We adopted a judgement sample, a non-random
sample where the elements are selected according to the judgement of someone
who is familiar with the target population (see Fowler 1984). The sample frame is a
list of email addresses comprising authors of articles in scientific
publications, key researchers in the field, and the members of five email
discussion lists:
o
simsoc
(simsoc@jiscmail.ac.uk)
o
cormas (cormas@cines.fr)
o
swarm‑modelling
(swarm‑modelling@santafe.edu)
o
distributed-AI
(distributed-ai@jiscmail.ac.uk)
o
agents
(agents@cs.umbc.edu).
Elements in the sample were individually
contacted through electronic mail. Solicitations to fill in the questionnaire
were also sent to the email lists. This kind of sample does not allow us to
generalise the survey results to the target population, but can suggest
qualitative indicators. This procedure was adopted due to the following
reasons: (i) the impossibility of addressing the entire universe of researchers
in the field; (ii) the difficult availability of sample elements,
since the respondents were volunteers; and (iii) the exploratory character of
the survey, that aims to draw an overview of thoughts and modus operandi of respondents.
Currently, there are a large number of
computational systems in agent-based social simulation. While analysing such
systems it is possible to detect several technologies, but among this diversity
there are certain groups of requirements that characterise different
technologies. Such groups of requirements will be called facilities. We
identify four facilities that can be found in these computational systems: technological, domain, development and analysis. Computational systems that
present at some degree of development these four facilities will be called ABSS platforms. Meanwhile, there are a
number of requirements that are not so systematised and developed. Most are
related to the need of balancing the effort spent on the verification and
validation of unexpected outcomes. In other words, the importance of validating
unexpected outcomes by comparison with the target, and the importance of
verifying those same outcomes against the model specification and the program
executions. We have clustered these services in a new group called exploration facilities (see Marietto et al. 2003):
Technological facilities: Comprises services that
(i) intermediate the platform with both the operational system and the network
services; (ii) provide services to support controlled simulation worlds.
Analysis facilities: It encompasses services to help
gathering and analysing simulation outcomes.
Domain facilities: Include two sub-types of
requirements: (i) the first deals with requirements that have a considerable
importance in the modelling and implementation of domains; (ii) the second type
deals with requirements whose technological and logical functionalities must be
modelled in a personalised, way according to the relevant domain.
Development facilities: It includes mechanisms and
tools to construct multiagent systems within an agent-centred approach or
organisation-centred approach.
Exploration facilities: It emphasises the
human-computer interactive character of simulations with respect to the
exploration of different results and emerging qualitative concepts. While most
classic software processes concentrate on the analysis and exploration of
system requirements and intended behaviours, the MABS software process is also
concerned with exploration of results. The interactive exploration of different
conditions, such as different sequences of method invocation, mental states or
assignment of variables, is thus crucial. The exploration can be facilitated if
those conditions are allowed to change interactively, during the simulation,
in-between simulation steps.
Table A.1: Respondents by country,
according to the institution where the researcher is working.
|
Country |
Respondents |
Country |
Respondents |
Country |
Respondents |
|
United States |
50 |
Ireland |
4 |
Czech Republic |
1 |
|
United Kingdom |
19 |
Belgium |
3 |
Finland |
1 |
|
France |
18 |
Austria |
2 |
Iran |
1 |
|
Germany |
17 |
Denmark |
2 |
Korea |
1 |
|
Portugal |
13 |
Greece |
2 |
Mexico |
1 |
|
Netherlands |
12 |
Hungary |
2 |
New Zealand |
1 |
|
Italy |
9 |
India |
2 |
Sweden |
1 |
|
Australia |
7 |
Israel |
2 |
Switzerland |
1 |
|
Spain |
7 |
Japan |
2 |
Taiwan |
1 |
|
Brazil |
6 |
Costa Rica |
1 |
Ukraine |
1 |
|
Canada |
4 |
Croatia |
1 |
Venezuela |
1 |
Table A.2: Goodness-of-fit test for the
variable type of model.
LModel |
|||
|
|
Observed
N |
Expected N |
Residual |
|
D.socio-concrete |
25 |
24.1 |
.9 |
|
D.socio-cognitive |
11 |
24.1 |
-13.1 |
|
D.socio-cognitive/concrete |
22 |
24.1 |
-2.1 |
|
PR.prototyping-resolution |
50 |
24.1 |
25.9 |
|
SS.artificial-social |
3 |
24.1 |
-21.1 |
|
SS.socio-cognitive |
22 |
24.1 |
-2.1 |
|
SS.socio-concrete |
35 |
24.1 |
10.9 |
|
SS.socio-cognitive/concrete |
25 |
24.1 |
.9 |
|
Total |
193 |
|
|
Test Statistics
|
|
|
|
LModel |
|
Chi-Square(a) |
58.731 |
|
df |
7 |
|
Asymp. Sig. |
.000 |
|
(a) 0 cells (.0%)
have expected frequencies less than 5. The minimum expected cell frequency is
24.1. |
|
Table A.3: Goodness-of-fit test for the variable domain of interest.
