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A plot of the density function (3) of the largest order statistic C * based on the optimal C ( R ) values C 1 , . . . , C M from the M = 20 runs of the optimization algorithm using the 2015/2016 NCAA basketball dataset.

A plot of the density function (3) of the largest order statistic C * based on the optimal C ( R ) values C 1 , . . . , C M from the M = 20 runs of the optimization algorithm using the 2015/2016 NCAA basketball dataset.

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Article
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In this paper, we introduce a new ranking system where the data are preferences resulting from paired comparisons. When direct preferences are missing or unclear, then preferences are determined through indirect comparisons. Given that a ranking of n subjects implies (2n) paired preferences, the resultant computational problem is the determination...

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Context 1
... φ and are the density function and the distribution function of the standard normal distribution, respectively. In Fig. 2 , we plot the density function of C * given by (3) based on the observed maxima C 1 , . . . , C M from the M = 20 runs. Based on the observed value C * = 53388 . 5 , the plot suggests that we can be confident that we are close to the global maximum. In particular, it looks unlikely that C * = 53388 . 5 could be off from the global ...

Citations

... The research circles involve in methodological advancements ritualistically facilitate the attainment of above documented delicacies through the application of paired comparison (PC) models or choice models. For example, Cattelan et al. (2013), demonstrated the applicability of PC mechanism to assess the outcomes of sports events while allowing the time-varying ability (Beaudoin & Swartz, 2018). Similarly, Johnson et al. (2019) elucidated the applicability of PC models in public health administration while facilitating the arduous task of project prioritization. ...
Chapter
This chapter fundamentally aims at the development of generalized framework encapsulating a wide range of dynamic utility functional and resultant latent choice models. The objectives are served by the application of well cherished exponential family of distributions capable of entertaining numerous probabilistic articulations through a single comprehensive and elegant expression. Moreover, the utility of the proposed scheme is further substantiated by delineating the working pedagogy in accordance with the rapidly embraced Bayesian paradigm. The legitimacy of the devised mechanism in the pursuit of optimal decision-making is advocated with respect to diverse experimental states. We entertained varying extent of worth parameters describing the preference ordering, different sample sizes and distinguished stochastic formations to inject the prior information or historic data in the demonstration of choice behaviors.KeywordsChoice behaviorsLatent choice modelsPrior informationUtility functional
... Recently, [9,10] elucidated the applicability of PC models in public health administration while facilitating the arduous task of project prioritization. For the account of more applications, one may see [11,12] in field of sensory analysis, [13,14] in engineering and reliability and [15,16] for measurement systems. ...
Article
Full-text available
This research fundamentally aims at providing a generalized framework to assist the launch of paired comparison models while dealing with discrete binary choices. The purpose is served by exploiting the fundaments of the exponential family of distributions. The proposed generalization is proved to cater to seven paired comparison models as members of this newly developed mechanism. The legitimacy of the devised scheme is demonstrated through rigorous simulation-based investigation as well as keenly persuaded empirical evaluations. A detailed analysis, covering a wide range of parametric settings, through the launch of Gibbs Sampler—a notable extension of Markov Chain Monte Carlo methods, is conducted under the Bayesian paradigm. The outcomes of this research substantiate the legitimacy of the devised general structure by not only successfully retaining the preference ordering but also by staying consistent with the established theoretical framework of comparative models.
... Market relations cover various aspects of the company's activities that cannot be characterized by a single indicator (Vrazhnova et al. 2019). The primary method of risk research in manufacturing industries is the use of heuristic methods, including pairwise comparison (Beaudoin and Swartz 2018;Dudin et al. 2015). This method, which allows adapting the conditions for assessing the risks to the transformation of market processes, is characterized by the presence of conditions of limited statistical and hard-to-measure factors (Beaudoin and Swartz 2018). ...
... The primary method of risk research in manufacturing industries is the use of heuristic methods, including pairwise comparison (Beaudoin and Swartz 2018;Dudin et al. 2015). This method, which allows adapting the conditions for assessing the risks to the transformation of market processes, is characterized by the presence of conditions of limited statistical and hard-to-measure factors (Beaudoin and Swartz 2018). In order to systematize the known risks in agricultural cooperation, it is recommended to build a risk map using the following methodology. ...
Book
This book is the very first book-length study devoted to the advances in technological development and systems research in cooperative economics. The chapters provide, first of all, a coherent framework for understanding and applying the concepts and approaches of complexity and systems science for the advanced study of cooperative networks and particular cooperative enterprises and communities. Second, the book serves as a unique source of reliable information on the frontier information technologies available for the production, consumer, credit, and agricultural cooperative enterprises, discussing predominant strategies, potential drivers of change, and responses to complex problems. Given the diverse range of backgrounds and advanced research results, researchers, decision-makers, and stakeholders from all fields of cooperative economics in any country of the world will undoubtedly benefit from this book.
... Based on the above, the authors suggest assessing risk related to market conditions in the petroleum machine-building sector using heuristic methods, more specifically paired comparison. This method, employed by the authors subsequently, can be adapted to market conditions to assess risks inherent in the operation of the market in a climate of limited statistical data and the impact of certain factors which yield themselves to measurement with some difficulty [14]. ...
... The examination procedure is based on the use of paired comparisons of a set of items whereby they are all compared in a pairwise fashion, with each subsequent assessment not linked with the previous one. All these paired assessments form a matrix of paired preferences, which, when subjected to special processing, helps produce the numerical parameters of indicators of an item's priority for a particular company [14,15]. ...
Article
Full-text available
This paper explores the essence and establishes the key characteristics of companies’ economic security in a climate of changing market conditions. Based on a summarization of research into the market conditions, the authors performed modeling of risk from changes in conditions in Russia’s petroleum machine-building market using the paired comparison method.
Chapter
The paper focuses on the methodology for creating a risk map applicable to agricultural cooperative enterprises. The primary purpose of the risk map for agricultural cooperation is to visually display on the map the relative location of each specific risk in relation to other significant risks in the context of determining the boundary of the admissible (critical) significance of the risk. The linear risk map is the best way to solve this issue. The paper determines that, as a result of the process of a qualitative risk assessment, it is possible to create a classified list of the most significant risks of business processes. These risks are visualized, allowing the organization to transform business processes as efficiently as possible to adapt to the influencing factors.