Opinion Dynamics over a Finite Set in Cooperative Multi-robot Systems: An Asynchronous Gossip-Based Consensus Approach

2020 ◽  
Author(s):  
Feres A. Salem ◽  
Renan Da S. Tchilian ◽  
Sidney R. D. Carvalho ◽  
Ubirajara F. Moreno
Keyword(s):  
Laboratory Experiments ◽  
Opinion Dynamics ◽  
Consensus Algorithm ◽  
Eigenvector Centrality ◽  
Consensus Approach ◽  
Robot Systems ◽  
Finite Set ◽  
Initial Opinion ◽  
Definition Of ◽  
Centrality Score

The focus of this paper is to present an algorithm that allows robotic teams to make decisions between a finite set of choices. The approach used was based on models that represent the way groups of humans evolve their opinions through time. Numerous works have explored models that consider the opinion as continuous values, while the literature less frequently considers groups trying to reach an agreement when only a finite set of possible opinions is given. The main contribution of this paper is to present a consensus algorithm that can be applied in those scenarios. For this purpose, it is briefly reviewed some crucial concepts for the definition of the proposed algorithm, which is based on asynchronous gossip. Due to the stochasticity of this approach, it is not possible to precisely predict the behavior of the network. However, the results from both computational and laboratory experiments indicate the eigenvector centrality score as a valuable metric to predict the probability of an initial opinion to become the prevailing one for the group when they reach consensus. Also, the asynchrony of the proposed algorithm made it possible to reach consensus in scenarios where synchronous approaches could not.

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

Towards a computational theory of social groups: A finite set of cognitive primitives for representing any and all social groups in the context of conflict

2021 ◽  
pp. 1-62
Author(s):  
David Pietraszewski
Keyword(s):  
Social Group ◽  
Group Representation ◽  
Social Groups ◽  
Study Groups ◽  
Human Mind ◽  
Computational Theory ◽  
Mistaken Belief ◽  
Finite Set ◽  
The One ◽  
Definition Of

Abstract We don't yet have adequate theories of what the human mind is representing when it represents a social group. Worse still, many people think we do. This mistaken belief is a consequence of the state of play: Until now, researchers have relied on their own intuitions to link up the concept social group on the one hand, and the results of particular studies or models on the other. While necessary, this reliance on intuition has been purchased at considerable cost. When looked at soberly, existing theories of social groups are either (i) literal, but not remotely adequate (such as models built atop economic games), or (ii) simply metaphorical (typically a subsumption or containment metaphor). Intuition is filling in the gaps of an explicit theory. This paper presents a computational theory of what, literally, a group representation is in the context of conflict: it is the assignment of agents to specific roles within a small number of triadic interaction types. This “mental definition” of a group paves the way for a computational theory of social groups—in that it provides a theory of what exactly the information-processing problem of representing and reasoning about a group is. For psychologists, this paper offers a different way to conceptualize and study groups, and suggests that a non-tautological definition of a social group is possible. For cognitive scientists, this paper provides a computational benchmark against which natural and artificial intelligences can be held.

Fuzzy Measures: A solution to deal with community detection problems for networks with additional information

2020 ◽  
Vol 39 (5) ◽  
pp. 6217-6230
Author(s):  
Inmaculada Gutiérrez ◽  
Daniel Gómez ◽  
Javier Castro ◽  
Rosa Espínola
Keyword(s):  
Community Detection ◽  
Weighted Graph ◽  
Fuzzy Relation ◽  
Fuzzy Measure ◽  
Fuzzy Measures ◽  
Additional Information ◽  
Fuzzy Graphs ◽  
Finite Set ◽  
Definition Of ◽  
Community Detection Problems

In this work we introduce the notion of the weighted graph associated with a fuzzy measure. Having a finite set of elements between which there exists an affinity fuzzy relation, we propose the definition of a group based on that affinity fuzzy relation between the individuals. Then, we propose an algorithm based on the Louvain’s method to deal with community detection problems with additional information independent of the graph. We also provide a particular method to solve community detection problems over extended fuzzy graphs. Finally, we test the performance of our proposal by means of some detailed computational tests calculated in several benchmark models.

