Showing papers on "Pairwise comparison published in 1992"

Evaluation of Advanced Construction Technology with AHP Method

Abstract: It is difficult to quantify the intangible benefits of advanced construction technologies and the risks involved in implementing such technologies with the use of traditional economic analysis techniques. An analytical approach to assessing the intangible aspects of technical innovation in construction is presented. The approach uses the analytical hierarchy process (AHP) technique and incorporates both favorable and unfavorable evaluation factors in one framework. The methods for constructing the comparison matrices, measuring the consistency of the pairwise comparisons, and aggregating the eigenvectors for the matrices to produce a final result are discussed. The sources of information for evaluation using the AHP method are identified and the significance of the method as a communication tool for group discussion is addressed. An example evaluation of two tower‐crane alternatives, one traditional and one semiautomated, is given to demonstrate the viability of the proposed approach. The effect of the ma...

A pairwise comparative method as illustrated by copulation frequency in birds

Abstract: The pairwise comparison of the value of the dependent variable between pairs of populations of one species or between pairs of congeners differing with respect to the character state of an independent variable is a very simple solution to two major problems of comparative studies, namely, poorly known phylogenies and unduly inflated sample sizes caused by using dependent observations. We illustrate the usefulness of this approach by testing the hypothesis that among birds frequent copulations between pair members may be one of several paternity-insurance mechanisms. The difference between each pair of observations was used as an independent observation in a sign test under the null hypothesis that equal numbers of observations should occur in each direction. A pairwise comparative approach may be superior to other comparative methods because few confounding variables are likely to influence the conclusions, the assumption of parsimony is more likely in closely related taxa, and problems of poorly known ph...

Multiple‐use planning of forest resources by using the analytic hierarchy process

Abstract: In this paper, a decision analysis method called the Analytic Hierarchy Process (AHP) is applied to multiple‐use planning of forest resources. Principles of the method are presented and its properties are discussed with regard to multiple‐use planning. An illustrative example is given, in which the decision alternatives are produced using linear programming. Both quantitative and qualitative decision elements can be dealt with when the decision alternatives are evaluated using the AHP. The preferences of the decision‐maker are accommodated by pairwise comparisons between the decision elements. An additive priority model is estimated based on comparisons. Due to its simplicity, flexibility, and high effectiveness in analysing complex decision problems, the AHP is very applicable in multiple‐use planning.

Use and misuse of multiple comparisons in animal experiments

Abstract: The objective of many animal experiments is to detect meaningful relationships among treatments and associated responses. Types of comparisons of means include pairwise multiple comparisons, planned orthogonal or nonorthogonal contrasts, and orthogonal polynomials. Some procedures are appropriate only for specific types of treatment designs and specific types of objectives. Pairwise, multiple comparisons are appropriate only for comparing unstructured, qualitative treatments. Planned comparisons partition the overall set of treatment effects into independent or nonindependent subsets, with special application to factorials. Orthogonal polynomial (regression) procedures assess relationships between quantitative treatments and response when a full range of responses or an optimal dose is of interest. Recommendations for appropriate use of each mean comparison procedure are illustrated using data from three Journal of Animal Science articles. Also mentioned are a number of computer graphics packages that provide creative ways to display biological relationships and can be linked to statistical packages for input and to word processors or 35-mm cameras for output.

Log-linear modelling of pairwise interobserver agreement on a categorical scale.

Abstract: This article uses log-linear models to describe pairwise agreement among several raters who classify a sample on a subjective categorical scale. The models describe agreement structure simultaneously for second-order marginal tables of a multidimensional cross-classification of ratings. Practical difficulties arise in fitting the models, because models refer to pairwise marginal tables of a very large and sparse table. A standard analysis that treats the marginal tables as independent yields consistent estimates of model parameters, but not of the covariance matrix of the estimates. We estimate the covariance matrix using the jackknife. We apply the models to describe agreement between evaluations made by seven pathologists of carcinoma in situ of the uterine cervix, using a five-level ordinal scale. Previous analyses showed differences among the pathologists in their pairwise levels of agreement, but we observe near homogeneity in the dependence structure of their ratings.

