Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of risk analyses. In order to successfully model the existing uncertainties, this paper presents a methodology for ecological risk analysis that is based on input evaluations in the form of intuitionistic fuzzy values (IFVs). The advantage of this approach is the ability to model a wide range of uncertainties in ecological risk analysis tasks.

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.

To select optimal solutions in multicriteria decision-making (MCDM) problems, many practical approaches have been developed. In almost all of these approaches, it is necessary to assess the importance of individual criteria for decision makers. Subjective assessments of importance are transformed into numerical assessments of decision weights by applying appropriate computational procedures. A large number of methods for determining the weights of the criteria have been proposed. These methods differ in their operating principles and in the calculation procedures underlying each method. The paper presents the most well-known methods and provides a brief comparative analysis.

One of the geometric figures that has symmetry properties is the Cassini oval. The Cassini oval is a curve defined as the locus of points in the plane such that the product of the distances to two fixed points is constant. Cassini ovals are named after the astronomer Giovanni Domenico Cassini, who studied them in 1680. Today, the geometric properties of Cassini ovals are used in many fields: analytical geometry, nuclear physics, radiolocation, and industrial applications. The bistatic radar uses Cassini ovals to detect various targets in radiolocation. Until now, there have been no studies on the clustering capabilities of Cassini ovals. As a novelty, it is hypothesized that clustering possibilities could be used for Cassini ovals. In this article, a study of the capabilities of Cassini ovals in radiolocation was carried out, and their suitability for clustering purposes was shown.

Choosing solutions under risk and uncertainty requires the consideration of several factors. One of the main factors in choosing a solution is modeling the decision maker’s attitude to risk. The expected utility theory was the first approach that allowed to correctly model various nuances of the attitude to risk. Further research in this area has led to the emergence of even more effective approaches to solving this problem. Currently, the most developed theory of choice with respect to decisions under risk conditions is the cumulative prospect theory. This paper presents the development history of various extensions of the original expected utility theory, and the analysis of the main properties of the cumulative prospect theory. The main result of this work is a fuzzy version of the prospect theory, which allows handling fuzzy values of the decisions (prospects). The paper presents the theoretical foundations of the proposed version, an illustrative practical example, and conclusions based on the results obtained.

The goal of this article is to forecast migration flows in Latvia. In comparison with many other countries with sufficiently symmetric emigration and immigration flows, in Latvia, migration flows are very asymmetric: the number of emigrants considerably exceed the number of immigrants. Since statistical data about migration are usually inaccurate, we employ fuzzy time series forecasting methods for prognosticating migration flows in Latvia forecasting. The use of this type of method is often useful not only for forecasting purposes. Three different methods for fuzzy time series forecasting are used. A detailed comparative analysis of the obtained results is given. Generalized forecasts of the expected net migration flow in the future are presented.