scholarly journals Cladistic hypotheses as degree of equivalence relational structures: implications for three-item statements

2021 ◽  
Author(s):  
Valentin Rineau ◽  
Stéphane Prin
Keyword(s):  
Phylogenetic Trees ◽  
Phylogenetic Reconstruction ◽  
Equivalence Relations ◽  
Strong Connection ◽  
Relational Structures ◽  
Degree Of Equivalence ◽  
Axiomatic Definition ◽  
Finite Set ◽  
Definition Of ◽  
Supertree Methods

AbstractThree-item statements, as minimal informative rooted binary phylogenetic trees on three items, are the minimal units of cladistic information. Their importance for phylogenetic reconstruction, consensus and supertree methods relies on both (i) the fact that any cladistic tree can always be decomposed into a set of three-item statements, and (ii) the possibility, at least under some conditions, to build a new cladistic tree by combining all or part of the three-item statements deduced from several prior cladistic trees. In order to formalise such procedures, several k-adic rules of inference, i.e., rules that allow us to deduce at least one new three-item statement from exactly k other ones, have been identified. However, no axiomatic background has been proposed, and it remains unknown if a particular k-adic rule of inference can be reduced to more basic rules. In order to solve this problem, we propose here to define three-item statements in terms of degree of equivalence relations. Given both the axiomatic definition of the latter and their strong connection to hierarchical classifications, we establish a list of the most basic properties for three-item statements. With such an approach, we show that it is possible to combine five three-item statements from basic rules although they are not combinable only from dyadic rules. Such a result suggests that all higher k-adic rules are well reducible to a finite set of simpler rules.

Parametric definition of the influence of a paper in a citation network using communicability functions

2019 ◽  
Vol 7 (4) ◽  
pp. 623-640 ◽  
Author(s):  
Juan A Pichardo-Corpus ◽  
J Guillermo Contreras ◽  
José A de la Peña
Keyword(s):  
Citation Network ◽  
Function Parameter ◽  
Test Case ◽  
Eigenvector Centrality ◽  
Pagerank Algorithm ◽  
Definition Of ◽  
Number Of Citations ◽  
Intermediate Value ◽  
Flow Of Information ◽  
Communicability Function

Abstract Communicability functions quantify the flow of information between two nodes of a network. In this work, we use them to explore the concept of the influence of a paper in a citation network. These functions depend on a parameter. By varying the parameter in a continuous way we explore different definitions of influence. We study six citation networks, three from physics and three from computer science. As a benchmark, we compare our results against two frequently used measures: the number of citations of a paper and the PageRank algorithm. We show that the ranking of the articles in a network can be varied from being equivalent to the ranking obtained from the number of citations to a behaviour tending to the eigenvector centrality, these limits correspond to small and large values of the communicability-function parameter, respectively. At an intermediate value of the parameter a PageRank-like behaviour is recovered. As a test case, we apply communicability functions to two sets of articles, where at least one author of each paper was awarded a Nobel Prize for the research presented in the corresponding article.

Prediction methods for coning process

2021 ◽  
pp. 18-25
Author(s):  
E.F. Veliyev ◽  
Keyword(s):  
Computer Modeling ◽  
Analytical Approach ◽  
Laboratory Experiments ◽  
Numerical Approach ◽  
Prediction Methods ◽  
Field Development ◽  
The Third ◽  
Modern Methods ◽  
Definition Of

Currently, the percentage of the mature fields steadily rise and the process of formation of water and gas cones becomes unavoidable. The prediction of this process is essential for successful field development. Correlation dependencies developed for this purpose can be divided into three main groups. The models in the first group are based on the analytical approach of definition of balance conditions for viscous and gravitational powers in the reservoir. The methods in the second group are based on empiric approach, i.e. on the data obtained as a result of laboratory experiments or computer modeling. The methods in the third group are based on numerical approach. The paper presents the analysis and classification of modern methods for prediction of coning process.

