Decision Speed Research Articles

AI-Powered Autonomous Trading: Enhancing Market Efficiency Through Predictive Analytics

Abstract: In the realm of financial markets, AI-powered autonomous trading systems are revolutionizing the way trades are executed and decisions are made. This paper presents a formal approach to developing an AI-powered trading system aimed at enhancing market efficiency through predictive analytics. The proposed system integrates machine learning algorithms, including deep learning and reinforcement learning, to analyze vast amounts of historical and real-time market data. Emphasis is placed on improving decision-making speed, accuracy, and risk management through techniques such as algorithmic trading, automated portfolio management, and sentiment analysis. The result is a robust, efficient, and scalable trading system that outperforms traditional models in terms of profitability, adaptability, and computational efficiency in high-frequency trading environments

The Contribution of Perceptual-Cognitive Skills to Reactive Agility in Early and Middle Adolescent Soccer Players.

Horníková, H, Hadža, R, and Zemková, E. The contribution of perceptual-cognitive skills to reactive agility in early and middle adolescent soccer players. J Strength Cond Res XX(X): 000-000, 2024-Agility performance increased from childhood to mid-adolescence, followed by a plateau until early adulthood. Because adolescence is crucial for the development of perceptual-cognitive skills, the question is whether and to what extent its contribution to reactive agility increases during developmental periods of adolescence in invasive sports athletes. This study investigated the association of reactive agility with the speed of decision making, sprint speed, and explosive strength in early and middle adolescent soccer players. They performed reactive and preplanned Y-shaped agility tests, choice reaction time test, modified half T-test, 30-m sprint (10 m split), and countermovement jump (CMJ). The reactive agility time in both age groups significantly correlated with 30-m sprint time (r = 0.839, p < 0.001, and r = 0.745, p = 0.001, respectively), pre-planned Y-shaped agility time (r = 0.828, p < 0.001, and r = 0.644, p = 0.007, respectively), 10-m sprint time (r = 0.823, p < 0.001, and r = 0.775, p < 0.001, respectively), time in modified half T-test (r = 0.723, p < 0.001, and r = 0.630, p = 0.009, respectively), and CMJ height (r = -0.744, p < 0.001, and r = -0.615, p = 0.013, respectively), although with the index of reactivity in middle adolescent soccer players only (r = 0.615, p = 0.011). Based on R2, the explained proportion of variance was 53.6% for linear sprint speed and 30.1% for a change of direction speed in early adolescence, whereas it was 54.6% for acceleration speed and 31.8% for decision-making in middle adolescence. This indicates the significant contribution of speed factors to reactive agility in both adolescent groups, whereas perceptual-cognitive factors in middle adolescent soccer players only. It seems that even during the relatively short period of adolescence, the changes occur in the contribution of perceptual-cognitive skills to reactive agility in athletes practising invasive sports.

Design of an Intelligent Computing-based Information System for Automated Decision Making

The rapid growth of information technology has increased the need for intelligent computing-based information systems across sectors, such as business, education, and government, to facilitate quick and accurate decision-making. Previous research primarily focused on data analysis without a seamless integration for automated decision support. This study aims to bridge this gap by designing an information system that leverages machine learning algorithms for automated decision-making. The system incorporates artificial intelligence and big data processing to provide accurate recommendations based on historical and real-time data patterns. Key processes include identifying user needs, selecting suitable algorithms, developing predictive models, and integrating them into a user-friendly, web-based platform. Results indicate that the intelligent system significantly enhances decision-making speed and accuracy, particularly in scenarios demanding real-time analysis. Tests with decision trees and neural network algorithms demonstrate the system's reliability and adaptability to various data types, supporting consistent, data-driven outcomes. This research concludes by highlighting the system's potential to address complex data challenges, enabling efficient decision-making in dynamic environments.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

فاعلية توظيف العلاقات العامة لربورتات الدردشة في التعريف بالخدمات في المؤسسات الحكومية