LDomain
|
|||
|
|
Observed N |
Expected N |
Residual |
|
RC.res-edu |
101 |
32.7 |
68.3 |
|
IND |
4 |
32.7 |
-28.7 |
|
ENG |
28 |
32.7 |
-4.7 |
|
BUS |
12 |
32.7 |
-20.7 |
|
POL |
22 |
32.7 |
-10.7 |
|
ALL |
29 |
32.7 |
-3.7 |
|
Total |
196 |
|
|
Test Statistics
|
|
|
|
LDomain |
|
Chi-Square(a) |
185.735 |
|
df |
5 |
|
Asymp. Sig. |
.000 |
|
(a) 0 cells (.0%) have expected frequencies less
than 5. The minimum expected cell frequency is 32.7. |
|
Table A.4: The requirements chosen as
Imperative or Important in a descending preference order.† Acronyms A, T, DO, DE and E stand
respectively for membership of Analysis,
Technological, Domain, Development and Exploration facilities.
|
Requirement |
Fac. |
% |
|
Observe Behavioural Events |
A |
83.2 |
|
Manage Communication |
T |
81.1 |
|
Control Tracking |
A |
75.5 |
|
Define Scenarios |
A |
72.4 |
|
Manage Agents Life Cycle |
T |
71.4 |
|
Manage Scheduling Techniques |
T |
70.4 |
|
Provide Graphical Interface |
A |
69.9 |
|
Model Scalability |
T |
65.8 |
|
Observe Cognitive Events |
A |
64.8 |
|
Provide Graphical Representation of Domain(s) |
DO |
61.2 |
|
Provide Sensitivity Analysis |
A |
59.7 |
|
Develop Agent Architecture |
DE |
57.7 |
|
Use Groups |
DE |
56.6 |
|
Use Roles |
DE |
53.6 |
|
Launch Agents |
DO |
51.0 |
|
Provide Data
Analysis |
A |
50.5 |
|
Use Organisational Abstractions |
DE |
46.9 |
|
Use Organisational
Rules |
DE |
45.9 |
|
Guarantee Independency from the Simulator |
DE |
43.9 |
|
Intervene in Behavioural Events |
E |
43.9 |
|
Integrate Controlled and Non-Controlled Environments |
DO |
41.3 |
|
Intervene in Cognitive Events |
E |
40.8 |
|
Manage Intentional Failures |
DO |
35.7 |
|
Use Ontologies |
DE |
34.7 |
|
Model the Platform Execution Model |
DO |
34.2 |
|
Use Multiple Societies |
DE |
31.1 |
|
Adopt Ontological Commitment |
DE |
29.1 |
|
Provide Models of Cognitive Reflectivity |
E |
28.6 |
|
Manage Mobility |
T |
23.0 |
|
Model Security |
T |
21.4 |
|
Manage Social Opacity |
E |
20.9 |
|
Provide Translation Mechanisms |
DE |
19.4 |
Table A.5: The requirements chosen as Desirable in a descending preference order.† Acronyms A, T, DO, DE and E stand respectively for membership of Analysis, Technological, Domain, Development and Exploration facilities.
|
Requirement |
Fac. |
% |
|
Intervene in
Behavioural Events |
E |
43.4 |
|
Intervene in
Cognitive Events |
E |
40.8 |
|
Model the Platform
Execution Mode |
DO |
39.8 |
|
Provide Models of
Cognitive Reflectivity |
E |
39.3 |
|
Manage Social
Opacity |
E |
38.8 |
|
Manage Intentional
Failures |
DO |
37.8 |
|
Use Ontologies |
DE |
36.7 |
|
Provide
Translation Mechanisms |
DE |
35.7 |
|
Provide Data Analysis |
DO |
33.2 |
|
Develop Agent
Architectures |
DE |
32.7 |
|
Use Multiple
Societies |
DE |
32.7 |
|
Integrate
Controlled and Non-Controlled Environments |
DO |
31.6 |
|
Use Organisational
Abstractions |
DE |
31.6 |
|
Adopt Ontological
Commitment |
DE |
31.6 |
|
Manage Security |
T |
31,1 |
|
Launch Agents |
DO |
30,6 |
|
Provide Graphical
Representation of Domain(s) |
DO |
29.6 |
|
Use Roles |
DE |
29.6 |
|
Use Organisational
Rules |
DE |
28.6 |
|
Model
Scalability |
T |
28.1 |
|
Use Groups |
DE |
28.1 |
|
Guarantee
Independency from the Simulator |
DE |
27 |
|
Manage Mobility |
T |
26.5 |
|
Provide
Sensitivity Analysis |
A |
26.5 |
|
Provide Graphical
Interface |
A |
25 |
|
Manage Scheduling
Techniques |
T |
23 |
|
Observe Cognitive
Events |
A |
21.9 |
|
Define Scenarios |
A |
21.4 |
|
Control Tracking |
A |
20.4 |
|
Manage Agents Life
Cycle |
T |
17.3 |
|
Observe
Behavioural Events |
A |
13.3 |
|
Manage
Communication |
T |
11.2 |
Table A.6: The requirements chosen as Domain Dependent in a descending easing preference order.† Acronyms A, T, DO, DE and E stand respectively for membership of Analysis, Technological, Domain, Development and Exploration facilities.
