! 2! a10 a11
-------------------------------------------------------------------------------
! 14! 273! 13! 3! 3! a2 a9 a3
-------------------------------------------------------------------------------
! 13! 199! 2! 8! 2! a2 a9
-------------------------------------------------------------------------------
! 12! 104! 7! 4! 2! a8 a5
-------------------------------------------------------------------------------
1REPRESENTATION DE LA CLASSIFICATION HIERARCHIQUE
+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
a4 -----*----------------------------------------------------*-
! !
a2 -**--- !
!! !
a9 --! !
! !
a3 --- !
!
a6 --*---------------*----------------------------------------
! !
a8 *-- !
! !
a5 - !
!
a7 ----**-------------
!!
a10 --*--!
! !
a11 --- !
!
a1 ------
1FIN NORMALE DU PROGRAMME CAHVOR
PLACE MEMOIRE RESERVEE : 10000
PLACE MEMOIRE UTILISEE : 279
Kendall Coefficient of Concordance
among the Points of view of the Actors = 0.72817660351
#$!KFUTURHIS for WINDOWS 2.0 is a Windows's Multiple Document Interface, that notably allows its users :
(a) to display and print Cognitive Maps as Trees, by using a Multiple Trees Builder ; that gives some facilities for verifying, comparing and discussing the modelled Actors's Logics; you can test this tool by loading the Orkneys.MAP file ...
(b) to display several Results Files at the same time, by using a Multiple Windows Editor ; that makes easier the comparision among the different simulated Logics and Scenarios.
NB :
1. The Tree Builder doesn't accept cyclic graphs ; that is why probably you should select the NonRepeat option for displaying CMaps
2. The Tree Builder makes the assumption you have previously loading the CMap ; i.e. you must running Decision-Making Analysis & Select a CMap option menu before Building a tree
2. The Tree Builder makes the assumption it exists minimum one policy-concep in the cognitive map ; if necessary run Decision-Making Analysis & Define Story Beginning menu for adding a fictive pX concept at the top of the CM
#$!KFuturhis is a Windows's Multiple Documents Interface.
Click twice on the Tile option of Window menu for correctly seeing the messages displayed by Futurhis
Click on the frame of the "For Building Scenarios" Window for using any option of the main menu
#$!
- A -
APM KBF Format
APM TXT Format
APM XLS Format
Accointances Table
Actors List
Actors Positions
Actors Positions Matrix
Alliances among all Actors
Allies of an Actor
- C -
CM TXT Format
CM XLS Format
Cognitive Map
Common Positions of 2 allies
Concertation
- D -
Decision Analysis
Decisions
Define Panorama
Differences between 2 Opposites
- E -
Evaluated Actions
Evaluation
- F -
Forecasting Analysis
- O -
Opposites of an Actor
Optimized Allotment
- P -
Positions Distances
Possible Story Continuations
Possible Strategy Continuations
- S -
Scenarios
Show Results
Simulate Actors Decision
Story Beginning
Strategic Analysis
Strategies
Strategy Beginning
Structural Analysis
- T -
Tools
- W -
Window
- c -
contents
