scholarly journals Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1350 ◽  
Author(s):  
Luis F. Espinoza-Audelo ◽  
Maricruz Olazabal-Lugo ◽  
Fabio Blanco-Mesa ◽  
Ernesto León-Castro ◽  
Victor Alfaro-Garcia
Keyword(s):  
Weighted Average ◽  
Global Market ◽  
Weighted Averaging ◽  
Agricultural Commodity ◽  
Data Set ◽  
Ordered Weighted Averaging ◽  
Price Estimation ◽  
Ordered Weighted Average ◽  
Price Analysis ◽  
Mathematical Process

Financial markets have been characterized in recent years by their uncertainty and volatility. The price of assets is always changing so that the decisions made by consumers, producers, and governments about different products is not still accurate. In this situation, it is necessary to generate models that allow the incorporation of the knowledge and expectations of the markets and thus include in the results obtained not only the historical information, but also the present and future information. The present article introduces a new extension of the ordered weighted averaging (OWA) operator called the Bonferroni probabilistic ordered weighted average (B-POWA) operator. This operator is designed to unify in a single formulation the interrelation of the values given in a data set by the Bonferroni means and a weighted and probabilistic vector that models the attitudinal character, expectations, and knowledge of the decision-maker of a problem. The paper also studies the main characteristics and some families of the B-POWA operator. An illustrative example is also proposed to analyze the mathematical process of the operator. Finally, an application to corn price estimation designed to calculate the error between the price of an agricultural commodity using the B-POWA operator and a leading global market company is presented. The results show that the proposed operator exhibits a better general performance than the traditional methods.

scholarly journals Induced intuitionistic fuzzy ordered weighted averaging: Weighted average operator and its application to business decision-making

2014 ◽  
Vol 11 (2) ◽  
pp. 839-857 ◽  
Author(s):  
Zeng Shouzhen ◽  
Wang Qifeng ◽  
José Merigó ◽  
Pan Tiejun
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Aggregation Operator ◽  
Weighted Averaging ◽  
Business Decision ◽  
Ordered Weighted Averaging ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Weighted Average ◽  
Decision Making Problem ◽  
Selection Of

We present the induced intuitionistic fuzzy ordered weighted averaging-weighted average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy weighted average (IFWA) and the induced intuitionistic fuzzy ordered weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.

A theory of palaeoclimate reconstruction

2020 ◽  
Author(s):  
Mengmeng Liu ◽  
Iain Colin Prentice ◽  
Cajo ter Braak ◽  
Sandy Harrison
Keyword(s):  
Climate Models ◽  
Compositional Data ◽  
Likelihood Function ◽  
Multinomial Distribution ◽  
Weighted Average ◽  
Weighted Averaging ◽  
Northern Eurasia ◽  
Data Set ◽  
Best Estimate ◽  
Special Case

<p>Past climate states can be used to test climate models for present-day changes and future responses. Past states can be reconstructed from fossil assemblages, and WA-PLS (weighted averaging–partial least squares) is one of the most widely used statistical methods to do this. However, WA-PLS has a marked bias. Whatever biotic indicator is being used, reconstructed climate values are artificially compressed and biased towards the centre of the range used for calibration.</p><p>Here we developed an improvement of the method, derived rigorously from theory. It makes three assumptions:</p><p>a) the theoretical abundance of each taxon follows a Gaussian (unimodal) curve with respect to each climate variable considered;</p><p>b) the abundances of taxa are compositional data, so they sum to unity and follow a multinomial distribution;</p><p>c) the best estimate of the climate value at the site to be reconstructed maximizes the log-likelihood function – in other words, it minimizes the difference between theoretical and actual abundances as assessed by the likelihood criterion.</p><p>The best estimate of the climate value is approximated by a tolerance-weighted version of the weighted average in which the abundances of taxa are weighted by the inverse square of their tolerances (a measure of the range of environments in which a taxon is found). WA-PLS thus corresponds to the special case when all taxon tolerances are equal. The fact that this special case is far from reality generally is part of the the cause of the “compression and bias”. The new method can be applied using the existing functions for WA-PLS in the R package rioja. We show that it greatly reduces the compression bias in reconstructions based on a large modern pollen data set from Europe, northern Eurasia and the Middle East.</p>

scholarly journals Web-Enabled Spatial Decision Analysis Using Ordered Weighted Averaging (OWA)

2021 ◽  
Author(s):  
Claus Rinner ◽  
Jacek Malczewski
Keyword(s):  
Graphical Representation ◽  
Decision Support Tool ◽  
Fine Tuning ◽  
Weighted Averaging ◽  
Decision Strategy ◽  
Spatial Decision Support ◽  
Data Set ◽  
Support Tool ◽  
Ordered Weighted Averaging ◽  
Exploratory Approach