|
Requirement |
Fac. |
% |
|
Manage Mobility |
T |
30.1 |
|
Manage Security |
T |
27.6 |
|
Use Multiple
Societies |
DE |
20.9 |
|
Manage Social
Opacity |
E |
20.4 |
|
Provide
Translation Mechanisms |
D |
19.4 |
|
Adopt Ontological
Commitment |
DE |
17.9 |
|
Integrate
Controlled and Non-Controlled Environments |
DO |
16.3 |
|
Provide Models of
Cognitive Reflectivity |
E |
15.8 |
|
Use Ontologies |
DE |
13.8 |
|
Use Organisational
Rules |
DE |
13.3 |
|
Manage Intentional
Failures |
DO |
12.8 |
|
Use Organisational
Abstractions |
DE |
12.2 |
|
Launch Agents |
DO |
11.2 |
|
Guarantee
Independency from the Simulator |
DE |
9.7 |
|
Intervene in
Cognitive Events |
E |
9.7 |
|
Model the Platform
Execution Model |
DO |
8.2 |
|
Use Roles |
DE |
7.7 |
|
Observe Cognitive
Events |
A |
7.7 |
|
Use Groups |
DE |
7.1 |
|
Intervene in
Behavioural Events |
E |
6.6 |
|
Manage Agents Life
Cycle |
T |
6.1 |
|
Manage
Communication |
T |
5.1 |
|
Manage Scheduling
Techniques |
T |
4.1 |
|
Provide Graphical
Representation of Domain(s) |
DO |
4.1 |
|
Provide
Sensitivity Analysis |
A |
3.6 |
|
Model Scalability |
T |
2.6 |
|
Define Scenarios |
A |
2.6 |
|
Provide Data
Analysis |
A |
2.6 |
|
Develop Agent
Architectures |
DE |
1 |
|
Observe
Behavioural Events |
A |
1 |
|
Control Tracking |
A |
1 |
|
Provide Graphical
Interface |
A |
1 |
Table A.7: The requirements chosen as Not Necessary in a descending preference order.† Acronyms A, T, DO, DE and E stand respectively for membership of Analysis, Technological, Domain, Development and Exploration facilities.
|
Requirement |
Fac. |
% |
|
Provide Translation Mechanisms |
DE |
18.9 |
|
Model the Platform
Execution Mode |
DO |
16.8 |
|
Manage Security |
T |
16.3 |
|
Manage Mobility |
T |
15.8 |
|
Guarantee Independency from the Simulator |
DE |
14.8 |
|
Manage Social Opacity |
E |
13.8 |
|
Adopt Ontological Commitment |
DE |
13.3 |
|
Provide Data Analysis |
A |
11.7 |
|
Manage Intentional Failures |
DO |
11.2 |
|
Use Multiple Societies |
DE |
9.7 |
|
Provide Models of Cognitive Reflectivity |
E |
9.7 |
|
Use Organisational Rules |
DE |
8.7 |
|
Use Ontologies |
DE |
8.7 |
|
Provide Sensitivity Analysis |
A |
7.1 |
|
Integrate Controlled and Non-Controlled Environments |
DO |
6.6 |
|
Develop Agent Architectures |
DE |
6.6 |
|
Use Roles |
DE |
6.6 |
|
Launch Agents |
D |
6.1 |
|
Use Groups |
DE |
6.1 |
|
Use Organisational Abstractions |
DE |
5.6 |
|
Intervene in Cognitive Events |
E |
4.6 |
|
Observe Cognitive Events |
A |
4.1 |
|
Provide Graphical
Interface |
A |
4.1 |
|
Intervene in Behavioural Events |
E |
4.1 |
|
Manage Agents Life Cycle |
T |
3.6 |
|
Provide Graphical Representation of Domain(s) |
DO |
3.1 |
|
Define Scenarios |
A |
2.6 |
|
Manage Communication |
T |
2 |
|
Manage Scheduling Techniques |
T |
2 |
|
Model Scalability |
T |
2 |
|
Observe Behavioural Events |
A |
1.5 |
|
Control Tracking |
A |
1.5 |
Table A.8: The requirements chosen as Undesirable in a descending preference order.† Acronyms A, T, DO, DE and E stand respectively for membership of Analysis, Technological, Domain, Development and Exploration facilities.
|
Requirement |
Fac. |
% |
|
Adopt Ontological Commitment |
DE |
5.6 |
|
Provide Translation Mechanism |
DE |
5.1 |
|
Provide Models of Cognitive Reflectivity |
E |
4.6 |
|
Manage Mobility |
T |
4.1 |
|
Guarantee Independency from the Simulator |
DE |
4.1 |
|
Use Ontologies |
DE |
4.1 |
|
Use Multiple Societies |
DE |
3.6 |
|
Manage Social Opacity |
E |
3.5 |
|
Manage Security |
T |
3.1 |
|
Use Organisational Abstractions |
DE |
3.1 |
|
Intervene in Cognitive Events |
E |
3.1 |
|
Integrate Controlled and Non-Controlled Environments |
DO |
2.6 |
|
Use Organisational Rules |
DE |
2 |
|
Manage Agents Life Cycle |
T |
1.5 |
|
Model Scalability |
T |
1.5 |
|
Manage Intentional Failures |
DO |
1.5 |
|
Provide Graphical Representation of Domain(s) |
DO |
1.5 |
|
Develop Agent Architectures |
DE |
1.5 |
|
Provide Data Analysis |
A |
1.5 |
|
Provide Sensitivity Analysis |
A |
1.5 |
|
Launch Agents |
DO |
1 |
|
Use Roles |
DE |
1 |
|
Observe Cognitive Events |
A |
1 |
|
Intervene in
Behavioural Events |
E |
1 |
|
Manage Communication |
T |
0.5 |
|
Manage Scheduling Techniques |
T |
0.5 |
|
Use Groups |
DE |
0.5 |
|
Observe Behavioural Events |
A |
0.5 |
|
Control Tracking |
A |
0.5 |
|
Model the Platform Execution Mode |
DO |
0 |
|
Define Scenarios |
A |
0 |
|
Provide Graphical Interface |
A |
0 |
Table A.9: Chi-Square test:
Type of Model (branches) vs. Domain of Interest (leafs).