Reconstruction of Evolutionary Trees from Pairwise Distributions on Current Species

Abstract: : Suppose that the evolution of a character possessed by a number of current species is modelled as a Markov random field on an evolutionary tree. Suppose that for each pair of current species we know the joint probability distribution of the pair of characters possessed by that pair of species. We give conditions under which the evolutionary tree can be reconstructed from knowledge of these pairwise joint distributions, that is, conditions under which there is only one evolutionary tree topology consistent with the given pairwise distributions. In this way we establish consistency of a method for reconstructing evolutionary trees using pairwise distributions estimated from observed homologous DNA sequences.

Interactive Multiobjective Analysis Embedding the Decision Maker's Implicit Preference Function

Abstract: Two simple interactive techniques are developed and illustrated by means of two different real-life examples in Thailand. The first technique, Evolutionary Sequential Multiobjective Problem Solving (ESEMOPS), is an open-ended algorithm designed for planning problems with discrete alternatives. ESEMOPS helps the decision making group (DMG) develop progressively a preference function over the alternatives. The algorithm follows an evolutionary “breeding” strategy to generate a small set of good alternative solutions. This heuristic search, which does not guarantee that the adopted ‘satisfactum’ is an efficient solution leads to plausible results when applied to the planning of the Mae Khlong-Chao Phraya interbasin water transfer and irrigation system. The second algorithm, Search Beam Method (SBM) is essentially a series of one-dimensional searches for an efficient point along a “beam” passing through a goal point. Repeated search towards displayed goal points is leading to a set of quasi non-dominated solutions. SBM is illustrated by the Ubol Ratana reservoir control problem with the two conflicting objectives of energy generation and irrigation water supply. Neither ESEMOPS nor SBM require that weights, utilities, or pairwise tradeoffs be assessed. These features have been very much appreciated by a real DMG presented with the two techniques.

The Analytic Hierarchy Process: Structure of the Problem and Its Solutions

Abstract: Many real-world problems involve the assignment of priorities to a set of objects, projects, products, etc. Preferences are often expressed in the form of pairwise comparisons, rather than as weights directly. In many environments, this is the most convenient format for extracting such preference data. Specifically, ifnobjects are being compared, then preferences are supplied by means of an n×n matrix A =(a ij )wherea ij is the degree to which objectiis preferred to objectj.Paired comparison information has been used extensively in multicriteria problems and in particular to derive weights for goal programming functionals (see Charnes and Cooper [2]; Gass [6]).

On frequency-hop multiple access sequence

Abstract: The design of frequency-hop spread spectrum multiple access sequences is discussed. In particular, it is pointed out that the minimum number of pairwise hits between sequences does not necessarily guarantee good sequences in terms of mutual interference between multiple users. The point is illustrated using pairwise optimum Reed-Solomon sequences with maximum one pairwise hit. It is shown that in this case, one user can even have a hit in every hop in the worst case. Since the worst-case assumption is often too restrict in engineering, the average number of hits appears to be a more appropriate criterion in sequence design. This is particularly true where error-correcting techniques are employed. Hit distributions are given for some hopping sequence sets in a frequency-hop, multiple-access, spread spectrum system. A generating function proves to be a useful tool. A special set of Reed-Solomon sequences is shown to approximate truly random sequences with respect to hit probability distribution, but with a reduced variance. >

Pairwise rank superiority of winter wheat genotype for spring stand

Abstract: Rank statistics can be used as a simple method of summarizing multienvironment data when the assumptions of uniform variances and main effects across environments are violates. A pairwise rank superiority measure is described for analysis of genotypes across environments when usual statistical measures are invalid or too complex for clear data presentation (.)