The study aimed to describe the effectiveness of employing public relations for chat bots in defining services in government institutions, by knowing the manifestations of public relations practitioners' interest in knowing information about chat bots and employing them in defining services in government institutions, services and areas of use, as well as the benefits achieved and the obstacles they face in this field, reaching their suggestions and their evaluation of the impact of these applications on their professional competence, and the future of public relations in light of chat bots. The study used the descriptive approach, and among the tools, the interview was used, and a questionnaire on the reality of public relations practitioners' use of chat bots in defining services in government institutions. The study sample consisted of (240) employees in the Corporate Communications and Public Relations Department in government institutions. As for the results of the study, it showed great interest from public relations practitioners in government institutions in chat bots and their use in managing corporate communication, as their aspects of use varied between crisis management and meeting the needs of the public. These technologies have contributed to improving the professional efficiency of most of the respondents by enhancing daily performance, speed of accomplishment, planning and decision-making. However, users faced challenges such as the difficulty of robots understanding customer feelings and fear of losing jobs. They suggested linking promotion to their ability to use them and provide training and technical support. Despite the benefits of robots, there are still concerns about the future of the public relations profession in light of this technology.

Task goals shape the relationship between decision and movement speed.

The speed at which we move is linked to the speed at which we decide to make these movements. Yet, the principles guiding such relationship remain unclear: while some studies point towards a shared invigoration process boosting decision and movement speed jointly, others rather indicate a tradeoff between both levels of control, with slower movements accompanying faster decisions. Here, we aimed (1) at further investigating the existence of a shared invigoration process linking decision and movement and (2) at testing the hypothesis that such a link is masked when detrimental to the reward rate. To this aim, we tested 62 subjects who performed the tokens task in two experiments (separate sessions): Experiment 1 evaluated how changing decision speed affects movement speed while Experiment 2 assessed how changing movement speed affects decision speed. In the latter experiment, subjects were either encouraged to favor decision speed (fast decision group) or decision accuracy (slow decision group). Various mixed model analyses revealed a coregulation of decision (urgency) and movement speed in Experiment 1 and in the fast decision group of Experiment 2, but not in the slow decision group despite the fact that these same subjects displayed a coregulation effect in Experiment 1. Altogether, our findings support the idea that coregulation occurs as a default mode but that this form of control is diminished or supplanted by a tradeoff relationship, contingent on reward rate maximization. Drawing from these behavioral observations, we propose that multiple processes contribute to shaping the speed of decisions and movements.

Fast or slow? A meta-analysis on the performance implications of decision, implementation, and response speed

Speed across various organizational processes, such as decision-making, executing operational activities, and responding to competitive challenges, represents an essential component of firms’ ability to achieve a competitive advantage. However, a theoretical and empirical consensus has yet to form whether decision speed (DS), implementation speed (IS), and response speed (RS) are beneficial or detrimental to firm performance. To help reconcile these inconclusive findings, we meta-analyze 127 studies (representing 239 effect sizes) and report overall direct effects for DS (0.21), IS (0.22), and RS (0.10) on firm performance. In addition, we examine contingencies which reveal that the performance consequences of DS, IS, and RS vary across contextual and methodological conditions.

Psychological value theory: predicting health-seeking behavior from symptom perception.

We recruit Psychological Value Theory (PVT) to understand how symptom value influences health-seeking decisions. Estimates of the Psychological Value of relief from a particular symptom were previously collected and used to predict the speed of participants' decision and the choice they make in three discrete choice experiments. Experiment 1 presented participants with a scenario and asked them to identify which of two symptoms they would seek healthcare services to treat. For each participant on every trial, two randomly chosen symptoms were inserted into the scenario. Experiment 2 addressed how the Psychological Value of a group of symptoms is predicted from the individual symptoms. Experiment 2 replicated Experiment 1 using groups of two symptoms, and predicted choice based on three grouping functions. Experiment 3 replicated Experiment 2 using a yes/no task, whereby participants were asked if they would pursue a health care visit for a single set of symptoms. The results showed that PVT accurately predicted speed and choice in all three experiments. The Psychological Value of relief from a symptom was the primary driver of choice along with a response bias in favor of avoiding symptoms labeled "severe."Health-seeking decisions are well modeled by a general-purpose, value-based computational model (PVT), with the Psychological Value of relief from health symptoms as a primary driver of health-seeking behavior.

