Search Results (1,707)

Flying Ad-Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV) are critical enablers of intelligent transportation and smart city ecosystems. Their reliance on shared wireless channels, however, exposes them to diverse jamming attacks that threaten communication reliability, mission effectiveness, and safety. This paper presents a comprehensive study of jamming threats in integrated FANET–IoT–IoV environments and analyzes conventional and advanced anti-jamming techniques across physical, link/MAC, spectral, spatial, temporal, and hybrid domains. To address the challenges posed by heterogeneous and dynamic network conditions, we propose a cross-layer anti-jamming framework that integrates Cognitive Radio (CR) for dynamic spectrum access and Multi-Agent Reinforcement Learning (MARL) for cooperative, adaptive decision-making. The framework employs a Perception Engine for local anomaly detection, a Cognitive Engine for constructing a collaborative jamming map, and a Decision and Action Engine for multi-agent DRL-based mitigation. Simulation results demonstrate that the proposed CR-MARL framework significantly improves packet delivery ratio, reduces latency, and adapts efficiently to varying jamming strategies, while maintaining low energy and computational overhead, making it suitable for resource-constrained UAVs, vehicles, and IoT sensors. Full article

The transition from traditional siloed to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart city as an urban cognitive Cyber–Physical System (CPS) across environmental, economic, and social sustainability pillars, metrics and their trade-offs. A methodology based on Deep Reinforcement Learning (DRL), specifically adopting Deep Q-Networks (DQNs), is proposed to represent and assess sustainability pillar dependencies and their interplay. A case study on Low-Power Wide-Area Network planning, deployment and management in a Sicilian municipality has been developed to demonstrate the effectiveness of the proposed approach in dealing with the dynamics and non-linear dependencies of the sustainability pillars. Full article

Background: Patients with head and neck cancer who are undergoing radiotherapy frequently suffer from oral mucositis and oral microecological disorders, which severely impair their quality of life. Oral microbiota transplantation is an emerging oral microecological intervention that offers a novel approach for reconstructing oral microecological balance and relieving mucositis. However, regarding this innovative therapy, there is a paucity of in-depth research into patients’ decision-making cognition, and existing evidence is insufficient to support individualized clinical decision-making guidance. Methods: A descriptive qualitative research design was employed. From July to December 2025, patients diagnosed with head and neck cancer undergoing radiotherapy were recruited from a tertiary hospital in Shanghai via purposive sampling. The data were collected through semi-structured interviews and analyzed using Colaizzi’s seven-step analysis method. The user label system was refined and summarized to construct user portraits. These portraits were visualized in the form of WordArt word clouds and character labels. Results: A total of 21 eligible patients with head and neck cancer undergoing radiotherapy participated in the study. The construct of decision-making cognition encompasses five dimensions: treatment prioritization, information needs, health literacy, psychological status, and decision quality. The patients were categorized into four types: proactive participation, passive dependence, weigh carefully, and symptom-driven. These classifications reflect the cognitive characteristics and group differences regarding the Oral Microbiota Transplantation decision-making process among different patients. Conclusions: Patients exhibit considerable variability in their decision-making cognition regarding the innovative OMT therapy. This phenomenon can be categorized into four distinct persona types, which, respectively, reflect unique information processing styles, risk assessments, and behavioral coping strategies when patients encounter novel therapeutic interventions. This typology provides a theoretical foundation for individualized clinical decision support, delineates targets for the formulation of targeted communication strategies, and ultimately enhances patient decision quality and treatment adherence. Full article

This article addresses the growing concern that generative artificial intelligence (AI) may replace human expertise in organizations. Instead of asking whether AI should be used, it examines why human judgment rooted in experience cannot be fully replaced by current AI systems and how organizations can work with AI more effectively. Drawing on research from cognitive science, neuroscience, and organizational studies, the paper explains how people use prior experience to interpret context, notice subtle cues, and make sense of ambiguous situations—capabilities that differ fundamentally from how large language models process data. Evidence from recent studies of AI use in hiring, performance management, healthcare, and knowledge work shows recurring problems, including mistakes in unusual cases, missed context, over-reliance on AI recommendations, and reduced visibility of real skill differences among employees. In response, we propose a five-part Human–AI Collaboration Framework designed to help organizations use AI for efficiency while keeping human judgment active and accountable in key Human Resource Management decisions. The analysis shows that AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding. By framing organizations as systems built on accumulated experience, this article offers practical guidance for responsible AI integration and outlines directions for future research on human–AI collaboration. Full article

