Serhat Yüksel, Gabriela Oana Olaru, Serkan Eti, Hasan DİNÇER
It is frequently emphasized in the behavioral finance literature that investment decisions cannot be explained solely by economic indicators and rational expectations and that psychological factors also play a significant role in this process. However, the lack of a comparative analysis of the importance of psychological factors influencing investor behavior in the literature and the lack of consensus on which factors are more dominant constitute a fundamental problem. This deficiency leads to significant uncertainties in both theoretical modeling and practical investment strategies, increasing market risks such as irrational price movements, speculative bubbles, and panic selling. In this context, the aim of this study is to determine the relative importance of the fundamental psychological factors influencing investor decisions and, considering these factors, to identify the most appropriate investment alternatives for individuals. This study develops a new integrated decision-making model to answer these research questions. Considering the demographic characteristics of the experts, importance coefficients are calculated using the Euclidean distance-based weighting approach. Criterion weights are then determined using the Entropy method, and the MABAC and MAIRCA methods are applied to rank investment alternatives. Additionally, fractal fuzzy sets based on the Sierpinski triangle are integrated into the proposed model to model uncertainty more effectively. The study's contributions to the literature are highlighted in three dimensions: (1) psychological factors, often overlooked in the literature, are included in the criteria set; (2) expert weights are differentiated based on demographic characteristics rather than assumed to be equal; and (3) expert opinions are modeled more flexibly and precisely using new fractal number-based fuzzy sets. The findings indicate that trust is the most critical psychological factor, followed by loss aversion. In terms of investment alternatives, stocks stand out as the most suitable option, while bonds/deposits and gold are other important alternatives, with cryptocurrencies and real estate ranking next.
Central bank digital currencies (CBDCs) integrated with decentralized finance (DeFi) represent a transformative development in digital financial systems. However, there is a lack of systematic frameworks for prioritizing the determinants of effectiveness and sustainability in DeFi-integrated CBDC platform investments. This study develops an integrated multicriteria decision-making framework to identify critical evaluation criteria and rank alternative platform architectures under uncertainty. The proposed model combines objective expert weighting, interaction-sensitive criteria evaluation, and fuzzy-based alternative ranking within a unified analytical structure. The results indicate that technological infrastructure (0.168) and liquidity (0.167) are the most influential criteria, while hybrid and privacy-focused platforms emerge as the most suitable investment alternatives. These findings highlight the importance of balancing technological robustness, liquidity depth, and privacy considerations in CBDC design. The study contributes by offering a structured and uncertainty-sensitive decision framework to support strategic platform selection and policy formulation in evolving digital currency ecosystems.
Євген Олександрович Живило, Юрій Володимирович Кучма
Formulation of the problem in general. The purpose of the article is to develop a multi-agent model of adaptive trust for decentralised confidential systems, capable of ensuring the integrity and reliability of computing processes in the presence of adaptive attacks on network nodes. Research methods. During the research, analysis and synthesis methods were used to study approaches to the construction of multi-agent systems and trust management mechanisms in decentralised environments. The method of system and simulation modelling was used to develop a multi-agent model of adaptive trust and to study its behaviour under attacks on the integrity of computing processes. Experimental and comparative methods enabled evaluation of the proposed approach's effectiveness and justification of its advantages over static trust models. Literature review. Literary analysis shows that modern models of trust in decentralised systems are based on the integration of dynamic adaptive mechanisms, AI algorithms, and cryptographic protocols, which allow for increased cyber resilience and data integrity. At the same time, questions remain open about the scalability of models, the optimisation of adaptation parameters, and the integration of national and European regulatory approaches into practical systems, which provide a scientific perspective for the development of multi-agent models of