The Science of Social Emergence

The Science of Social Emergence

R. Keith Sawyer (Washington University in St. Louis, USA)
DOI: 10.4018/978-1-60566-236-7.ch001
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Abstract

Sociology should be the foundational science of social emergence. But to date, sociologists have neglected emergence, and studies of emergence are more common within microeconomics. Moving forward, I argue that a science of social emergence requires two advances beyond current approaches—and that sociology is better positioned than economics to make these advances. First, consistent with existing critiques of microeconomics, I argue that we need a more sophisticated representation of individual agents. Second, I argue that multi-agent models need a more sophisticated representation of interaction processes. The agent communication languages currently used by multi-agent systems researchers are not appropriate for modeling human societies. I conclude by arguing that the scientific study of interaction and emergence will have to migrate out of microeconomics and become a part of sociology. Sociologists, for their part, should embrace multi-agent modeling to pursue a more rigorous study of these traditional sociological issues.
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Introduction

Social emergence is the central phenomenon of the social sciences. The science of social emergence is the basic science underlying all of the social sciences, because social emergence is foundational to all of them. Political science, economics, education, history, and sociology study phenomena that socially emerge from complex systems of individuals in interaction. In this chapter, I argue that sociology should become the basic science of social emergence, and I outline a theoretical framework to guide this study.

But this is not the sociology we see today; few sociologists study social emergence. In the second half of the twentieth century, economics has made the best case for being the foundational social science, by making social emergence central to its theory and practice. Perhaps the most important strength of the neoclassical economic approach is that it has rigorous formalisms for modeling the ways that individual action generates aggregate outcomes at the level of an entire population (Bowles, 2001; Durlauf & Young, 2001). Because social emergence is the central phenomenon of the social sciences, and economics has developed the most successful model of social emergence, this has naturally led to “economic imperialism,” with neoclassical economists beginning to analyze non-economic phenomena traditionally associated with sociology (Boulding, 1969, p. 8; Hirshleifer, 1985; Radnitzky & Bernholz, 1987; Tullock, 1972). These imperialists argue that economics is “the universal grammar of social science” (Hirshleifer, 1985, p. 53), and that it simply represents “straight thinking” applied to social science (Radnitzky, 1992, p. 15). And, in fact, microeconomics has been the only game in town for those interested in studying social emergence.

However, there are many problems with the models of social emergence dominant in microeconomics. Critics such as the “New Economic Sociologists” (see Krier, 1999; Zafirowski, 1999) claim that the microeconomic account of social emergence is empirically unfounded, methodologically individualist, neglects the social embeddedness of actors, neglects the importance of institutions and social networks, and neglects the unavoidable inefficiencies introduced by institutions, power, and path dependence. I focus on two specific critiques in this chapter. The first one is well known: many critics of microeconomics have called for a more sophisticated representation of the individual agents. Some agent models have begun to develop more accurate agent representations by drawing on the field of cognitive psychology, and occasionally on sociological theories of agency.

My second critique is less widely acknowledged: I argue that microeconomics radically simplifies important elements of social emergence—particularly, the key role played by symbolic interaction. Microeconomics uses formalisms that impose a simplistic representation of individual agents, and a simplistic representation of agent interaction. Some microeconomists have begun to use multi agent system models, but when they do, they tend to reproduce the overly simplistic models of agents and agent interaction associated with the optimizing mathematics of rational choice. Multi-agent models, whether developed by economists or by sociologists, need a more sophisticated representation of interaction processes. The most sophisticated of these are modeled using what is called an Agent Communication Language (ACL), but the ACLs developed to date in the MAS research community are not appropriate for modeling human societies. Social modelers can develop better representations of interaction by drawing on the science of microinteraction within sociology. I have done several empirical studies of emergence in conversation, and I have shown that different communication mechanisms change the processes of social emergence (e.g. Sawyer, 2003b). This leads to a second critique of rational choice models: such models of social emergence have a radically simplified account of human interaction.

