Minding Norms: Mechanisms and Dynamics of Social Order in Agent Societies

ISBN : 9780199812677

Rosaria Conte; Giulia Andrighetto; Marco Campenni
208 ページ
162 x 241 mm

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.


1.1 Why a new book on norms
1.2 Why a book on cognition
1.3 Our perspective and approach
1.4 Presentation of the volume and questions addressed
1.5 How to read the volume
1.6 Acknowledgements
1.7 References
Loops in Social Dynamics
Chapter 2
2.1 Introduction
2.2 The Way Up: Emergence
2.3 The Way Back: Downward Causation
2.3.1 Simple loop
2.3.2 Complex loop (Incorporation) Second Order Emergence Immergence
2.4 Advantages of the Present Approach
2.5 Concluding Remarks
2.6 References
Agent Based Social Simulation and its necessity for understanding socially embedded phenomena
Chapter 3
3.1 Cognitive Simulation Modelling
3.2 Agent Based Architectures and Frameworks
3.3 The Social Intelligence Hypothesis
3.4 Social Embeddedness
3.5 Micro-Macro Complexity
3.6 Types of Social Simulation
3.7 Linking Plausible Theory and Observed Evidence
3.8 Relevance vs. Generality in Simulation
3.9 Emergence and Immergence in Simulations
3.10 Conclusion
How are norms brought about? A state of the art
Chapter 4
4.1 Norms between conventions and legal norms
4.2 The game theoretical framework of simulating norms
4.3 The cognitive method of modelling norms
4.3.1 Analysis
4.4 Norms in current architectures
4.4.1 Normative modules
4.4.2 Norm conflicts
4.4.3 Concepts of norms
4.4.4 Drawbacks of cognitive architectures
4.5 Results and unresolved questions
5.1 Introduction and motivation
5.2 Interaction structure and specialization
5.3 The structure: Local groups and a central market
5.4 Matching agents
5.5 Learning
5.7 The evolution of trust and division of labor - some first simulation studies
Norms' Dynamics as a Complex Loop
Chapter 6
6.1 Normative Prescriptions
6.2 The missing link in the formal treatment of obligations
6.3 The mental dynamics of norms
6.3.1 Norm recognition
6.3.2 Norm adoption
6.3.3 Norm compliance
6.4 Concluding Remarks
Hunting for norms in unpredictable societies
Chapter 7
7.1 Introduction
7.2 Related Work
7.3 The Norm Recognition Module
7.4 Norm Detectives Vs. Social Conformers
7.4.1 Results of comparison
7.5 Norm Detectives in a segregated world
7.5.1 Effects of segregation
7.6 Concluding remarks
The derivation of EMIL-S from EMIL-A: From cognitive architecture to software architecture
Chapter 8
8.1 General Requirements of a Multi-Agent Simulation System with Normative Agents
8.2 System Architecture
8.3 EMIL-S
8.4 Overview of the cognitive and normative architecture of EMIL-A
8.5 Correspondence between EMIL-S and EMIL-A
8.6 Differences between the cognitive and the implemented model
8.7 Additional assumptions about cognitive processes used in EMIL-S
Demonstrating the Theory: The case of Wikipedia
Chapter 9
9.1 Empirical background
9.2 The Case: Wikipedia
9.2.1 Social Self-Regulation in Wikipedia
9.2.2 Methodology
9.2.3 Results
9.2.4 Discussion, Conclusions and Ideas for Further Empirical Research
9.3 Designing the Wikipedia Simulation
9.4 Simulation runs and results
9.5 Conclusion: Comparison between the NetLogo prototype and the EMIL-S/Repast version
The Role of Norm Internalizers in Mixed Populations
Chapter 10
10.1 Introduction
10.2 Related Work
10.3 A multi-step and flexible model of norm internalization
10.4 Factors affecting internalization
10.5 Internalizer: the EMIL-I-A architecture
10.6 Simulating a social dilemma
10.6.1 Experimental Design
10.6.2 Experimental Results
10.7. Conclusions
Summary and Conclusions
11.1 Summary
11.2 Conclusions
11.2.1 What are norms
11.2.2 How norms emerge
10.2.3 How much mental complexity is needed
11.4 Balance and open questions
11.5 References


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