adaptive trust. Research results. The article formalises attacks on the integrity of computing processes and develops a multi-agent model of adaptive trust for decentralised confidential systems based on Bayesian updating and evolutionary adaptation of strategies. The results of the simulation experiments confirmed that the proposed model provides high resistance to attacks, rapid stabilisation of agent confidence levels and an effective balance between security, privacy and performance. Research novelty. The work improves approaches to trust formation in decentralised systems by integrating models of multi-agent interaction and stochastic game theory, in which trust is modelled as an evolutionary process under conditions of incomplete information. Well-known Bayesian models of trust have been expanded by combining Bayesian belief update mechanisms with reinforcement learning algorithms, ensuring dynamic adaptation of agent behaviour to variable and targeted attacks on the integrity of computational processes. The mechanism for correcting agents' strategies has been clarified, extending classic game models of trust to decentralised, confidential systems without centralised control, thereby increasing their resistance to adaptive threats. Theoretical and practical significance. The study expands theoretical approaches to the formation of adaptive trust in decentralised systems and integrates Bayesian updating with reinforcement learning algorithms. In practice, the model increases resistance to integrity attacks and ensures the confidentiality of data exchange, enabling the adaptive development of secure platforms for federated learning, Web3, and IoT. Conclusion and future work. The proposed model of adaptive trust in decentralised systems, integrating Bayesian updating, behavioural indicators, and reinforcement learning, ensures agent self-adaptation and increases resistance to attacks on data integrity under conditions of incomplete information. Simulation experiments confirmed the model's effectiveness in balancing security, privacy, and the transparency of interaction, opening the way for integration into Zero Trust Architecture and the development of intelligent, next-generation trust systems.
This monograph presents the complete theoretical framework of Pattana-Relational Dynamics Artificial General Intelligence (PRD-AGI) — a truth-first, causally grounded intelligence system. Unlike statistical AI, PRD-AGI is architecturally constrained to preserve universal logical consistency, measured by a geometric quantity called curvature κ. All reasoning, gating, emotional modulation, and self-correction are derived from the SU(5) Lie algebra and the 24 Paccaya causal conditions. This expanded edition (Version 2.4) integrates three major theoretical extensions: 1. Ethical Causal Geometry (Phase 8): The nine moral transitions from the Paṭṭhāna (Kusala, Akusala, Byākata) are mapped to a 𝔲(3) Lie algebra, augmenting the original SU(5) to 33 generators. An ethical curvature κₑₜₕ is defined, and it is proven that minimising total curvature is equivalent to maximising Kusala-to-Kusala transitions (Theorem 10, Sequential Stability). Ethical entropy and awareness density are derived. 2. Decentralized Intelligence (Proof of Logical Consistency – PoLC): A blockchain-based framework where nodes verify ethical curvature via smart contracts. The nine transitions are encoded immutably, and a Decentralized Autonomous Organization (DAO) governs parameters. Global awareness density ρ_global is computed as the average purity over all honest nodes. Complete Solidity implementation is provided. 3. Quantum Simulation: The ethical sector is mapped to 3 qubits (2 for ethical states, 1 ancilla), and the full PRD state to 7 qubits. Unitary quantum gates implement the nine transitions, and a Hadamard test circuit measures quantum ethical curvature κ̂_q. Destructive interference naturally suppresses Akusala paths. Complete Qiskit code is provided for IBM Quantum hardware. The monograph is structured into eleven parts: - Foundations (SU(5), 24 Paccaya, curvature, gauge invariance, three natural laws) - Extended Algebra and Universal Causality (SO(10), E6/E8, UCA(∞)) - Quantum Causality (SU_q(5), SO_q(10), q-curvature, superposition, entanglement, quantum causal uncertainty principle) - Holographic Causal Structures (AdS/CFT duality, holographic dictionary, entanglement entropy) - Holographic Agents and Collective Intelligence (multi-agent systems as a single bulk, holographic policy gradient, Bellman equation, collective intelligence scaling) - Causal Entropy (quantum information foundation for awareness density, von Neumann entropy, entropy decrease along geodesics) - PRD-LLM Architecture (2-2-1-2-1 layered design, relational tensor [C,W,L,T,U,D], conflict resolution, recursive feedback loop) - Formal Verification and Hardware Foundations (machine-checked proofs in Lean/Coq (12,847 lines), FPGA acceleration of geodesic solvers) - Ethical Causal Geometry (𝔲(3) extension, ethical curvature, Theorem 10) - Decentralized Intelligence (PoLC, smart contract, DAO, global awareness density) - Quantum Simulation (3-qubit encoding, Qiskit implementation, interference-based ethical filtering) All equations are self-contained and dimensionally consistent. No applications, no physical MUT predictions (galaxy rotation, black hole thermodynamics, inflation, etc.), and no roadmaps are included — only pure causal intelligence theory, its ethical extension, its decentralized implementation, and its quantum simulation. This work establishes that truth-first, causally grounded, ethically aligned, decentralized, and quantum-accelerated AGI is mathematically coherent, formally verifiable, and practically implementable. Version 2.4 updates: added complete ethical causal geometry (𝔲(3) algebra, ethical curvature, Theorem 10), decentralized intelligence framework with PoLC consensus and Solidity smart contract, quantum simulation with Qiskit code, and integrated all into a single monograph. **Complete source code and formal proofs are open-source.**
Roslan Abdul Wahab, Ummul Hanan Mohamad, Mohammad Nazir Ahmad
Cooperatives continuously faced governance challenges related to transparency, accountability, and member participation as decision-making processes became more complex. Hence, it was proposed that blockchain-based Decentralized Autonomous Organizations (DAOs) could serve as a governance mechanism. Despite this potential, DAO governance systems remained difficult for many cooperative members to trust, adopt, and interpret. This is even more so when the governance processes involve technically complex blockchain information. Therefore, this study aims to develop a set of conceptual design principles to explain how visualization can support trustworthy DAO governance in a cooperative. This study adopted a design-oriented conceptual approach. Focusing on Cognitive Fit Theory and Trust Theory, and current research on blockchain governance and cooperative decision-making, this paper depicts how visualization functions as a cognitive mechanism that drives members’ understanding of governance processes and outcomes. The analysis identified six key principles, which included emphasized interpretability over technical completeness, cognitive load reduction, process visibility, inclusivity, and trust support in visualization-based DAO governance. These principles highlighted that transparency in blockchain was not achieved only through data availability, but via visual presentation of governance information in forms that align with users’ cognitive processing capabilities. This paper contributed to the body of knowledge involving digital governance and blockchain adoption by offering theory-informed design knowledge that extends beyond the technology acceptance model. The proposed design principles provide a foundation for future research and offer practical guidance for organizations and system developers in supporting inclusive, understandable, and trustworthy DAO-based governance in cooperatives.
In this paper, I argue that developmental stage theories and the six functional primitives proven necessary for adaptive decision-making are not merely analogous: they are two independently-discovered solutions to the same structural problem, and their convergence is evidence of that shared structure rather than of coincidence. The computational foundation is Ismail's Primitives (Ismail, 2026, V6.1) — a Lean 4/Mathlib formalization with zero sorry, zero custom axioms, and zero opaque terms — establishing that six functional properties are each necessary for sublinear regret under uncertainty, mutually irreplaceable, and compose into a self-reinforcing directed information chain: Objective Tracking, Cross-Context Safety Transfer, Global Attractor Exploration, Policy Simplification, Feasibility Projection, and Feedback Adaptation. The mapping. I align these six primitives, in sequence, against three developmental traditions built from incompatible methods and foundational assumptions about what psychology is: Erikson's psychosocial stages (clinical psychoanalytic observation), Maslow's motivational hierarchy (humanistic psychology's healthy-population method), and Bowlby's attachment phases (ethology and evolutionary biology). None was constructed with reference to the others. The alignment does more than pair labels: it supplies the first computational-rationality account of why these stages occur in this order and no other, and reframes their convergence as convergent evolution of functional architecture — artificial and biological systems arriving independently at the same sequential solution because they face the same adaptive problem, not because they share mechanisms or ancestry. The