Subjective Expert Judgment Research Articles

The Visual Grasp of the Fragmented Landscape

Between 1925 and 1980, landscape ecology underwent important changes through the gradual imposition of the view from above, through the uses of aerial photography. A new concept emerged, “the smallest unit of landscape,” also called ecotope and land unit, expressing a direct visual grasp of the landscape. This article compares the view from above as introduced and promoted by geographers Carl Troll and Isaak Zonneveld, with its (problematic) history vis-à-vis a school of ecology, i.e., plant sociology, led by Josias Braun-Blanquet and Reinhold Tüxen. This school’s internal struggles with balancing the physiognomic gaze (at the ground) and numerical methods are discussed. In comparison, the geographers based themselves on the mechanical objectivity of standardized aerial surveys, whereas the plant sociologists relied on their subjective expert judgment of plant recognition together with the structural objectivity of their numerical methods. An important communality of both schools was their inductive building of a landscape from its constituent landscape fragments. Landscape fragments were identified through abstraction and categorization, emanating from a taxonomical style of science.

How to watch the watchmen? The role and measurement of regulatory governance

This article proposes a method to evaluate regulatory governance. For this purpose, we develop a novel multicriteria decision analysis modeling approach. Performance criteria were organized into key dimensions and descriptors were constructed for each of the criteria. Reference regulator profiles were developed for scoring and comparison purposes. The subjective judgment of experts was incorporated in the modeling process. The “Regulatory Governance Scorecard” is calculated for the Portuguese regulatory agencies to illustrate the model outputs and demonstrate its potential usefulness.

Towards Predicting Trend of Scientific Research Topics using Topic Modeling

This paper presents an alternative method to study and forecast the trend of research topics. Prediction of scientific research topics’ trend can be conducted using subjective judgments of experts or quantitative analysis. Due to the fact that subjective judgments of experts might be biased, researchers have been employing quantitative analysis such as bibliometrics, scientometrics, or informetrics. However, using these measures has limitations which require the use of an alternative approach. Results of the scientific research have been digitized and stored as news, scientific articles, and books. Such abundant information can be analyzed, taking the whole content of the papers into account. Hence, this paper proposes prediction of the trend of research topics using topic modeling. The proposed method was experimented using the proceedings of the International Conference on Computational Science (ICCS) which contains a total of 5982 papers over seventeen years (2001-2017). Non-negative Matrix Factorization (NMF) topic modeling method was utilized to discover topics. The result was structured as time series data and used to predict the trend of research topics. Auto-Regressive Integrated Moving Averages (ARIMA) prediction method was implemented and the performance of the model was evaluated using Root Mean Squared Error (RMSE). The proposed method may allow researchers, policy makers, funding agencies, and government to understand the current and the future state of research areas and take corrective actions.

Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

In this paper, we have developed an interval type-2 fuzzy logic controller (T2FLC) approach for assessment of the risks that workers expose to at construction sites. Using this novel approach, past accident data, subjective judgments of experts, and the current safety level of a construction site are to be combined. The method is then implemented on a tunneling construction site and risk level for all type of accidents is formulated. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of risk assessment can be predicted in industry. Finally, a comparative study has been successfully performed with type-1 and type-2 fuzzy dataset for improving risk assessment that can be easily determined in the type-2 fuzzy prediction model for improving accuracy. Validity of the proposed model is done with the help of statistical analysis and multiple linear regressions.

Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory

Clostridium difficile infection is one of the major patient safety concerns in hospitals worldwide. Clostridium difficile infection can have high economic burden to patients, hospitals, and government. Limited work has been done in the area of predictive modeling. In this article, A new predictive model based on Gaussian mixture model and Dempster–Shafter theory is proposed to predict Clostridium difficile infection incidence in hospitals. First, the Gaussian mixture model and expectation–maximization algorithms are used to generate explicit probability criteria of risk factors based on the given data. Second, Dempster–Shafter theory is used to predict the Clostridium difficile infection incidence based on the generated probability criteria that have different beliefs attributing to their different credits. The main procedure includes (1) generate the probability criteria model using Gaussian mixture model and expectation–maximization algorithm; (2) determine the credit of the probability criteria; (3) generate the basic probability assignment; (4) discount the evidences; (5) aggregate the evidences using Dempster combining rule; (6) predict Clostridium difficile infection incidence using pignistic probability transformation. Results show that the model has a higher accuracy than an existing model. The proposed model can generate the criteria ratings of risk factors automatically, which would potentially prevent the imprecision caused by the subjective judgement of experts. The proposed model can assist risk managers and hospital administrators in the prediction and control of Clostridium difficile infection incidence with optimizing their resources.

How Much Should We Spend to Protect Privacy?: Data Breaches and the Need for Information We Do Not Have

A cost/benefit approach to privacy confronts two tradeoff issues. One is making appropriate tradeoffs between privacy and many goals served by the collection, distribution, and use of information. The other is making tradeoffs between investments in preventing unauthorized access to information and the variety of other goals that also make money, time, and effort demands. Much has been written about the first tradeoff. We focus on the second. The issue is critical. Data breaches occur at the rate of over three a day, and the aggregate social cost is extremely high. The puzzle is that security experts have long explained how to defend better. So why does society tolerate a significant loss that it has the means to avoid? Some may object that society does not tolerate breaches. Laws—current and proposed—impose requirements aimed at improving information security. However, as Thomas Smedinghoff notes, most of the laws “simply obligate companies to establish and maintain ‘reasonable’ or ‘appropriate’ security measures, controls, safeguards, or procedures, but give no further direction or guidance.” The approach has so far failed to provide an adequate incentive to improve information security. As one commentator notes, the “bad guys basically go where they want to go and do what they want to do, and they're not being stopped. Maybe for every one organization that's effectively stopping attacks, there are 100 that are being breached.” We argue that the problem is not so much a lack of legal guidance as it is a lack of information. A standard cost/benefit approach is particularly suitable here. In the information security context, a business should adopt the following risk management goal: choose the most effective defense meeting the condition that the defense cost is not greater than the expected (business and relevant third-party) losses thereby avoided (over some appropriate short- or long-term time period). Businesses fall far short of a good approximation to business risk management goal. Applying this standard requires reasonably accurate information about the probability of a breach and the losses that will occur if it happens. Unfortunately, we are currently very far from having adequate information about either. The World Economic Forum report paints an accurate, if disturbing, picture of the consequences: “businesses are increasingly delaying the adoption of technological innovations due to inadequate understandings of required countermeasures. . . . A vicious circle results: uncertainty regarding proper levels of preparedness leads to forestalled investments in safeguards as interconnection expands exponentially.” A lack—even a severe lack—of objective information about probability and cost does not imply a complete inability to make better and worse decisions about information security. One can turn to subjective expert judgments and a variety of sophisticated analytic techniques that make use of them. Security outsourcing companies like AllClearID, BayDynamics, Healthguard Cyber Risk Management, and FireEye take this approach. Whatever its virtues, the “subjective judgment” approach can still spend far less or far more than the risk management goal requires. It is not a reliable guide to the optimal investment. We think the best course is to turn the unanswerable question into an answerable one by taking steps to discover the information need to adequately approximate the risk management goal. We see no alternative to the slow road of discovering the necessary information. This solution may seem singularly unappealing. Isn’t there a quicker fix? Data breach notification laws may seem to offer just that. There are at least two reasons to have breach notification laws. One is that the notifications are a source of precisely the information we need. The second is—so it is claimed—that they improve security. We focus on the second claim, which we argue is likely false. Indeed, such laws may make security worse (by focusing resources on avoiding reportable breaches, not on meeting the risk management goal). Breach notification laws are at best an uncertain road to improving risk management. The more certain, if longer, road is to get the information risk management needs. We argue for mandatory anonymous report by businesses of relevant information about data breaches.

