scholarly journals How Does the Heterogeneity of Members Affect the Evolution of Group Opinions?

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
An Lu ◽  
Haifeng Ling ◽  
Zhengping Ding
Keyword(s):  
Decision Making ◽  
Random Network ◽  
Small World ◽  
Important Influence ◽  
The Other ◽  
Network Structures ◽  
Small World Network ◽  
Average Amount ◽  
Regular Network ◽  
The Impact

Understanding the impact of heterogeneity on the evolution of group opinions can enlighten us on how to effectively organize, redesign, and improve decision-making efficiency. This article explores mainly the effects of heterogeneity on the evolution of group opinions. It is found that the heterogeneity of individuals’ openness has an important influence on the ability to aggregate group opinions. According to the average amount of clusters and Herfindahl–Hirschman Index (HHI) under different network structures, heterogeneity often improves the ability. In addition, for the small-world network and random network, there is little difference in the aggregation ability from both the average amount of clusters and the Herfindahl–Hirschman Index. While for the regular network, the ability is obviously weaker than that of the other two. This result also shows that the randomness of interaction between members will enhance the cohesion of a group.

MICROSCOPIC SELF-ORGANIZATION IN NETWORKS AND REGULAR LATTICE

2002 ◽  
Vol 16 (25) ◽  
pp. 923-935
Author(s):  
QI OUYANG ◽  
KAI SUN ◽  
HONGLI WANG
Keyword(s):  
Correlation Length ◽  
Random Network ◽  
Reaction System ◽  
Small World ◽  
Self Organization ◽  
Fast Diffusion ◽  
Small World Network ◽  
Regular Lattice ◽  
Numerical Studies ◽  
Regular Network

We report our numerical studies on the microscopic self-organizations of a reaction system in three types of networks: a regular network, a small-world network, and a random network as well as on a regular lattice. Our simulation results show that the topology of the network has an important effect on the communication among reaction molecules, and plays an important role in microscopic self-organization. The correlation length among reacting molecules in a random or a small-world network is much shorter compared with that in the regular network. As a result, it is much easier to obtain a microscopic self-organization in a small-world or a random network. A phase transition from a stochastic state to a synchronized state was observed when the randomness of a small-world network was increased. We also demonstrate that good synchronization activities of enzymatic turnover cycles can be developed on a regular lattice when the correlation length created by the fast diffusion of regulatory particles is large enough.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

scholarly journals Stochastic Fuzzy Algorithms for Impairment of Assets Management

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcel Ioan Bolos ◽  
Victoria Bogdan ◽  
Ioana Alexandra Bradea ◽  
Claudia Diana Sabau Popa ◽  
Dorina Nicoleta Popa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Numbers ◽  
Cash Flows ◽  
The Other ◽  
Dual Purpose ◽  
Fuzzy Algorithms ◽  
Tangible Assets ◽  
The Impact

The present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. Stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. Estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. Also, based on the Lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. Within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. Information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance.

Abstract 17426: Pulmonary Vein Isolation ‘Rewires’ Electrical Communications to Enhance Small-world Network Topology During Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Hiroshi Ashikaga ◽  
Jonathan Chrispin ◽  
Degang Wu ◽  
Joshua Garland
Keyword(s):  
Atrial Fibrillation ◽  
Communication Network ◽  
Mutual Information ◽  
Path Length ◽  
Random Network ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Network ◽  
Z Score ◽  
Network Properties

Recent evidence suggests that pulmonary vein isolation (PVI) may perturb the electrophysiological substrate for maintenance of atrial fibrillation (AF). Our previous work indicates that information theory metrics can quantify electrical communications during arrhythmia. We hypothesized that PVI ‘rewires’ the electrical communication network during AF such that the topology exhibits higher levels of small-world network properties, with higher clustering coefficient and lower path length, than would be expected by chance. Thirteen consecutive patients (n=6 with prior PVI and n=7 without) underwent AF ablation using a 64-electrode basket catheter in the left atrium. Multielectrode recording was performed during AF for 60 seconds, followed by PVI. Mutual information was calculated from the time series between each pair of electrodes using the Kraskov-Stögbauer-Grassberger estimator. The all-to-all mutual information matrix (64x64; Figure, upper panels) was thresholded by the median and standard deviations of mutual information to build a binary adjacency matrix for electrical communication networks. The properties of small-world network ( swn ; ‘small-world-ness’) were quantified by the ratio of the observed average clustering coefficient to that of a random network over the ratio of the observed average path length to that of a random network. swn was expressed in normal Z standard deviation units. As the binarizing threshold increased, the Z-score of swn decreased (Figure, lower panel). However, the Z-score at each threshold value was consistently higher with prior PVI than those without (p<0.05). In conclusion, electrical communication network during AF with prior PVI is associated with higher levels of small-world network properties than those without. This finding supports the concept that PVI perturbs the underlying substrate. In addition, swn of electrical communication network may be a promising metric to quantify substrate modification.

scholarly journals A Discussion of the Favorable Factors for the Economic System’s Utility Growth Rate

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Lunhan Luo ◽  
Jianan Fang
Keyword(s):  
Growth Rate ◽  
Utility Function ◽  
Group Size ◽  
Growth Rates ◽  
Small World ◽  
Economic Systems ◽  
Network Structures ◽  
Small World Network ◽  
Proper Volume

Some virtual economic systems are constructed. The agents in the systems are linked crossover by different network structures and endowed with goods and the same symmetric Cobb-Douglas utility function. Their Cobb-Douglas utility growth rates (UGR) are compared with each other after the systems reach their eventual equilibriums. We try to discover how the network structures and how the endowments affect the UGR. Furthermore, the possible factors including group size, trade sequence, turnover, and number of trades are taken into account. Some but not all of the factors listed above may impact the UGR. The aim of this paper is to select the effective ones and try to improve the UGR by manipulating these factors. We discovered that the small-world network surpasses other networks to achieve the highest UGR. Besides, another way to improve UGR is to endow the agents with proper volume of goods.

