scholarly journals Stochastic model of network formation with asymmetric players

2020 ◽  
Vol 12 (4) ◽  
pp. 62-92
Author(s):  
Пин Сунь ◽  
Ping Sun ◽  
Елена Михайловна Парилина ◽  
Elena Parilina
Keyword(s):  
Stochastic Model ◽  
Network Structure ◽  
Stochastic Games ◽  
Network Formation ◽  
Dynamic Consistency ◽  
Cooperative Solution ◽  
Joint Project ◽  
Cooperative Approach ◽  
Terminal Time ◽  
Theoretical Results

We propose a model of a network formation using the theory of stochastic games with random terminal time. Initially, the leader proposes a joint project in the form of a network to the players. Then, the players have the opportunities to form new links with each other to update the network proposed by the leader. Any player's payoff at any stage is determined by the network structure. It is also assumed that the formation of links proposed by the players is random. The duration of the game is also random. As a result of the players' actions and the implementation of the random steps of the Nature, a network is formed. We consider a cooperative approach to network formation, and we use the CIS-value as a cooperative solution. In this paper, a recurrent formula for its derivation in any cooperative subgame is obtained. The paper also investigates the dynamic consistency of CIS-value. The theoretical results are demonstrated by a numerical example.

scholarly journals Risk Aversion and Social Networks

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Jaromír Kovářík ◽  
Marco J. van der Leij
Keyword(s):  
Risk Aversion ◽  
Undergraduate Students ◽  
Network Structure ◽  
Network Formation ◽  
Empirical Finding ◽  
Local Network ◽  
Network Clustering ◽  
Strongly Correlated ◽  
Social Network Structure ◽  
The One

AbstractThis paper first investigates empirically the relationship between risk aversion and social network structure in a large group of undergraduate students. We find that risk aversion is strongly correlated to local network clustering, that is, the probability that one has a social tie to friends of friends. We then propose a network formation model that generates this empirical finding, suggesting that locally superior information on benefits makes it more attractive for risk averse individuals to link to friends of friends. Finally, we discuss implications of this model. The model generates a positive correlation between local network clustering and benefits, even if benefits are distributed independently ex ante. This provides an alternative explanation of this relationship to the one given by the social capital literature. We also establish a linkage between the uncertainty of the environment and the network structure: risky environments generate more clustered and more unequal networks in terms of connectivity.

Simulating Social Network Formation

2011 ◽  
pp. 581-599
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Model And Simulation ◽  
Graph Theoretic Models

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

scholarly journals Elastic Modulus of ECM Hydrogels Derived from Decellularized Tissue Affects Capillary Network Formation in Endothelial Cells

2020 ◽  
Vol 21 (17) ◽  
pp. 6304
Author(s):  
Mako Kobayashi ◽  
Junpei Kadota ◽  
Yoshihide Hashimoto ◽  
Toshiya Fujisato ◽  
Naoko Nakamura ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Network Structure ◽  
Network Formation ◽  
Three Dimensional ◽  
Sodium Deoxycholate ◽  
Capillary Network ◽  
Matrix Stiffness ◽  
Collagen Hydrogel ◽  
Urinary Bladder Matrix ◽  
Decellularized Tissue

Recent applications of decellularized tissue have included the use of hydrogels for injectable materials and three-dimensional (3D) bioprinting bioink for tissue regeneration. Microvascular formation is required for the delivery of oxygen and nutrients to support cell growth and regeneration in tissues and organs. The aim of the present study was to evaluate the formation of capillary networks in decellularized extracellular matrix (d-ECM) hydrogels. The d-ECM hydrogels were obtained from the small intestine submucosa (SIS) and the urinary bladder matrix (UBM) after decellularizing with sodium deoxycholate (SDC) and high hydrostatic pressure (HHP). The SDC d-ECM hydrogel gradually gelated, while the HHP d-ECM hydrogel immediately gelated. All d-ECM hydrogels had low matrix stiffness compared to that of the collagen hydrogel, according to a compression test. D-ECM hydrogels with various elastic moduli were obtained, irrespective of the decellularization method or tissue source. Microvascular-derived endothelial cells were seeded on d-ECM hydrogels. Few cells attached to the SDC d-ECM hydrogel with no network formation, while on the HHP d-ECM hydrogel, a capillary network structure formed between elongated cells. Long, branched networks formed on d-ECM hydrogels with lower matrix stiffness. This suggests that the capillary network structure that forms on d-ECM hydrogels is closely related to the matrix stiffness of the hydrogel.

scholarly journals Asymptotic Behavior Analysis for a Three-Species Food Chain Stochastic Model with Regime Switching

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Junmei Liu ◽  
Yonggang Ma
Keyword(s):  
Asymptotic Behavior ◽  
Lyapunov Function ◽  
Stochastic Model ◽  
Numerical Simulations ◽  
Behavior Analysis ◽  
Food Chain ◽  
Regime Switching ◽  
Sufficient Conditions ◽  
Stationary Distributions ◽  
Theoretical Results

This paper discusses the asymptotic behavior of a class of three-species stochastic model with regime switching. Using the Lyapunov function, we first obtain sufficient conditions for extinction and average time persistence. Then, we prove sufficient conditions for the existence of stationary distributions of populations, and they are ergodic. Numerical simulations are carried out to support our theoretical results.

scholarly journals A Survey on Two Viruses Extensions of Epidemic Model with Continuous and Impulse Control

2021 ◽  
Vol 14 ◽  
pp. 127-154
Author(s):  
Elena Gubar ◽  
◽  
Vladislav Taynitskiy ◽  
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Network Structure ◽  
Epidemic Model ◽  
Impulse Control ◽  
Sir Model ◽  
Initial Population ◽  
Protection Measures ◽  
Virus Spreading ◽  
Theoretical Results

The current study represents a survey on several modifications of compartment epidemic models with continuous and impulse control policies. The main contribution of the survey is the modification of the classical Susceptible Infected Recovered (SIR) model with the assumption that two types of viruses are circulating in the population at the same time. Moreover, we also take into consideration the network structure of the initial population in two-virus SIIR models and estimate the e ectiveness of protection measures over complex networks. In each model, the optimal control problem has been formalized to minimize the costs of the virus spreading and find optimal continuous and impulse antivirus controllers. All theoretical results are corroborated by a large number of numerical simulations.

scholarly journals Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data

2020 ◽  
Vol 110 (8) ◽  
pp. 2454-2484 ◽  
Author(s):  
Emily Breza ◽  
Arun G. Chandrasekhar ◽  
Tyler H. McCormick ◽  
Mengjie Pan
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Field Experiments ◽  
Network Formation ◽  
Relational Data ◽  
Network Data ◽  
Formation Model ◽  
Social Network Data ◽  
Level Statistics ◽  
Aggregated Relational Data

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form “how many of your links have trait k ?” Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone. (JEL C81, C93, D85, Z13)

scholarly journals Toward Curation and Personality-Driven Social Networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Bas Hofstra
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Social Stratification ◽  
Network Formation ◽  
Research Question ◽  
Network Size ◽  
Personal Network ◽  
Online Technologies ◽  
Emergent Technologies ◽  
Stable Network

Do humans have bigger or smaller social networks today? We reflect on the state of this research question and assert that an updated approach is needed to understand the effects of emergent technologies on network structure. Although the absolute changes in average network size are likely to remain elusive, recent perspectives converge on the idea that online technologies make it easier for individuals to shape—or curate—their social connections. Here we merge conflicting views and specify mechanisms through which curation technologies may impact personal network structure. Looking forward, we suggest personality will become more influential in network formation and maintenance when aided by technological levers. Consequently, curation technologies have the potential to increase differences in networks between types of people (for example, extroverts vs introverts) and thus generate new forms of social stratification, despite preserving a stable network size on average. The Comment concludes with empirical and theoretical implications, including the importance of attending to dispersion and examining the societal ramifications of personality-driven curation.

Simulating Social Network Formation

2009 ◽  
Vol 1 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Systems Research ◽  
Model And Simulation

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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