Case Processing Summary
|
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
BModel * LDomain |
190 |
100.0% |
0 |
.0% |
190 |
100.0% |
BModel * BDomain Crosstabulation
|
|||||||||
|
|
Ldomain |
Total |
|||||||
|
ALL |
BUS |
ENG |
IND |
POL |
RES.EDU |
||||
|
BModel |
PR |
Count |
8 |
4 |
13 |
2 |
|
23 |
50 |
|
% within BModel |
16.0 |
8.0 |
26.0 |
4.0 |
|
46.0 |
100.0 |
||
|
% within LDomain |
28.6 |
33.3 |
48.1 |
50.0 |
|
23.7 |
26.3 |
||
|
% of Total |
4.2 |
2.1 |
6.8 |
1.1 |
|
12.1 |
26.3 |
||
|
D |
Count |
13 |
4 |
10 |
2 |
7 |
22 |
58 |
|
|
% within BModel |
22.4 |
6.9 |
17.2 |
3.4 |
12.1 |
37.9 |
100.0 |
||
|
% within LDomain |
46.4 |
33.3 |
37.0 |
50.0 |
31.8 |
22.7 |
30.5 |
||
|
% of Total |
6.8 |
2.1 |
5.3 |
1.1 |
3.7 |
11.6 |
30.5 |
||
|
SS |
Count |
7 |
4 |
4 |
|
15 |
52 |
82 |
|
|
% within BModel |
8.5 |
4.9 |
4.9 |
|
18.3 |
63.4 |
100.0 |
||
|
% within LDomain |
25.0 |
33.3 |
14.8 |
|
68.2 |
53.6 |
43.2 |
||
|
% of Total |
3.7 |
2.1 |
2.1 |
|
7.9 |
27.4 |
43.2 |
||
|
Total |
Count |
28 |
12 |
27 |
4 |
22 |
97 |
190 |
|
|
% within BModel |
14.7 |
6.3 |
14.2 |
2.1 |
11.6 |
51.1 |
100.0 |
||
|
% within LDomain |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
||
|
% of Total |
14.7 |
6.3 |
14.2 |
2.1 |
11.6 |
51.1 |
100.0 |
||
Chi-Square Tests
|
|||
|
|
Value |
df |
Asymp.
Sig. (2-sided) |
|
Pearson Chi-Square |
32.058(a) |
10 |
.000 |
|
Likelihood Ratio |
39.649 |
10 |
.000 |
|
N of Valid Cases |
190 |
|
|
|
(a) 5 cells (27.8%) have expected count less than 5.
The minimum expected count is 1.05. |
|||
Table A.10:
Chi-Square test: Type of Model (branches) vs. Domain of Interest (branches).
Case Processing Summary |
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
BModel * BDomain |
190 |
100.0 % |
0 |
.0% |
190 |
100.0% |
BModel * BDomain Crosstabulation |
|||||
|
|
BDomain |
Total |
|||
|
APP |
RES |
||||
|
BModel |
PR |
Count |
27 |
23 |
50 |
|
% within BModel |
54.0 |
46.0 |
100.0 |
||
|
% within BDomain |
29.0 |
23.7 |
26.3 |
||
|
% of Total |
14.2 |
12.1 |
26.3 |
||
|
D |
Count |
36 |
22 |
58 |
|
|
% within BModel |
62.1 |
37.9 |
100.0 |
||
|
% within BDomain |
38.7 |
22.7 |
30.5 |
||
|
% of Total |
18.9 |
11.6 |
30.5 |
||
|
SS |
Count |
30 |
52 |
82 |
|
|
% within BModel |
36.6 |
63.4 |
100.0 |
||
|
% within BDomain |
32.3 |
53.6 |
43.2 |
||
|
% of Total |
15.8 |
27.4 |
43.2 |
||
|
Total |
Count |
93 |
97 |
190 |
|
|
% within BModel |
48.9 |
51.1 |
100.0 |
||
|
% within BDomain |
100.0 |
100.0 |
100.0 |
||
|
% of Total |
48.9 |
51.1 |
100.0 |
||
Chi-Square Tests
|
|||
|
|
Value |
df |
Asymp.
Sig. (2-sided) |
|
Pearson Chi-Square |
9.522(a) |
2 |
.009 |
|
Likelihood Ratio |
9.624 |
2 |
.008 |
|
N of Valid Cases |
190 |
|
|
|
(a) 0 cells (.0%) have expected count less than 5.
The minimum expected count is 24.47. |
|||
Table A.11:
Chi-Square test: Type of Model (leafs) vs. Domain of Interest (leafs).
