Literatura académica sobre el tema "Euclidean preferences"

Three studies tested the hypothesis that human mate choice psychology uses a Euclidean algorithm to integrate mate preferences into estimates of mate value. In Study 1, a series of agent-based models identify a pattern of results relatively unique to mating markets where individuals high in Euclidean mate value experience greater power of choice: strong preference fulfillment overall and correlations between mate value and (a) preference fulfillment, (b) ideal standards, and (c) partner mate value. Studies 2 and 3 demonstrated that this pattern of results that emerges in human romantic relationships, is specific to mate value as a long-term partner, and is not accounted for by participant biases. These results suggest that human mate choice psychology uses a Euclidean algorithm to integrate mate preferences in mate choice, providing insight into the computational design of human mating psychology and validating this algorithm as a useful tool for future research.

We suggest a concept of convexity of preferences that does not rely on any algebraic structure. A decision maker has in mind a set of orderings interpreted as evaluation criteria. A preference relation is defined to be convex when it satisfies the following condition: If, for each criterion, there is an element that is both inferior to b by the criterion and superior to a by the preference relation, then b is preferred to a. This definition generalizes the standard Euclidean definition of convex preferences. It is shown that under general conditions, any strict convex preference relation is represented by a maxmin of utility representations of the criteria. Some economic examples are provided.

In social network analysis, the studies on weighted adjacency matrix of nodes are increasing day by day. In thispaper, a method is proposed by including node properties to neighbourhood matrix, in order to see the structures of weightedadjacency matrix that defines the relationship between the nodes. In accordance with this proposal, the relationship betweenthe faculties of Turkish universities is studied according to student preferences. Weighted adjacency matrix between facultiesis composed based on the frequency of faculty preference of students. By using the properties of faculties, this matrix ismultiplied by the adjacency matrix, calculated by Squared Euclidian Distance. The weighted adjacency matrix of the facultiesis compared with the re-calculated weighted adjacency matrix. It is observed that the relations between faculties are turnedout to be more meaningful in new weighted neighbourhood matrix which is multiplied by Squared Euclidean Distance.

Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the definition of what we call a preference basis that shows the preferred optimization directions in the objective space. Associated to this preference basis a new basis in the objective space—dominance basis—is computed. With this new basis the meaning of dominance is reinterpreted to include the designer’s preferences. In this paper, we show the effect of changing the geometric properties of the underlying structure of the Euclidean objective space by including preferences. This way of incorporating preferences is very simple and can be used in two ways: by redefining the optimization problem and/or in the decision-making phase. The approach can be used with any multi-objective optimization algorithm. An advantage of including preferences in the optimization process is that the solutions obtained are focused on the region of interest to the designer and the number of solutions is reduced, which facilitates the interpretation and analysis of the results. The article shows an example of the use of the preference basis and its associated dominance basis in the reformulation of the optimization problem, as well as in the decision-making phase.

Cette thèse étudie des aspects structurels et algorithmiques des restrictions de domaines de préférences, en se focalisant sur les préférences unimodales et les préférences Euclidiennes. Dans la première partie de la thèse, nous introduisons d'abord une généralisation des préférences unimodales sur des graphes quelconques, en se focalisant sur des aspects algorithmiques, notamment le problème de reconnaissance. Dans un deuxième temps, nous nous intéressons aux préférences presque unimodales. Plus précisément, nous proposons une nouvelle métrique d'unimodalité approchée et nous étudions ses propriétés théoriques et computationnelles. La deuxième partie de la thèse est consacrée à l'étude des préférences d-Euclidiennes (où d est la dimension) par rapport à différentes normes. Nous proposons d'abord une heuristique de reconnaissance des préférences 2-Euclidiennes par rapport à la norme l_2, et étudions son efficacité en pratique. Enfin, nous étudions des aspects structurels des préférences 2-Euclidiennes par rapport à la norme l_1
This thesis studies structural and algorithmic aspects of preference domain restrictions, namely single-peaked preferences and Euclidean preferences. In the first part of the thesis, we first introduce a generalization of the notion of single-peakedness on an arbitrary graph. We focus, in particular, on algorithmic aspects, namely the problem of recognition. The notion of nearly single-peakedness is then studied. More precisely, we introduce a new metric of nearly single-peakedness, and we study its theoretical and computational properties. The second part of the thesis is devoted to the study of d-Euclidean preferences (where d is the dimension of the real space) with respect to different norms. We first propose a heuristic algorithm for recognizing 2-Euclidean preferences with respect to the l_2 norm, and study its practical efficiency in practice. Finally, we focus on structural aspects of 2-Euclidean preferences with respect to the l_1 norm