This paper presents a spatial decision support tool that implements the Ordered Weighted Averaging (OWA) method. OWA is a family of multicriteria evaluation operators characterised by two sets of weights: criterion importance weights and order weights. We propose a highly interactive way of choosing, modifying, and fine-tuning the decision strategy defined by the order weights. This exploratory approach to OWA is supported by a graphical representation of the operator's behaviour in terms of decision risk and tradeoff/dispersion between criteria. Our prototype implementation is based on the CommonGIS software, and thus, Web-enabled and working with vector data. We successfully demonstrate online, exploratory support of spatial decision strategies using a data set of skiing resorts in Wallis, Switzerland.<div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s101090300095 <br></div>

scholarly journals Web-Enabled Spatial Decision Analysis Using Ordered Weighted Averaging (OWA)

2021 ◽  
Author(s):  
Claus Rinner ◽  
Jacek Malczewski
Keyword(s):  
Graphical Representation ◽  
Decision Support Tool ◽  
Fine Tuning ◽  
Weighted Averaging ◽  
Decision Strategy ◽  
Spatial Decision Support ◽  
Data Set ◽  
Support Tool ◽  
Ordered Weighted Averaging ◽  
Exploratory Approach

This paper presents a spatial decision support tool that implements the Ordered Weighted Averaging (OWA) method. OWA is a family of multicriteria evaluation operators characterised by two sets of weights: criterion importance weights and order weights. We propose a highly interactive way of choosing, modifying, and fine-tuning the decision strategy defined by the order weights. This exploratory approach to OWA is supported by a graphical representation of the operator's behaviour in terms of decision risk and tradeoff/dispersion between criteria. Our prototype implementation is based on the CommonGIS software, and thus, Web-enabled and working with vector data. We successfully demonstrate online, exploratory support of spatial decision strategies using a data set of skiing resorts in Wallis, Switzerland.<div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s101090300095 <br></div>

scholarly journals DECISION MAKING WITH DEMPSTER-SHAFER BELIEF STRUCTURE AND THE OWAWA OPERATOR

2014 ◽  
Vol 19 (Supplement_1) ◽  
pp. S100-S118 ◽  
Author(s):  
José M. Merigo ◽  
Kurt J. Engemann ◽  
Daniel Palacios-Marques
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Weighted Average ◽  
Weighted Averaging ◽  
Arithmetic Means ◽  
Belief Structure ◽  
Ordered Weighted Averaging ◽  
Decision Making Problem ◽  
Political Management

A new decision making model that uses the weighted average and the ordered weighted averaging (OWA) operator in the Dempster-Shafer belief structure is presented. Thus, we are able to represent the decision making problem considering objective and subjective information and the attitudinal character of the decision maker. For doing so, we use the ordered weighted averaging – weighted average (OWAWA) operator. It is an aggregation operator that unifies the weighted average and the OWA in the same formulation. This approach is generalized by using quasi-arithmetic means and group decision making techniques. An application of the new approach in a group decision making problem concerning political management of a country is also developed.

Fuzzy Imputation Method for Database Systems

2011 ◽  
pp. 805-821
Author(s):  
J. I. Peláez ◽  
J. M. Doña ◽  
D. La Red
Keyword(s):  
Missing Data ◽  
Weighted Average ◽  
Real Data ◽  
Database Systems ◽  
Data Sets ◽  
Data Set ◽  
Actual Problem ◽  
Owa Operators ◽  
Ordered Weighted Average ◽  
Linguistic Quantifiers

Missing data is often an actual problem in real data sets, and different imputation techniques are normally used to alleviate this problem. Imputation is a method to fill in missing data with plausible values to produce a complete data set. In this chapter, we analyze the performance of the different traditional data imputation methods. A new fuzzy imputation approach is proposed using ordered weighted average operators and the majority concept. In order to form the majority concept, we propose the use of neat OWA operators and linguistic quantifiers with two fusion strategies for aggregation operators.

scholarly journals Developing a GIS-Based Decision Rule for Sustainable Marine Aquaculture Site Selection: An Application of the Ordered Weighted Average Procedure

2021 ◽  
Vol 13 (5) ◽  
pp. 2672
Author(s):  
Elham Haghshenas ◽  
Mehdi Gholamalifard ◽  
Nemat Mahmoudi ◽  
Tiit Kutser
Keyword(s):  
Site Selection ◽  
Caspian Sea ◽  
Weighted Average ◽  
Weighted Averaging ◽  
Maximum Temperature ◽  
Risk Level ◽  
Operating Characteristics ◽  
The Caspian Sea ◽  
Marine Aquaculture ◽  
Ordered Weighted Average

