Minding Norms: Mechanisms and dynamics of social order in agent societies by Rosaria ConteMinding Norms: Mechanisms and dynamics of social order in agent societies by Rosaria Conte

Minding Norms: Mechanisms and dynamics of social order in agent societies

EditorRosaria Conte, Giulia Andrighetto, Marco Campennl

Hardcover | November 8, 2013

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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 evenaesthetical 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 affecttheir 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, acognitive 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 differentsimulated 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, thanksto their detecting, reasoning, and deciding whether to respect them or not.
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, and Professor Communication Sciences and Social Psychology at the University of Siena. Giulia Andrighetto is a Post-Doctoral Researcher at LABSS. Marco Campenn...
Title:Minding Norms: Mechanisms and dynamics of social order in agent societiesFormat:HardcoverDimensions:208 pages, 9.25 × 6.12 × 0.98 inPublished:November 8, 2013Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199812675

ISBN - 13:9780199812677

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Table of Contents

1. Introduction1.1 Why a new book on norms1.2 Why a book on cognition1.3 Our perspective and approach1.4 Presentation of the volume and questions addressed1.5 How to read the volume1.6 Acknowledgements1.7 References2. Loops in Social Dynamics2.1 Introduction2.2 The Way Up: Emergence2.3 The Way Back: Downward Causation2.3.1 Simple loop2.3.2 Complex loop (Incorporation) Second Order Emergence2.3.2.2 Immergence2.4 Advantages of the Present Approach2.5 Concluding Remarks2.6 References3. Agent Based Social Simulation and its necessity for understanding socially embedded phenomena3.1 Cognitive Simulation Modelling3.2 Agent Based Architectures and Frameworks3.3 The Social Intelligence Hypothesis3.4 Social Embeddedness3.5 Micro-Macro Complexity3.6 Types of Social Simulation3.7 Linking Plausible Theory and Observed Evidence3.8 Relevance vs. Generality in Simulation3.9 Emergence and Immergence in Simulations3.10 ConclusionReferences4. How are norms brought about? A state of the art4.1 Norms between conventions and legal norms4.2 The game theoretical framework of simulating norms4.3 The cognitive method of modelling norms4.3.1 Analysis4.4 Norms in current architectures4.4.1 Normative modules4.4.2 Norm conflicts4.4.3 Concepts of norms4.4.4 Drawbacks of cognitive architectures4.5 Results and unresolved questionsReferences5. 5.1 Introduction and motivation5.2 Interaction structure and specialization5.3 The structure: Local groups and a central market5.4 Matching agents5.5 Learning5.6 The evolution of trust and division of labor - some first simulation studiesReferences6. Norms' Dynamics as a Complex Loop6.1 Normative Prescriptions6.2 The missing link in the formal treatment of obligations6.3 The mental dynamics of norms6.3.1 Norm recognition6.3.2 Norm adoption6.3.3 Norm compliance6.4 Concluding RemarksReferences7. Hunting for norms in unpredictable societies7.1 Introduction7.2 Related Work7.3 The Norm Recognition Module7.4 Norm Detectives Vs. Social Conformers7.4.1 Results of comparison7.5 Norm Detectives in a segregated world7.5.1 Effects of segregation7.6 Concluding remarksReferences8. The derivation of EMIL-S from EMIL-A: From cognitive architecture to software architecture8.1 General Requirements of a Multi-Agent Simulation System with Normative Agents8.2 System Architecture8.3 EMIL-S8.4 Overview of the cognitive and normative architecture of EMIL-A8.5 Correspondence between EMIL-S and EMIL-A8.6 Differences between the cognitive and the implemented model8.7 Additional assumptions about cognitive processes used in EMIL-SReferences9. Demonstrating the Theory: The case of Wikipedia9.1 Empirical background9.2 The Case: Wikipedia9.2.1 Social Self-Regulation in Wikipedia9.2.2 Methodology9.2.3 Results9.2.4 Discussion, Conclusions and Ideas for Further Empirical Research9.3 Designing the Wikipedia Simulation9.4 Simulation runs and results9.5 Conclusion: Comparison between the NetLogo prototype and the EMIL-S/Repast versionReferences10. The Role of Norm Internalizers in Mixed Populations10.1 Introduction10.2 Related Work10.3 A multi-step and flexible model of norm internalization10.4 Factors affecting internalization10.5 Internalizer: the EMIL-I-A architecture10.6 Simulating a social dilemma10.6.1 Experimental Design10.6.2 Experimental Results10.7. ConclusionsReferences11. Summary and Conclusions11.1 Summary11.2 Conclusions11.2.1 What are norms11.2.2 How norms emerge11.2.3 How much mental complexity is needed11.4 Balance and open questions11.5 References