Rhythm is at the heart of all music. It is the variation of the duration of sound over time. A rhythm has two components: one is the striking of an instrument – called the "onset" – and the other is silence. Historically, musical forms and works were preferred and became popular by their rhythmic properties. Therefore, to study rhythm is to study the underpinnings of all of music. In this thesis, we explore basic rhythmic preferences in traditional music and, using this as a point of reference, methods are implemented to generate similar types of rhythms. Finally, a software platform to facilitate such an analysis is developed – it is the first of its kind available to our best knowledge as this research field has only recently emerged.

Since at least the Greek classic period that thought, within the western civilizations, has been sharing a strict relationship with Euclidean principles, which have influenced and characterized it, leading to a specific type of reasoning and identity. In turn, as expressions of the mind, the forms that we have been thinking about and have brought to material reality, have been following these same Euclidean principles. Thought has shared also a closed relationship with architecture and architecture space form. A relationship that became even more pronounced with the incoming phenomenon of the Industrial Revolution with its standardization and mass production techniques and technologies. Ever since, the majority of architecture spaces that we have been thinking about and eventually building, follow and share Euclidean-orthogonal principles and relationships. However, with the arrival of the 20th and 21st centuries’ Digital Revolutions and their novel representation, visualization and production techniques and technologies, the forms that we think about and manage to produce, have achieved an unprecedented range of freedom, in which both Euclidean and non-Euclidean free forms are considered. This happening opened a pertinent and relevant thinking and discussion on whether humans, in their nature and within a valid freedom of choice, tend to prefer the long settled Euclidean, orthogonal-based architecture spaces, with all the elements that such geometry implies, namely, the presence of angular, sharp edges and vertices, or, on the contrary, they tend to prefer non-Euclidean, curved, rounded architecture spaces. This thesis proposes to address the problem of the preference for form, namely architecture space form, divided in two sub-problems that the literature review helped to identify: aesthetic judgements and approach-avoidance decisions, two judgements that, in turn, may rely in two knowledge ‘databases’: a subjective-based one, build through our life time sensible and rational experiences, and, a more objective-based one, which hides behind our genetic legacy and lays on basic evolutionary defense functions or mechanisms. We will approach this thesis Problem and Research Question, through (i) a free discourse on historic key events that, through our evolutionary stages, may have contributed to the fact that we have been closer to some elements, namely forms and architecture form, with certain geometric characteristics, over others; (ii) the evolution of aesthetics and our basic evolutionary defense functions or mechanisms, through qualitative and quantitative research methodologies, (iii) state-of-the-art review on the topic of preference for elements with distinct geometric characteristics, and (iv) our own developed experimental user study on abstract architecture spaces with distinct geometric characteristics at the contour level, which, based on the thesis two sub-problems, tried to validate our raised hypotheses. The results of this thesis suggest that humans prefer abstract architecture spaces with curved, rounded elements, rather than those equipped with angular, sharp ones. On the other hand, they were inconclusive on whether we prefer Euclidean-orthogonal or full non-Euclidean abstract architecture spaces, possibly due to familiarity (“mere-exposure”) and ‘strangeness’ effects. These results validate and partial validate hypotheses ‘H1’ and ‘H2’, respectively, the two major hypotheses of this thesis.
