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

208 Pages·2013·0.67 MB·English

by  Rosaria Conte

Minding Norms
OXFORD SERIES ON
COGNITIVE MODELS AND ARCHITECTURES
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Frank E. Ritter
Series Board
Rich Carlson
Gary Cottrell
Robert L. Goldstone
Eva Hudlicka
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Robert St. Amant
Richard M. Young
Integrated Models of Cognitive Systems
Edited by Wayne D. Gray
In Order to Learn:
How the Sequence of Topics Infl uences Learning
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Erno Lehtinen, and Timothy O’Shea
How Can the Human Mind Occur
in the Physical Universe?
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Principles of Synthetic Intelligence PSI:
An Architecture of Motivated Cognition
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The Multitasking Mind
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How to Build a Brain:
A Neural Architecture for Biological Cognition
By Chris Eliasmith
Minding Norms: Mechanisms and Dynamics of Social Order
in Agent Societies
Edited by Rosaria Conte, Giulia Andrighetto, and Marco Campennì
Minding Norms
Mechanisms and Dynamics of Social
Order in Agent Societies
Edited by
Rosaria Conte
Giulia Andrighetto
Marco Campennì
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Library of Congress Cataloging-in-Publication Data
Minding norms : mechanisms and dynamics of social order in
agent societies / edited by Rosaria Conte, Giulia Andrighetto, Marco Campennì.
pages cm
Includes bibliographical references and index.
ISBN 978–0–19–981267–7
1. Social norms. I. Conte, Rosaria, 1952– II. Andrighetto, Giulia, 1979–
III. Campennì, Marco.
HM676.M56 2014
306—dc23
2013014487
9 8 7 6 5 4 3 2 1
Printed in the United States of America
on acid-free paper
Contents
Foreword ix
Contributors xi
1. Introduction 1
1.1 Why a new book on norms? 1
1.2 Why a book on cognition? 5
1.3 Our perspective and approach 8
1.4 Presentation of the volume and questions addressed 10
1.5 How to read the book 11
1.6 Acknowledgements 12
2. Loops in Social Dynamics 17
2.1 Introduction 17
2.2 The way up: Emergence 18
2.3 The way back: Downward causation 19
2.3.1 Simple loop 20
2.3.2 Complex loop Incorporation 22
2.4 Advantages of the present approach 28
2.5 Concluding remarks 29
3. Agent-Based Social Simulation and Its Necessity for
Understanding Socially Embedded Phenomena 34
3.1 Cognitive simulation modeling 34
3.2 Agent-based architectures and frameworks 35
v
vi Contents
3.3 The social intelligence hypothesis 36
3.4 Social embeddedness 37
3.5 Micro-macro complexity 38
3.6 Types of social simulation 39
3.7 Linking plausible theory and observed evidence 44
3.8 Relevance vs. generality in simulation 46
3.9 Emergence and immergence in simulations 46
3.10 Conclusion 47
4. How Are Norms Brought About? A State of the
Art of Current Research 50
4.1 Norms between conventions and legal norms 50
4.2 The game-theoretical framework of simulating norms 52
4.2.1 Simulation models 53
4.2.2 Analysis 55
4.3 The cognitive method of modelling norms 56
4.3.1 Analysis 58
4.4 Norms in current architectures 59
4.4.1 Normative modules 60
4.4.2 Norm conflicts 60
4.4.3 Concepts of norms 61
4.4.4 Drawbacks of cognitive architectures 62
4.5 Results and unresolved questions 62
5. Modeling Hume’s Theory of Norm Emergence 68
5.1 Introduction and motivation 68
5.2 Interaction structure and specialization 69
5.3 The structure: Local groups and a central market 72
5.4 Matching agents 73
5.5 Learning 74
5.6 A benchmark: Wealth under universal trust
and trustworthiness 75
5.7 The evolution of trust and division of labor—some first
simulation studies 76
Contents vii
6. Norms’ Dynamics as a Complex Loop 81
6.1 Normative prescriptions 81
6.2 The missing link in the formal treatment of
obligations 82
6.3 The mental dynamics of norms 83
6.3.1 Norm recognition 84
6.3.2 Norm adoption 87
6.3.3 Norm compliance 90
6.4 Concluding remarks 91
7. Hunting for Norms in Unpredictable Societies 94
7.1 Introduction 94
7.2 Related work 95
7.3 The Norm Recognition Module 96
7.4 Norm Detectives vs. Social Conformers 100
7.4.1 Results of comparison 102
7.5 Norm Detectives in a segregated world 106
7.5.1 Effects of segregation 107
7.6 Concluding remarks 114
8. The Derivation of EMIL-S from EMIL-A: From Cognitive
Architecture to Software Architecture 118
8.1 General requirements of a multi-agent simulation
system with normative agents 118
8.2 System architecture 118
8.3 EMIL-S 120
8.4 Overview of the cognitive and normative architecture
of EMIL-A 121
8.5 Correspondence between EMIL-S and EMIL-A 123
8.6 Differences between the cognitive and the
implemented models 124
8.7 Additional assumptions about cognitive processes used
in EMIL-S 124
viii Contents
9. Demonstrating the Theory: The Case of Wikipedia 127
9.1 Empirical background 127
9.2 The case: Wikipedia 128
9.2.1 Social self-regulation in Wikipedia 128
9.2.2 Methodology 129
9.2.3 Results 132
9.2.4 Discussion, conclusions and ideas for
further empirical research 138
9.3 Designing the Wikipedia simulation 140
9.4 Simulation runs and results 142
9.5 Conclusion: Comparison between the NetLogo
prototype and the EMIL-S/Repast version 149
10. The Role of Norm Internalizers in Mixed Populations 153
10.1 Introduction 153
10.2 Related work 155
10.3 A multi-step and flexible model of norm internalization 157
10.4 Factors affecting internalization 159
10.5 Internalizer: the EMIL-I-A architecture 162
10.6 Simulating a social dilemma 166
10.6.1 Experimental design 167
10.6.2 Experimental results 168
10.7 Conclusions 170
11. Summary and Conclusions 175
11.1 Summary 176
11.2 Conclusions 177
11.2.1 What are norms? 177
11.2.2 How do norms emerge? 178
11.2.3 How much mental complexity is needed? 178
11.3 Balance and open questions 179
Index 181
Foreword
Our behavior is influenced all the time by social norms, what is expected
(i.e., customs), what is required (i.e., laws), and what is moral (i.e., what is
ethical). Other names for norms are standard operating procedures, regu-
lations, standing orders, rules of engagement, and customs. All of the data
that have been collected to develop and test theories of cognition has
been collected where norms actively influenced the behavior observed.
However, the effect of norms, how other agents’ expectations and behav-
ior influence behavior, has not been very often addressed by cognitive
models, architectures, or agent-based research.
As cognitive models become more social and social models become
more cognitive, the role of norms in modeling behavior will become
increasingly important. Cognitive modelers who read this book by Conte
and her colleagues can learn about norms and how to include them
in models and architectures. Researchers who study norms can better
understand the role of simulation and norms.
This book presents a theory of how norms, arising through interactions
between individuals, are implemented in and by those individuals. A series
of simulations using a new architecture, EMIL, help explain how bottom-
up and top-down processes can produce emergent behavior, and provide
lessons for work on norms (e.g., how norms can arise through small inter-
actions), and for other architectures (e.g., the many ways norms influence
cognition). Through a thoughtful series of models, capabilities are added
building up to a system demonstrating necessary and sufficient capabili-
ties for the emergence and function of norms. While the models are not
complete and may at times appear far from complete—for some even too
ix