Showing papers on "Pairwise comparison published in 1999"

Estimation of pairwise relatedness with molecular markers

Abstract: Applications of quantitative genetics and conservation genetics often require measures of pairwise relationships between individuals, which, in the absence of known pedigree structure, can be estimated only by use of molecular markers. Here we introduce methods for the joint estimation of the two-gene and four-gene coefficients of relationship from data on codominant molecular markers in randomly mating populations. In a comparison with other published estimators of pairwise relatedness, we find these new "regression" estimators to be computationally simpler and to yield similar or lower sampling variances, particularly when many loci are used or when loci are hypervariable. Two examples are given in which the new estimators are applied to natural populations, one that reveals isolation-by-distance in an annual plant and the other that suggests a genetic basis for a coat color polymorphism in bears.

Multiview registration for large data sets

Abstract: We present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used in conjunction with any pairwise method based on aligning overlapping surface sections.

Multi-Criteria Decision Analysis via Ratio and Difference Judgement

Abstract: 1. Introduction. 2. SMART, direct rating. 3. The AHP, pairwise comparisons. 4. Scale sensitivity and rank preservation. 5. The alternatives in perspective. 6. Group decision making. 7. Resource allocation. 8. Scenario analysis. 9. Conflict analysis and negotiations. 10. Multi-objective linear programming. 11. MCDA in the hands of its masters. 12. Prospects of MCDA. Subject index. About the author.

Guidelines for Applying Multi-Criteria Analysis to the Assessment of Criteria and Indicators

Abstract: Multi-Criteria Analysis (MCA) is a decision-making tool developed for complex problems. In a situation where multiple criteria are involved confusion can arise if a logical, well-structured decision-making process is not followed. Another difficulty in decision making is that reaching a general consensus in a multidisciplinary team can be very difficult to achieve. By using MCA the members don't have to agree on the relative importance of the Criteria or the rankings of the alternatives. Each member enters his or her own judgements, and makes a distinct, identifiable contribution to a jointly reached conclusion. This manual is written for an audience that needs a clear, easy to follow manual that can be used in the field to implement MCA. The information is structured so that the reader is first introduced to the general concepts involved before dwelving into the more specific applications of Multi Criteria Analysis. The manual reviews the conceptual framework of C&I and introduces the theoretical basis of MCA, and methods such as ranking, rating and pairwise comparisons in the Analytic Hierarchy Process (AHP). It provides an example of how MCA can be applied to C&I in a Forest Certification context both from a 'top-down' perspective as well as in a more 'bottom-up' context.

Mathematical Structure of Voting Paradoxes: II. Positional Voting

Abstract: A theory is developed to explain all positional voting outcomes that can result from a single but arbitrarily chosen profile. This includes all outcomes, paradoxes, and disagreements among positional procedure outcomes as well as all discrepancies in rankings as candidates are dropped or added. The theory explains why each outcome occurs while identifying all illustrating profiles. It is shown how to use this approach to derive properties of methods based on pairwise and positional voting outcomes. Pairwise voting is addressed in the preceding companion paper [15]; the theory for positional methods is developed here.

An analytic hierarchy process based simulation model for entry mode decision regarding foreign direct investment

Abstract: A methodology is developed for ranking entry mode alternatives encountered by individual firms considering foreign direct investment (FDI). The methodology deals with the risks and uncertainties related to FDI. The analytic hierarchy process (AHP) is used to solve the multiple criteria decision-making problem using input from a firm's management. A simulation approach is incorporated into the AHP to handle the uncertainty considerations encountered in an FDI environment. The uncertainties include: (1) uncertainty regarding the future characteristics of the FDI decision making environment, (2) uncertainty associated with the decision maker's judgment regarding pairwise comparisons necessitated by the AHP.

Pairwise likelihood methods for inference in image models

Abstract: SUMMARY We consider parameter estimation for certain random set models for image data. For the spatial models considered here, a full likelihood-based approach to this problem is often difficult, since the likelihood generally cannot be computed for images of typical size. This has motivated the consideration of various less computationally demanding parameter estimation techniques. We discuss one method based on likelihoods for small subsets of the data. Consideration of the basic technique within the framework of the theory of estimating functions leads to a weighted version of the basic approach. Results of a simulation study are reported for some variants of the Boolean model and excursion sets of random fields. The methods are illustrated for some data on the spatial incidence of heather.

Computing a consistent approximation to a generalized pairwise comparisons matrix

Abstract: This paper presents an algorithm for computing a consistent approximation to a generalized pairwise comparisons matrix (that is, without the reciprocity property or even 1s on the main diagonal). The algorithm is based on a logarithmic transformation of the generalized pairwise comparisons matrix into a linear space with the Euclidean metric. It uses both the row and (reciprocals of) column geometric means and is thus a generalization of the ordinary geometric means method. The resulting approximation is not only consistent, but also closest to the original matrix, i.e., deviates least from an expert's original judgments. The computational complexity of the algorithm is O ( n 2 ).

Comparison analysis of methods for deriving priorities in the analytic hierarchy process

Abstract: Presents a comparison study of some methods for prioritisation in the analytic hierarchy process. The main objective of this analysis is to compare and evaluate a fuzzy preference programming method with the most popular prioritisation techniques: the eigenvector method, the weighted least squares method, the logarithmic least squared method and the goal programming method. The analysis is based on three evaluation criteria: the total deviation, measuring the Euclidean distance between the ratios obtained by the derived weights and the decision-maker ratios; the minimum violations criterion, measuring the rank reversal properties of the methods; and the conformity, indicating the outliers with respect to the other methods. The evaluation procedure uses randomly generated inconsistent pairwise comparison matrices of different dimensions.

