Published Papers

This paper presents a methodology for forming an adaptive investment portfolio structure based on a multi-module architecture implemented using an ensemble of neural network models. The developed system comprises five specialized analytical modules, each responsible for processing a specific class of factors: macroeconomic, fundamental, technical, and credit. The central element of the architecture is a neural network model for forecasting the Bank of Russia key interest rate, which defines scenario conditions for the valuation of all asset classes. Fundamental equity analysis, technical analysis of market dynamics, credit risk assessment of debt instruments, and an adapted Markowitz portfolio optimization model are integrated within a digital twin that reconciles the outputs of individual modules. The digital twin performs the functions of coordinating and aggregating the ensemble results, identifying inconsistencies between partial recommendations, and generating a coherent investment decision. The system produces probabilistic investment recommendations, thereby enabling scenario analysis and quantitative risk assessment. The proposed approach can be used as a decision-support tool in both individual and institutional asset management, taking into account changes in the macroeconomic environment.

Decision-making in omnichannel logistics networks requires balancing multiple performance dimensions under dynamic and uncertain operating conditions. In practice, key performance indicators (KPIs) are frequently applied for ex-post evaluation, while their direct integration into decision formulation remains limited, leading to a structural separation between performance management and alternative selection. This study proposes a KPI-driven decision framework that embeds multidimensional performance indicators at the modeling stage. Alternatives are represented within a unified multi-criteria structure, where KPI dimensions are normalized and aggregated into a weighted composite index. The decision problem is formulated as the maximization of expected composite performance over discrete alternatives under scenario uncertainty. A simulation-based operationalization is developed to incorporate stochastic variation in key indicators while preserving a consistent comparison logic across scenarios. The resulting structure enables robust ranking and selection of alternatives based on expected multidimensional performance. The study provides an interpretable and computationally reproducible framework for multi-indicator decision support in omnichannel logistics networks.

The article examines the problem of formalizing investment cash flow in a distributed ledger environment. Within the framework of the digital transformation of financial relations, the cash flow of an investment project can be represented as a digital twin, recorded in the distributed ledger infrastructure and implemented through smart contracts. The aim of the study is to develop a mathematical model of the digital twin of investment cash flow and an algorithm for its forecasting using neural networks. Theoretical approaches to the interpretation of digital twins are systematized, and the limitations of the classical discounted cash flow model in relation to the digital environment are analyzed. A formalized model of digital cash flow is proposed, taking into account transaction fees of the distributed ledger, algorithmically accrued income, and an extended discount rate structure including technological and regulatory risk premiums. An algorithm for neural network forecasting of the digital twin is developed based on a feature vector integrating financial and infrastructure parameters. A comparative analysis of the digital and classical models is performed, which allowed establishing the structural modification of the investment process in the digital environment. The obtained results can be used in the valuation of digital financial assets and the construction of adaptive systems for forecasting their cash flows.

Modern metaverse platforms, populated by heterogeneous multi-agent systems (MAS), generate vast streams of experiential data whose epistemic value remains largely untapped. This paper introduces the Enigma framework — a formal theory of collective intelligence emergence in metaverse ecosystems, grounded in a novel logical-probabilistic learning theory that extends classical first-order logic with probabilistic confidence annotations and distributed knowledge semantics. We define a Distributed Knowledge Lattice (DKL) over multi-agent interactions and prove that, under precisely stated monotonicity and convergence conditions, the collective knowledge of an agent population forms a complete lattice whose least upper bound represents an emergent cognitive state unreachable by any individual agent. We formalize the dynamics of knowledge creation, verification, and propagation through a system of Logical-Probabilistic Agents (LP-agents) that interact with LLM-driven entities, providing trustworthy and explainable reasoning via symbolic proof chains stored on a multi-blockchain ledger. Central results include: (i) a Collective Intelligence Convergence Theorem establishing conditions under which the system's aggregate knowledge monotonically approaches a fixed point; (ii) a Probabilistic Inference Soundness Theorem guaranteeing that confidence propagation through distributed reasoning chains preserves logical consistency; and (iii) a polynomial-time algorithm for optimal knowledge retrieval from the distributed lattice. The framework is instantiated within the Enigma Metaverse architecture, where smart contracts govern knowledge tokenization, cross-chain knowledge interoperability protocols enable seamless sharing, and decentralized governance mechanisms ensure epistemic accountability. We demonstrate that this synthesis of mathematical logic, probability theory, LLM-based hypothesis generation, and blockchain-secured knowledge persistence provides a rigorous foundation for building self-optimizing, trustworthy, and explainable collective intelligence (CI) in virtual worlds.