Optimizing Society: The Social Impact Theory Based Optimizer

Optimizing Society: The Social Impact Theory Based Optimizer

Martin Macaš (Czech Technical University in Prague, Czech Republic) and Lenka Lhotská (Czech Technical University in Prague, Czech Republic)
DOI: 10.4018/978-1-60566-310-4.ch021
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Abstract

A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of social interactions. The algorithm is based on society of individuals. Each individual holds a set of its attitudes, which encodes a candidate solution of a binary optimization problem. Individuals change their attitudes according to their spatial neighbors and their fitness, which leads to convergence to local (or global) optimum. This chapter also tries to demonstrate different aspects of the SITO’s behavior and to give some suggestions for potential user. Further, a comparison to similar techniques – genetic algorithm and binary particle swarm optimizer – is discussed and some possibilities of formal analysis are briefly presented.
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Background

The connections between social and natural sciences have mostly laid in the use of natural sciences for a formal description of social phenomena. A particular case is the use of physical and mathematical models of society and social interaction, whose historical roots date back to 17th century (Ball, 2002).

The main source of inspiration for the SITO development comes from the area of models of social interactions. One of characteristics shared by these models is the presence of many, more or less simple, individuals representing the participants of the social processes. These individuals, sometimes called agents, form an artificial society. They are very often situated in an environment which could be defined as a medium separate from the agents, on which the agents operate and with which they interact (Epstein, 1996). There is wide variety of such models differing in their purpose or structure of agents and environment.

Epstein and Axtell describe a number of experiments with a virtual ecosystem (Epstein, 1996). The computer simulation techniques are presented, which show how social structure and group behaviors arise from simple local interactions of simple individuals. They follow a particular instance of the artificial society concept that has come to be known as “The Sugarscape Model”. Actually, it is a two-dimensional grid on which agents interact and move on the basis of agent’s rules. Such model of artificial society is an example of analysis study, which could help the social scientists to model and explore the behavior of a society from the bottom-up point of view. However the sugarscape model is not the first agent-based computer model of social interactions.

The first computer simulation of social interaction was the checkerboard model introduced by Sakoda (Sakoda, 1971). The checkerboard represented an environment (a square lattice) on which two groups of individuals (checkers) are situated. The individuals have different attitudes to members of their own group and different attitudes to members of the other group. The individuals are moving on the board on basis of positive, neutral or negative attitudes toward one another. The model has been capable of demonstrating the intimate connection between attitudes of group members toward their own group and toward others to a social interactional process and to the resulting social structure. The resulting social structure is a consequence of local interactions defined by simple attitude combinations. Another model, very similar to the Sakoda’s one, is the Schelling’s model of segregation (Schelling, 1969). The two types of individuals prefer that at least some fraction of their neighbors is of their own group. If this condition is not met, the individuals move to the nearest site where it is. The results explain and describe the emergence of segregation.

Key Terms in this Chapter

Social Impact Theory: Formulates a mathematical model concerning how social processes operate at a given point in time. It specifies principles how individuals are affected by the society.

Social Impact: Any of a great variety of changes in physiological states and subjective feelings, motives and emotions, cognitions and beliefs, values and behavior, that occur in an individual, human or animal, as a result of the real, implied, or imagined presence or actions of other individuals.

Social Psychology: Discipline dealing with formation and changes of human’s mentation and personality under the influence of social stimulation. It addresses influence of groups and societies on individual.

Statistical Physics: Deals with general laws of macroscopic systems compound of a large number of particles. A macro-process does not depend on the particular types of the particles, but depends on mean numbers of these particles. In statistical physics, the too complex problem of solving the equations of motion is solved using calculus of probabilities and mathematical statistics.

Neighborhood: The set of cells that are considered to be sources of social influence. The social impact is computed purely from the neighboring cells. In this chapter, von Neumann and Moore neighborhood is considered. The more general neighborhood types can be time dependent or random.

Social Impact Theory Based Optimizer: A binary optimization technique inspired by psychological phenomena, namely the processes of social influence.

Topology (Social Topology): Describes the spatial structure of the society. It can be understood as a non-oriented graph structure, where nodes are the cells (on which the individuals are situated) and the edges symbolize the relation of neighborhood.

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Table of Contents
Foreword
Lipo Wang
Preface
Hongwei Mo
Chapter 1
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Chapter 2
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Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
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Chapter 3
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Chapter 4
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Chapter 5
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Applications of Artificial Immune Systems in Agents
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Chapter 6
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Chapter 7
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Chapter 8
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Chapter 9
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Chapter 10
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Chapter 11
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A Static Web Immune System and Its Robustness Analysis
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Chapter 12
Alexander O. Tarakanov
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Chapter 13
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Chapter 14
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Chapter 15
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Chapter 16
Mark Burgin, Eugene Eberbach
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Chapter 17
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Chapter 18
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Chapter 19
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Chapter 20
Bo-Suk Yang
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Chapter 21
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Chapter 22
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Chapter 24
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