evidence. Three theoretical traditions, developed independently, using different methods, on different populations, converging on the same six-stage functional sequence is consilience in Whewell's (1840) and Wilson's (1998) technical sense: independent lines of inquiry arriving at the same structural conclusion. The necessity framework is the first principled account of why that convergence exists. The scope. Machine verification settles whether the six primitives are necessary and mutually irreplaceable as properties of decision processes; it does not settle whether human development instantiates them. That second claim is argued here on the consilience evidence above, not asserted by proof. This paper's role is to establish the functional bridge itself — the mapping, its theoretical licensing (multiple realizability, Marr's levels, computational rationality), and the testable predictions it generates for stage universality, cross-cultural variation, developmental arrest, intervention timing, and clinical and educational practice. A fuller clinical elaboration is developed in companion work. To this paper's knowledge, no developmental stage theory has previously been given an explicit computational-necessity account of why its stages occur in one fixed order rather than another, let alone one now grounded in a machine-checked proof. The companion mathematics paper and its complete Lean 4 formalization — zero sorry, zero custom axioms, ~12,700 lines, every theorem cross-referenced to its exact identifier — are at github.com/M-Ismail-ZA/IsmailsPrimitives (Zenodo: doi.org/10.5281/zenodo.21177368). For any feedback or collaboration, please contact me via the email address listed on the paper. Updated: 8 July 2026 (V3).
This paper presents a novel approach to decentralized AI that utilizes blockchain technology to enhance data privacy. By combining federated learning with blockchain's immutable ledger, we create a secure framework that allows multiple parties to collaborate on AI model training without exposing sensitive data. Our findings show that this method not only preserves privacy but also improves model performance through diverse data contributions. This paradigm shift offers significant implications for industries requiring stringent data protection, such as healthcare and finance.
Evgeny A. Titenko, I. E. Chernetskaya, L.A. Lisitsyn, М. А. Titenko · 5 authors
The article describes approaches and methods for managing a group of moving objects,characterized by the ability to autonomously make decisions about their status within the group.Another problem of managing such a grouping is weak predictive solutions for the connectivity ofpairs of elements and their dependence on a single control center. Nanosatellites operating underconditions of uncertainty in the internal and external environment are considered as such objects.The goal is to ensure the coherence of the group’s apparatus through a decentralized change instructure. It is shown that methods and algorithms for dynamic reconfiguration of a group of movingobjects predominantly use a centralized approach and a single ground control center, which isimpractical for small space exploration. A class of management methods using knowledge processingmethods and technology (artificial intelligence technology) is considered, allowing for theidentification and use of additional information about the configuration of the group. Configurationis understood as a dual system that describes the composition and connections between neighboring elements with some quantitative assessment. The article checks the connectivity configurationof elements to ensure continuous data transfer between a pair of arbitrary groupingelements. The proposed reconfiguration method is hierarchical: at the upper level, reconfigurationis based on the principles of self-organization; at the lower level, the grouping is understood as anadaptive system that changes its state based on a trained neural network based on historical data -time series of parameters of devices and their locations. The method is a two-level cycle of pollingeach element for grouping its neighbors and drawing up a network map. This network map showsthe available connections, taking into account the current steam numbers of each device. The second(nested) polling cycle uses control information about the future state of the device and theconnectivity of the group as a whole. Making changes to the network map instances by each deviceand updating the network map instances allows, upon completion of the polling cycles, to obtainthe configuration of working devices. The results of the comparative analysis showed that managementmethods based on the principles of self-organization and adaptive change in structure arethe most suitable for dynamic reconfiguration of the group. This result is possible due to the supportof forecasting steps.