Chainwise Paired Comparisons

Abstract: The pairwise comparison technique is a building block of the Analytic Hierarchy Process (AHP), which has been popularly used for multicriteria decision analysis. This paper develops a shortcut technique in which only n paired comparisons forming a closed chain are needed for n decision elements. Together with the development of a simple and intuitive measure of (inconsistency, this technique derives the relative weights of decision elements via easy step-by-step calculations on a spreadsheet format. Its performance has been tested on Saaty's wealth of nations example. It is important to notice that ranking and weights yielded from this alternative technique are identical to Harker's incomplete pairwise comparison solution for the same chain orientation for the example tested.

Fuzzy assessment of priorities with application to competitive bidding

Abstract: This paper presents a new fuzzy preference programming method for deriving priorities from inconsistent interval pairwise comparison matrices in the Analytical Hierarchy Process (AHP). The preference programming method of Arbel for elicitation of the priorities from interval matrices transforms the prioritisation problem into a linear program, which is attractive from a computational point of view, however it cannot deal with inconsistent decision—maker judgements. The proposed fuzzy preference programming method represents the inconsistent interval judgements as fuzzy linear constraints and defines a convex non—empty fuzzy feasible area. The assessment of the priorities is stated as an optimisation problem, maximising the decision-maker's satisfaction with the solution of an approximate fuzzy programming problem. The new method is applicable for both crisp and interval inconsistent judgements and can be considered as a generalisation of the classical prioritisation methods in the AHP. An applica...

DIVA: Applying Decision Theory to Collaborative Filtering

Abstract: This paper describes DIVA, a decision-theoretic agent for recommending movies that contains a number of novel features. DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings. It uses a novel similarity measure based on the concept of the probability of conflict between two orderings of ¯ items. The system has a rich representation of preference, distinguishing between a user’s general taste in movies and his immediate interests. It takes an incremental approach to preference elicitation in which the user can provide feedback if not satisfied with the recommendation list. We empirically evaluate the performance of the system using the EachMovie collaborative filtering database.

Reduction of pairwise comparisons in decision making via a duality approach

Abstract: Although pairwise comparisons have been seen by many as an effective and intuitive way for eliciting qualitative data for multi -criteria decision making problems, a major drawback is that the number of the required comparisons increases quadratically with the number of the entities to be compared. Thus, often even data for medium size decision problems may be impractical to be elicited via pairwis e comparisons. The more the comparisons are, the higher is the likelihood that the decision maker will introduce erroneous data. This paper introduces a dual formulation to a given multi-criteria decision making problem, which can significantly alleviate the previous problems. Some theoretical results establish that this is possible when the number of alternatives is greater than the number of decision criteria plus one.

A Framework for Automated Landmark Generation for Automated 3D Statistical Model Construction

Abstract: We describe a method of pairwise 3D surface correspondence for the automated generation of landmarks on a set of examples from a class of shape. We show how the pairwise corresponder can be used in an extension of an existing framework for establishing dense correspondences between a set of training examples to build a 3D statistical model. The framework relies upon additional algorithms for the production of surface paths between vertices on a polyhedral mesh, and these are described. An example statistical model is shown for the left lateral ventricle of the brain.

Managing Null Entries in Pairwise Comparisons

Abstract: This paper shows how to manage null entries in pairwise comparisons matrices. Although assessments can be imprecise, since subjective criteria are involved, the classical pairwise comparisons theory expects all of them to be available. In practice, some experts may not be able (or available) to provide all assessments. Therefore managing null entries is a necessary extension of the pairwise comparisons method. It is shown that certain null entries can be recovered on the basis of the transitivity property which each pairwise comparisons matrix is expected to satisfy.

The Analytic Hierachy Process (AHP): Standard Version, Forestry Application and Advances

Abstract: The Analytic Hierarchy Process (AHP) has attracted a lot of attention worldwide both in research and in supporting practical decision-making. It has been found especially suitable for environmental planning with multiple criteria. In natural resource management, the AHP has been most frequently applied to planning at strategic level. The AHP, as any planning method and technique, has its advantages but also drawbacks. Research related to the AHP and to its applications has been vigorous, and many methodological advances and extensions have been developed. These include, for example, statistical analyses of pairwise comparisons data and hybrid methods aiming at embody some fundamental features of the AHP together with benefits of other approaches, such as mathematical optimisation, Delphi technique, and SWOT. In Finland, a heuristic optimisation method called HERO has been developed for purposes of tactical forest planning. HERO can make use of pairwise comparisons and the eigenvalue technique in the formulation of optimisation problems in accordance with decision-makers’ objectives and preferences. It can be applied to group decision support and participatory planning, too. In this article, the standard version of the AHP is first presented. After that, some experiences of the use of the method in forest management planning are discussed, including also the drawbacks. The research efforts carried out in Finland in order to further develop forestry decision support methods, which utilise some ideas and techniques as applied in the AHP, are then briefly presented. A more closer look is taken at HERO.

The decision-theoretic interactive video advisor

Abstract: The need to help people choose among large numbers of items and to filter through large amounts of information has led to a flood of research in construction of personal recommendation agents. One of the central issues in constructing such agents is the representation and elicitation of user preferences or interests. This topic has long been studied in Decision Theory, but surprisingly little work in the area of recommender systems has made use of formal decision-theoretic techniques. This paper describes DIVA, a decision-theoretic agent for recommending movies that contains a number of novel features. DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings. It uses a novel similarity measure based on the concept of the probability of conflict between two orderings of items. The system has a rich representation of preference, distinguishing between a user's general taste in movies and his immediate interests. It takes an incremental approach to preference elicitation in which the user can provide feedback if not satisfied with the recommendation list. We empirically evaluate the performance of the system using the EachMovie collaborative filtering database.