Case Processing Summary
|
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
LModel * LDomain |
190 |
100.0% |
0 |
.0% |
190 |
100.0% |
LModel * LDomain Crosstabulation |
|||||||||
|
|
LDomain |
Total |
|||||||
|
ALL |
BUS |
ENG |
IND |
POL |
RES.EDU |
||||
|
LModel |
PR.prototyping-resolution |
Count |
8 |
4 |
13 |
2 |
|
23 |
50 |
|
% within LModel |
16.0 |
8.0 |
26.0 |
4.0 |
|
46.0 |
100.0 |
||
|
% within LDomain |
28.6 |
33.3 |
48.1 |
50.0 |
|
23.7 |
26.3 |
||
|
% of Total |
4.2 |
2.1 |
6.8 |
1.1 |
|
12.1 |
26.3 |
||
|
D.socio-cognitive |
Count |
|
1 |
3 |
|
|
7 |
11 |
|
|
% within LModel |
|
9.1 |
27.3 |
|
|
63.6 |
100.0 |
||
|
% within LDomain |
|
8.3 |
11.1 |
|
|
7.2 |
5.8 |
||
|
% of Total |
|
.5 |
1.6 |
|
|
3.7 |
5.8 |
||
|
D.socio-cognitive/concrete |
Count |
8 |
|
3 |
|
2 |
9 |
22 |
|
|
% within LModel |
36.4 |
|
13.6 |
|
9.1 |
40.9 |
100.0 |
||
|
% within LDomain |
28.6 |
|
11.1 |
|
9.1 |
9.3 |
11.6 |
||
|
% of Total |
4.2 |
|
1.6 |
|
1.1 |
4.7 |
11.6 |
||
|
D.socio-concrete |
Count |
5 |
3 |
4 |
2 |
5 |
6 |
25 |
|
|
% within LModel |
20.0 |
12.0 |
16.0 |
8.0 |
20.0 |
24.0 |
100.0 |
||
|
% within LDomain |
17.9 |
25.0 |
14.8 |
50.0 |
22.7 |
6.2 |
13.2 |
||
|
% of Total |
2.6 |
1.6 |
2.1 |
1.1 |
2.6 |
3.2 |
13.2 |
||
|
SS.socio-cognitive |
Count |
1 |
|
1 |
|
4 |
16 |
22 |
|
|
% within LModel |
4.5 |
|
4.5 |
|
18.2 |
72.7 |
100.0 |
||
|
% within LDomain |
3.6 |
|
3.7 |
|
18.2 |
16.5 |
11.6 |
||
|
% of Total |
.5 |
|
.5 |
|
2.1 |
8.4 |
11.6 |
||
|
SS.socio-cognitive/concrete |
Count |
1 |
1 |
|
|
7 |
16 |
25 |
|
|
% within LModel |
4.0 |
4.0 |
|
|
28.0 |
64.0 |
100.0 |
||
|
% within LDomain |
3.6 |
8.3 |
|
|
31.8 |
16.5 |
13.2 |
||
|
% of Total |
.5 |
.5 |
|
|
3.7 |
8.4 |
13.2 |
||
|
SS.socio-concrete |
Count |
5 |
3 |
3 |
|
4 |
20 |
35 |
|
|
% within LModel |
14.3 |
8.6 |
8.6 |
|
11.4 |
57.1 |
100.0 |
||
|
% within LDomain |
17.9 |
25.0 |
11.1 |
|
18.2 |
20.6 |
18.4 |
||
|
% of Total |
2.6 |
1.6 |
1.6 |
|
2.1 |
10.5 |
18.4 |
||
|
Total |
Count |
28 |
12 |
27 |
4 |
22 |
97 |
190 |
|
|
% within LModel |
14.7 |
6.3 |
14.2 |
2.1 |
11.6 |
51.1 |
100.0 |
||
|
% within LDomain |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
||
|
% of Total |
14.7 |
6.3 |
14.2 |
2.1 |
11.6 |
51.1 |
100.0 |
||
Chi-Square Tests |
|||
|
|
Value |
df |
Asymp.
Sig. (2-sided) |
|
Pearson Chi-Square |
60.085(a) |
30 |
.001 |
|
Likelihood Ratio |
72.529 |
30 |
.000 |
|
N of Valid Cases |
190 |
|
|
|
(a) 31 cells (73.8%) have expected count less than
5. The minimum expected count is .23. |
|||
Table A.12:
Chi-Square test: Type of Model (leafs) vs. Domain of Interest (branches).
Case Processing Summary
|
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
LModel * BDomain |
190 |
100.0% |
0 |
.0% |
190 |
100.0% |
LModel * BDomain Crosstabulation |
|||||
|
|
BDomain |
Total |
|||
|
APP |
RES |
||||
|
LModel |
PR.prototyping-resolution |
Count |
27 |
23 |
50 |
|
% within Lmodel |
54.0 |
46.0 |
100.0 |
||
|
% within Bdomain |
29.0 |
23.7 |
26.3 |
||
|
% of Total |
14.2 |
12.1 |
26.3 |
||
|
D.socio-cognitive |
Count |
4 |
7 |
11 |
|
|
% within Lmodel |
36.4 |
63.6 |
100.0 |
||
|
% within Bdomain |
4.3 |
7.2 |
5.8 |
||
|
% of Total |
2.1 |
3.7 |
5.8 |
||
|
D.socio-cognitive/concrete |
Count |
13 |
9 |
22 |
|
|
% within Lmodel |
59.1 |
40.9 |
100.0 |
||
|
% within Bdomain |
14.0 |
9.3 |
11.6 |
||
|
% of Total |
6.8 |
4.7 |
11.6 |
||
|
D.socio-concrete |
Count |
19 |
6 |
25 |
|
|
% within Lmodel |
76.0 |
24.0 |
100.0 |
||
|
% within Bdomain |
20.4 |
6.2 |
13.2 |
||
|
% of Total |
10.0 |
3.2 |
13.2 |
||
|
SS.socio-cognitive |
Count |
6 |
16 |
22 |
|
|
% within Lmodel |
27.3 |
72.7 |
100.0 |
||
|
% within Bdomain |
6.5 |
16.5 |
11.6 |
||
|
% of Total |
3.2 |
8.4 |
11.6 |
||
|
SS.socio-cognitive/concrete |
Count |
9 |
16 |
25 |
|
|
% within Lmodel |
36.0 |
64.0 |
100.0 |
||
|
% within Bdomain |
9.7 |
16.5 |
13.2 |
||
|
% of Total |
4.7 |
8.4 |
13.2 |
||
|
SS.socio-concrete |
Count |
15 |
20 |
35 |
|
|
% within Lmodel |
42.9 |
57.1 |
100.0 |
||
|
% within Bdomain |
16.1 |
20.6 |
18.4 |
||
|
% of Total |
7.9 |
10.5 |
18.4 |
||
|
Total |
Count |
93 |
97 |
190 |
|
|
% within Lmodel |
48.9 |
51.1 |
100.0 |
||
|
% within Bdomain |
100.0 |
100.0 |
100.0 |
||
|
% of Total |
48.9 |
51.1 |
100.0 |
||
Chi-Square Tests
|
|||
|
|
Value |
df |
Asymp.
