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The study presents a probabilistic approach to assessing undiscovered hydrocarbon resources in well explored areas of the West Siberian petroleum province. The relevance of this work arises from the mature exploration stage of the region, where further resource growth is expected mainly from small and very small accumulations. The methodology is based on analyzing the size distribution of hydrocarbon accumulations using a truncated Pareto distribution, whose parameters are estimated through maximum likelihood applied to the largest discovered fields. Probabilistic characteristics of initial and remaining resources are obtained via Monte Carlo simulations, which account for variations in the total number of accumulations and their aggregate volumes. The Jurassic and Cretaceous complexes of the continental part of the province, characterized by high geological knowledge and a representative dataset of discovered fields, serve as the study area. The results obtained confirm the applicability of the probabilistic approach for assessing undiscovered resources in mature oil and gas provinces and allow for the evaluation of their degree of uncertainty.

The article discusses which approach can be used to solve the problem of theoretical justification of machine learning methods in the natural sciences. The authors point out that the current strategy of philosophical, epistemological and applied research related to ML (countering bias and minimizing subjective assumptions) fail to solve the “black box” problem, which makes it difficult to interpret the results and reduces their scientific value. The article suggests an alternative approach to the theoretical justification of ML based on the extension of computational powers. Using Paul Humphreys’ concept of “extending ourselves,” the authors show how computing technologies can overcome the limitations of human thinking and model complex phenomena that are inaccessible to “traditional” mathematical methods. The idea of a “bias compensation” mechanism is put forward, which can neutralize the influence of subjective factors in the framework of natural science research using ML. Special attention is paid to comparing ML with computer simulations, where the influence of assumptions/bias can be compensated by analyzing the global dynamics of the model, whereas in ML this problem remains unresolved. This entails the need to separate the “black box” problem in ML from the “epistemic opacity” that is common to both machine learning and computer simulations. It is specifically emphasized that the “black box” in machine learning arises not from the “opacity” of the model or the complexity of computational operations, but from the lack of clarity of the model’s connections with real physical processes (the “target system”). Thus, the authors demonstrate that the application of ML in natural science requires a rethinking of existing methodological prerequisites. The development of mechanisms of bias compensation in the field of ML is becoming a key task in order to overcome the “black box” problem and successfully integrate ML into scientific research.

The paper defends the thesis that nomic necessity cannot be adequately interpreted either as a regularity of fact distribution, or a primitive metaphysical fact, or a derivative of dispositional entities. A dynamic reconstruction of structural realism is proposed, in which the fundamental structure is the ordered pair 〈S, T 〉 where S is the set of ontological states, and T is the geometrically organized structure of admissible transitions between them. It is argued that the very determinacy of physical dynamics logically presupposes the existence of such a structure. The laws of nature are interpreted as expressions of the invariance of T , and nomic necessity as an internal property of its geometry. It is shown that without recognizing the accessibility structure, it is impossible to explain the counterfactual force of laws, the stability of symmetries, and the scale universality of physical theories. The proposed position formulates a modal-dynamic version of ontic structural realism and offers an alternative to Humean and primitivist theories of the laws of nature. The issues of ontological identity of objects, emergence, and the arrow of time will be addressed in a separate paper.

The article shows that the polysemy of the term “scientific theory” and the diffuse nature of its meaning distort the application of the tools of scientific knowledge theory. It proposes options for optimizing the concept of “scientific theory” and its relationship to the concepts of a system of theoretical knowledge and a system of supra-empirical knowledge.

The article continues the discussion of the concept of semantic modeling developed by the authors. It examines the features, limitations, and characteristics of information systems created either using AI technologies or traditional methods. The possibility of expanding the provisions and tools of the semantic modeling concept is analyzed with regard to the situation of creating information systems focused on solving an open class of problems. It is proposed to develop behavioral strategies for such IT systems based on the possibility to leverage the experience of their previous actions in similar circumstances, selecting those actions that have previously yielded the best results, and then, using the task-based approach, select the most appropriate problem-solving methods under given conditions, either through simulation modeling or decision-making in order to determine the method most appropriate to the problem statement, paying particular attention to contextual conditions and the criterion for solution. The article presents and discusses the goal of further developing a semantic modeling methodology aimed at creating IT systems that behave in the manner described above, which explains the title of the article. In conclusion, it describes a possible variant of an extended semantic modeling language designed to specify the behavioral logic of such systems.

The article provides an overview of the critical amount of knowledge required to confidently understand the emergence of living nature and its qualitative differences from inanimate objects. Possible scientific and methodological approaches are described, as well as the image of a node of interdependent problems, the full solution of which requires a synergistic approach to studying the entire complex. The latter highlights the main, determinative issues that are at the focus of the major force of global science. Accordingly, it describes the skills that a researcher in this field must possess in order to achieve a real, effective, and quick result.

The article proposes a variant of classification of assessments of the significance of philosophical ideas and the involvement of philosophers in science, as formulated by renowned scientists: A. Einstein, R. Feynman, P. Medawar and S. Weinberg. Much attention is paid to analyzing Weinberg’s critical arguments, such as the lack of universal philosophical theories adequate for research in physics, the absolute conservatism of philosophers, and the paucity of approaches for explaining and understanding physical phenomena. It is shown that only the last argument is justified; however, new approaches to understanding science are being vigorously studied in contemporary philosophy of science. Based on a literature review, it is shown that biologists highly appreciate the involvement of philosophers in science and their results in applying conceptual analysis to the life sciences, particularly in cancer stem cell research. The potential for applying critical philosophical analysis to certain fields of knowledge, such as data analysis, is demonstrated.

In this article, the author pays tribute to the founder and first editor-in-chief of the journal “Philosophy of Science,” Doctor of Philosophy Aleksander Leonidovich Simanov. This renowned scientific methodologist focused primarily on the philosophy of physics, where he covered a large number of topics in detail, imparting his unique insight into them. For example, he understood scientific metaphysics methodologically, i.e. as a way to generate scientific hypotheses that are subsequently tested empirically. A.L. Simanov viewed metaphysics and methodology dialectically from the standpoint of historical materialism, emphasizing the fundamental incompleteness of scientific knowledge and pointing to the gradual transition from the metaphysics of objects to the metaphysics of states and structures. His approach differed from scientific structuralism in recognizing the existence of an object’s internal structure, whose change occurs not only due to the intervention of external forces, but also due to its internal evolution, as well as through changes in its internal qualities in response to external influences.

The article examines the place and role of metaphysical ideas in the development of the concept of unification based on the basic constants of the geometric representation of spacetime. It is noted that the concept of symmetry-asymmetry can serve as such a basic foundation, allowing for building an explanatory, rather than a phenomenological, theory.

This is a review of A.V. Rodin’s book “Axiomatic Architecture of Scientific Theories” (Moscow, DirectMedia, 2025. In Russ.).