Desde pelo menos o período grego clássico que o pensamento das civilizações ocidentais têm partilhado uma relação estreita com princípios Euclidianos, algo que tem influenciado e caracterizado este pensamento, orientando-o para uma forma específica de raciocínio e identidade. Por sua vez, enquanto expressões da mente, as formas que temos vindo a pensar e a trazer para a realidade material têm seguido estes mesmos princípios Euclidianos. Enquanto extensão do pensamento, o mesmo se tem aplicado à arquitetura, nomeadamente à sua forma. Esta relação tornou-se ainda mais pronunciada com a chegada do fenómeno da Revolução Industrial e as suas técnicas e tecnologias de estandardização e produção em série. Desde então, a maioria dos espaços de arquitetura que temos pensado e construído seguem e partilham princípios e relações ortogonais-Euclidianos. No entanto, com a entrada em cena das revoluções digitais dos séculos XX e XXI e as suas inovadoras técnicas e tecnologias de representação, visualização e produção, as formas que temos vindo a pensar e a conseguir produzir, atingiram um grau de liberdade sem precedentes, no qual entram em consideração tanto as formas Euclidianas como não-Euclidianas. Este acontecimento abriu uma discussão pertinente e relevante sobre se, na eventualidade de uma válida liberdade de escolha, os humanos efetivamente preferem os, tão presentes e enraizados, espaços de arquitetura ortogonais-Euclidianos, com todos os elementos que esta geometria implica, nomeadamente, a presença de arestas e vértices angulares e afiados, ou, pelo contrário, preferem espaços de arquitetura não-Euclidianos, com elementos curvos e arredondados. Esta tese de doutoramento propõe, neste sentido, abordar o problema da preferência pela forma, nomeadamente, pela forma dos espaços de arquitetura, dividido pelos dois sub-problemas que a revisão bibliográfica ajudou a identificar: Julgamentos estéticos e decisões de aproximação ou afastamento, dois julgamentos que, por sua vez, podem estar baseados em duas ‘bases de dados’ de conhecimento: uma subjetiva, construída ao longo das nossas experiências sensíveis e racionais, e, uma mais objetiva, que se esconde atrás do nosso legado genético e assenta em funções ou mecanismos básicos de defesa evolutiva. O Problema e a Pergunta de Investigação desta tese serão abordados através de (i) um discurso livre sobre eventos históricos chave que, ao longo da nossa evolução, possam ter contribuído para o facto de podermos ter mantido um maior grau de aproximação em relação a determinados elementos, nomeadamente, formas e a forma da arquitetura, com determinadas características geométricas, sobre outros; (ii) a evolução do estudo da estética e as nossas funções ou mecanismos básicos de defesa evolutiva, através metodologias de investigação qualitativa e quantitativa, (iii) a revisão do estado-da-arte sobre a preferência de elementos com características geométricas distintas e (iv) o desenvolvimento de um estudo experimental sobre espaços abstratos de arquitetura com características geométricas distintas ao nível do contorno, que, baseado nos dois sub-problemas desta tese, procurou validar as hipóteses de estudo levantadas. Os resultados desta tese sugerem que os humanos preferem espaços abstratos de arquitetura com elementos curvos e arredondados em relação àqueles dotados de elementos angulares e afiados. Por outro lado, foram inconclusivos quanto ao facto de podermos preferir espaços de arquitetura Euclidianos-ortogonais àqueles puramente não-Euclidianos. Estes resultados validam e validam parcialmente as hipóteses H1 e H2, respetivamente, as duas hipóteses principais desta tese de doutoramento.

A set of voters’ preferences on a set of candidates is 2-Euclidean if candidates and voters can be mapped to the plane so that the preferences of each voter decrease with the Euclidean distance between her position and the positions of candidates. Based on geometric properties, we propose a recognition algorithm, that returns either “yes” (together with a planar positioning of candidates and voters) if the preferences are 2-Euclidean, or “no” if it is able to find a concise certificate that they are not, or “unknown” if a time limit is reached. Our algorithm outperforms a quadratically constrained programming solver achieving the same task, both in running times and the percentage of instances it is able to recognize. In the numerical tests conducted on the PrefLib library of preferences, 91.5% (resp. 4.5%) of the available sets of complete strict orders are proven not to be (resp. to be) 2-Euclidean, and the status of only 4.5% of them could not be decided. Furthermore, for instances involving 5 (resp. 6, 7) candidates, we were able to find planar representations that are compatible with 87.4% (resp. 58.1%, 60.1%) of voters’ preferences.