Symbolic clustering

Abstract: Clustering is the problem of finding a good organization for data. Because there are many kinds of clustering problems, and because there are many possible clusterings for any data set, clustering programs use knowledge and assumptions about individual problems to make clustering tractable. Cluster analysis techniques allow knowledge to be expressed in the choice of a pairwise distance measure and in the choice of clustering algorithm. Conceptual clustering adds knowledge and preferences about cluster descriptions. In this dissertation, I describe symbolic clustering, which adds representation choice to the set of ways a data analyst can use problem-specific knowledge. I will develop an informal model for symbolic clustering, and use it to suggest where and how knowledge can be expressed in clustering. A language for creating symbolic clusterers, based on the model, has been developed and tested on three real clustering problems. The dissertation concludes with a discussion of the implications of the model and the results for clustering in general.

Procurement process: Decision by exclusion and pairwise comparisons

Abstract: Two case studies illustrate multicriteria decision making techniques frequently used in procurement practice: least-cost sufficient performance and weighted utility optimization. Both techniques have defects and are rarely robust. Decision by exclusion and pairwise comparisons is proposed as a third approach which eliminates inferior alternatives by generalized forms of minimum performance requirements established under multicriteria framework. It is more robust and maintains high level of accountability.

A problem identification taxonomy for classification and automated formulation of multiple objective linear programming models

Abstract: This paper presents a knowledge-based Decision Support System (DSS) for classification, formulation and solution of multiple objective linear programming problems. The authors propose a generic taxonomy which is used to classify commonly encountered types of linear programming problems. Classification of the problem, vis-a-vis the proposed taxonomy, is based on the interaction with the user to determine the attributes which best describe the context or setting of the problem. A total of twenty-four problem types are included in the taxonomy. Following classification, a problem type-specific rule base is invoked to assist the user in constraint formulation. A product blending linear programming problem is used to demonstrate this component of the system since these types of problems typically include more varied constraints, including ratio as well as additive types. A second rule base is invoked for formulation of the multiple criteria objective function, model solution and sensitivity analysis. An initial goal prioritization scheme is obtained by use of the Analytical Hierarchy Process [17]; the optimal goal ordering is obtained by interaction with the user in the form of pairwise attribute value tradeoffs. The system developed is intended for use by the OR-naive user who is more familiar with the content of a problem than he/she is with the mathematical tools needed to formulate and solve such models. The system is a model management tool designed to interpret user inputs and translate those inputs into a solvable multiple objective LP. This interface alleviates the technical burdens of content specification and solution. The approach expands previous formulation tools, such as those based on natural language processing [8], to a broader range of problem types in a multiple criteria environment. The system was implemented on a personal computer using VP-Expert, BASIC and LINDO, and is demonstrated on a multiple objective blending problem. The ability of the approach to accurately classify LP problems was tested on thirty-six subjects. Results suggest that correct classification of problems was more likely to occur when the system was used.

Stepwise multi-criteria evaluation

Abstract: In this article we show that quantitative methods for multi-criteria evaluation have three serious shortcomings First, the addition of an alternative which has bad criterion scores may distort the comparison between other alternatives Second, the addition of a criterion which does not differentiate between alternatives may distort the comparison between alternatives Third, when a ranking of alternatives is derived from a multi-criteria evaluation, it suggests complete transitivity It is shown that intransitivity may occur Therefore a stepwise procedure is recommended It involves the removal of all criteria which do not or hardly differentiate between the alternatives and a pairwise evaluation of alternatives

Algorithms to find the maximum set of pairwise incomparable weighted vertices in a directed acyclic graph

Abstract: Two algorithms are considered that find the largest set of pairwise incomparable weighted vertices. The branch-and-bound method is used to find the set subject to additional constraints. Results of a computational experiment are reported.

