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## Self-Organized Collective Decision Making: The Weighted Voter Model (2014)

Citations: | 9 - 9 self |

### Citations

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Citation Context ...n. However, the resulting master equations are characterized by nonlinearities that prevent the use of analytical approaches. We use therefore numerical methods, in particular the Gillespie algorithm =-=[13]-=-. Given a swarm of N agents, we define quantities WA and WB as the number of agents in the nest dancing, respectively, for site A and site B. Equivalently, we denote the number of agents surveying sit... |

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Citation Context ...kov chains as done in [14, 15, 31]). To study how finite-size effects influence the weighted voter model, we define a macroscopic mathematical model using the formalism of (chemical) master equations =-=[32]-=-, i.e., by means of stochastic differential equations. As in Section 3, we assume null traveling times and we neglect the influence of agents’ displacement periods. We model the proposed collective de... |

338 |
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Citation Context ...and general model of democratic voting. To consider opinion qualities, we introduce a positive feedback mechanism inspired by the house hunting behavior of honeybee swarms. In the classic voter model =-=[5, 18]-=-, agents are distributed over a static lattice and they interact only with their neighbors. At each round, a randomly picked agent adopts the opinion of a random neighbor. The evolution of the process... |

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Citation Context ...ide a valuable alternative to classical centralized solutions (e.g., robotic swarms [3, 16, 17, 20, 22, 23], wireless sensor networks [19, 27, 28], virtual agents operating in high-dimensional spaces =-=[6, 7, 11, 12]-=-). Artificial selforganized systems achieve high degrees of scalability, flexibility, and robustness by relying on limited perception and communication capabilities (e.g., few and noisy sensors, only ... |

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Citation Context ...tiagent Systems (www.ifaamas.org). All rights reserved. a positive feedback mechanism inspired by the house hunting behavior of honeybee swarms [9, 24, 25, 33]. We apply methods from opinion dynamics =-=[4, 10]-=- to define mathematical models that reliably predict the system behavior as a function of a number of relevant parameters. The key idea is to develop a control algorithm along with a set of models tha... |

197 | Stability Analysis of Swarms,”
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Citation Context ...ide a valuable alternative to classical centralized solutions (e.g., robotic swarms [3, 16, 17, 20, 22, 23], wireless sensor networks [19, 27, 28], virtual agents operating in high-dimensional spaces =-=[6, 7, 11, 12]-=-). Artificial selforganized systems achieve high degrees of scalability, flexibility, and robustness by relying on limited perception and communication capabilities (e.g., few and noisy sensors, only ... |

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Citation Context ...and general model of democratic voting. To consider opinion qualities, we introduce a positive feedback mechanism inspired by the house hunting behavior of honeybee swarms. In the classic voter model =-=[5, 18]-=-, agents are distributed over a static lattice and they interact only with their neighbors. At each round, a randomly picked agent adopts the opinion of a random neighbor. The evolution of the process... |

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Citation Context ...ion behaviors operate in continuous space [17] which defines a continuum of opinions (e.g., at which position to form a cluster) in contrast to the discrete number of alternatives in opinion dynamics =-=[4, 10]-=-. Although it is possible to reduce the opinion continuum of aggregation-based approaches to a discrete set of opinions [15], these reductions are coarse and the modeling of spatial inhomogeneous agen... |

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Citation Context ...inion is associated to the alternative of highest quality. We study self-organized solutions to the best-of-n decision problem within the context of embodied agents, i.e., swarms of autonomous robots =-=[1]-=-. We consider agents acting within a bounded, two-dimensional environment which is divided in a number of regions. In the following, we restrict our study to binary decision problems (n = 2) and we re... |

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Citation Context ...elf-organized systems. Notably, for those systems designed to provide a valuable alternative to classical centralized solutions (e.g., robotic swarms [3, 16, 17, 20, 22, 23], wireless sensor networks =-=[19, 27, 28]-=-, virtual agents operating in high-dimensional spaces [6, 7, 11, 12]). Artificial selforganized systems achieve high degrees of scalability, flexibility, and robustness by relying on limited perceptio... |

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Citation Context ...lem of finding a collective agreement over the most favorable choice among a set of alternatives, namely the best-of-n decision problem, is a general and abstract cognitive challenge for both natural =-=[8, 9, 24, 25, 29, 33]-=- and artificial [2, 20, 22, 26, 31] self-organized systems. In this paper, we focus on artificial systems and we describe a control algorithm that solves the best-of-n decision problem with a self-org... |

30 |
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Citation Context ...lem of finding a collective agreement over the most favorable choice among a set of alternatives, namely the best-of-n decision problem, is a general and abstract cognitive challenge for both natural =-=[8, 9, 24, 25, 29, 33]-=- and artificial [2, 20, 22, 26, 31] self-organized systems. In this paper, we focus on artificial systems and we describe a control algorithm that solves the best-of-n decision problem with a self-org... |

28 |
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Citation Context ...making is a significant challenge for artificial self-organized systems. Notably, for those systems designed to provide a valuable alternative to classical centralized solutions (e.g., robotic swarms =-=[3, 16, 17, 20, 22, 23]-=-, wireless sensor networks [19, 27, 28], virtual agents operating in high-dimensional spaces [6, 7, 11, 12]). Artificial selforganized systems achieve high degrees of scalability, flexibility, and rob... |