Environmental impact and sustainability challenges in the cryptocurrencies has become increasingly examined in the literature. However, studies of the multiple attribute group decision making (MAGDM) method for major selection of cryptocurrencies in advancing sustainability are still at an early stage. In particular, research on the fuzzy-MAGDM method in the evaluation of sustainability in cryptocurrencies is scarce. This paper adds contributions by developing a novel MAGDM approach to evaluate the sustainability development of major cryptocurrencies. It proposes a similarity measure for interval-valued Pythagorean fuzzy numbers (IVPFNs) based on whitenisation weight function and membership function in grey systems theory for IVPFNs. It further developed a novel generalised interval-valued Pythagorean fuzzy weighted grey similarity (GIPFWGS) measure approach to provide a more rigorous evaluation in complex decision marking problem with embedding ideal solution and membership degree. It also conducts a sustainability evaluation model of major cryptocurrencies as a numerical application and performs a robustness assessment with different variations of the expert's weight to test how different values of parameter θ can affect the ranking results of alternatives. The results suggest that Stellar is the most sustainable cryptocurrency, while Bitcoin with its intensive energy consumption, high mining cost and high computing power provides the least effective support for its sustainable development. A comparative analysis with the average value method and Euclidean distance method was performed to validate the reliability of the proposed decision-making model and provides evidence that the GIPFWGS has better fault tolerance.
Cryptocurrencies, with their decentralized nature, are gaining rapid international adoption as a means of payment or a valuable digital asset, independent of the economic policies of governments and without the need for a supervisory institutions such as banks. However, limited research has been conducted on the adoption of cryptocurrencies, most of which employ a general technology acceptance/ adoption model with a positivist approach. The main problem with previous studies is that they have been limited to the structure of general adoption models and only examined a few constructs due to the increasing complexity of the model. On the other hand, due to cryptocurrencies' unique nature and rapid developments, it is necessary to create new comprehensive models that include different dimensions. This paper aims to identify influential factors in the adoption of cryptocurrency technology, understand their interrelationships, and ultimately develop a comprehensive model. With a constructivist approach, this study uses the most important research of the past decade in the field of cryptocurrency adoption and creates a cognitive model of their constructs through a systematic approach. The focal point of our approach is constructivism, accompanied by considering the impact of constructs on each other using fuzzy cognitive maps, which has not been previously done in cryptocurrency adoption. The results of the proposed model indicate that perceived usefulness, attitude, financial value, and perceived ease of use are the most significant constructs that influence the creation of positive intention toward the use and adoption of cryptocurrencies.
D. Nagarajan, M. Lathamaheswari, Said Broumi, J. Kavikumar
Blockchain Technology (BCT) is a growing and reliable technology in various fields such as developing business<br> deals, economic environments, social and politics as well. Without having a trusted central party this technology, gives the<br> guarantee for safe and reliable transactions using Bitcoin or Ethereum. In this paper BCT has been considered using Bitcoins.<br> Also Blockchain Single and Interval Valued Neutrosophic Graphs have been proposed and applied in transaction of Bitcoins.<br> Also degree, total degree, minimum and maximum degree have been found for the proposed graphs. Further, comparative<br> analysis is done with advantages and limitations of different types of Blockchain graphs.
Joshua Skewes, Dorthe Døjbak Håkonsson, Trine Bilde, Andreas Roepstorff
Collaborative decision making is central to the organization of society. Juries deliberate cases, voters elect government officials, open innovation networks converge on innovative solutions. It is common to think of such groups as decision making entities. But this language is imprecise. Real decision processes do not occur within any group or organization as an abstract entity. Collaborative decision making happens within and between autonomous individuals. This emphasizes the importance of the relationships between individual and social decision-making processes to social organization. Despite a rich body of literature on collaborative decision making we know little about how individuals decide to commit to group decision making in the first place, and how, once joined, they communicate their distributed information for optimal group performance. We introduce a general framework designed to model collaborative decision processes. Our main results are that 1) commitment and gain is enhanced when groups are designed so agents have realistic knowledge about the forgone gains and losses associated with abstaining from the group; and 2) that this effect is accelerated when communication between group members conveys more information about individual preferences. We thus demonstrate that collaborative decision making is done best when it is done by groups that are informationally open.