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Table of Contents
Foreword
Georgi Stojanov
Chapter 1
R. Keith Sawyer
Sociology should be the foundational science of social emergence. But to date, sociologists have neglected emergence, and studies of emergence are... Sample PDF
The Science of Social Emergence
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Chapter 2
Christopher Goldspink, Robert Kay
This chapter critically examines our theoretical understanding of the dialectical relationship between emergent social structures and agent... Sample PDF
Agent Cognitive Capabilities and Orders of Social Emergence
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Chapter 3
Joseph C. Bullington
Social interaction represents a powerful new locus of research in the quest to build more truly human-like artificial agents. The work in this area... Sample PDF
Agents and Social Interaction: Insights from Social Psychology
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Chapter 4
M. Afzal Upal
This chapter will critically review existing approaches to the modeling transmission of cultural information and advocate a new approach based on a... Sample PDF
Predictive Models of Cultural Information Transmission
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Chapter 5
Jorge A. Romero
Despite the popularity of agents for the information technology infrastructure, questions remain because it is not clear what do e-business agents... Sample PDF
Interaction of Agent in E-Business: A Look at Different Sources
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Chapter 6
Adam J. Conover
This chapter presents a description of ongoing experimental research into the emergent properties of multi-agent communication in “temporally... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Passive Inquiry
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Chapter 7
Richard Schilling
This chapter presents a generalized messaging infrastructure that can be used for distributed agent systems. The principle of agent feedback... Sample PDF
Agent Feedback Messaging: A Messaging Infrastructure for Distributed Message Delivery
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Chapter 8
Yu Zhang, Mark Lewis, Christine Drennon, Michael Pellon, Coleman
Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at... Sample PDF
Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics
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Chapter 9
Scott Watson, Kerstin Dautenhahn, Wan Ching (Steve) Ho, Rafal Dawidowicz
This chapter discusses certain issues in the development of Virtual Learning Environments (VLEs) populated by autonomous social agents, with... Sample PDF
Developing Relationships Between Autonomous Agents: Promoting Pro-Social Behaviour Through Virtual Learning Environments Part I
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Chapter 10
Martin Takác
In this chapter, we focus on the issue of understanding in various types of agents. Our main goal is to build up notions of meanings and... Sample PDF
Construction of Meanings in Biological and Artificial Agents
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Chapter 11
Myriam Abramson
In heterogeneous multi-agent systems, where human and non-human agents coexist, intelligent proxy agents can help smooth out fundamental... Sample PDF
Training Coordination Proxy Agents Using Reinforcement Learning
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Chapter 12
Deborah V. Duong
The first intelligent agent social model, in 1991, used tags with emergent meaning to simulate the emergence of institutions based on the principles... Sample PDF
The Generative Power of Signs: The Importance of the Autonomous Perception of Tags to the Strong Emergence of Institutions
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Chapter 13
Josefina Sierra, Josefina Santibáñez
This chapter addresses the problem of the acquisition of the syntax of propositional logic. An approach based on general purpose cognitive... Sample PDF
Propositional Logic Syntax Acquisition Using Induction and Self-Organisation
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Chapter 14
Giovanni Vincenti, James Braman
Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial... Sample PDF
Hybrid Emotionally Aware Mediated Multiagency
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Chapter 15
Samuel G. Collins, Goran Trajkovski
In this chapter, we give an overview of the results of a Human-Robot Interaction experiment, in a near zerocontext environment. We stimulate the... Sample PDF
Mapping Hybrid Agencies Through Multiagent Systems
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Chapter 16
Scott Watson, Kerstin Dautenhahn, Wan Ching (Steve) Ho, Rafal Dawidowicz
This chapter is a continuation from Part I, which has described contemporary psychological descriptions of bullying in primary schools and two... Sample PDF
Developing Relationships Between Autonomous Agents: Promoting Pro-Social Behaviour Through Virtual Learning Environments Part II
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Chapter 17
Mario Paolucci, Rosaria Conte
This chapter is focused on social reputation as a fundamental mechanism in the diffusion and possibly evolution of socially desirable behaviour... Sample PDF
Reputation: Social Transmission for Partner Selection
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Chapter 18
Adam J. Conover
This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous”... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Belief Promulgation
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Chapter 19
David B. Newlin
Following the discovery in Rhesus monkeys of “mirror neurons” that fire during both execution and observation of motor behavior, human studies have... Sample PDF
The Human Mirror Neuron System
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Chapter 20
Eric Baumer, Bill Tomlinson
This chapter presents an argument that the process of emergence is the converse of the process of abstraction. Emergence involves complex behavior... Sample PDF
Relationships Between the Processes of Emergence and Abstraction in Societies
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Chapter 21
Vern R. Walker
In modern legal systems, a large number of autonomous agents can achieve reasonably fair and accurate decisions in tens of thousands of legal cases.... Sample PDF
Emergent Reasoning Structures in Law
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Chapter 22
Theodor Richardson
Network Intrusion Detection Systems (NIDS) are designed to differentiate malicious traffic, from normal traf- fic, on a network system to detect the... Sample PDF
Agents in Security: A Look at the Use of Agents in Host-Based Monitoring and Protection and Network Intrusion Detection
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Chapter 23
Michael J. North, Thomas R. Howe, Nick Collier, Eric Tatara, Jonathan Ozik, Charles Macal
Search has been recognized as an important technology for a wide range of software applications. Agentbased modelers often face search challenges... Sample PDF
Search as a Tool for Emergence
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About the Contributors