Sig. (2-sided) |
|
Pearson Chi-Square |
15.768(a) |
6 |
.015 |
|
Likelihood Ratio |
16.319 |
6 |
.012 |
|
N of Valid Cases |
190 |
|
|
|
(a) 0 cells (.0%) have expected count less than 5.
The minimum expected count is 5.38. |
|||
Table A.13:
Requirements in descending preference order, classified as Imperative or Important
for each leaf of the variable type of model.
|
SS.socio-cognitive |
% |
SS.socio-concrete |
% |
SS.
socio-cognitive/ concrete |
% |
D.
socio-cognitive |
% |
D.
socio-concrete |
% |
D.
socio-cognitive/ concrete |
% |
|
Manage Communication |
81.8 |
Manage Scheduling Techniques |
85.7 |
Manage Communication |
92.0 |
Manage Communication |
90.9 |
Observe Behavioural Events |
88.0 |
Observe Behavioural Events |
81.8 |
|
Observe Behavioural Events |
81.8 |
Observe Behavioural Events |
85.7 |
Manage Scheduling Techniques |
88.0 |
Manage Agents Life Cycle |
81.8 |
Manage Communication |
84.0 |
Define Scenarios |
77.3 |
|
Control Tracking |
77.3 |
Control Tracking |
82.9 |
Model Scalability |
88.0 |
Manage Scheduling Techniques |
63.6 |
Define Scenarios |
80.0 |
Manage Communication |
72.7 |
|
Manage Scheduling Techniques |
68.2 |
Provide Graphical Interface |
82.9 |
Manage Agents Life Cycle |
84.0 |
Use Roles |
63.6 |
Manage Agents Life Cycle |
76.0 |
Manage Scheduling Techniques |
72.7 |
|
Define Scenarios |
68.2 |
Observe Cognitive Events |
77.1 |
Control Tracking |
80.0 |
Observe Behavioural Events |
63.6 |
Manage Scheduling Techniques |
76.0 |
Control Tracking |
72.7 |
|
Provide Graphical Interface |
68.2 |
Manage Communication |
74.3 |
Provide Sensitivity Analysis |
80.0 |
Model Scalability |
54.5 |
Use Groups |
76.0 |
Manage Agents Life Cycle |
68.2 |
|
Manage Agents Life Cycle |
63.6 |
Define Scenarios |
74.3 |
Provide Graphical Interface |
80.0 |
Launch Agents |
54.5 |
Control Tracking |
68.0 |
Provide Graphical Representation of Domain(s) |
68.2 |
|
Provide Graphical Representation of Domain(s) |
63.6 |
Manage Agents Life Cycle |
71.4 |
Develop Agent Architecture |
76.0 |
Guarantee Independency from the Simulator |
54.5 |
Provide Graphical Interface |
68.0 |
Provide Sensitivity Analysis |
68.2 |
|
Model Scalability |
59.1 |
Model Scalability |
68.6 |
Observe Behavioural Events |
76.0 |
Use Organisational Abstractions |
54.5 |
Use Roles |
64.0 |
Provide Graphical Interface |
68.2 |
|
Observe Cognitive Events |
59.1 |
Provide Graphical Representation of Domain(s) |
68.6 |
Define Scenarios |
76.0 |
Use Groups |
54.5 |
Observe Cognitive Events |
64.0 |
Model Scalability |
63.6 |
|
Provide Sensitivity Analysis |
59.1 |
Provide Sensitivity Analysis |
65.7 |
Provide Graphical Representation of Domain(s) |
72.0 |
Define Scenarios |
54.5 |
Provide Sensitivity Analysis |
64.0 |
Develop Agent Architecture |
63.6 |
|
Provide Data Analysis |
54.5 |
Develop Agent Architecture |
57.1 |
Provide Data Analysis |
68.0 |
Intervene in Behavioural Events |
54.5 |
Model Scalability |
60.0 |
Use Groups |
63.6 |
|
Develop Agent Architecture |
50.0 |
Use Groups |
54.3 |
Use Groups |
64.0 |
Integrate Controlled and Non-Controlled Environments |
45.5 |
Provide Graphical Representation of Domain(s) |
60.0 |
Observe Cognitive Events |
63.6 |
|
Intervene in Cognitive Events |
45.5 |
Use Organisational ëRules |
54.3 |
Use Organisational Rules |
64.0 |
Develop Agent Architecture |
45.5 |
Develop Agent Architecture |
60.0 |
Launch Agents |
59.1 |
|
Launch Agents |
40.9 |
Provide Data Analysis |
54.3 |
Observe Cognitive Events |
64.0 |
Use Organisational Rules |
45.5 |
Launch Agents |
48.0 |
Manage Intentional Failures |
59.1 |
|
Manage Intentional Failures |
40.9 |
Use Organisational Abstractions |
51.4 |
Use Organisational Abstractions |
60.0 |
Use Ontologies |
45.5 |
Use Organisational Rules |