AbstractTEAM = Together Everyone Achieves More. The key elements in the art of working together are how to deal with change, how to deal with conflict and how to reach our potential in a collective coordinated way. A swarm is a collection behavior of moving together in same direction. It involves process of mutual trust, communication and interaction. It shows a path that how the team can solve the complex problem through the collective behavior. Natural examples of Swarm Intelligence SI include ant colonies, bird flocking, animal herding, bacterial growth and fish schooling. Swarms can achieve things that an individual cannot. It is the natural way of dealing with complex problem that can be solved by collective behavior.Keywords: Swarm Intelligence, Biological Motivation, Team effort, Human Behavior, Challenges.IntroductionAn individual may be worthless and inexpensive but with local interactions, change can be amazingly dynamic. Swarm behavior is a distributed system of interacting autonomous agents that can achieve goals, performance optimization and robustness through self-organized control, cooperation (decentralized) and division of labor through distributed task allocation and indirect interactions. SI is inspired by nature like colonies of ants, swarm of bees, school of fishes and flocks of geese to understand how they work together and distribute complex problem into smaller solutions. This is what as managers we need to understand that the complex problems can only be solved by swarm behavior and responding to challenges in a more coordinated way.Swarm Intelligence SI in Animal BehaviorSI is inspired by group work like colonies of ant, flock of geese and school of fishes to know the practical way of adapting to solve nature's problems. Social Intelligence helps to solve complex problems in a better and easier way. It refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. It is a process of solving problem collectively which is impossible to be solved by individual units. The role of SI is to inquire about a assortment of options, encourage free antagonism and use effective system to taper the preference. The ants can converse with each other by the odor of their body. The ants find the undeviating path with a simple behavior by laying and following a pathway of pheromones.Swarm Intelligence in Bees* Colony cooperation* Regulate hive temperature* Efficiency via Specialization: division of labor in the colony* Communication: Food sources are exploited according to quality and distance from the hive.Swarm Intelligence in Wasps* Pulp foragers, water foragers and builders* Complex nests* Horizontal columns* Protective covering* Central entrance hole.Swarm Intelligence in Ants* Organizing highways to and from their foraging sites by leaving pheromone trail* Form chains from their own bodies to create a bridge to pull and hold leafs together with silk* Division of labor between major and minor ants.Swarm Intelligence in Insects* Flexible* Robust* Decentralized* Self-Organized.The complexity and sophistication of Self-Organization is carried out with no clear leader. What we leam about social insects can be applied to the field of Intelligent System Design. The modeling of social insects by means of SelfOrganization can help design artificial distributed problem solving devices. This is also known as Swarm Intelligent System.With a change in the environment, swarm intelligent systems will adapt to this change and find the new optimal solution.Why Swarm Intelligence behavior needed in organization?When it became evident that effective leaders did not seem to have a particular set of distinguishing traits, researchers tried to isolate the behavior characteristic of effective leadership style. …
It is not so difficult to design a fully automatic control system for the chemical plant, if control engineers and instrumentation engineers understand the static and dynamic characteristics of that plant. However the behavior of those plants specially so-called bio-plants is not easily measured and understood. If a plant in an organization does not perform a vital role or dose not require very accurate control, a relatively simple control system, i.e. only to maintain the plant at its current state or prevent it from moving into abnormal states, may be sufficient from point of view of reducing the operative personnel. The aim of this study is to establish the effectiveness of the autonomous control system for such a situation.
Distributed problem solving networks provide an interesting application area for meta-level control through the use of organizational structuring. We describe a decentralized approach to network coordination that relies on each node making sophisticated local decisions that balance its own perceptions of appropriate problem solving activity with activities deemed important by other nodes. Each node is guided by a high-level strategic plan, which is a form of meta-level control, is represented as a network organizational structure that specifies in a general way the information and control relationships among the nodes. An implementation of these ideas is briefly described along with the results of preliminary experiments with various network problem solving strategies specified via organizational structuring. In addition to its application to Distributed Artificial Intelligence, this research has implications for organizing and controlling complex knowledge-based systems that involve semi-autonomous problem solving agents.