In a Group Decision Making problem, several people try to reach a single common decision by selecting one of the possible alternatives according to their respective preferences. So, a consensus process is performed in order to increase the level of accord amongst people, called experts, before obtaining the final solution. Improving the consensus degree as much as possible is a very interesting task in the process. In the evaluation of the consensus degree, the measurement of the distance representing disagreement among the experts’ preferences should be considered. Different distance functions have been proposed to implement in consensus models. The Euclidean distance function is one of the most commonly used. This paper analyzes how to improve the consensus degrees, obtained through the Euclidean distance function, when the preferences of the experts are slightly modified by using one of the properties of the Uniform distribution. We fulfil an experimental study that shows the betterment in the consensus degrees when the Uniform extension is applied, taking into account different number of experts and alternatives.

Sustainable supplier selection is one of the most important decisions for companies in sustainable supply chain management. Therefore, sustainable supplier performance evaluation and selection are of great importance in terms of economic, environmental, and social aspects of sustainable development. Sustainable supplier selection problem can be considered as a multiple-criteria decision-making (MCDM) problem. In the MCDM problems, decision-makers evaluate conflicting criteria and alternatives according to their own preferences/judgments. One of the most well-known MCDM methods is the technique for order preference by similarity to an ideal solution (TOPSIS). Neutrosophic set (NS) is an extension of fuzzy set where each element of the universe has the degrees of truth, indeterminacy, and falsity. A single-valued neutrosophic set (SVNS) is a special case of the neutrosophic set. This chapter aims to evaluate sustainable supplier selection in a Turkish discount market chain using the single-valued neutrosophic TOPSIS method based on normalized Euclidean and Hamming distances.

Abstract This paper presents a method of learning a class of preference order over interpretations in a propositional language which is a generalization of circumscription. The order considered in this paper is based on the distance in a weighted Euclidean space between the most preferable interpretation among all the interpretations and an interpretation represented as lattice points whose elements take values of either O or 1. Nonmonotonic reasoning defined by this order is a subclass of lazy nonmonotonic reasoning (Satoh 1992) where nonmonotonicity emerges only when new added knowledge is contradictory to the previous belief.

The aim of this paper is to propose a satisfactory degree method by using nonlinear programming for solving multi-attribute decision making (MADM) problems in which ratings of alternatives on attributes is expressed via interval-valued intuitionistic fuzzy (IVIF) sets and preference information on attributes is incomplete. Concretely, a nonlinear programming model is firstly explored to determine the satisfactory degree which is the ratio of the square of the weight Euclidean distance between an alternative and the IVIF negative ideal solution (IVIFNIS) to the sum of the square of the weight Euclidean distance between the IVIF negative ideal solution (IVIFNIS) and the IVIF positive ideal solution (IVIFPIS). Another nonlinear programming model is also developed to obtain satisfactory intuitionistic fuzzy sets, and then the general satisfactory degrees of the satisfactory intuitionistic fuzzy sets are used to generate the ranking order of the alternatives. Finally, a real example is employed to verify the applicability of the proposed approach and illustrate its practicality and effectiveness.

In this chapter, the authors present a new strategy for multi-attribute decision making in interval rough neutrosophic environment. They define Hamming distance and Euclidean distance between interval rough neutrosophic numbers. They also define interval rough neutrosophic relative positive ideal solution (IRNRPIS) and interval rough neutrosophic relative negative ideal solution (IRNRNIS). Then the ranking order of the alternatives is obtained by the technique for order preference by similarity to ideal solution (TOPSIS) strategy. Finally, a numerical example is provided to demonstrate the applicability and effectiveness of the proposed interval rough neutrosophic TOPSIS strategy.

