Relationships Between the Processes of Emergence and Abstraction in Societies

Relationships Between the Processes of Emergence and Abstraction in Societies

Eric Baumer (University of California-Irvine, USA) and Bill Tomlinson (University of California, Irvine, USA)
DOI: 10.4018/978-1-60566-236-7.ch020
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This chapter presents an argument that the process of emergence is the converse of the process of abstraction. Emergence involves complex behavior resulting from simple rules, while abstraction forming simple rules that describe complex behavior. This converse relationship suggests the possibility that similar mechanisms underlie both processes, and a greater understanding of one can lead to a greater understanding of the other. Especially in the case of human and artificial social systems, the processes of abstraction and emergence are inextricably interconnected; the abstractions that individuals make will determine what behaviors emerge, and the behaviors that emerge in the society determine what abstractions will be made. This relationship between the two processes, which we call the abstraction-emergence loop, can be used to gain a better understanding of both. It is argued that the abstraction-emergence loop functions over various degrees of complexity and levels of detail, and that the loop has the greatest efficacy in certain ranges of detail. This way of understanding the two processes has particular bearing on social interactions; in order to understand macro-level emergent social phenomena, we must also simultaneously understand the micro-level phenomena from which they arise. In considering when emergence occurs, the role of the observer in the emergence abstraction loop is also discussed. In addition to describing various properties of the abstraction-emergence loop, this chapter presents descriptions of several ongoing and future research projects in the creation of autonomous agent societies, and offers pointers to future research directions aimed at exploring and understanding the nature of the abstraction-emergence loop. Such an understanding of the relationship between abstraction and emergence can be helpful in designing communities of autonomous agents that interact socially with each other and with humans, and may also be a helpful step toward understanding the phenomena of emergence and abstraction in general.
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A number of different disciplines have taken on the task of studying emergent phenomena, trying to understand how and why they emerge, and delineating what makes emergent phenomena different from other phenomena exhibited by complex systems. Within computer science, much of this work has fallen under the auspices of artificial life (ALife). This subfield focuses on creating computer programs and simulations that exhibit qualities we otherwise attribute to living things, such as the ability to reproduce. A common environment for such work is cellular automata (CA), a grid where each cell on the grid is in a certain state at each tick of a system clock, and each cell’s state at the next iteration is determined according to a set of rules that refer to its neighbor’s states in the current clock tick (see (Sarkar 2000) for a survey). One of the earliest examples of this is von Neumann’s self-replicating machine (von Neumann 1966), the goal of which was to create a theoretical machine capable of universal computation. This CA has the ability to produce any other cellular automaton if given a description in the proper format of the automaton to be produced. If the automaton is given a description of itself, it is thus able to reproduce itself. A reproducing CA was also developed by Christopher Langton (1984), whose goal was not to create a CA capable of universal computation, but rather the simplest possible CA still capable of self-replication. These automata’s capacity for reproduction is a well-known example of emergence, in that the high-level phenomenon of reproduction emerging from the low-level rules of the system, where none of the low-level rules explicitly describe the process of reproduction.

Another classic example from ALife is the cellular automaton known as the Game of Life, first developed by John Conway (Gardner 1970). The cells in this relatively simple CA have only 2 states, which are called alive and dead. A cell’s state at the next iteration is given by three simple rules. Any cell with one or zero live neighbors is dead. Any cell with two or three live neighbors is alive. Any cell with four or more neighbors is dead. From these relatively simple rules, vastly complex patterns emerge. One of the better know is that of the glider (Figure 1), a patter which moves one cell down and one cell to the right every four iterations (the direction of this movement depends on the orientation of the glider pattern). The high-level behavior of a unified pattern moving is not actually built into the system. Indeed, the automaton has no representation of this glider pattern, only the representation of the states of its cells. Rather, the behavior emerges from the interactions between individual cells in the system based on the rules that govern it.

Figure 1.

A glider from Conway’s Game of Life

These are a few examples of the types of emergence described in ALife. A system based on fairly simple, low-level rules exhibits some high-level behavior not directly or explicitly built into the system; the high-level behavior emerges from the low-level interactions within the system. It is important to note here that predictability has little to do with whether or not a phenomenon is emergent. As has been noted by Damper (Damper 2000), the property of self-replication exhibited by von Neumann’s machine is not only predictable, it was in fact designed into the machine. This does not mean, however, that the property is not emergent. It is still emergent, because the high-level phenomenon of self-replication occurs as a result of interactions between low-level rules that do not explicitly describe the property of self-replication. It can be seen here that as the system exhibits emergent properties, an observer must be present to observe those properties and note that they are indeed emergent. The importance of level of detail and the role of the observer in emergence will be addressed later in this chapter.

Complete Chapter List

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List of Reviewers
Table of Contents
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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