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Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior

In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if...

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Autores principales: Kochemazov, Stepan, Semenov, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272282/
https://www.ncbi.nlm.nih.gov/pubmed/25526612
http://dx.doi.org/10.1371/journal.pone.0115156
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author Kochemazov, Stepan
Semenov, Alexander
author_facet Kochemazov, Stepan
Semenov, Alexander
author_sort Kochemazov, Stepan
collection PubMed
description In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set [Image: see text] (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where the majority of simple agents are inactive to a state with the majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with the majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained.
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spelling pubmed-42722822014-12-26 Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior Kochemazov, Stepan Semenov, Alexander PLoS One Research Article In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set [Image: see text] (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where the majority of simple agents are inactive to a state with the majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with the majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained. Public Library of Science 2014-12-19 /pmc/articles/PMC4272282/ /pubmed/25526612 http://dx.doi.org/10.1371/journal.pone.0115156 Text en © 2014 Kochemazov, Semenov http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kochemazov, Stepan
Semenov, Alexander
Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior
title Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior
title_full Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior
title_fullStr Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior
title_full_unstemmed Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior
title_short Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior
title_sort using synchronous boolean networks to model several phenomena of collective behavior
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272282/
https://www.ncbi.nlm.nih.gov/pubmed/25526612
http://dx.doi.org/10.1371/journal.pone.0115156
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