24 |
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Citation Context ...over the most favorable choice among a set of alternatives, namely the best-of-n decision problem, is a general and abstract cognitive challenge for both natural [8, 9, 24, 25, 29, 33] and artificial =-=[2, 20, 22, 26, 31]-=- self-organized systems. In this paper, we focus on artificial systems and we describe a control algorithm that solves the best-of-n decision problem with a self-organized approach. Our control algori... |

19 | A class of attractions/repulsion functions for stable swarm aggregations,”
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Citation Context ...ide a valuable alternative to classical centralized solutions (e.g., robotic swarms [3, 16, 17, 20, 22, 23], wireless sensor networks [19, 27, 28], virtual agents operating in high-dimensional spaces =-=[6, 7, 11, 12]-=-). Artificial selforganized systems achieve high degrees of scalability, flexibility, and robustness by relying on limited perception and communication capabilities (e.g., few and noisy sensors, only ... |

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Citation Context ...over the most favorable choice among a set of alternatives, namely the best-of-n decision problem, is a general and abstract cognitive challenge for both natural [8, 9, 24, 25, 29, 33] and artificial =-=[2, 20, 22, 26, 31]-=- self-organized systems. In this paper, we focus on artificial systems and we describe a control algorithm that solves the best-of-n decision problem with a self-organized approach. Our control algori... |

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17 |
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Citation Context ...over the most favorable choice among a set of alternatives, namely the best-of-n decision problem, is a general and abstract cognitive challenge for both natural [8, 9, 24, 25, 29, 33] and artificial =-=[2, 20, 22, 26, 31]-=- self-organized systems. In this paper, we focus on artificial systems and we describe a control algorithm that solves the best-of-n decision problem with a self-organized approach. Our control algori... |

16 | A brief survey of self-organization in wireless sensor networks
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Citation Context ...elf-organized systems. Notably, for those systems designed to provide a valuable alternative to classical centralized solutions (e.g., robotic swarms [3, 16, 17, 20, 22, 23], wireless sensor networks =-=[19, 27, 28]-=-, virtual agents operating in high-dimensional spaces [6, 7, 11, 12]). Artificial selforganized systems achieve high degrees of scalability, flexibility, and robustness by relying on limited perceptio... |

16 |
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Citation Context ...lem of finding a collective agreement over the most favorable choice among a set of alternatives, namely the best-of-n decision problem, is a general and abstract cognitive challenge for both natural =-=[8, 9, 24, 25, 29, 33]-=- and artificial [2, 20, 22, 26, 31] self-organized systems. In this paper, we focus on artificial systems and we describe a control algorithm that solves the best-of-n decision problem with a self-org... |

12 |
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Citation Context ...making is a significant challenge for artificial self-organized systems. Notably, for those systems designed to provide a valuable alternative to classical centralized solutions (e.g., robotic swarms =-=[3, 16, 17, 20, 22, 23]-=-, wireless sensor networks [19, 27, 28], virtual agents operating in high-dimensional spaces [6, 7, 11, 12]). Artificial selforganized systems achieve high degrees of scalability, flexibility, and rob... |

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Citation Context ...making is a significant challenge for artificial self-organized systems. Notably, for those systems designed to provide a valuable alternative to classical centralized solutions (e.g., robotic swarms =-=[3, 16, 17, 20, 22, 23]-=-, wireless sensor networks [19, 27, 28], virtual agents operating in high-dimensional spaces [6, 7, 11, 12]). Artificial selforganized systems achieve high degrees of scalability, flexibility, and rob... |

9 | Towards swarm calculus: Urn models of collective decisions and universal properties of swarm performance.
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Citation Context ...om those predicted in the thermodynamic limit. It is thus important to develop also mathematical models with dependency on the actual size of the swarm (e.g., timehomogeneous Markov chains as done in =-=[14, 15, 31]-=-). To study how finite-size effects influence the weighted voter model, we define a macroscopic mathematical model using the formalism of (chemical) master equations [32], i.e., by means of stochastic... |

8 | Tessone, Finite size effects in the dynamics of opinion formation
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Citation Context ...s deterministically. However, despite selforganized systems are usually composed of a large number of agents, their finite size (N < ∞) often plays a crucial role in their dynamics. Toral and Tessone =-=[30]-=-, for instance, show that for a number of collective decision making systems finite-size effects may produce unexpected dynamics that differ from those predicted in the thermodynamic limit. It is thus... |

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5 | A reductionist approach to hypothesis-catching for the analysis of self-organizing decision- making systems
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Citation Context ...om those predicted in the thermodynamic limit. It is thus important to develop also mathematical models with dependency on the actual size of the swarm (e.g., timehomogeneous Markov chains as done in =-=[14, 15, 31]-=-). To study how finite-size effects influence the weighted voter model, we define a macroscopic mathematical model using the formalism of (chemical) master equations [32], i.e., by means of stochastic... |

2 |
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Citation Context ...r is reduced for larger swarms as higher agent densities reduce the effects of small r. For these finite interaction ranges r, bigger swarms have faster decision processes as found in honeybee swarms =-=[25]-=-. Hence, contrary to the accuracy of the decision, the consensus time is affected by both the size of the swarm and the magnitude of the interaction range. 4.3 Robustness to Noise In real-world applic... |