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E-grāmata: Minding Norms

Edited by , Edited by , Edited by (Director and Professor, Institute of Cognitive Sciences and Technologies (ISTC-CNR), Rome)
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Norms are prescribed conducts applied by the majority of people. Getting across cultures and centuries, norms evolved to rule all human relationships, from the most formal to the most intimate. Impinging on any sphere of life, from religious to political, norms affect social, moral, and even aesthetical behaviours. They are enforced through centralized sanctions or distributed control, and originate through deliberate acts of issuing or from spontaneous interaction in informal settings. Despite ubiquity and universality, norms are still awaiting for a general comprehensive theory, simultaneously doing justice to three intuitions: that, under variable contents, norms correspond to a common notion; that, once brought about, norms feedback on their producers, affecting their conducts; and finally that before and in order to drive the behaviours of individuals, norms must affect their beliefs and goals: people must detect and accept norms before converting them into observable behaviours.

This volume presents an unprecedented attempt to account for all the three intuitions at once, providing a systematic view of norms. Based on a unitary and operational notion of norms, as behaviours spreading thanks to and to the extent that the corresponding prescriptions spread as well, a cognitive architecture, EMIL-A, which is the main output of a research project on norm emergence, is described. EMIL-A is a BDI-like platform for simulation, endowed with modules for detecting, reasoning and deciding upon norms. Next, the EMIL-A platform is applied to generate norms in different simulated scenarios (from a multi-setting world to a virtual Wikipedia), through a complex bidirectional dynamics, i.e., the bottom-up emergence of norms thanks to a gradual, top-down process, denoted as immergence. As simulations results show, norms emerge while immerging in agents' minds, thanks to their detecting, reasoning, and deciding whether to respect them or not.
Foreword ix
Contributors xi
1 Introduction
1(16)
1.1 Why a new book on norms?
1(4)
1.2 Why a book on cognition?
5(3)
1.3 Our perspective and approach
8(2)
1.4 Presentation of the volume and questions addressed
10(1)
1.5 How to read the book
11(1)
1.6 Acknowledgements
12(5)
2 Loops in Social Dynamics
17(17)
2.1 Introduction
17(1)
2.2 The way up: Emergence
18(1)
2.3 The way back: Downward causation
19(9)
2.3.1 Simple loop
20(2)
2.3.2 Complex loop Incorporation
22(6)
2.4 Advantages of the present approach
28(1)
2.5 Concluding remarks
29(5)
3 Agent-Based Social Simulation and Its Necessity for Understanding Socially Embedded Phenomena
34(16)
3.1 Cognitive simulation modeling
34(1)
3.2 Agent-based architectures and frameworks
35(1)
3.3 The social intelligence hypothesis
36(1)
3.4 Social embeddedness
37(1)
3.5 Micro-macro complexity
38(1)
3.6 Types of social simulation
39(5)
3.7 Linking plausible theory and observed evidence
44(2)
3.8 Relevance vs. generality in simulation
46(1)
3.9 Emergence and immergence in simulations
46(1)
3.10 Conclusion
47(3)
4 How Are Norms Brought About? A State of the Art of Current Research
50(18)
4.1 Norms between conventions and legal norms
50(2)
4.2 The game-theoretical framework of simulating norms
52(4)
4.2.1 Simulation models
53(2)
4.2.2 Analysis
55(1)
4.3 The cognitive method of modelling norms
56(3)
4.3.1 Analysis
58(1)
4.4 Norms in current architectures
59(3)
4.4.1 Normative modules
60(1)
4.4.2 Norm conflicts
60(1)
4.4.3 Concepts of norms
61(1)
4.4.4 Drawbacks of cognitive architectures
62(1)
4.5 Results and unresolved questions
62(6)
5 Modeling Hume's Theory of Norm Emergence
68(13)
5.1 Introduction and motivation
68(1)
5.2 Interaction structure and specialization
69(3)
5.3 The structure: Local groups and a central market
72(1)
5.4 Matching agents
73(1)
5.5 Learning
74(1)
5.6 A benchmark: Wealth under universal trust and trustworthiness
75(1)
5.7 The evolution of trust and division of labor---some first simulation studies
76(5)
6 Norms' Dynamics as a Complex Loop
81(13)
6.1 Normative prescriptions
81(1)
6.2 The missing link in the formal treatment of obligations
82(1)
6.3 The mental dynamics of norms
83(8)
6.3.1 Norm recognition
84(3)
6.3.2 Norm adoption
87(3)
6.3.3 Norm compliance
90(1)
6.4 Concluding remarks
91(3)
7 Hunting for Norms in Unpredictable Societies
94(24)
7.1 Introduction
94(1)
7.2 Related work
95(1)
7.3 The Norm Recognition Module
96(4)
7.4 Norm Detectives vs. Social Conformers
100(6)
7.4.1 Results of comparison
102(4)
7.5 Norm Detectives in a segregated world
106(8)
7.5.1 Effects of segregation
107(7)
7.6 Concluding remarks
114(4)
8 The Derivation of EMIL-S from EMIL-A: From Cognitive Architecture to Software Architecture
118(9)
8.1 General requirements of a multi-agent simulation system with normative agents
118(1)
8.2 System architecture
118(2)
8.3 EMIL-S
120(1)
8.4 Overview of the cognitive and normative architecture of EMIL-A
121(2)
8.5 Correspondence between EMIL-S and EMIL-A
123(1)
8.6 Differences between the cognitive and the implemented models
124(1)
8.7 Additional assumptions about cognitive processes used in EMIL-S
124(3)
9 Demonstrating the Theory: The Case of Wikipedia
127(26)
9.1 Empirical background
127(1)
9.2 The case: Wikipedia
128(12)
9.2.1 Social self-regulation in Wikipedia
128(1)
9.2.2 Methodology
129(3)
9.2.3 Results
132(6)
9.2.4 Discussion, conclusions and ideas for further empirical research
138(2)
9.3 Designing the Wikipedia simulation
140(2)
9.4 Simulation runs and results
142(7)
9.5 Conclusion: Comparison between the NetLogo prototype and the EMIL-S/Repast version
149(4)
10 The Role of Norm Internalizers in Mixed Populations
153(22)
10.1 Introduction
153(2)
10.2 Related work
155(2)
10.3 A multi-step and flexible model of norm internalization
157(2)
10.4 Factors affecting internalization
159(3)
10.5 Internalizer: the EMIL-I-A architecture
162(4)
10.6 Simulating a social dilemma
166(4)
10.6.1 Experimental design
167(1)
10.6.2 Experimental results
168(2)
10.7 Conclusions
170(5)
11 Summary and Conclusions
175(6)
11.1 Summary
176(1)
11.2 Conclusions
177(2)
11.2.1 What are norms?
177(1)
11.2.2 How do norms emerge?
178(1)
11.2.3 How much mental complexity is needed?
178(1)
11.3 Balance and open questions
179(2)
Index 181
Rosaria Conte is Director, LABSS (Laboratory of Agent Based Social Simulation) at the Institute of Cognitive Science and Technology of the National Research Council (NRC), Rome.

Giulia Andrighetto is a researcher at the Institute of Cognitive Sciences and Technologies (ISTC-CNR) in Rome and at the European University Institute in Florence, Italy



Marco Campennģ is a postdoctoral researcher at Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany