scholarly journals Optimal Consumer Network Structure Formation Under Network Effects: Seeds Controllability and Visibility

2011 ◽  
Author(s):  
Yifan Dou ◽  
Marius Florin Niculescu ◽  
D. J. Wu
Keyword(s):  
Structure Formation ◽  
Network Structure ◽  
Network Effects

scholarly journals Cascading Losses in Reinsurance Networks

2020 ◽  
Vol 66 (9) ◽  
pp. 4246-4268
Author(s):  
Ariah Klages-Mundt ◽  
Andreea Minca
Keyword(s):  
Fixed Points ◽  
Network Structure ◽  
Real World ◽  
Stochastic Models ◽  
Network Effects ◽  
Proportional Reinsurance ◽  
Graph Structure ◽  
Excess Costs ◽  
Extreme Sensitivity ◽  
Contagion Risk

We develop a model for contagion in reinsurance networks by which primary insurers’ losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature—namely proportional reinsurance—greatly underestimate contagion risk. We characterize the fixed points of our model and develop efficient algorithms to compute contagion with guarantees on convergence and speed under conditions on network structure. We characterize exotic cases of problematic graph structure and nonlinearities, which cause network effects to dominate the overall payments in the system. Last, we apply our model to data on real-world reinsurance networks. Our simulations demonstrate the following. (1) Reinsurance networks face extreme sensitivity to parameters. A firm can be wildly uncertain about its losses even under small network uncertainty. (2) Our sensitivity results reveal a new incentive for firms to cooperate to prevent fraud, because even small cases of fraud can have outsized effect on the losses across the network. (3) Nonlinearities from excess of loss contracts obfuscate risks and can cause excess costs in a real-world system. This paper was accepted by Baris Ata, stochastic models and simulation.

Processes of Silica Network Structure Formation in Reverse Micellar Systems

Langmuir ◽  
2001 ◽  
Vol 17 (14) ◽  
pp. 4189-4195 ◽  
Author(s):  
Makoto Harada ◽  
Shintaro Itakura ◽  
Akihisa Shioi ◽  
Motonari Adachi
Keyword(s):  
Structure Formation ◽  
Network Structure ◽  
Silica Network ◽  
Micellar Systems ◽  
Reverse Micellar Systems ◽  
Reverse Micellar

Studies on Effects of Initialization on Structure Formationand Generalization of Structural Learning with Forgetting

2004 ◽  
Vol 8 (6) ◽  
pp. 621-626 ◽  
Author(s):  
Hiroshi Shiratsuchi ◽  
◽  
Hiromu Gotanda ◽  
Katsuhiro Inoue ◽  
Kousuke Kumamaru ◽  
...  
Keyword(s):  
Neural Networks ◽  
Structure Formation ◽  
Network Structure ◽  
Center Of Gravity ◽  
Input Pattern ◽  
Structural Learning ◽  
Generalization Ability ◽  
Multilayer Neural Networks ◽  
Pass Through

In this paper, our proposed initialization for multilayer neural networks (NN) applies to the structural learning with forgetting. Initialization consists of two steps: weights of hidden units are initialized so that their hyperplanes pass through the center of gravity of an input pattern set, and weights of output units are initialized to zero. Several simulations were performed to study how the initialization effects the structure formation of the NN. From the simulation result, it was confirmed that the initialization gives better network structure and higher generalization ability.

scholarly journals Consumption Network Effects

2019 ◽  
Vol 87 (1) ◽  
pp. 130-163 ◽  
Author(s):  
Giacomo De Giorgi ◽  
Anders Frederiksen ◽  
Luigi Pistaferri
Keyword(s):  
Panel Data ◽  
Network Structure ◽  
Risk Sharing ◽  
Network Effects ◽  
Peer Groups ◽  
Firm Level ◽  
Multiplier Effect ◽  
Record Data ◽  
Definition Of ◽  
Employee Data

Abstract In this article we study consumption network effects. Does the consumption of our peers affect our own consumption? How large is such effect? What are the economic mechanisms behind it? We use administrative panel data on Danish households to construct a measure of consumption based on tax records on income and assets. We combine tax record data with matched employer–employee data to identify peer groups based on workplace, which gives us a much tighter and credible definition of networks than used in previous literature. We use the non-overlapping network structure of one’s peers group, as well as firm-level shocks, to build valid instruments for peer consumption. We estimate non-negligible and statistically significant network effects, capable of generating sizable multiplier effect at the macro-level. We also investigate what mechanisms generate such effects, distinguishing between intertemporal and intratemporal consumption effects as well as a more traditional risk sharing view.

Network structure formation in thermally-induced gelation of glycinin

1984 ◽  
Vol 32 (2) ◽  
pp. 349-352 ◽  
Author(s):  
Takashi Nakamura ◽  
Shigeru Utsumi ◽  
Tomohiko Mori
Keyword(s):  
Structure Formation ◽  
Network Structure ◽  
Thermally Induced
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