Software Tool Implementing the Fuzzy AHP Method in Ecological Risk Assessment

Due to the increased spread of invasive animals and plants in the territory of Latvia, the necessity of ecological risk assessment related to such kind of spread has grown lately. In cases with sufficient statistical data, the risk assessment may be successfully performed on the basis of statistical methods. The amount of statistical data in the context of spread of invasive animals and plants is pretty poor; therefore, the only method of ecological risk assessment remains subjective judgements of experts. The present paper proposes using a programming tool for ecological risk analysis elaborated by the authors. With the help of this programming tool the method of Fuzzy Analytical Hierarchical Process is implemented. The elements of the pairwise comparison matrix are allowed to be expressed by triangular and trapezoidal fuzzy sets. The presented tool makes it possible to design the fuzzy pair-wise comparison matrix and process the results in a user-friendly way.

Ecopreneur Selection Using Fuzzy Similarity TOPSIS Variants

A supply chain network coordinates flow of components from different suppliers to enhance a product that meets customers’ value expectations. Traditionally, companies consider price, quality, and lead time for selecting appropriate suppliers. Increasing environmental concerns, government regulations, and long-term profitability have forced companies to reduce pollution. In addition, in a complex business environment, supply managers should have flexibility skills to act entrepreneurially. Being ecopreneur is a paradigm modification from being a green supplier and entrepreneur toward global economy and sustainability. Although ecopreneur selection is a strategic key to project success, construction industry lacks a systematic approach for decision-making processes. When historical data are not available or adequate, subjective judgment of experts is the only source of information for decision. Fuzzy logic helps decision makers to translate qualitative terms into quantitative information, whereas TOPSIS method ranks alternatives according to their relative distance to the ideal solution. This paper aims to rank ecopreneurs according to the criteria for green entrepreneur selection. In this case, construction-related experts are interviewed to first rate the importance of each criterion as their point of view, and then rate performance of each candidate in accordance to the most important criteria for ecopreneur selection. The final response is expressed based on the average of new variants of fuzzy TOPSIS each using a different fuzzy similarity measure. A numerical example is also presented to illustrate the process in detail.

Expert Judgment for Climate Change Adaptation

Climate change adaptation is largely a local matter, and adaptation planning can benefit from local climate change projections. Such projections are typically generated by accepting climate model outputs in a relatively uncritical way. We argue, based on the IPCC’s treatment of model outputs from the CMIP5 ensemble, that this approach is unwarranted and that subjective expert judgment should play a central role in the provision of local climate change projections intended to support decision-making.

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.

A hybrid human error probability determination approach: The case of cargo loading operation in oil/chemical tanker ship

HEART (human error assessment and reduction technique) is widely recognized a robust empirical tool in determination of HEP (human error probability) since prediction of human error is of paramount importance in marine industry (Williams, 1988). The method is successfully applied to a variety of domains such as aviation, railway, petrochemical, nuclear power plants, etc. The method has two fundamental parameters; generic error probability (GEP) and error-producing conditions (EPCs). The EPCs are the factors that affect the human performance negatively. The paper deals in principle with one of the methods used for human error probabilities estimation and it suggests its enhancement using analytic hierarchy process (AHP) (Saaty, 1980). Thus, quantification of the subjective judgements of experts during assessed proportion of affect (APOA) calculation is achieved in the course of HEP. To demonstrate the proposed approach, cargo loading operation in oil/chemical tanker ship is selected since the process is very critical to prevent loss of life and marine environment pollution. Consequently, besides its theoretical insight, the paper has practical outcomes to safety practitioners and maritime professionals for estimation of HEP in a specific task.

