Shortfall Risk Models When Information on Loss Function Is Incomplete

Utility-based shortfall risk measures effectively captures a decision maker's risk attitude on tail losses. In this paper, we consider a situation where the decision maker's risk attitude toward tail losses is ambiguous and introduce a robust version of shortfall risk, which mitigates the risk arising from such ambiguity. Specifically, we use some available partial information or subjective judgement to construct a set of plausible utility-based shortfall risk measures and define a so-called preference robust shortfall risk as through the worst risk that can be measured in this (ambiguity) set. We then apply the robust shortfall risk paradigm to optimal decision-making problems and demonstrate how the latter can be reformulated as tractable convex programs when the underlying exogenous uncertainty is discretely distributed.

Optimal categorisation

The debate as to whether language influences cognition has been long standing but has yielded conflicting findings across domains such as colour and kinship categories. Fewer studies have investigated systems such as nominal classification (gender, classifiers) across different languages to examine the effects of linguistic categorisation on cognition. Effective categorisation needs to be informative to maximise communicative efficiency but also simple to minimise cognitive load. It therefore seems plausible to suggest that different systems of nominal classification have implications for the way speakers conceptualise relevant entities. A suite of seven experiments was designed to test this; here we focus on our card sorting experiment, which contains two sub-tasks — a free sort and a structured sort. Participants were 119 adults across six Oceanic languages from Vanuatu and New Caledonia, with classifier inventories ranging from two to 23. The results of the card sorting experiment reveal that classifiers appear to provide structure for cognition in tasks where they are explicit and salient. The free sort task did not incite categorisation through classifiers, arguably as it required subjective judgement, rather than explicit instruction. This was evident from our quantitative and qualitative analyses. Furthermore, the languages employing more extreme categorisation systems displayed smaller variation in comparison to more moderate systems. Thus, systems that are more informative or more rigid appear to be more efficient. The study implies that the influence of language on cognition may vary across languages, and that not all nominal classification systems employ this optimal trade-off between simplicity and informativeness. These novel data provide a new perspective on the origin and nature of nominal classification.

Comparative analysis of subjective/advanced objective technique of tropical cyclone intensity estimation

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Is eliciting dependency worth the effort? A study for the multivariate Poisson-Gamma probability model

We examine whether it is worthwhile eliciting subjective judgements to account for dependency in a multivariate Poisson-Gamma probability model. The challenge of estimating reliability during product design motivated the choice of model class. For the multivariate Poisson-Gamma model we adopt an empirical Bayes methodology to present an estimator with improved accuracy. A simulation study investigates the estimation error of this estimator for different degrees of dependency and examines the impact of dependency being mis-specified when assessed by subjective judgement. Our theoretical and simulation findings give analysts insights about the value of eliciting dependency.

Image-Based Automated Width Measurement of Surface Cracking

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.

Wahdat Al-Wujūd, Pesantren, and Religious Moderation Model in Indonesia

This article is based on a research on religious moderation in Pesantren (Islamic Boarding School) reviewed from the perspective of Ibn Arabi. It focuses on the violation of belief and religious freedom (KKB) in Indonesia, with its intensity through coercion and subjective judgement against other beliefs and sects. This study aims to relate wahdat al-wujūd, the concept of Ibn Arabi, with religious moderation concept in pesantren. This study found that (1) the concept of wahdat al-wujūd has philosophically constructed moderate mental and mindset; (2) pesantrens are able to instill the concept of religious moderation into students through their educational system; and (3) through wahdat al-wujūd Ibn Arabi proposes unifying concept and the nature of human being supporting the existence of religious moderation.

Reasons for and against Nutritional Interventions. An Exploration in the Nursing Home Setting

Malnutrition (MN) is widespread in nursing homes. Sometimes, but not always, nutritional interventions (NIs) are made, and the reasons for or against NIs are unknown. The aim of this cross-sectional study was to describe these reasons for residents with and without MN according to nurses’ subjective judgement and according to objective signs of MN. The nutritional status of 246 nursing home residents was subjectively judged by nurses (MN, at risk of MN, no MN) and objectively assessed by body mass index (BMI), weight loss (WL), and low food intake. NIs (enriched meals and/or oral nutritional supplements) were recorded using a standardized questionnaire, and nurses’ main reasons for (not) giving NIs were obtained in an open question. Of the residents, 11.0% were subjectively malnourished, and 25.6% were at risk of MN; 32.9% were malnourished according to objective criteria. Overall, 29.7% of the residents received NIs, 70.4% of those with MN as assessed by the nurses, 53.0% of those with objective MN, and 11.0% and 18.0% of non-malnourished residents, respectively. Reasons for NIs most often stated were low intake (47.9%), WL (23.3%), and low BMI (13.7%). Reasons against NIs mostly mentioned were adequate BMI (32.9%) and sufficient intake (24.3%). The lack of NIs for residents with MN was partially—but not always—explained by valid reasons. As residents without MN frequently received NIs, criteria for both MN rating and providing NIs, require closer scrutiny.