How to Quantify 'Small-World Networks'?

Fractals ◽  
1998 ◽  
Vol 06 (04) ◽  
pp. 301-303 ◽  
Author(s):  
Hanspeter Herzel
Keyword(s):  
Small World ◽  
Small World Network ◽  
Small Worlds ◽  
Small World Networks ◽  
Complete Regularity ◽  
Local Clustering ◽  
Regular Network ◽  
Analytical Expressions ◽  
Global Connection

Recently Watts and Strogatz emphasized the widespread relevance of 'small worlds' and studied numerically networks between complete regularity and complete randomness. In this letter, I derive simple analytical expressions which can reproduce the empirical observations. It is shown how a few random connections can turn a regular network into a 'small-world network' with a short global connection but persisting local clustering.

scholarly journals Revisiting the Small-World Phenomenon

2016 ◽  
Vol 20 (1) ◽  
pp. 149-173 ◽  
Author(s):  
Tore Opsahl ◽  
Antoine Vernet ◽  
Tufool Alnuaimi ◽  
Gerard George
Keyword(s):  
Significant Variation ◽  
Random Network ◽  
Small World ◽  
Random Networks ◽  
Significant Heterogeneity ◽  
Small World Network ◽  
Small World Networks ◽  
Firm Outcomes ◽  
Baseline Levels

Research has explored how embeddedness in small-world networks influences individual and firm outcomes. We show that there remains significant heterogeneity among networks classified as small-world networks. We develop measures of the efficiency of a network, which allow us to refine predictions associated with small-world networks. A network is classified as a small-world network if it exhibits a distance between nodes that is comparable to the distance found in random networks of similar sizes—with ties randomly allocated among nodes—in addition to containing dense clusters. To assess how efficient a network is, there are two questions worth asking: (a) What is a compelling random network for baseline levels of distance and clustering? and (b) How proximal should an observed value be to the baseline to be deemed comparable? Our framework tests properties of networks, using simulation, to further classify small-world networks according to their efficiency. Our results suggest that small-world networks exhibit significant variation in efficiency. We explore implications for the field of management and organization.

Situation Awareness and the Decision-Making Process in a Dynamic Situation: Avoiding Collisions at Sea

2008 ◽  
Vol 2 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Christine Chauvin ◽  
J. P. Clostermann ◽  
Jean-Michel Hoc
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
The Other ◽  
Decision Making Process ◽  
Secondary Importance ◽  
Training Course ◽  
Training Facility ◽  
Level 1 ◽  
The One ◽  
The Impact

In this study, the authors aim to determine the impact of situation awareness (SA) in the decision-making process of “young” watch officers of a Merchant Marine training facility. The trainees were shown an ambiguous interaction situation in which they could choose among several actions. The results show that Level 1 SA (perception of the elements in the environment) tends to be of secondary importance in decision making. The major variables of the decision-making process are the interpretation of the rules and anticipation of the other vessel's intentions. Moreover, four different trainee “profiles” emerged. The main difference between them lies in the distance at which they decided to change course, the direction of this maneuver (port or starboard), the way in which they interpreted the other vessel's intentions (is it going to change course?), and whether the trainees referred to the rules. Of the trainees, 55% performed a maneuver that was against regulations, and 34% did so in an unsafe manner. This result provides an incentive to rethink the training course to put more stress on recognizing prototypical situations and choosing which actions to take in situations such as the one presented here.

Network and equation-based models in epidemiology

2018 ◽  
Vol 11 (03) ◽  
pp. 1850046 ◽  
Author(s):  
Kossi Edoh ◽  
Elijah MacCarthy
Keyword(s):  
Network Models ◽  
Small World ◽  
Control Measures ◽  
Network Structures ◽  
Scale Free ◽  
Measles Outbreak ◽  
Spanish Flu ◽  
Spread Of Infection ◽  
Aggregate Population ◽  
The Impact

Network and equation-based (EB) models are two prominent methods used in the study of epidemics. While EB models use a global approach to model aggregate population, network models focus on the behavior of individuals in the population. The two approaches have been used in several areas of research, including finance, computer science, social science and epidemiology. In this study, epidemiology is used to contrast EB models with network models. The methods are based on the assumptions and properties of compartmental models. In EB models we solve a system of ordinary differential equations and in network models we simulate the spread of epidemics on contact networks using bond percolation. We examine the impact of network structures on the spread of infection by considering various networks, including Poisson, Erdős Rényi, Scale-free, and Watts–Strogatz small-world networks, and discuss how control measures can make use of the network structures. In addition, we simulate EB assumptions on Watts–Strogatz networks to determine when the results are similar to that of EB models. As a case study, we use data from the 1918 Spanish flu pandemic and that from measles outbreak to validate our results.

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