Emergence in Hierarchical Complex Systems Structured as Social Networks

2017 ◽  
Author(s):  
Aimee-Theodora Dumitrescu ◽  
Ecaterina Oltean ◽  
Daniel Merezeanu ◽  
Radu Dobrescu
Keyword(s):  
Social Networks ◽  
Complex Systems

scholarly journals The emergence of consensus: a primer

2018 ◽  
Vol 5 (2) ◽  
pp. 172189 ◽  
Author(s):  
Andrea Baronchelli
Keyword(s):  
Game Theory ◽  
Social Networks ◽  
Complex Systems ◽  
Social Systems ◽  
Short Review ◽  
Systems Science ◽  
Social Consensus ◽  
Population Scale ◽  
Complex Systems Science

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

scholarly journals Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems

2020 ◽  
Vol 8 (4) ◽  
pp. 7-45
Author(s):  
E. G. Andrianova ◽  
S. A. Golovin ◽  
S. V. Zykov ◽  
S. A. Lesko ◽  
E. R. Chukalina
Keyword(s):  
Social Networks ◽  
Time Series ◽  
Complex Systems ◽  
Human Factor ◽  
General Information ◽  
Commodity Markets ◽  
Measurement Scale ◽  
Advantages And Disadvantages ◽  
Technical Systems ◽  
Analysis Of Time Series

The directions of perspective research in the field of analysis and modeling of the dynamics of time series of processes in complex systems with the presence of the human factor are described. The dynamics of processes in such systems is described by nonstationary time series. Predicting the evolution of such systems is of great importance for managing processes in social (election campaigns), economic (stock, futures and commodity markets) and socio-technical systems (social networks). The general information on time series and tasks of their analysis is given. Modern methods of time series analysis for economic processes are considered. The results show that economic processes cannot be considered completely random, since they tend to self-organize and, moreover, are subject to the influence of memory of previous states. It was revealed that one of the main tasks in modeling processes in sociotechnical systems (for example, social networks) is the development of a mathematical apparatus for bringing data to a single measurement scale. The modern models of analysis and forecasting of electoral processes based on the analysis of time series: structural, polling, hybrid. Based on the analysis, their advantages and disadvantages are considered. In conclusion, it was concluded that to describe processes in complex systems with the presence of the human factor, in addition to traditional factors, it is necessary to develop and use methods and tools to take into account the possibility of self-organization of human groups and the presence of memory about previous states of the system.

scholarly journals Reconsidering Social Dynamics: Fundamentality and Social Simulations

2012 ◽  
Vol 10 (1) ◽  
pp. 122-125
Author(s):  
Ioannis Katerelos ◽  
Charalambos Tsekeris
Keyword(s):  
Social Networks ◽  
Complex Systems ◽  
Everyday Life ◽  
Social Dynamics ◽  
Social World ◽  
Agent Based ◽  
Fundamental Law ◽  
Human Complex ◽  
The Ideal

We live in a ceaselessly changing and inescapably dynamic social world. Given the inherent unpredictability of human complex systems, this brief article seeks to show that agent-based social simulations can possibly approach the ideal of a fundamental law of social dynamics, including all forms or processes of social dynamics, articulated with everyday life and action, individual or collective. This ultimately tends to recover the explanatory potential of social networks and offer an efficient research basis for the creative re-conceptualization of social dynamics.

scholarly journals Reconsidering Social Dynamics: Fundamentality and Social Simulations

2012 ◽  
Vol 10 (1) ◽  
pp. 122-125
Author(s):  
Ioannis Katerelos ◽  
Charalambos Tsekeris
Keyword(s):  
Social Networks ◽  
Complex Systems ◽  
Everyday Life ◽  
Social Dynamics ◽  
Social World ◽  
Agent Based ◽  
Fundamental Law ◽  
Human Complex ◽  
The Ideal

