Stratified network mapping decision making technique based decision support framework for R&D budget allocation in South Korea

In this research work, we proposed and developed a stratified network mapping (SNM) decision making method and used it to improve the industry–university specialization in R&D in each region selected in this study. The proposed method considers the influence of criteria on and their priority in alternatives performance evaluation process. By analyzing the influence of these criteria on decision-making, we can easily improve the performance of alternatives. The SNM gives a clear understanding of each alternatives performance efficiency level. It explores possible and inefficient states and high-level influence states in inefficient states. Narrowly using multi-criteria decision-making methods to rank alternatives does not improve the performance of alternatives. The proposed method helps rank alternatives and improve the performance level of alternatives in each state. We analyzed the R&D investment of central and local governments of South Korea. It is an attempt to invigorate and facilitate R&D collaboration using a decision support model. We analyzed industry–academia research networks and enhanced the efficiency of the research.     

A fuzzy AHP approach based on the concept of possibility extent

The main purpose of this paper is to develop a fuzzy AHP method for tackling the uncertainty and imprecision existing in multi-criteria decision process. The proposed method uses fuzzy pair-wise comparison judgments in place of exact numerical values of the comparison ratios. The geometric mean technique is used to integrate all decision-makers’ opinions and construct the fuzzy positive reciprocal matrices. The algebraic operations of triangular fuzzy numbers are utilized to calculate the fuzzy suitability indices of all alternatives. The extent analysis method is used to compute the degree of possibility of priority among fuzzy suitability indices. Besides, two principles are presented to solve the multi-criteria decision problem in a fuzzy decision environment. Principle I provides a partial preorder, and Principle II gives a total preorder on the set of the possible alternatives. Finally, a numerical example of selecting the company with optimal performance in performing customer relationship management is used to demonstrate the decision process of proposed method.     

第四方物流下的供应链选择

利用供应链运作参考模型（SCOR）第一层的指标作为评判指标体系，并将它们与供应链关系指标综合考虑，采用模糊层次分析法（Fuzzy AHP）来解决每条供应链的多层次和MCDM问题，并耳最终得到对供应链的最优选择。     

层次分析法在解决工程款拖欠问题中的应用

层次分析法(Analytic Hierarchy Process简称AHP)是一种多目标、多判据的系统评价方法,是一种可以对定性问题进行定量分析的多准则决策方法。用层次分析法的思想对工程款拖欠风险进行分析,可以有效的弥补定性研究的不足,为解决工程款拖欠提供了一种新的思路。     

Bayes estimates of muIti-criteria decision alternatives using Monte Carlo integration

A Bayesian procedure is proposed for the estimation of the weights of the alternatives in a multi-criteria decision model with data that stem from pair-wise comparison of alternatives. The prior information restricts the weights to the unit simplex. The posterior results are computed by Monte Carlo integration procedures based on importance sampling. The Bayesian procedure is applied to a case study concerning the choice of a professor of Operations Research (OR). Results are: (1) according to the Bayesian procedure a different candidate would be chosen as professor of OR than according to the maximum likelihood procedure; (2) given the prior and data information, there exists a substantial probability of taking the wrong decision; (3) there exists a ranking of the candidates with a posterior probability greater than one half.     

A supplier evaluation model based on customer demand in blockchain tracing anti-counterfeiting platform project management

This study assesses supplier selection at the beginning of project management to establish an evaluation system corresponding to blockchain tracing anti-counterfeiting platforms (BTAP). First, this paper determines 20 evaluation criteria from the four dimensions of platform overview, core technology, application support, and operations management. On this basis, multi-criteria decision making (MCDM) based on customer needs is proposed, which consists of three main steps. First, quality function deployment (QFD) and the best and worst method (BWM) are used to evaluate the four dimensions of the BTAP and specific evaluation criteria from the perspective of customers to obtain the criteria weight. Then, this method uses the extended Vlse Kriterjumska Optimizacija I Kompromisno Resenje (VIKOR) approach to sort the alternatives. Finally, the improved decision making trial and evaluation laboratory (DEMATEL) method is used to analyse the relationships between the 20 criteria in the four dimensions. The feasibility and effectiveness of this method are verified by an example. According to the sensitivity analysis and comparative analysis, the results show that this method can evaluate blockchain anti-counterfeiting enterprises. The main conclusions are as follows: the core technology is the most important factor influencing the choice of a BTAP project, and the role of application support in evaluation cannot be ignored.     

