managing information uncertainty and complexity in decision...

EditorialManaging Information Uncertainty andComplexity in Decision-Making

Jurgita Antucheviciene,1 Madjid Tavana,2,3 Mehrbakhsh Nilashi,4,5 and Romualdas Bausys6

1Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University,Sauletekio Al. 11, LT-10223 Vilnius, Lithuania2Business Systems and Analytics Department, La Salle University, Philadelphia, PA 19141, USA3Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn,33098 Paderborn, Germany4Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia5Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran6Department of Graphical Systems, Faculty of Technological Sciences, Vilnius Gediminas Technical University,Sauletekio Al. 11, LT-10223 Vilnius, Lithuania

Correspondence should be addressed to Jurgita Antucheviciene; [email protected]

Received 19 September 2017; Accepted 19 September 2017; Published 16 November 2017

Copyright © 2017 Jurgita Antucheviciene et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

This special issue aims to provide recent developments inmanaging information uncertainty and complexity in deci-sion-making. Information uncertainty and complexity area common paradigm in modern decision-making becauseperfect information is seldom available to decision-makers.Awide range of statistical and nonstatistical decision-makingmodels have been proposed in the literature to model com-plex systems under uncertainty. Statistical methods (i.e.,probability theory) are useful in modeling complex sys-tems with incomplete or inaccurate data while nonstatisticalmethods (i.e., fuzzy set theory, rough set theory, possibilitytheory, or fuzzy neural networks) are useful for model-ing complex systems with imprecise, ambiguous, or vaguedata.

Today’s real-world problems involve multiple data sets,some precise or objective and some uncertain or subjective.Hybrid decision-making models are quickly emerging asalternative methods of choice for modeling complex systemsunder uncertainty. Managing uncertainty is a prerequisite toeffective problem-solving and decision-making in complexsystems. Therefore, we invited authors to submit origi-nal research articles that proposed formal decision-makingmethods to describe and rationalize the process of decision-making in complex systems under uncertainty.

This special issue received twenty-four articles, and afterrigorous review, we accepted six articles for inclusion. Wewould like to introduce each of them by a short description.The articles are described in the order of their appearance inthe issue.

D. Zhou et al. in their paper titled “An Improved BeliefEntropy and Its Application in Decision-Making” propose animproved belief entropy that enhances the performance ofDeng entropy and some other uncertainty measures inDempster–Shafer framework.The new belief entropy consid-ers the uncertain information comprised not only in themassfunction and the cardinality of the proposition, but also inthe scale of the frame of discernment (FOD) and the relativescale of each proposition with respect to the FOD. Numericalexamples show that the new belief entropy can quantifythe uncertain degree of the body of evidence (BOE) moreaccurately than the other uncertaintymeasures. A newuncer-taintymeasure-based decision-making approach is presentedin this paper and applied to a case study.The approach showsthe efficiency and effectiveness of the new belief entropy tosolve problems related to uncertain information processingin real-world applications.

J. Ma and Z. Guo, in their paper titled “Implicationsfor Firms with Limited Information to Take Advantage of

HindawiComplexityVolume 2017, Article ID 1268980, 3 pageshttps://doi.org/10.1155/2017/1268980

https://doi.org/10.1155/2017/1268980

2 Complexity

Reference Price Effect in Competitive Settings,” explore someuseful implications for decision-makers of firms with partialinformation to take advantage of the reference price effectin competitive settings. The work was closely related to theresearch on decision-making mechanisms of bounded ratio-nal players. Specifically, the authors set up a dynamic systemmodel to represent the evolution process of prices andreference prices. Attempts have also been made to revealthe impacts of key parameters on stability and profits.Accordingly, the researchers considered a market with twocompetitive firms which sell similar products to customersand compete in price. To model the evolution of referenceprices, the authors adopted an exponential smoothing modelwhich has been one of the most commonly used models forreference prices. In addition, to model the decision-makingprocesses of firms with limited information, the authorsadopted the widely used gradient mechanism, which hasproven to be robust in providing a good approximation to thepractical adjustment when only marginal profit is available.They show that if the adjustment process is unstable, toomuch advertising may be harmful even when the advertisingcost is negligible. Accordingly, firms should take potentialevolution processes into consideration before implementingtheir advertising strategies. The authors finally suggest thatincorporating random factors into the study of referenceprice effect makes implications more profound and robustwhich can be further investigated in the future research.

