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Mob Control: Models of Threshold Collective Behavior

verfasst von: Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Verlag: Springer International Publishing

Buchreihe : Studies in Systems, Decision and Control

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book presents mathematical models of mob control with threshold (conformity) collective decision-making of the agents. Based on the results of analysis of the interconnection between the micro- and macromodels of active network structures, it considers the static (deterministic, stochastic and game-theoretic) and dynamic (discrete- and continuous-time) models of mob control, and highlights models of informational confrontation. Many of the results are applicable not only to mob control problems, but also to control problems arising in social groups, online social networks, etc. Aimed at researchers and practitioners, it is also a valuable resource for undergraduate and postgraduate students as well as doctoral candidates specializing in the field of collective behavior modeling.

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Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Mob is understood below as an active or aggressive gathering of people, i.e. an active or aggressive group, crowd, etc. In scientific literature, mob control has several stable and widespread interpretations.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 2. Models of Threshold Collective Behavior

Abstract

Consider the following model of an active network structure that includes several interacting agents. Each agent chooses between two decisions resulting in one of two admissible states, namely, “1” (active, the excited state) or “0” (passive, the normal or unexcited state). For instance, possible examples are a social network [48] or a mob [23], where the active state means participation in mass riots.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 3. Micro- and Macromodels

Abstract

This section considers two approaches to the design and analysis of ANSs, namely, macro- and microdescriptions [13, 24]. According to the former approach, the structure of relations in a network is averaged, and agents’ behavior is studied “in the mean”.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 4. Deterministic Models of Mob Control

Abstract

This section studies a threshold behavior model for a group of agents. Making binary decisions (choosing between active or passive states), agents take into account the choice of other members of the group. Control problems for thresholds and agents’ reputation are stated and solved in order to minimize the number of agents choosing “to be active” [23].

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 5. Stochastic Models of Mob Control

Abstract

This section explores the following model of agents’ threshold behavior. Making binary decisions (choosing between “activity” and “passivity”), the agents consider the choice of other members in a group. We formulate and solve an associated control problem, i.e., the random choice problem for the initial states of some agents in order to vary the number of agents preferring “activity” in an equilibrium [25].

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 6. Game-Theoretic Models of Mob Control

Abstract

This paper studies models of centralized, decentralized and distributed control of excitation in a network of interacting purposeful agents [73].

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 7. Dynamic Models of Mob Control in Discrete Time

Abstract

Written jointly with I.N. Barabanov, this section formulates and solves the mob excitation control problem in the discrete-time setting by introducing an appropriate number of “provokers” at each step of control [9].

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 8. Dynamic Models of Mob Control in Continuous Time

Abstract

This section written jointly with I.N. Barabanov is dedicated to the continuous-time models of mob control [10]. We formulate and solve the mob excitation control problem in the continuous-time setting by introducing an appropriate number of “provokers” at each moment of control.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 9. Micromodels of Informational Confrontation

Abstract

To characterize the informational confrontation problems solved at level 5 of the ANS description (see the Introduction), one needs simple results in the fields of informational interaction analysis and informational control of the agents.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 10. Macromodels of Informational Confrontation

Abstract

Within the stochastic models of mob control (see Chap. 5), we explore the game-theoretic models of informational confrontation when the agents are simultaneously controlled by two subjects with noncoinciding interests regarding the number of active agents in an equilibrium state. And the ANS is described by the macromodel with the proportion of active agents as the main parameter.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Chapter 11. Models of Mob Self-excitation

Abstract

In the previous sections of this book, the proportion of active agents has evolved according to the difference or differential equations of type (2.7), (3.12), (7.1), (8.1), etc., with the distribution function of the agents’ thresholds in their right-hand sides.

Vladimir V. Breer, Dmitry A. Novikov, Andrey D. Rogatkin

Backmatter

Titel: Mob Control: Models of Threshold Collective Behavior
verfasst von: Vladimir V. Breer
Dmitry A. Novikov
Andrey D. Rogatkin
Copyright-Jahr: 2017
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-51865-7
Print ISBN: 978-3-319-51864-0
DOI: https://doi.org/10.1007/978-3-319-51865-7

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