Inference of three-dimensional structure from weak perspective projections based on pairwise comparisons of images

Abstract: A new method is proposed for extraction of 3-D rigid depth interpretations from pairwise comparisons of weak perspective projections. The method provides a simple criterion to test for the correctness of correspondence for a pair of images; the method also provides a description of a one-parameter family of interpretations for each pair of images that satisfies this criterion. We show that if at least three projections of a volumetric object are known, then a 3-D rigid interpretation can be inferred from pairwise comparisons between any one of these images and other images in the set. The 3-D interpretation is derived from the intersection of corresponding one-parameter families. The method provides a uniform computational basis for different processes of depth perception (for example, depth-from-stereo and depth-from- motion). In fact, a single mechanism for these processes in the human visual system could be sufficient. The proposed method does not require information about relative positions of eye(s) or camera(s) for different projections, but this information can be easily incorporated. This method can be applied for pairwise comparison within a single image. If any non-trivial correspondence is found it means that several views of the same object are present in the same image or that the object with volumetric symmetry is presented within the image. Results of pairwise comparison within a single image may also be considered in the process of depth reconstruction. If the object possesses two or more symmetries, its depth can be reconstructed from single image. Symmetry as a source of structural information is widely used by the visual system.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

AHP Version 5.1 user`s manual

Abstract: As decisions become more and more complex, decision makers are faced with the challenge of sorting through many variables to arrive at a sound decision. The Analytical Hierarchy Process (AHP) is a tool, that allows a systematic, logical approach to reducing complex issues into manageable pieces. The decision maker can then sort through the variables and determine to what degree a particular variable should influence the final decision. The power of the AHP as a management tool comes from the fact that it reduces complex problems to many simple pairwise decisions. Only two items need be compared against one another - a much simpler task than comparing an item to all the others simultaneously. By arranging the items that influence a decision in the form of a matrix and comparing appropriate pairs in this matrix to each other, each item can be compared with every other item. Matrix algebra can then operate on this matrix and rank each item according to its importance to the final decision.

AHP Version 5. 1 user's manual. [Analytical Hierarchy Process (AHP)]

Abstract: As decisions become more and more complex, decision makers are faced with the challenge of sorting through many variables to arrive at a sound decision The Analytical Hierarchy Process (AHP) is a tool, that allows a systematic, logical approach to reducing complex issues into manageable pieces The decision maker can then sort through the variables and determine to what degree a particular variable should influence the final decision The power of the AHP as a management tool comes from the fact that it reduces complex problems to many simple pairwise decisions Only two items need be compared against one another - a much simpler task than comparing an item to all the others simultaneously By arranging the items that influence a decision in the form of a matrix and comparing appropriate pairs in this matrix to each other, each item can be compared with every other item Matrix algebra can then operate on this matrix and rank each item according to its importance to the final decision

Fuzzy sets, rough sets, and modeling evidence: Theory and Application. A Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection

Abstract: The Dempster-Shafer theory of evidence is applied to a multiattribute decision making problem whereby the decision maker (DM) must compromise with available alternatives, none of which exactly satisfies his ideal. The decision mechanism is constrained by the uncertainty inherent in the determination of the relative importance of each attribute element and the classification of existing alternatives. The classification of alternatives is addressed through expert evaluation of the degree to which each element is contained in each available alternative. The relative importance of each attribute element is determined through pairwise comparisons of the elements by the decision maker and implementation of a ratio scale quantification method. Then the 'belief' and 'plausibility' that an alternative will satisfy the decision maker's ideal are calculated and combined to rank order the available alternatives. Application to the problem of selecting computer software is given.

A Bayesian Network for Temporal Segmentation

Abstract: A recurrent network which segments an unlabeled externally timed sequence of data is presented. The proposed method uses a Bayesian learning scheme earlier investigated, where the relaxation scheme is modified with a few extra parameters, a pairwise correlation threshold and a pairwise conditional probability threshold. The method studied is able to find start and end positions of words which are in an unlabeled continuous stream of characters. The robustness against noise during both learning and recall is studied.