We live in a ceaselessly changing and inescapably dynamic social world. Given the inherent unpredictability of human complex systems, this brief article seeks to show that agent-based social simulations can possibly approach the ideal of a fundamental law of social dynamics, including all forms or processes of social dynamics, articulated with everyday life and action, individual or collective. This ultimately tends to recover the explanatory potential of social networks and offer an efficient research basis for the creative re-conceptualization of social dynamics.

scholarly journals Complex Neutrosophic Hypergraphs: New Social Network Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 234 ◽  
Author(s):  
Anam Luqman ◽  
Muhammad Akram ◽  
Florentin Smarandache
Keyword(s):  
Social Networks ◽  
Sustainable Development ◽  
Social Network ◽  
Complex Systems ◽  
Network Models ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Score Functions ◽  
Proposed Model ◽  
Complex Valued

A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.

scholarly journals Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks

2019 ◽  
Vol 56 (1) ◽  
pp. 107-129
Author(s):  
James D. Westaby ◽  
Adam K. Parr
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Systems ◽  
Network Theory ◽  
Betweenness Centrality ◽  
Dynamic Network ◽  
Quadratic Assignment ◽  
Organizational Systems ◽  
Goal Striving ◽  
Shared Goal

Grounded in dynamic network theory, this study examined network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social networks. Goal nodes can also represent missions, objectives, or desires, thus having wide applicability. The theory ties social networks to goal nodes through a parsimonious set of social network role linkages, such as independent goal striving, system supporting, feedback, goal preventing, supportive resisting, and system negating (i.e., those who are upset with others in the pursuit). Moreover, we extend the theory’s system reactance role linkage to better account for constructive conflicts. Two complex systems were examined: a team’s mission and an individual’s work project. In support of dynamic network theory, using the Quadratic Assignment Procedure, results demonstrated significant shared goal striving, system supporting, and shared connections between goal striving and system supporting. These findings manifest what we coin as multipendence: Systems having some actions independently involved with goals, while others are dependently involved in the associated network. NGA also demonstrated that the goal nodes manifested strong betweenness centrality, indicating that goal striving and feedback links were connecting entities across the wider system. Strategies to plan network goal interventions are illustrated with implications for practice.

scholarly journals Bots increase exposure to negative and inflammatory content in online social systems

2018 ◽  
Vol 115 (49) ◽  
pp. 12435-12440 ◽  
Author(s):  
Massimo Stella ◽  
Emilio Ferrara ◽  
Manlio De Domenico
Keyword(s):  
Social Networks ◽  
Complex Systems ◽  
Online Social Networks ◽  
Social System ◽  
Large Scale ◽  
Social Conflict ◽  
Social Systems ◽  
Social Data ◽  
The Social ◽  
Peripheral Areas

Societies are complex systems, which tend to polarize into subgroups of individuals with dramatically opposite perspectives. This phenomenon is reflected—and often amplified—in online social networks, where, however, humans are no longer the only players and coexist alongside with social bots—that is, software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives, and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.

scholarly journals Fuzzy mixed graphs and its application to identification of COVID19 affected central regions in India

2021 ◽  
Vol 40 (1) ◽  
pp. 1051-1064
Author(s):  
Kousik Das ◽  
Usman Naseem ◽  
Sovan Samanta ◽  
Shah Khalid Khan ◽  
Kajal De
Keyword(s):  
Social Networks ◽  
Complex Systems ◽  
Central Regions ◽  
Mixed Graphs ◽  
Isomorphism Theorems

In the recent phenomenon of social networks, both online and offline, two nodes may be connected, but they may not follow each other. Thus there are two separate links to be given to capture the notion. Directed links are given if the nodes follow each other, and undirected links represent the regular connections (without following). Thus, this network may have both types of relationships/ links simultaneously. This type of network can be represented by mixed graphs. But, uncertainties in following and connectedness exist in complex systems. To capture the uncertainties, fuzzy mixed graphs are introduced in this article. Some operations, completeness, and regularity and few other properties of fuzzy mixed graphs are explained. Representation of fuzzy mixed graphs as matrix and isomorphism theorems on fuzzy mixed graphs are developed. A network of COVID19 affected areas in India are assumed, and central regions are identified as per the proposed theory.

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