On multigrid and iterative aggregation methods for nonsymmetric problems

1986 ◽  
pp. 219-231
Author(s):  
Jan Mandel
Keyword(s):  
Aggregation Methods

scholarly journals Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors

2021 ◽  
Author(s):  
LeSheng Jin ◽  
Ronald R. Yager ◽  
Zhen-Song Chen ◽  
Jana Špirkovà ◽  
Daniel Paternain ◽  
...  
Keyword(s):  
Aggregation Methods

Rank aggregation methods dealing with ordinal uncertain preferences

2017 ◽  
Vol 78 ◽  
pp. 103-109 ◽  
Author(s):  
Esther Dopazo ◽  
María L. Martínez-Céspedes
Keyword(s):  
Rank Aggregation ◽  
Aggregation Methods ◽  
Uncertain Preferences

Improving Time Delay Neural Network Based Speaker Recognition with Convolutional Block and Feature Aggregation Methods

2021 ◽  
Author(s):  
Yu-Jia Zhang ◽  
Yih-Wen Wang ◽  
Chia-Ping Chen ◽  
Chung-Li Lu ◽  
Bo-Cheng Chan
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Speaker Recognition ◽  
Aggregation Methods ◽  
Feature Aggregation

scholarly journals Distributive Justice, Public Policies and the Comparison of Legal Rules: Quantify the “Price of Equity”

2017 ◽  
Vol 14 (3) ◽  
Author(s):  
Marco Fabbri ◽  
Diogo GC Britto
Keyword(s):  
Social Welfare ◽  
Social Welfare Function ◽  
Social Preferences ◽  
Public Policies ◽  
Quantitative Approach ◽  
Legal Rules ◽  
Aggregation Methods ◽  
Equity And Efficiency ◽  
Special Circumstances ◽  
Total Wealth

Abstract This paper proposes a quantitative approach to study two methodological problems arising when a costly redistribution of resources is implemented through public policies or legal rules: (a) aggregating individual into social preferences and (b) choosing the object of maximization. We consider a redistribution intervention that reduces inequality but diminishes total wealth and we specify a set of social welfare functions combining different preferences aggregation methods and maximands. For each social welfare function, we calculate its “price of equity”, defined as the maximum fraction of total wealth that a society is willing to sacrifice in order to implement the redistribution. Comparing the prices for equity across different social welfare function specifications, we identify systematic relationships and we rank them according to the efficiency-equity orientation. Results show that social welfare functions characterized by aggregation methods conventionally considered equity-oriented may reject redistribution interventions that are evaluated as welfare-improving by social welfare functions using efficiency-oriented aggregation methods. Similarly, social welfare functions considered equity-oriented because using utility as object of maximization may reject distributive policies that are evaluated as welfare-improving by social welfare functions using wealth as maximand. We argue that the quantitative approach proposed, by expounding the trade-off between equity and efficiency connected to different social welfare functions, may prove useful in areas of public law where policy-makers have to engage in the choice of a normative criterion for the evaluation of social welfare. Additionally, our results may inform rule-makers interested in comparing the distributive effects of alternative legal rules in special circumstances where private remedies can efficiently achieve redistribution goals.

scholarly journals A Reliable Peer-to-Peer Platform for Adding New Node Using Trust Based Model

2017 ◽  
Vol 7 (6) ◽  
pp. 3515
Author(s):  
Vimal S. ◽  
Srivatsa S K.
Keyword(s):  
Success Rate ◽  
Trust Model ◽  
Peer To Peer ◽  
Peer Networks ◽  
Aggregation Methods ◽  
Peer To Peer Networks

In order to evaluate the trustworthiness of participating peers in unstructured peer-to-peer networks, Reputation aggregation methods are used in this method. Each and every peer of the network will collect the local scores of each transaction and will compute global scores by aggregating all the local scores with the help of global scores, each individual peer can interact with its suitable peers. But the existing method will not consider the score of the new peer. In this condition, requests are handled by existing peers who leads to failure in downloading process. To rectify this, NP-TRUST model is used to distribute the request to all peers including the newly joined peers. The proposed method is compared with gossip and DFR-TRUST model in Transaction Success rate and variation in file request.

scholarly journals Logic-based aggregation methods for ranking student applicants

2017 ◽  
Vol 27 (4) ◽  
pp. 463-479
Author(s):  
Pavle Milosevic ◽  
Ivan Nesic ◽  
Ana Poledica ◽  
Dragan Radojevic ◽  
Branislav Petrovic
Keyword(s):  
Boolean Algebra ◽  
Aggregation Method ◽  
Team Members ◽  
Aggregation Methods ◽  
Consistent Aggregation ◽  
The University ◽  
Depen Dencies

In this paper, we present logic-based aggregation models used for ranking student applicants and we compare them with a number of existing aggregation methods, each more complex than the previous one. The proposed models aim to include depen- dencies in the data using Logical aggregation (LA). LA is a aggregation method based on interpolative Boolean algebra (IBA), a consistent multi-valued realization of Boolean algebra. This technique is used for a Boolean consistent aggregation of attributes that are logically dependent. The comparison is performed in the case of student applicants for master programs at the University of Belgrade. We have shown that LA has some advantages over other presented aggregation methods. The software realization of all applied aggregation methods is also provided. This paper may be of interest not only for student ranking, but also for similar problems of ranking people e.g. employees, team members, etc.

scholarly journals Mathematically aggregating experts' predictions of possible futures

2021 ◽  
Author(s):  
Anca Hanea ◽  
David Peter Wilkinson ◽  
Marissa McBride ◽  
Aidan Lyon ◽  
Don van Ravenzwaaij ◽  
...  
Keyword(s):  
Data Sets ◽  
Future Event ◽  
Linear Combinations ◽  
Aggregation Methods ◽  
Mathematical Rule ◽  
Multiple Experts ◽  
Combine Probability ◽  
Final Group ◽  
The Ideal

Experts are often asked to represent their uncertainty as a subjective probability. Structured protocols offer a transparent and systematic way to elicit and combine probability judgements from multiple experts. As part of this process, experts are asked to individually estimate a probability (e.g., of a future event) which needs to be combined/aggregated into a final group prediction. The experts' judgements can be aggregated behaviourally (by striving for consensus), or mathematically (by using a mathematical rule to combine individual estimates). Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. However, the choice of a rule is not straightforward, and the aggregated group probability judgement's quality depends on it. The quality of an aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction.In the ideal case, individual experts' performance (as probability assessors) is scored, these scores are translated into performance-based weights, and a performance-based weighted aggregation is used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. We use several data sets to investigate the relative performance of multiple aggregation methods informed by previous experience and the available literature. Even though the accuracy, calibration, and informativeness of the majority of methods are very similar, two of the aggregation methods distinguish themselves as the best and worst.

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