The paper “Some Generalized Pythagorean Fuzzy Bon-ferroni Mean Aggregation Operators with Their Applicationto Multiattribute Group Decision-Making” written by R.Zhang et al. develops a few aggregation operators for fusingthe Pythagorean fuzzy information and introduces a novelapproach to decision-making based on the proposed oper-ators. First, the generalized Bonferroni mean is extendedto the Pythagorean fuzzy environment, and the generalizedPythagorean fuzzy Bonferroni mean and the generalizedPythagorean fuzzy Bonferroni geometric mean are intro-duced. Second, a new generalization of the Bonferroni mean,namely, the dual generalizedBonferronimean, is proposed byconsidering the shortcomings of the generalized Bonferronimean. Thereafter, the new operators are used as bases topropose a novel approach to multiattribute group decision-making with Pythagorean fuzzy information. The authorsapply the new approach to illustrate its validity to the problemof selecting the best airline. As the calculation process of theproposed method is a little more complicated than existingmethods, the focus of future research of the authors is toreduce the complexity of the calculation of the proposedmethod.

K. Yan et al., in their paper titled “Managing InformationUncertainty inWaveHeightModeling for theOffshore Struc-tural Analysis through Random Set,” present a reliabilitystudy for an offshore jacket structure with emphasis on thefeatures of nonconventional modeling. First, a random setmodel is formulated for modeling the random waves inan ocean site. Then, a jacket structure is investigated in apushover analysis to identify the critical wave direction andkey structural elements. The selected probabilistic modelsare adopted for the important structural members. The wave

height model is processed in a P-box format when it is usedin the numerical analysis. The models are applied to find thebounds of the failure probabilities for the jacket structure.The propagation of this wave model is investigated in aninterval analysis and Monte Carlo simulation. The resultsare compared in context of information content and numer-ical accuracy. Further, the failure probability bounds arecompared with the conventional probabilistic approach. Thekey findings are that the P-box model possesses significantadvantages over the traditional probability and it providesmore information for engineers to make decisions especiallyfor an existing structure which is exposed to a changingenvironment. The findings from the case analysis show itsbenefits for practical use in reliability engineering.

The paper titled “An Approach to Integrating TacticalDecision-Making in Industrial Maintenance Balance Score-cards Using Principal Components Analysis and MachineLearning” written byN. Rodriguez-Padial et al. deals with thedesign of the customized maintenance plans in a productionplant. Applying Custom Balance Scorecard design frame-work, two data sets are constructed. The first data set reflectsthe maintenance work orders received in the productive areaand the second data set consists of the production values andmachine responses that are expressed as efficiency variablesand failure times. Later on, the first data set is consideredas input information at the exploratory phase, at which theprincipal component analysis (PCA)methodology is applied.The second data set is employed in the analysis phase. At thisphase, machine learning framework is implemented for twodifferent tasks: supervised learning, which is training amodelon known input and output data to predict future outputs,and unsupervised learning, which is finding hidden patternsand intrinsic structures in the input data. The proposedapproach enables the measurement of certain indicatorsof those productive areas that were previously consideredstrategic.

A. Mardani et al. in their paper titled “Recent Fuzzy Gen-eralisations of Rough Sets Theory: A Systematic Review andMethodological Critique of the Literature” present a compre-hensive and systematic review of methodological approachesand applications of fuzzy-rough set theory including fuzzylogic and fuzzy sets, rough sets, fuzzy logical operators,and fuzzy relations. They use preferred reporting items forsystematic reviews and meta-analyses (PRISMA) to clas-sify the selected articles according to author name, authornationality, publication year, application domain, method,contribution, and the publication outlet. A total of 132 paperswere selected for this systematic review and meta-analysisfor a seven-year period from 2010 to 2016. Many reviewedarticles were published between 2013 and 2016. The authorsalso classify these papers into six application areas: infor-mation systems, decision-making, approximation operators,feature and attribute selection, fuzzy set theories, and otherapplication areas. This study found that fuzzy set theorycombined with rough set theory was the most widely usedmethod. The study also ranks the Journal of InformationSystems first in publishing fuzzy-rough set papers among28 journals identified as relevant publication outlets in thefield. The authors encourage future research on expanding

Complexity 3

this collection with books, dissertations, and non-Englishsources.

We think the special issue is interesting to all those whohave to make decisions in uncertain environments, includingengineers,managers, and economists interested in the analyt-ical and practical aspects of hybridmultiple-criteria decision-making methods. Because multiple-criteria decision-makingis highly interdisciplinary, the audience of the issue couldinclude the research community specialists who study orteach decision-making related disciplines in academia andpractitioners who work as consultants or in-house experts inbusiness, industry, and government.

Jurgita AntuchevicieneMadjid Tavana

Mehrbakhsh NilashiRomualdas Bausys

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