Appendix A

 

A.1 Further Information about the Survey Methodology

 

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.

 

 

A.2 Facilities Definitions

 

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.

 

 

A.3 Statistical Analysis

 

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