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This paper is intended to enable competitive intelligence practitioners using an important method for everyday work when confronted with conditional uncertainties: the Bayes' theorem.

The paper shows how the mathematical concept of the Bayes theorem applies to competitive intelligence problems. The main approach is to illustrate the concepts by a near‐real world example. The paper also provides background for further reading, especially for psychological problems connected with Bayes' theorem.

The main finding is that conditional uncertainties represent a common problem in competitive intelligence. They should be computed explicitly rather than estimated intuitively. Otherwise, serious misinterpretations and complete project failures might follow.

In psychological literature it is a known fact that conditional uncertainties sometimes cannot be handled correctly. Conditional uncertainties seem to be handled well when they are about human properties. This should be verified or falsified in the competitive intelligence context.

In general, the application of Bayes' theorem should be seen as one of the foundations of competitive intelligence education. Especially, when it is clear in which intelligence research situations conditional uncertainties can or cannot be handled intuitively, competitive intelligence education and practice should be adapted to these findings.

CI practitioners can underestimate the value of Bayes' theorem in practice as they are often unaware of the (psychological) problems around handling conditional uncertainties intuitively. The article demonstrates how to take a computational approach to conditional uncertainties in CI projects. Thus, it can be used as part of appropriate CI training material.

The purpose of this paper is to inform facility managers of the type of failure affecting certain pipe types more than others. This is useful in asset management as preventive maintenance can be undertaken for those pipe types that experience high probabilities of failure.

The probability of a specific pipe type failing given the cause of break, age at failure, pipe diameter, and type of soil at the location of the break was found using inventory and main break data from the Honolulu Board of Water Supply (HBWS). Bayes’ theorem was then applied to find the posterior probabilities of failure starting from the prior probabilities of failure.

It was observed that the greatest probabilities of failure involved corrosion, pipes aged between 20‐30 years, 8″ pipes, and pipes in fill material. The pipe types were ranked and scored based on their probability of failing due to break cause, age, diameter, and soil type. Cast iron pipes were shown to have the highest probability of failing. As such, attention should be given to replace segments of cast iron pipes as they reach the end of their service lives.

This study serves to address a major query in asset management at a public utility, that of which pipes should be selected for replacement when they reach the end of their service life. In addition, this study helps to understand the causes of failure for the various types of pipe.

The importance of having reliable water supply at low cost has immense social implications in modern communities. To deliver such service, water pipe assets have to be managed efficiently.

This paper addresses the probability of failure in a straightforward manner that the water utility can easily apply to its own data, both in its design and asset management.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

Stable approach concept has great importance for the safe operation of an airline during the approach and landing phases. The purpose of this study is to analyse the unstabilized approaches with bow-tie method and determine the threats that may cause risk in an unstable approach.

In this study, risk assessment of the unstabilized approaches is carried out by using fuzzy bow-tie method and Bayesian networks. Bow-tie method is the combination of event tree analysis and fault tree analysis. Bayesian network is used in the analysis to see interrelationship of basic and intermediate events as well as to update posterior probabilities. Finally, analysis results are verified by the safety performance indicator values.

In this study, the probabilistic values of the numerical model presented by the risk assessment system for risks were calculated using the fuzzy bow-tie method. Thus, the risk assessment system has been transformed into a structure that can be expressed in a probabilistic manner, and the relationship of the risks within the system has been examined and the effect of a possible change on the risk value has been found to be prevalent.

The bow-tie model is widely applied to assess the risks in aviation. Obtaining prior probabilities is not always possible in the risk assessment process. In this paper, innovative fuzzy bow-tie method is used to assess the risks to overcome the lack of prior probability problem in aviation operations.

ASRS maintenance reports from 1996 through 2000 (n = 937) were subjected to posterior probability analysis to determine the probability of causal factors leading to a specific maintenance‐related event. Unintentional release of an unairworthy aircraft into revenue service was found to be the most frequent maintenance‐related event (40 per cent). The top three maintenance errors leading to such an event were improper documentation (33 per cent), improper installation (27 per cent), and sign‐off of work not performed (13 per cent). The probabilities of certain causal factors responsible for each of the maintenance errors were as follows: (a) For documentation errors – lack of awareness (22 per cent), poor procedures (15 per cent), and lack of training (4 per cent); (b) For improper installation – complacency (21 per cent), lack of awareness (21 per cent), and poor procedures (15 per cent); and (c) For sign‐off of work not performed – maintenance management (27 per cent), complacency (21 per cent), and poor procedures (14 per cent).

The problem of automatic diagnosis by use of a computer is expressed as an optimization problem in which parameters are chosen to minimize the diagnosis errors in reference to a previously treated set of patients. The results are expressed in terms of statistical measures of mutual associations of symptoms, and of symptoms with diseases. A decision criterion is discussed, and a formula is derived to describe the diagnostic value of each symptom. No assumptions are made regarding mutual exclusiveness of diseases or statistical independence of symptoms.

Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.

The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.

The results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.

The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.

Research in both psychology and accounting indicates that humans, in making decisions, resort to using decision strategies known as heuristics. One heuristic of particular interest in the field of accounting is that of anchoring and adjustment. Empirical research has shown that subjects will sometimes bias judgements towards the anchor even in situations where the anchor is of little value or is irrelevant. Explains that the presence of a primary or recency effect in the context of the anchoring and adjustment heuristic may be the existence of an “internal anchor”. Combining these theories, hypothesizes that auditors would use their initial mindset as an anchor. A laboratory experiment indicated that auditors did employ the anchoring and adjustment heuristic; they did have a negative internal anchor; and the inertia effect could be used to predict whether a primary or recency effect would be present in a particular likelihood estimation. The results gave strong support for the idea that auditors place over‐reliance on negative information. However, the results indicated that students did not have an internal anchor, did not employ the anchoring and adjustment heuristic and that the inertia effect was not useful in predicting whether a primary or recency effect would be present in a particular likelihood estimation.