Legal mediation is an important mechanism for resolving social conflicts and handling disputes. It involves complex interpersonal interactions and unstructured decision-making processes, and therefore holds significant research value as a domain. Leveraging the outstanding logical reasoning capabilities of large language models, multi-agent systems for simulating complex social interactions have become a cutting-edge research direction in artificial intelligence, providing a new supporting vehicle and research pathway for the intelligent study and practical application of legal mediation. However, directly applying general-purpose multi-agent techniques or general-purpose, opaque LLMs to long-horizon, multi-party, and high-conflict professional mediation tasks exposes several deep-seated structural cognitive deficiencies, including a lack of process awareness, insufficient domain-specific intervention capabilities, and limited theory-of-mind reasoning. To address these challenges, this study proposes CogMed, a cognitively enhanced multi-agent framework for legal mediation simulation, which aims to compensate for the limitations of general models in professional strategic interactions through an explicit cognitive architecture. Rather than introducing entirely new individual reasoning modules, the proposed framework focuses on cognitively coordinated integration of process control, strategic intervention, and belief modeling mechanisms under legal mediation settings. CogMed models the mediation process as a Finite State Machine (FSM) to capture macro-level decision logic and introduces a Strategic Toolkit (STK) that serves as a set of action primitives for micro-level interventions. Meanwhile, a Dynamic Belief Tracking (DBT) mechanism is incorporated into party agents to simulate psychological anticipation and strategic reasoning during negotiation. Experimental results demonstrate that CogMed effectively improves both mediation success rates and the quality of negotiated outcomes. Furthermore, the findings suggest a preliminary framework-level compensation pattern under the current experimental setting, where cognitively structured coordination mechanisms may partially enhance the mediation capability of medium-scale models. These preliminary experimental observations suggest that cognitively structured coordination mechanisms may partially compensate for certain limitations associated with model scale under the current controlled mediation setting, thereby offering a potential research direction for cognitively structured legal mediation simulation systems under controlled experimental settings. Full article

The growing adoption of artificial intelligence and autonomous decision-making systems in maritime operations introduces significant challenges related to safety, accountability, and human oversight. Although Human-in-the-Loop approaches are commonly proposed to maintain human control over autonomous systems, continuous supervision is often impractical due to cognitive workload, operator fatigue, alert saturation, and scalability constraints. This study introduces an Adaptive Human Oversight framework for Maritime Agentic AI Systems, extending the Controlled Agentic AI Systems paradigm with a risk-aware, constraint-preserving, and auditable human governance layer. The framework employs a two-stage risk mechanism in which hard safety conditions, including critical loss of separation, boundary violations, infeasible actions, and excessive speed conditions, override the weighted composite risk score and trigger human oversight or fallback behavior independently of activation thresholds. Under elevated but non-critical conditions, a Composite Risk Assessment Module regulates human activation using interpretable indicators of separation, congestion, speed, and uncertainty. The framework also defines the behavioral semantics of human oversight actions, ensuring that approval, modification, override, and rejection remain compatible with governance constraints before execution. To reduce unnecessary supervisory burden, the framework incorporates hysteresis and trend-aware cooldown mechanisms that suppress redundant repeated requests while preserving responsiveness to critical safety events. Simulation experiments conducted using the MARIS-AI platform evaluated risk thresholds, CRAM weight sensitivity, cooldown diagnostics, operator reliability, intervention delay, and stress-test scenarios. Results show that threshold tuning primarily regulates non-critical elevated-risk states, while critical states are governed by hard safety overrides. Trend-aware cooldown reduced intervention frequency without suppressing critical safety events, whereas operator reliability and intervention timeliness strongly determined safety outcomes. The findings suggest that effective maritime human oversight should rely on hard safety constraints, interpretable risk assessment, timely human activation, workload-aware cooldown, and auditable decision traces rather than continuous monitoring alone. The proposed framework provides a pathway toward scalable, trustworthy, and accountable human–AI collaboration in maritime agentic AI systems. Full article