Risk Prediction Models of Chronic Disease in Chinese Medicine: A Systematic Review

Objective: To summarize the risk prediction models of chronic disease in Chinese medicine, describe their performance, and assess suitability of clinical or administrative use. Methods: The China National Knowledge Infrastructure and Wanfang Data were searched through February 2021, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies of prediction models of chronic disease in Chinese medicine. Results: From 399 citations reviewed, 17 studies were included in the analysis. Most of the studies were from single-centers (50%) or did not external validated (81.25%). The sample sizes were smaller and the models’ discrimination were larger compared with studies in fully western medicine. All the models used both laboratory findings and subjective judgements from doctors or patients. 9 models concentrated on diabetes mellitus or cardiovascular disease, and showed better performance and clinical application. Conclusions: The prediction models of chronic disease in Chinese medicine have unique advantages due to their considerations of doctors’ and patients’ subjective judgement. Diabetes mellitus and cardiovascular disease prediction models were in higher quality and clinical usability. Efforts to improve their quality are needed as use becomes more widespread.

Statistical Methods in Bidding Decision Support for Construction Companies

On the border of two phases of a building life cycle (LC), the programming phase (conception and design) and the execution phase, a contractor is selected. A particularly appropriate method of selecting a contractor for the construction market is the tendering system. It is usually based on quality and price criteria. The latter may involve the price (namely, direct costs connected with works realization as well as mark-ups, mainly overhead costs and profit) or cost (based on the life cycle costing (LCC) method of cost efficiency). A contractor’s decision to participate in a tender and to calculate a tender requires an investment of time and company resources. As this decision is often made in a limited time frame and based on the experience and subjective judgement of the contractor, a number of models have been proposed in the literature to support this process. The present paper proposes the use of statistical classification methods. The response obtained from the classification model is a recommendation to participate or not. A database consisting of historical data was used for the analyses. Two models were proposed: the LOG model—using logit regression and the LDA model—using linear discriminant analysis, which obtain better results. In the construction of the LDA model, the equation of the discriminant function was sought by indicating the statistically significant variables. For this purpose, the backward stepwise method was applied, where initially all input variables were introduced, namely, 15 identified bidding factors, and then in subsequent steps, the least statistically significant variables were removed. Finally, six variables (factors) were identified that significantly discriminate between groups: type of works, contractual conditions, project value, need for work, possible participation of subcontractors, and the degree of difficulty of the works. The model proposed in this paper using a discriminant analysis with six input variables achieved good performance. The results obtained prove that it can be used in practice. It should be emphasized, however, that mathematical models cannot replace the decision-maker’s thought process, but they can increase the effectiveness of the bidding decision.

Vulnerability of Road Pavements to Climate Change in Abia and Imo States of Nigeria

Around the world today, road pavement is increasingly vulnerable and Southeast Nigeria is no exception. To this end, the Delphi technique is used in this study to ascertain experts opinions in order to identify and prioritize road pavements vulnerable to climate change in Abia and Imo states of Southeast Nigeria. As a measure of stability, standard deviation of two rounds of questionnaire responses was used to assess the level of convergence of opinions of 10-member Delphi panel. The Delphi panel achieved acceptable level of agreement at threshold values of standard deviation = 0.10 for stability and mean = 0.20 for consensus. Out of 14 federal roads considered in the study, 1 road had high vulnerability status, 5 roads had medium vulnerability status, 5 roads were rated low vulnerability and 2 roads were rated very low vulnerability. The only highly vulnerable road is from Abia state. This study has shown that the vulnerability of road pavements to climate change can be assessed comprehensively using collective and subjective judgement in the form of Delphi technique. Government should therefore apply adaptive maintenance plans that reflect the relative vulnerability rating of these roads.