With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be…
Abstract
With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.
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Federico Echenique, SangMok Lee and Matthew Shum
We propose a methodology for estimating preference parameters in matching models. Our estimator applies to repeated observations of matchings among a fixed group of individuals…
Abstract
We propose a methodology for estimating preference parameters in matching models. Our estimator applies to repeated observations of matchings among a fixed group of individuals. Our estimator is based on the stability conditions in matching models; we consider both transferable (TU) and nontransferable utility (NTU) models. In both cases, the stability conditions yield moment inequalities which can be taken to the data. The preference parameters are partially identified. We consider simple illustrative examples, and also an empirical application to aggregate marriage markets.
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WILLIAM H. DESVOUSGES, F. REED JOHNSON, RICHARD W. DUNFORD, K. NICOLE WILSON and KEVIN J. BOYLE
WE publish this issue on the eve of the Brighton Conference and our hope is that this number of The Library World will assist the objects of that meeting. Everything connected…
Abstract
WE publish this issue on the eve of the Brighton Conference and our hope is that this number of The Library World will assist the objects of that meeting. Everything connected with the Conference appears to have been well thought out. It is an excellent thing that an attempt has been made to get readers of papers to write them early in order that they might be printed beforehand. Their authors will speak to the subject of these papers and not read them. Only a highly‐trained speaker can “get over” a written paper—witness some of the fiascos we hear from the microphone, for which all papers that are broadcast have to be written. But an indifferent reader, when he is really master of his subject, can make likeable and intelligible remarks extemporarily about it. As we write somewhat before the Conference papers are out we do not know if the plan to preprint the papers has succeeded. We are sure that it ought to have done so. It is the only way in which adequate time for discussion can be secured.
Aarhus Kommunes Biblioteker (Teknisk Bibliotek), Ingerslevs Plads 7, Aarhus, Denmark. Representative: V. NEDERGAARD PEDERSEN (Librarian).
The following requirement will be included in due course in an amendment to Air Publication 1208.
Yun‐Sheng Chung, D. Frank Hsu, Chun‐Yi Liu and Chun‐Yi Tang
Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single…
Abstract
Purpose
Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP).
Design/methodology/approach
In particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D.
Findings
Bounds for these three cases are shown to be tight using the PDP for the input set.
Originality/value
As a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance Pm resulting from a maximum (information‐theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.
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Charles M. Cameron, John M. de Figueiredo and David E. Lewis
We examine personnel policies and careers in public agencies, particularly how wages and promotion standards can partially offset a fundamental contracting problem: the inability…
Abstract
We examine personnel policies and careers in public agencies, particularly how wages and promotion standards can partially offset a fundamental contracting problem: the inability of public-sector workers to contract on performance, and the inability of political masters to contract on forbearance from meddling. Despite the dual contracting problem, properly constructed personnel policies can encourage intrinsically motivated public-sector employees to invest in expertise, seek promotion, remain in the public sector, and work hard. To do so requires internal personnel policies that sort “slackers” from “zealots.” Personnel policies that accomplish this task are quite different in agencies where acquired expertise has little value in the private sector, and agencies where acquired expertise commands a premium in the private sector. Even with well-designed personnel policies, an inescapable trade-off between political control and expertise acquisition remains.