Mixing induced by ISWs breaking over a sloping boundary: an analytical heuristic model

2020 ◽  
Author(s):  
Davide Cavaliere ◽  
Giovanni la Forgia ◽  
Federico Falcini
Keyword(s):  
Laboratory Experiments ◽  
Nonlinear Models ◽  
Wave Model ◽  
Real Field ◽  
Mixing Efficiency ◽  
Layer System ◽  
Geophysical Research ◽  
Strongly Nonlinear ◽  
Symmetric Density ◽  
Definition Of

<p>We propose an analytical approach to estimate mixing efficiency in Internal Solitary Waves (ISWs) breaking processes. We make use of the theoretical framework of Winters et al. [1995] to describe the energetics of a stratified fluid flow, calculating the Available Potential Energy (APE) of an ISW of depression in a two-layer system, assuming that the symmetric density structure on both sides of the feature is exactly the same. Starting from the definition of mixing efficiency given by Michallet and Ivey [1999], through the Ozmidov and Thorpe length-scales we derive an expression for the mixing efficiency avoiding the use of any wave model (as KdV-type models or strongly nonlinear models) to estimate the wave energy. The model is successfully verified through laboratory experiments performed in a wave tank and is meant to be applied by using real field CTD casts.</p><p> </p><p>References:</p><p>Winters, K., Lombard, P., Riley, J., and D’Asaro, E. (1995). <em>Available potential</em></p><p><em>energy and mixing in density-stratified fluids</em>. J. Fluid Mech., 289, 115-128.</p><p>Michallet, H. and Ivey, G. (1999). <em>Experiments on mixing due to internal solitary</em></p><p><em>waves breaking on uniform slopes</em>. Journal of Geophysical Research: Oceans,</p><p>104(C6), 13467-13477</p>

scholarly journals Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3005 ◽  
Author(s):  
Baishen Wei ◽  
Brett Nener
Keyword(s):  
Sensor Network ◽  
Efficient Solution ◽  
Space Debris ◽  
Situational Awareness ◽  
Consensus Algorithm ◽  
Time Varying ◽  
Global Average ◽  
Random Finite Set ◽  
Finite Set ◽  
Simulation Results

Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized δ -generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results.

scholarly journals Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Oyelola A. Adegboye ◽  
Faiz Elfaki
Keyword(s):  
Healthcare Workers ◽  
Arabian Peninsula ◽  
Healthcare Facility ◽  
Disease Outbreak ◽  
Control Measures ◽  
Human Populations ◽  
Eigenvector Centrality ◽  
Infectious Disease Outbreak ◽  
Centrality Score ◽  
Important Nodes

Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 (n=210) and 17 (n=1), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5–17). The eigenvector centrality was significantly (p<0.05) associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases.

scholarly journals A Synergetic Theory of Information

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 142 ◽  
Author(s):  
Viktor Vyatkin
Keyword(s):  
Average Length ◽  
Structural Characteristics ◽  
Discrete Systems ◽  
Information Law ◽  
Finite Set ◽  
The Law ◽  
Definition Of ◽  
Synergetic Theory ◽  
First Time

A new approach is presented to defining the amount of information, in which information is understood as the data about a finite set as a whole, whereas the average length of an integrative code of elements serves as a measure of information. In the framework of this approach, the formula for the syntropy of a reflection was obtained for the first time, that is, the information which two intersecting finite sets reflect (reproduce) about each other. Features of a reflection of discrete systems through a set of their parts are considered and it is shown that reproducible information about the system (the additive syntropy of reflection) and non-reproducible information (the entropy of reflection) are, respectively, measures of the structural order and the chaos. At that, the general classification of discrete systems is given by the ratio of the order and the chaos. Three information laws have been established: The law of conservation of the sum of chaos and order; the information law of reflection; and the law of conservation and transformation of information. An assessment of the structural organization and the level of development of discrete systems is presented. It is shown that various measures of information are structural characteristics of integrative codes of elements of discrete systems. A conclusion is made that, from the information-genetic positions, the synergetic approach to the definition of the quantity of information is primary in relation to the approaches of Hartley and Shannon.

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