Influence of contribution-based resource allocation mechanism on individual resource sharing cooperation in social networks

2019 ◽  
Vol 30 (12) ◽  
pp. 2050007
Author(s):  
Guanghai Cui ◽  
Zhen Wang ◽  
Ling Dong ◽  
Xiaoli Cao ◽  
Yue Liu ◽  
...  
Keyword(s):  
Social Networks ◽  
Resource Allocation ◽  
Resource Sharing ◽  
Evolutionary Dynamics ◽  
Allocation Mechanism ◽  
Total Contribution ◽  
Cooperation Strategy ◽  
Individual Strategies ◽  
The Cost ◽  
Work First

In social networks, resource sharing behaviors always take place in groups of individuals and rely on voluntary cooperation. In this work, first, a multi-player donor recipient game in which strategies describe individuals’ varying degrees of willingness to share resources is formulated, instead of using the limited binary decisions (e.g. share or not share) in a classical donor-recipient game. Second, the evolutionary dynamics of individual strategies are explored under the influence of two contribution-based resource allocation mechanisms: the total contribution-based allocation mechanism (TCAM) and the direct contribution-based allocation mechanism (DCAM). The results indicate that the network is dominated by the full-cooperation strategy when the cost-to-benefit ratio of resources is not too large and the DCAM is more effective than TCAM. Furthermore, the underlying reason why some strategies with higher sharing willingness can coexist in specific situations, is also explained in detail by leveraging macroscopic and microscopic perspective analysis. Finally, the influences of slandering and whitewashing behaviors conducted by a few malicious individuals on the allocation mechanisms are also studied. Current research will offer new insights into understanding the influence and optimizing the resource allocation policies in social networks.

Resource Sharing and Allocation in P2P Social Networks

2014 ◽  
Vol 610 ◽  
pp. 588-594
Author(s):  
Qing Feng Zhang ◽  
Sheng Wang ◽  
Dan Liao
Keyword(s):  
Game Theory ◽  
Social Networks ◽  
Resource Allocation ◽  
Nash Equilibrium ◽  
Public Goods ◽  
Transaction Costs ◽  
Resource Sharing ◽  
Fixed Costs ◽  
Average Price ◽  
Resource Pricing

this paper investigated resource sharing and allocation in P2P social networks which based on game theory. Firstly, resources are divided into two categories: Public goods (PG) and Club goods (CG). The PG has the following characteristics: self-less, Non-exclusive and un-competitive; but the CG has some self-ish, exclusive and competitive. The PG only to get the sharing fixed costs and transaction costs, but the CG needs to obtain more benefits over than costs. We demonstrated that when providers sharing resource is CG within sharing capacities can achieve the maximum benefits and Nash equilibrium. Secondly, peers are divided into two sets: friends set (FS) and strangers set (SS), providers allocate the CG in different sets within different pricing by the average price. Finally, simulations analyzed benefits of peers sharing the PG or the CG, and then discussed resource allocation in different sets within different payment strategies and resource pricing in the same set.

scholarly journals Economic and mathematical modeling as an effective tool of the analysis of economic processes in industry

2019 ◽  
Vol 12 (3) ◽  
pp. 316-322
Author(s):  
A. S. Kulyasova ◽  
A. R. Esina ◽  
V. D. Svirchevskiy
Keyword(s):  
Mathematical Model ◽  
Resource Allocation ◽  
Foreign Currency ◽  
High Tech ◽  
Calculation Error ◽  
Allocation Mechanism ◽  
Industrial Enterprises ◽  
Statistical Regularities ◽  
Cost Of Materials ◽  
The Cost

In conditions of market volatility, an important issue for industrial enterprises is the issue of creating an efficient resource allocation mechanism. The article gives an example about using of individually adapted economic and mathematical model for forecasting the cost of materials and purchased products, that takes into account both internal and external factors affecting the planning figures. In order to create an effective predictive model, an analysis was conducted of statistical data for the period from 2009 to 2016, data was represented by high-tech enterprises of the radioelectronic industry. As a result of analysis it was revealed the presence of statistical regularities in the nature of the distribution of the analyzed data.On the basis of the calculated distribution parameters, a prediction procedure was performed using the exponential smoothing method and the total projected cost of materials and purchased products was obtained. The use of elements of probability theory and mathematical statistics, as well as methods for forecasting time series as basic methods of the model allows to take into account probabilistic economic factors, such as, for example, a change in the exchange rate of a foreign currency, as well as the presence of defects in the production process. Application of a special mathematical apparatus provides an ability to create a flexible, individually-adapted forecasting model. As a result of application of the model intended for forecasting the cost of materials and purchased products at one of industry enterprises it was revealed that the developed model has lover calculation error than the method that is used at the enterprise at present. Thus economic and mathematical model allows increasing the efficiency of the enterprise’s planned system and ensuring a rational resource allocation by increasing the accuracy of the forecasting process.

scholarly journals Reinforcement Learning Based Resource Management for Network Slicing

2019 ◽  
Vol 9 (11) ◽  
pp. 2361 ◽  
Author(s):  
Yohan Kim ◽  
Sunyong Kim ◽  
Hyuk Lim
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Resource Management ◽  
Resource Sharing ◽  
Service Level Agreement ◽  
End Users ◽  
Allocation Mechanism ◽  
Network Slicing ◽  
Resource Adjustment ◽  
Dynamic Resource

Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.

scholarly journals The impact of predictability on dual-task performance and implications for resource-sharing accounts

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Laura Broeker ◽  
Harald Ewolds ◽  
Rita F. de Oliveira ◽  
Stefan Künzell ◽  
Markus Raab
Keyword(s):  
Resource Allocation ◽  
Task Performance ◽  
Dual Task ◽  
Resource Sharing ◽  
Visual Information ◽  
Allocation Policy ◽  
Capacity Sharing ◽  
Dual Task Performance ◽  
Sound Sequence ◽  
The Impact

AbstractThe aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

scholarly journals Evolutionary dynamics of higher-order interactions in social networks

2021 ◽  
Author(s):  
Unai Alvarez-Rodriguez ◽  
Federico Battiston ◽  
Guilherme Ferraz de Arruda ◽  
Yamir Moreno ◽  
Matjaž Perc ◽  
...  
Keyword(s):  
Social Networks ◽  
Evolutionary Dynamics ◽  
Higher Order

“All Lives Matter”: The Cost of Colorblind Racial Attitudes in Diverse Social Networks

2016 ◽  
Vol 8 (2) ◽  
pp. 196-208 ◽  
Author(s):  
John Tawa ◽  
Ruqian Ma ◽  
Shinji Katsumoto
Keyword(s):  
Social Networks ◽  
Racial Attitudes ◽  
The Cost

scholarly journals An Efficient Resource Allocation Mechanism for LTE–GEPON Converged Networks

2013 ◽  
Vol 22 (3) ◽  
pp. 437-461 ◽  
Author(s):  
Chathurika Ranaweera ◽  
Elaine Wong ◽  
Christina Lim ◽  
Ampalavanapillai Nirmalathas ◽  
Chamil Jayasundara
Keyword(s):  
Resource Allocation ◽  
Allocation Mechanism ◽  
Efficient Resource ◽  
Resource Allocation Mechanism
Sign in / Sign up

Export Citation Format

Share Document