44.0 |
Use Multiple Societies |
59.1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Use Groups |
40.9 |
Use Roles |
48.6 |
Use Roles |
60.0 |
Provide Graphical Interface |
45.5 |
Provide Data Analysis |
44.0 |
Intervene in Cognitive Events |
59.1 |
|
Use Roles |
40.9 |
Integrate Controlled and Non-Controlled Environments |
42.9 |
Intervene in Behavioural Events |
60.0 |
Model the Platform Execution Model |
36.4 |
Intervene in Behavioural Events |
44.0 |
Provide Data Analysis |
54.5 |
|
Intervene in Behavioural Events |
40.9 |
Manage Intentional Failures |
37.1 |
Intervene in Cognitive Events |
56.0 |
Provide Graphical Representation of Domain(s) |
36.4 |
Integrate Controlled and Non-Controlled Environments |
40.0 |
Use Organisational Abstractions |
50.0 |
|
Guarantee Independency from the Simulator |
36.4 |
Launch Agents |
34.3 |
Launch Agents |
52.0 |
Control Tracking |
36.4 |
Guarantee Independency from the Simulator |
40.0 |
Use Roles |
50.0 |
|
Use Organisational Abstractions |
36.4 |
Guarantee Independency from the Simulator |
31.4 |
Manage Social Opacity |
52.0 |
Provide Data Analysis |
36.4 |
Use Organisational Abstractions |
40.0 |
Use Organisational Rules |
50.0 |
|
Integrate Controlled and Non-Controlled Environments |
27.3 |
Use Ontologies |
31.4 |
Integrate Controlled and Non-Controlled Environments |
44.0 |
Provide Sensitivity Analysis |
36.4 |
Intervene in Cognitive Events |
40.0 |
Intervene in Behavioural Events |
50.0 |
|
Use Organisational Rules |
27.3 |
Adopt Ontological Commitment |
31.4 |
Model the Platform Execution Model |
44.0 |
Manage Security |
27.3 |
Model the Platform Execution Model |
36.0 |
Integrate Controlled and Non-Controlled Environments |
40.9 |
|
Use Multiple Societies |
27.3 |
Provide Models of Cognitive Reflectivity |
31.4 |
Use Multiple Societies |
44.0 |
Manage Mobility |
27.3 |
Manage Security |
28.0 |
Guarantee Independency from the Simulator |
40.9 |
|
Use Ontologies |
27.3 |
Intervene in Behavioural Events |
28.6 |
Provide Models of Cognitive Reflectivity |
40.0 |
Manage Intentional Failures |
27.3 |
Manage Intentional Failures |
28.0 |
Use Ontologies |
40.9 |
|
Provide Models of Cognitive Reflectivity |
27.3 |
Intervene in Cognitive Events |
28.6 |
Guarantee Independency from the Simulator |
32.0 |
Use Multiple Societies |
27.3 |
Provide Models of Cognitive Reflectivity |
28.0 |
Adopt Ontological Commitment |
40.9 |
|
Manage Mobility |
22.7 |
Model the Platform Execution Model |
25.7 |
Manage Intentional Failures |
28.0 |
Adopt Ontological Commitment |
27.3 |
Use Ontologies |
24.0 |
Provide Models of Cognitive Reflectivity |
40.9 |
|
Adopt Ontological Commitment |
22.7 |
Use Multiple Societies |
25.7 |
Manage Mobility |
24.0 |
Observe Cognitive Events |
27.3 |
Manage Mobility |
20.0 |
Manage Social Opacity |
36.4 |
|
Manage Security |
13.6 |
Manage Social Opacity |
17.1 |
Use Ontologies |
20.0 |
Intervene in Cognitive Events |
27.3 |
Use Multiple Societies |
16.0 |
Manage Mobility |
27.3 |
|
Manage Social Opacity |
13.6 |
Manage Security |
14.3 |
Adopt Ontological Commitment |
12.0 |
Manage Social Opacity |
27.3 |
Adopt Ontological Commitment |
16.0 |
Manage Security |
22.7 |
|
Model the Platform Execution Model |
9.1 |
Manage Mobility |
14.3 |
Manage Security |
8.0 |
Provide Models of Cognitive Reflectivity |
18.2 |
Manage Social Opacity |
8.0 |
Model the Platform Execution Model |
18.2 |
|
Provide Translation Mechanisms |
0.0 |
Provide Translation Mechanisms |
2.9 |
Provide Translation Mechanisms |
0.0 |
Provide Translation Mechanisms |
0.0 |
Provide Translation Mechanisms |
4.0 |
Provide Translation Mechanisms |
13.6 |
Table A.14: Chi-Square
test: Requirement Manage Agents Life Cycle vs.Type
of Model (branches).