The identification of new service opportunities: a case-based morphological analysis

Typically, firms try to differentiate their products through the integration of innovative services. For this reason, much recent research into new service development has focused on methods of identifying and generating new service ideas. The most prevalent method for generating a new service is a morphological analysis that decomposes a system into several dimensions and values, and then recombines those values to generate new services. Despite the popularity of morphological analysis, how to build morphological matrix has been an area of subjective expert judgment. In this paper, we focused on the possibility to utilize the big data to the morphological analysis to address the subjective morphological building process, by suggesting a case-based morphological analysis. By employing case-based reasoning and network analysis, firms can easily identify direct and indirect clues for the new services and integrate these results to the morphological building processes. To support this approach, this study first employs a case-based reasoning strategy to collect and identify similar services, and then assesses the patterns in those services through network analysis. By engaging in network analysis, firms can identify key aspects of new services, and determine what kinds of keywords or aspects should be employed for the dimensions and values of morphological matrices.

A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams.

Delineation of Seismic Sources in Probabilistic Seismic‐Hazard Analysis Using Fuzzy Cluster Analysis and Monte Carlo Simulation

Abstract Determination of seismic sources is the first step in probabilistic seismic‐hazard analysis (PSHA); however, this step, especially in low seismic regions, is often controversial. In conventional PSHA procedure, determination of seismic sources is merely based on the subjective judgments of experts, and in many cases, there are great differences among proposed seismic models in a specific region. As a result, one important source of uncertainty in PSHA is due to determination of seismic sources. In this article, by combination of fuzzy clustering analysis and Monte Carlo simulation, an objective method for determination and probabilistic modeling of seismic sources is presented. By clustering spatial locations of earthquakes, it is possible to specify the extent of each seismic source in an objective way. A cluster quality index is used to identify the optimum number of clusters. The density and spread of events in each cluster determines the geometrical shape of seismic sources. Moreover, in this article a method is proposed to construct spatial probability density functions (PDFs) of earthquake locations based on the results of fuzzy clustering analysis. The spatial PDF of earthquakes can be used for the generation of synthetic events in Monte Carlo simulation. The Azarbaijan region, with its varied seismotectonics and generally high seismicity, is used as an important area of seismicity in which to develop and demonstrate the application and capability of fuzzy clustering analysis in specifying seismic sources. The PSHA is performed for the city of Tabriz, and a comprehensive comparison is made between the results of conventional PSHA, ordinary Monte Carlo hazard analysis, and the proposed method. The results indicate there is an objective relationship between observed seismicity and seismotectonic evidences in the region. Moreover, the distribution of synthetic events is highly correlated with the observed seismicity, seismotectonic, and geological information of the region.

Combined medical quality assessment using the evidential reasoning approach

Due to increasing demand for healthcare, medical quality has attracted significant attention in recent years. Most studies to date have tried to assess medical quality from objective quality indicators or subjective expert judgments or patient feedback perspective. In this study, the evidential reasoning approach is employed to combine objective quality indicators, subjective expert judgments and patient feedback in a multiple criteria framework to assess the quality of hospitals systematically and comprehensively. The evidential reasoning approach has the advantages of consistently handling both subjective evaluations and objective indicators under uncertainty within the same framework, and it can help to provide a robust alternative ranking. This study contributes to the literature with not only a novel medical quality assessment and aggregation framework, but also a pragmatic data transformation technique which can facilitate the combination of quantitative data and qualitative judgments using the evidential reasoning approach. A case study of three top-ranked teaching hospitals in Beijing is presented to demonstrate the framework and methodology proposed in this study.

Developing a reduced-form ensemble of climate change scenarios for Europe and its application to selected impact indicators

This paper presents a method for identifying a representative subset of global climate models (GCMs) for use in large-scale climate impact research. Based on objective criteria (GCM performance in reproducing the seasonal cycle of temperature and precipitation, and a subset ability to represent future inter-GCM variability), two candidate subsets are selected from a reference set of 16 GCMs. An additional subset based on subjective expert judgement is also analysed. The representativeness of the three subsets is validated (with respect to the reference set) and compared for future changes in temperature, precipitation and Palmer drought index Z (direct validation), and occurrence of the European corn borer and snow-cover characteristics implemented in the CLIMSAVE Integrated Assessment Platform (indirect validation). The direct validation indicates that one of the objective-based subsets (ECHAM5/MPI-OM, CSIRO-Mk3.0, HadGEM1, GFDL-CM2.1 and IPSL-CM4 models) provides the best choice for the Europe-wide climate change impact study. Its performance is balanced between regions, seasons and validation statistics. However, the expert-judgement-based subset achieved slightly better results in the indirect validation. The differences between the subsets and the reference set are generally much lower for the impact indices compared to their mean (across all GCMs in the subset) changes due to projected climate change. The ranking of the candidate subsets differs between regions, climatic characteristics and seasons, demonstrating that the subset suitability for a specific impact study depends on the target region and the roles of individual seasons and/or climatic variables on the processes being studied.