Design management in Industry 5.0 faces a persistent gap due to fragmented conceptualizations that treat AI, human creativity, and machine capabilities as largely separate elements, limiting their effective integration within complex socio-technical systems. Addressing this gap, the present study develops and empirically validates a Design Management Model grounded in human-centered, ethical, and sustainability-oriented principles, framing design management as an adaptive and relational system rather than a linear or technology-driven process. Departing from the automation-oriented logic of Industry 4.0, the study adopts an augmented cognition perspective in which AI functions as a collaborative partner supporting, rather than replacing, human judgment. A sequential mixed-methods approach was employed, integrating systematic literature review, qualitative content analysis, expert evaluation, and Structural Equation Modeling (SEM) based on data from 316 participants, followed by empirical examination in two service-oriented case contexts, namely tourism/hospitality and healthcare services. The findings identify and validate six interrelated domains and demonstrate that Human–AI–Machine Synergy plays a central role in shaping design outcomes. More specifically, the results show that effective design management in Industry 5.0 depends on the coordinated interaction between cognitive processes, technological infrastructures, and organizational strategies, rather than on isolated technological advancement. Empirical applications further illustrate how the model supports ethically guided AI integration, enhances adaptive decision-making, and improves experience-oriented innovation across different contexts. By providing a validated structural framework that connects previously disjointed elements, this study contributes a clearer operational understanding of how human–AI collaboration can be embedded within design management practices in Industry 5.0. Full article

Background: Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems. Full article

The rainfed paddy farming system faces profound vulnerabilities due to daily climate non-stationarity. This mixed-methods study in Central Java analyses daily climate signals, total rice production, and household adaptation over 25 years (2001–2025). Moving beyond simple correlation, a Principal Component Regression model integrating five climate variables and three agronomic confounders reveals a profound climate–production decoupling. The composite climate index explains only 7.9% of total production variation, while non-climate factors account for 92.1%. Physical stability is maintained through asymmetric temporal scheduling and a distinct hierarchy of responses, employing active, planned adaptations alongside passive, reactive coping. However, quantitative household evaluation reveals this tonnage stability incurs severe hidden costs; the titip gabah post-harvest system maintains a high Yield Stability Index (0.93) but yields a negative Return on Storage (−7.15%), functioning as a risk-mitigation buffer rather than a profit-maximising tool. Furthermore, climate anomalies drive the progressive alienation of traditional ethnoclimatological knowledge, forcing a cognitive shift toward hybridised decision-making. To prevent passive coping from evolving into systemic maladaptation, we propose a stratified policy framework ranging from village-level knowledge integration and Subdistrict daily risk warnings to regency-level subsidies targeted at smallholders (<0.5 ha). Full article

This paper examines whether the persistent difficulty in addressing the eco-social crisis may partly stem from an inadequate representation of human decision-making within mainstream economic models. Although pro-environmental behaviors (PEBs) and sustainable consumption are increasingly recognized as essential for sustainability transitions, neoclassical economics still largely relies on the homo oeconomicus paradigm, which assumes fully rational and utility-maximizing decision-making. Building on contributions from psychology, behavioral economics, neuroscience, and sustainability studies, this integrative narrative review examines how cognitive biases challenge the foundational assumptions of homo oeconomicus and explores the potential role of emotional intelligence in sustainability-related decision-making. Adopting the integrative narrative review approach, this paper integrates literature on (1) cognitive biases and bounded rationality; (2) emotional intelligence and judgment bias; and (3) emotional intelligence, pro-environmental behaviors, and sustainable consumption. The evidence reviewed suggests that sustainability-related decisions are strongly shaped by cognitive and emotional processes operating under uncertainty and socially embedded consumption patterns. Within this framework, EI may represent a psychological resource capable of influence of cognitive biases by supporting emotional regulation, impulse control, self-awareness, and long-term orientation. Overall, the paper proposes a conceptual framework linking cognitive biases, emotional intelligence, and sustainable behavior beyond the traditional homo oeconomicus paradigm. Full article

Manned–unmanned teaming (MUMT) represents a critical evolution in collaborative operations across domains including search and rescue, firefighting, surveillance, and defense. Despite widespread interest in MUMT capabilities, the field lacks a unified taxonomy for classifying and comparing system capabilities, hindering systematic development and technology integration. This paper presents a comprehensive framework for MUMT that addresses the fundamental challenge of organizing and assessing cognitive agent capabilities within human–machine teams. Building upon established automation frameworks, we propose a three-dimensional framework comprising information analysis and inference, decision-making, and action execution. Each dimension defines six hierarchical levels of teaming, ranging from human-only operations to fully autonomous cognitive agent capabilities. The framework distinguishes itself from existing taxonomies by explicitly modeling collaborative teaming rather than simple task delegation, incorporating transparency requirements, and addressing dynamic authority relationships between humans and cognitive agents. The proposed taxonomy provides researchers and engineers with a common vocabulary for MUMT development, enables gap analysis for technology roadmaps, and facilitates the identification of integration opportunities across organizational boundaries. Full article