Fish consumption is on the increase due to the increase in growth of the global population. Therefore, taking advantage of new methods such as marine aquaculture can be a reliable source for the production of fish in the world. It is necessary to allocate suitable sites from environmental, economic, and social points of view in the decision-making process. In this study, in order to specify suitable areas for marine aquaculture by the Ordered Weighted Averaging (OWA) methodology in the Caspian Sea (Iran), efforts were made to incorporate the concept of risk into the GIS-based analysis. By using the OWA-based method, a model was provided which can generate marine aquaculture maps with various pessimistic or optimistic strategies. Eighteen modeling criteria (14 factors and 4 constraints) were considered to determine the appropriate areas for marine aquaculture. This was done in 6 scenarios using multi-criteria evaluation (MCE) and ordered weighted average (OWA) methodologies. The results of the sensitivity analysis showed that most of the parameters affecting the marine aquaculture location in the region were as follows: Social-Economic, Water Quality, and Physical–Environmental parameters. In addition, based on Cramer’s V coefficient values for each parameter, bathymetry and distance from the coastline with the most effective and maximum temperature had the least impact on site selection of marine aquaculture. Finally, the final aggregated suitability image (FASI) of weighted linear combination (WLC) scenario was compared with existing sites for cage culture on the southern part of the Caspian Sea and the ROC (Relative Operating Characteristics) value turned out to be equal to 0.69. Although the existing sites (9 farms) were almost compatible with the results of the study, their locations can be transferred to more favorable areas with less risk and the mapping risk level can be controlled and low- or high-risk sites for marine aquaculture could be determined by using the OWA method.

A Hybrid Method for Pythagorean Fuzzy Multiple-Criteria Decision Making

2016 ◽  
Vol 15 (02) ◽  
pp. 403-422 ◽  
Author(s):  
Shouzhen Zeng ◽  
Jianping Chen ◽  
Xingsen Li
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Multiple Criteria Decision Making ◽  
Weighted Average ◽  
Multiple Criteria ◽  
Distance Measures ◽  
Weighted Averaging ◽  
Ordered Weighted Averaging ◽  
Special Cases

As a generalization of intuitionistic fuzzy set, the Pythagorean fuzzy set is interesting and very useful in modeling uncertain information in real-world decision-making problems. In this paper, we develop a new method for Pythagorean fuzzy multiple-criteria decision-making (MCDM) problems with aggregation operators and distance measures. First, we present the Pythagorean fuzzy ordered weighted averaging weighted average distance (PFOWAWAD) operator. The main advantage of the PFOWAWAD operator is that it uses distance measures in a unified framework between the ordered weighted averaging (OWA) operator and weighted average (WA) that considers the degree of importance of each concept in the aggregation. Some of its main properties and special cases are studied. Then, based on the proposed operator, a hybrid TOPSIS method, called PFOWAWAD-TOPSIS is introduced for Pythagorean fuzzy MCDM problem. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed method.

scholarly journals A Study on Evaluating Water Resources System Vulnerability by Reinforced Ordered Weighted Averaging Operator

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiqin Suo ◽  
Jing Zhang ◽  
Lixin He ◽  
Qian Zhou ◽  
Tengteng Kong
Keyword(s):  
Water Resources ◽  
Weighted Average ◽  
Principal Component ◽  
Weighted Averaging ◽  
Water Resource System ◽  
Water Resources System ◽  
Ordered Weighted Averaging ◽  
System Vulnerability ◽  
Ordered Weighted Averaging Operator ◽  
Handan City

Evaluating the vulnerability of a water resources system is a multicriteria decision analysis (MCDA) problem including multiple indictors and different weights. In this study, a reinforced ordered weighted averaging (ROWA) operator is proposed by incorporating extended ordered weighted average operator (EOWA) and principal component analysis (PCA) to handle the MCDA problem. In ROWA, the weights of indicators are calculated based on component score coefficient and percentage of variance, which makes ROWA avoid the subjective influence of weights provided by different experts. Concretely, the applicability of ROWA is verified by assessing the vulnerability of a water resources system in Handan, China. The obtained results can not only provide the vulnerable degrees of the studied districts but also denote the trend of water resources system vulnerability in Handan from 2009 to 2018. And the indictor that most influenced the outcome is per capita GDP. Compared with EOWA referred to various indictor weights, the represented ROWA shows good objectivity. Finally, this paper also provides the vulnerability of the water resource system in 2025 based on ROWA for water management in Handan City.

scholarly journals A Study of OWA Operators Learned in Convolutional Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Practical Method ◽  
Local Information ◽  
Weighted Averaging ◽  
Global Information ◽  
Owa Operator ◽  
Owa Operators ◽  
Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

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