On scales and decision-making based on arithmetic mean

The scales used in schools for the purpose of student assessment are ordinal. The average of ordinal values is often used for the evaluation and comparison of overall student performance. We demonstrate a theorem for the selection of scales invariant with respect to rank of average and compare scales according to this property. A uniformity criterion is also defined for the choice of the scale on which to calculate the average. Concatenated sets of grades from scales not belonging to the same category may bring about errors of rank and absurd averaging, which may have a heavy impact on related decision-making processes.     

Role of culture in water resources management via sustainable social automated negotiation

Increased competitions for water resources in many regions worldwide call for cooperative approaches. The competitions are complex for humans to resolve due to numerous alternatives and different or conflicting preferences of multiple stakeholders over multiple criteria, which might even oppose desirable environmental objectives. Parties also have incomplete information about the preferences of the counterparties. Electronic negotiation, empowered by intelligent agent technology, is a combination of artificial intelligence, economics, and psychology to find beneficial joint agreements in complex paradigms such as this. This study investigates a multilateral sustainable automated negotiation among intelligent agents representing stakeholders, including the legal party ‘nature’ as one of the stakeholders. It defines decision criteria and alternatives in the framework of cultural factors, elicits preferences of the stakeholders regarding the criteria without their intervention using a multi-criteria decision-making method, prunes the solution space before starting the negotiation by recognizing a general social treaty, determines the multi-issue specific treaty by learning the stakeholders, and demonstrates bidding and acceptance strategies.     

Optimal comparison of misspecified moment restriction models under a chosen measure of fit

Suppose that the econometrician is interested in comparing two misspecified moment restriction models, where the comparison is performed in terms of some chosen measure of fit. This paper is concerned with describing an optimal test of the Vuong (1989) and Rivers and Vuong (2002) type null hypothesis that the two models are equivalent under the given measure of fit (the ranking may vary for different measures). We adopt the generalized Neyman–Pearson optimality criterion, which focuses on the decay rates of the type I and II error probabilities under fixed non-local alternatives, and derive an optimal but practically infeasible test. Then, as an illustration, by considering the model comparison hypothesis defined by the weighted Euclidean norm of moment restrictions, we propose a feasible approximate test statistic to the optimal one and study its asymptotic properties. Local power properties, one-sided test, and comparison under the generalized empirical likelihood-based measure of fit are also investigated. A simulation study illustrates that our approximate test is more powerful than the Rivers–Vuong test.     

TACKLING FALSE POSITIVES IN BUSINESS RESEARCH: A STATISTICAL TOOLBOX WITH APPLICATIONS

Serious concerns have been raised that false positive findings are widespread in empirical research in business disciplines. This is largely because researchers almost exclusively adopt the ‘p‐value less than 0.05’ criterion for statistical significance; and they are often not fully aware of large‐sample biases which can potentially mislead their research outcomes. This paper proposes that a statistical toolbox (rather than a single hammer) be used in empirical research, which offers researchers a range of statistical instruments, including a range of alternatives to the p‐value criterion such as the Bayesian methods, optimal significance level, sample size selection, equivalence testing and exploratory data analyses. It is found that the positive results obtained under the p‐value criterion cannot stand, when the toolbox is applied to three notable studies in finance.     

Investigating the satisfaction of citizens in municipality services using fuzzy modelling

Rapid developments in communication and information technologies in the world force local administrators to be more effective. For a local administrator to be elected again, satisfaction of citizens with the duties that need to be performed and the services provided is significant. In this study, citizen satisfaction with municipality services was measured using a questionnaire on a household level. It was aimed to develop a new approach in evaluation of municipal service quality.The data that were obtained were analyzed with fuzzy multi-criteria decision-making techniques. For determination of criterion weights, FDEMATEL was utilized, and for household-based ranking, the FTOPSIS technique was implemented. A raster map was created in the ArcGIS geostatistics module by applying IDW interpolation on the resulting values. The map that was produced visually revealed the satisfaction of citizens regarding the quality of municipality services based on location. When the map was analyzed, it was seen that the satisfaction levels in the study areas of two neighborhoods were high, while the levels in the study area of one neighborhood was low.     