We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization. Full article

Problem: Entrepreneurial decision-making is widely recognized as central to startup outcomes, yet how founders make decisions during the startup execution phase remains underexplored. Prior research rarely distinguishes between strategic decisions (e.g., market entry, scaling) and operational decisions (e.g., coordination, problem-solving), even though these two decision types differ in their uncertainty, reversibility, and cognitive demands. Objective: This study investigates how founder attributes relate to self-reported decision-making styles across strategic and operational decision contexts during startup execution. Methodology: Drawing on Dual-Process Theory, decision-making is viewed as an interplay between intuitive (System 1) and analytical (System 2) cognitive processes. A sequential exploratory mixed-methods design was employed, beginning with semi-structured interviews with 20 Indian startup founders to develop the conceptual framework, followed by quantitative examination using Partial Least Squares Structural Equation Modelling (PLS-SEM) on data from 350 funded startup founders, with separate structural models estimated for strategic and operational decision contexts. Results: The findings revealed context-specific patterns of association between founder attributes and self-reported decision-making styles across strategic and operational decision contexts. In the strategic model, cognitive orientation, domain experience, and risk appetite were significantly associated with decision-making style, explaining 49.7% of the variance (R² = 0.497). In the operational model, only risk appetite remained significant, with substantially lower explanatory power (R² = 0.125). Taken together, the findings indicate stronger patterns of association between founder attributes and decision-making style in the strategic context than in the operational context. Conclusions: The study contributes to entrepreneurial cognition research by demonstrating that founder attributes exhibit context-specific patterns of association with decision-making styles. These findings underscore the importance of considering decision context when examining entrepreneurial decision-making. Full article

Neuromarketing has emerged as a prominent methodological approach for investigating the implicit cognitive and affective processes underlying consumer decision making. By employing neuroscientific and psychophysiological techniques, it enables researchers to move beyond self-report measures and capture responses that consumers cannot always articulate explicitly. Despite these advances, a fundamental component of experimental design—the selection of affective stimuli—remains conceptually underexamined within neuromarketing research. This article adopts a structured narrative review to examine how affective stimuli are selected, documented, and justified in neuromarketing research. It develops a conceptual and normative framework that reconceptualizes stimulus selection as a decision with ethical, scientific, and sustainability implications rather than a purely technical methodological choice. The review critically examines the widespread reliance on ad hoc stimuli, discusses the potential and limitations of standardized affective databases and related resources, and highlights the need for marketing-specific stimulus repositories. By reframing stimulus selection as a core component of responsible research practice, this study contributes to emerging debates on responsible neuromarketing and provides guidance for more transparent, replicable, ethical, and sustainable neuromarketing research in academic and applied contexts. Full article

To investigate the influence of decision-making styles on college students’ sports and health consumption preferences and the underlying cognitive neural mechanisms, this study recruited 39 college students as participants, adopted a one-factor within-subjects design, and combined behavioral experiments with functional near-infrared spectroscopy (fNIRS). It examined consumption preferences and brain activation characteristics in maximizers and satisficers under three conditions: no promotion, discount promotion, and public welfare promotion. In behavioral terms, college students demonstrated the highest inclination towards public welfare promotions, with discounts being the second most favored, while the no-promotion condition received the lowest preference. Maximizers preferred discount promotion, while satisficers prioritized public welfare promotion. In neural terms, public welfare promotion widely activated the left dorsolateral prefrontal cortex, whereas discount promotion only activated a local region of this cortex. Maximizers showed the strongest activation in the corresponding region under discount promotion, and satisficers exhibited more significant activation in the corresponding region under public welfare promotion. Decision-making styles shaped consumption preferences through depth of information processing and brain activation patterns: maximizers focused on rational calculation and benefit maximization, while satisficers relied on intuitive experience and value perception. These findings provide behavioral and neuroscientific evidence for precision marketing in the sport and health consumption market and the implementation of the national fitness program. Full article