A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks

Methods of uncertainty analysis are being included increasingly in regulatory chemical risk assessment. Although best practices have been established by several safety agencies in Europe and the United States, they exist only in the grey literature – there has been no comprehensive analysis of the scientific, peer-reviewed literature on these methods. We therefore conducted a systematic review of the recent peer-reviewed literature (2007–2013) on uncertainty analysis relevant to chemical risks. The main objective was to determine whether current methods are robust enough for regulatory use, because the methods used to protect public health must meet the most stringent scientific standards. Based on 297 papers, we concluded that the peer-reviewed literature is much more critical about the disadvantages of those methods, compared to the grey literature. Furthermore, uncertainty analyses can be influenced significantly by subjective expert judgment. As a suggested improvement, we developed guidelines for transparent reporting of uncertainty assessment results.

Research on Credit Evaluation and Verification Model in Equipment Manufacture Industry

The paper aims to develop the credit evaluation and verification model in equipment manufacture industry. The study defines the vague set of linguistic variable firstly. Then the calculation rules are defined with the vague set. The research uses the expert subjective judgment to build the initial evaluation model. And the relevant 128 data items are collected by survey. These items are used to make factor analysis to test the structure of the evaluation system and build the initial SEM. The result of SEM shows the possible modification. Finally, the empirical research is given to verify the developed approach.

Market demand estimation for new product development by using fuzzy modeling and discrete choice analysis

Market demand estimation is an important process to assess the financial feasibility of new product development (NPD) projects. The development of models for market demand estimation involves market potential estimation and choice modeling. Previous studies commonly used conjoint analysis to develop utility functions which were then used in discrete choice models to generate market share models. The jury of executive opinion method is commonly used in industries wherein a number of experts and/or consultants are always involved in the market potential estimation. However, a high degree of fuzziness always exists in the data obtained from conjoint surveys and the market potential estimation because of the subjective judgments of respondents and experts. However, ignorance of the fuzziness would lead to the over-estimation of market demands. This research aims to tackle the fuzziness associated with market potential estimation and survey data in the development of market demand models. In this paper, a new methodology of developing fuzzy market demand models for NPD is proposed to address the fuzziness by which market demands can be estimated for the worst, normal, and best scenarios. The proposed methodology involves fuzzy choice modeling based on fuzzy regression and discrete choice analysis, and fuzzy estimate generation of market potential. To evaluate the effectiveness of the proposed methodology, a case study of market demand estimation of a new tablet PC is conducted based on the proposed methodology. The results of the implementation are compared with those based on a popular multinominal logit (MNL) based demand model. From the comparison, it can be noted that the estimated market demand based on the MNL model is very close to that for the normal scenario based on the proposed fuzzy demand model. However, the MNL model cannot provide estimates for other scenarios.

Fuzzy Analytic Hierarchy Process Based Reputation Evaluation for Generating Companies

It is not easy to evaluate the reputation of generating companies as the influence factors are multiple, complex, and partly subjective. A fuzzy Analytic Hierarchy Process method (AHP) with a set of power industry relevant criteria is applied in this paper. The fuzzy triangular number is introduced to characterize the subjective judgment of experts, and AHP is used to deal with the multiple evaluation factors, and to calculate the relevant weights with the aid of the arithmetic laws of fuzzy triangular number. Numerical examples illustrate the effectiveness of this model.