Assessment of CSR based supply chain performance system using an integrated fuzzy AHP-TOPSIS approach

The objective of this research was to identify Corporate Social Responsibility (CSR) criteria as well as propose and prioritise the alternatives to improve the supply chain performance system of an organisation. To meet the objective, firstly, CSR-based criteria, sub-criteria and alternatives were identified based on the literature review and discussion with the field experts, taken from automobile industries located at the Delhi region of India. Then, kappa statistics has been applied to check the internal consistency between identified criteria and sub-criteria. After finding the evidence of internal consistency, a hierarchical-type model has been developed and analysed using a multi-criteria decision-making as an integrated fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order performance by similarity to the ideal solution (TOPSIS) approach. To make the results more robust and feasible, a sensitivity analysis has also been performed.     

Efficient rank tests for semiparametric competing risk models

We consider a semiparametric competing risk model given by k independent survival times. The paper offers an asymptotic treatment of tests for the semiparametric null hypothesis of equality of the underlying risks. It turns out that modified rank tests are asymptotically efficient for certain semiparametric submodels, where the baseline hazard is a nuisance parameter. In addition, the asymptotic relative efficiency of the present tests is derived. A comparison of asymptotic power functions can then be used to classify various tests proposed earlier in the literature. For instance a chi-square type test is efficient for proportional hazards. Data driven tests of likelihood ratio type are proposed for cones of alternatives. We will consider certain stochastically increasing alternatives as a special example. The paper shows how the concept of local asymptotic normality of Le Cam works for hazard oriented models.     

EVALUATING ALTERNATIVE REPRESENTATIONS OF THE CHOICE SETS IN MODELS OF LABOR SUPPLY

Abstract.  During the last two decades, the discrete choice modeling of labor supply decisions has become increasingly popular. Within the literature adopting this approach, however, there are two potentially important issues that so far have not been given the attention they might deserve. A first issue concerns the procedure by which the discrete alternatives are selected to enter the choice set. Most authors choose (not probabilistically) a set of fixed points identical for every individual. Some authors adopt instead a sampling procedure and also assume that the choice set may differ across households. A second issue concerns the availability of the alternatives. Most authors assume all the values of hours of work within some range are equally available. At the other extreme, some authors assume only two or three alternatives (for example, nonparticipation, part-time and full-time) are available for everyone. Some studies account instead for the fact that not all the hour opportunities are equally available to everyone specifying a probability density function of opportunities for each individual. In this paper we explore by simulation the implications of (i) the procedure used to build the choice set (fixed alternatives versus sampled alternatives); (ii) accounting or not accounting for a different availability of alternatives. The results of the evaluation performed in this paper show that the way the choice set is represented has little impact on the fitting of observed values, but a more significant and important impact on the out-of-sample prediction performance. Thus, the treatment of the choice sets might have a crucial effect on the result of policy evaluations.     

基于情景判断的管理者胜任特征评价及效度研究

通过两次样本调查,研究了情景判断测验在测量管理者问题解决、人际胜任和伦理诚信等胜任特征方面的构思效度和效标关联效度。样本1运用验证型因素分析方法,通过替代模型策略,对测验的单维模型和多维模型进行比较,研究结果支持了多维模型假设,测验表现出较好的效标关联效度;在交叉效度检验研究中,测验存在一定程度的效度缩水,但仍表现出较好的效度水平。研究支持了胜任特征的情景判断测量,并对构思导向情景评价方法和意义进行了讨论。     

Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures

Airline traffic forecasting is important to airlines and regulatory authorities. This paper examines a number of approaches to forecasting short- to medium-term air traffic flows. It contributes as a rare replication, testing a variety of alternative modelling approaches. The econometric models employed include autoregressive distributed lag (ADL) models, time-varying parameter (TVP) models and an automatic method for econometric model specification. A vector autoregressive (VAR) model and various univariate alternatives are also included to deliver unconditional forecast comparisons. Various approaches for taking into account interactions between contemporaneous air traffic flows are examined, including pooled ADL models and the enhanced models with the addition of a “world trade” variable. Based on the analysis of a number of forecasting error measures, it is concluded that pooled ADL models that include the “world trade” variable outperform the alternatives, and in particular univariate methods; and, second, that automatic modelling procedures are enhanced through judgmental intervention. In contrast to earlier results, the TVP models do not improve accuracy. Depending on the preferred error measure, the difference in accuracy may be substantial.     

Roots and effects of financial misperception in a stochastic dominance framework

This work deals with the issue of investors’ irrational behavior and financial products’ misperception. The theoretical analysis of the mechanisms driving erroneous assessment of investment performances is explored. The study is supported by the application of Monte Carlo simulations to the remarkable case of structured financial products. Some motivations explaining the popularity of these complex financial instruments among retail investors are also provided. In particular, investors are assumed to compare the performances of different projects through stochastic dominance rules. Unreasonably and in contrast with results obtained by the application of the selected criteria, investors prefer complex securities to standard ones. In this paper, introducing a new definition for stochastic dominance which presents asymmetric property, we provide theoretical and numerical results showing how investors distort stochastic returns and make questionable investment choices. Results are explained in terms of framing and representative effects, which are behavioral finance type arguments showing how decisions may depend on the way the available alternatives are presented to investors.     

Presenting dynamics of social phenomena: should we use absolute,relative or time differences?

Empirical studies within social sciences face an important decision about how to express key findings to the target audience. Simplicity is an important selection criterion here, because the findings need to be conveyed in an efficient manner (i.e., briefly and concisely), but also because stakeholders (e.g., policy makers, the media, general public) are heterogeneous in their methodological backgrounds. The corresponding ways of measuring thus need to be not only exhaustive and message-delivering but also simple and intuitively understandable. This is particularly important when dynamics in time are discussed. There, most typically, either absolute or relative differences are used. This review paper critically elaborates these two popular measures and, in addition, discusses the alternatives of time distance and time step. The paper demonstrates that even in simple linear examples, the results of these four types of measures may sharply contradict. The empirical example of the digital divide is also elaborated, which illustrates many tempting possibilities for biased, one-sided interpretations that match the needs of certain stakeholders. Finally, the paper alerts users about possible misleading conclusions and suggests comprehensive treatments, using several measures simultaneously.     

A hybrid multi-criteria decision making approach for strategic retail location investment: Application to Turkish food retailing

Retail location selection decision is a critical and complex process which requires the evaluation and aggregation of multiple criteria and also the usage of appropriate data related to them. This study handles the problem at a strategic level and proposes a Monte Carlo simulation based multi-criteria strategic location decision model for food retailing. This model integrates two multi-criteria decision making (MCDM) methods which are Hesitant Analytic Hierarchy Process (H-AHP) and Grey Relational Analysis (GRA) methods. Firstly, H-AHP method is used to obtain the weights of criteria to be used in GRA based on the experts' judgements. Secondly, simulation based GRA is used for ranking the alternative locations. Finally, the effectiveness and the applicability of the proposed model is illustrated with an application of strategic location investment decision of food retail stores in Turkey.     

Adjusting inconsistency through learning in group decision-making, and its application to China's MBA recruiting interview

In group decision-making, because of limitations on individual knowledge and information bases, or because of the existing decision rule, an individual decision maker may not be capable of evaluating selected alternatives. Such circumstances can lead to inconsistencies across group decision matrices. These inconsistencies are difficult to remedy under existing approaches. Based on Rough Set Theory, we thus propose a new approach that integrates two types of learning techniques. It first applies a machine-learning procedure that extracts possible alternatives from other decision makers that are currently not included in a given decision maker's alternative set. It then applies a group knowledge-learning model to determine corresponding attribute values of those newly learned alternatives in meeting a group's consistency requirement. Efficacy of the approach is illustrated by its application to China's MBA recruiting interview.