Case Processing Summary
|
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
Manage Agents Life Cycle - Imperative + Important * BModel |
190 |
100.0% |
0 |
.0% |
190 |
100.0% |
Manage Agents Life Cycle - Imperative + Important * BModel Crosstabulation |
||||||
|
|
BModel |
Total |
||||
|
PR |
D |
SS |
||||
|
Manage
Agents Life Cycle - Imperative + Important |
Not
Selected |
Count |
16 |
15 |
22 |
53 |
|
% within Manage Agents Life Cycle - Imperative +
Important |
30.2 |
28.3 |
41.5 |
100.0 |
||
|
% within BModel |
32.0 |
25.9 |
26.8 |
27.9 |
||
|
% of Total |
8.4 |
7.9 |
11.6 |
27.9 |
||
|
Selected |
Count |
34 |
43 |
60 |
137 |
|
|
% within Manage Agents Life Cycle - Imperative +
Important |
24.8 |
31.4 |
43.8 |
100.0 |
||
|
% within BModel |
68.0 |
74.1 |
73.2 |
72.1 |
||
|
% of Total |
17.9 |
22.6 |
31.6 |
72.1 |
||
|
Total |
Count |
50 |
58 |
82 |
190 |
|
|
% within Manage Agents Life Cycle - Imperative +
Important |
26.3 |
30.5 |
43.2 |
100.0 |
||
|
% within BModel |
100.0 |
100.0 |
100.0 |
100.0 |
||
|
% of Total |
26.3 |
30.5 |
43.2 |
100.0 |
||
Chi-Square Tests
|
|||
|
|
Value |
df |
Asymp.
Sig. (2-sided) |
|
Pearson Chi-Square |
.584(a) |
2 |
.747 |
|
Likelihood Ratio |
.575 |
2 |
.750 |
|
N of Valid Cases |
190 |
|
|
|
(a) 0 cells (.0%) have expected count less than 5.
The minimum expected count is 13.95. |
|||
Table A.15: Chi-Square
test: Requirement Manage Communication vs.
Type of Model (branches).
Case Processing Summary
|
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
Manage Communication - Imperative + Important * BModel |
190 |
100.0% |
0 |
.0% |
190 |
100.0% |
Manage Communication - Imperative + Important * BModel Crosstabulation |
||||||
|
|
BModel |
Total |
||||
|
PR |
D |
SS |
||||
|
Manage
Communication - Imperative + Important |
Not
Selected |
Count |
9 |
11 |
15 |
35 |
|
% within Manage Communication - Imperative +
Important |
25.7 |
31.4 |
42.9 |
100.0 |
||
|
%
within BModel |
18.0 |
19.0 |
18.3 |
18.4 |
||
|
% of Total |
4.7 |
5.8 |
7.9 |
18.4 |
||
|
Selected |
Count |
41 |
47 |
67 |
155 |
|
|
% within Manage Communication - Imperative +
Important |
26.5 |
30.3 |
43.2 |
100.0 |
||
|
% within BModel |
82.0 |
81.0 |
81.7 |
81.6 |
||
|
% of Total |
21.6 |
24.7 |
35.3 |
81.6 |
||
|
Total |
Count |
50 |
58 |
82 |
190 |
|
|
% within Manage Communication - Imperative +
Important |
26.3 |
30.5 |
43.2 |
100.0 |
||
|
% within BModel |
100.0 |
100.0 |
100.0 |
100.0 |
||
|
% of Total |
26.3 |
30.5 |
43.2 |
100.0 |
||
Chi-Square Tests
|
|||
|
|
Value |
df |
Asymp.
Sig. (2-sided) |
|
Pearson Chi-Square |
.018(a) |
2 |
.991 |
|
Likelihood Ratio |
.018 |
2 |
.991 |
|
N of Valid Cases |
190 |
|
|
|
(a) 0 cells (.0%) have expected count less than 5.
The minimum expected count is 9.21. |
|||
Table A.16: Chi-Square
test: Requirement Manage Scheduling Techniques vs. Type of Model (branches).
Case Processing Summary
|
||||||
|
|
Cases |
|||||
|
Valid |
Missing |
Total |
||||
|
N |
Percent |
N |
Percent |
N |
Percent |
|
|
Manage Scheduling Techniques - Imperative + Important * BModel |
190 |
100.0% |
0 |
.0% |
190 |
100.0% |
Manage Scheduling Techniques - Imperative + Important *
BModel Crosstabulation
|
||||||
|
|
BModel |
Total |
||||
|
PR |
D |
SS |
||||
|
Manage
Scheduling Techniques - Imperative + Important |
Not
Selected |
Count |
24 |
16 |
15 |
55 |
|
% within Manage Scheduling Techniques - Imperative +
Important |
43.6 |
29.1 |
27.3 |
100.0 |
||
|
% within BModel |
48.0 |
27.6 |
18.3 |
28.9 |
||
|
% of Total |
12.6 |
8.4 |
7.9 |
28.9 |
||
|
Selected |
Count |
26 |
42 |
67 |
135 |
|
|
% within Manage Scheduling Techniques - Imperative +
Important |
19.3 |
31.1 |
49.6 |
100.0 |
||
|
% within BModel |
52.0 |
72.4 |
||||