This paper extends the technique for order preference by similarity to ideal solution (TOPSIS) for solving multi-attribute group decision making (MAGDM) problems under Atanassov intuitionistic fuzzy set (IFS) environments. In this methodology, weights of attributes and ratings of alternatives on attributes are extracted from fuzziness inherent in decision data and making process and described using Atanassov IFSs. An Euclidean distance measure is developed to calculate the differences between alternatives for each decision maker and an Atanassov IFS positive ideal solution (IFSPIS) as well as an Atanassov IFS negative ideal-solution (IFSNIS). Degrees of relative closeness to the Atanassov IFSPIS for all alternatives with respect to each decision maker in the group are calculated. Then all decision makers in the group may be regarded as “attributes” and a corresponding classical MADM problem is generated and hereby solved by the TOPSIS. The proposed methodology is validated and compared with other similar methods. A numerical example is examined to demonstrate the implementation process of the methodology proposed in this paper.

This paper presents the results of an empirical study of the learning styles of a group of computing students and the teaching styles of their tutors at The Open Polytechnic of New Zealand. This study of learning styles is based on Kolb’s learning model and the Felder-Soloman learning style instrument. To identify how close students’ learning styles match the teaching styles of their tutors we have used two indicators: the self-perception of the students about the proximity of their learning styles and the teaching styles of their tutors and a Euclidean distance measure between students’ and tutors’ learning preferences. Using survey data and the learning styles instrument results we identify the differences between the learning and teaching styles. Both indicators show consistent and significant differences between the learning and teaching styles, in the way students and their tutors perceive and understand information. Finally we make recommendation to tutors on how to bridge this gap and address the learning styles of their students.

Spatial models of preference, in the form of vector embeddings, are learned by many deep learning and multiagent systems, including recommender systems. Often these models are assumed to approximate a Euclidean structure, where an individual prefers alternatives positioned closer to their "ideal point", as measured by the Euclidean metric. However, previous work has shown there are ordinal preference profiles that cannot be represented with this structure if the Euclidean space has two fewer dimensions than there are individuals or alternatives. We extend this result, showing that there are situations in which almost all preference profiles cannot be represented with the Euclidean model, and derive a theoretical lower bound on the expected error when using the Euclidean model to approximate non-Euclidean preference profiles. Our results have implications for the interpretation and use of vector embeddings, because in some cases close approximation of arbitrary, true ordinal relationships can be expected only if the dimensionality of the embeddings is a substantial fraction of the number of entities represented.

Abstract The computing paradigm where sensor and actuator technology work in tandem to track and act on events in real Euclidean space, known as ambient intelligence (AmI), is likely to become increasingly common due to the rapid maturation of computing technology. Installing AmI in the built environment creates ambient intelligent environments (AmIE), which strive to make the places we inhabit (invisibly) sensitive and responsive to our presence, needs, wants, and preferences. Given that built environments and the goings-on therein are complicated in an of them selves, implementing AmI for (increasingly) complicated tasks in (increasingly) complicated scenarios, increases the difficulty of managing the outcomes in AmIEs. Our previous research indicates that industry practitioners attribute the agency of AmI artifacts as responsible for these outcomes; especially when harm perpetuation is (one of) the outcome(s), which we codified as the Agency/Intelligence Axis [1]. Due to the nascence of AmI, research on best practices for the design-engineering of AmI is still emerging. This research seeks to add to this literature by evaluating our formerly identified Agency/Intelligence Axis in the context of AmIE through a case study of VyZee, a retail company working on transitioning their retail stores to “smart” stores. Our findings highlight that while VyZee seems largely unaware of any relationship between agency and perpetuating un-anticipated/-desired outcomes, they do implement an array of levels of AmI agency in their retail stores, and their justifications for their choices are presented in the discussion. Finally, coding the data revealed more nuance then previously documented in the Agency/Intelligence Axis, and a new Ambient Intelligent Agent Model, which suggests that AmI agents have six properties, is proposed.