Quantum-Inspired AI for Optimized High-Frequency Trading

This article explores the transformative role of quantum-inspired AI in optimizing financial practices, particularly within high-frequency trading (HFT) in the financial sector. As HFT operates in an environment of rapid transactions and significant market volatility, the need for advanced optimization techniques becomes paramount. Quantum-inspired algorithms leverage principles from quantum mechanics, such as superposition and tunneling, to enhance various aspects of trading strategies. These algorithms enable rapid optimization of asset allocation, real-time trade execution, and proactive fraud detection, effectively addressing the challenges posed by traditional financial models. By facilitating simultaneous evaluation of multiple strategies and enabling real-time analysis of complex trading patterns, quantum-inspired AI significantly improves decision-making speed and accuracy. The financial implications of this advancement are profound, leading to higher profitability, improved market integrity, and enhanced trust among market participants. Ultimately, integrating quantum-inspired AI in finance represents a crucial step towards harnessing cutting-edge technology to reshape trading dynamics, paving the way for innovative strategies that can adapt to the evolving landscape of financial markets. This study underscores the potential of quantum-inspired AI to redefine operational efficiency in finance, ensuring competitiveness in an increasingly complex trading environment.

GRRIS: A Real-time Intrasite Observation Scheduling Scheme for Distributed Survey Telescope Arrays

The distributed telescope array offers promise for conducting large-sky-area, high-frequency time-domain surveys. Multiple telescopes can be deployed at each observation site, so intrasite observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time-domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for a time-domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intrasite observation scheduling scheme for the telescope array using graph and reinforcement learning (RL). It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent RL is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and subsecond decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays.

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

SOME ASPECTS OF ACTIONS TO RESPONSE TO EXTERNAL SECURITY THREATS AND CRISES OF THE EUROPEAN UNION

The European Union is taking action in various areas of security to improve its ability to respond to crises and increase its resilience to future challenges, so this article examines the evolution of crisis response actions of the European Union countries in the face of challenges and threats in a rapidly changing geopolitical environment in relation to European security. This, in turn, is reflected in the speed of decision-making by the competent associations of the European Union regarding legal regulation that corresponds to the current situation. The authors identify and analyze the main directions of action and response measures of member countries in the legal field. In this context, the authors pay special attention to the consideration of the European Union's security policy in relation to hybrid threats from neighboring states, ensuring cyber security, the use of modern information communication technologies, as well as mediation tools in the prevention of these threats. The authors analyze issues of cooperation between the European Union and the United Nations in the field of peacekeeping and military missions. The authors also touched on the specifics of lessons learned from addressing the consequences of crisis situations and the impact of the EU countries’ integrated approach to responding to crisis situations in the context of the past pandemic, as well as the impact of hybrid threats. The results of this study show that the relevant institutions of the European Union skillfully, promptly and effectively coordinate decision-making on response and management in crisis situations, and as part of improving the anti-crisis management strategy, taking measures to prevent the consequences of crisis situations in the EU area (hybrid threats, illegal migration, terrorism, organized crime, etc.)

Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm

UAV technology is advancing rapidly, and variable-sweep wing UAVs are increasingly valuable because they can adapt to different flight conditions. However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. To address these challenges, this study presents an innovative framework that integrates dynamic modeling with morphing control, optimized by the multi-agent deep deterministic policy gradient for two-sweep control (MADDPG–VSC) algorithm. This approach enables real-time sweep angle adjustments based on current flight states, significantly enhancing aerodynamic efficiency and overall UAV performance. The precise motion state model for wing morphing developed in this study underpins the MADDPG–VSC algorithm’s implementation. The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. Extensive simulations and real-world experiments consistently demonstrate that the proposed algorithm outperforms contemporary methods in multiple aspects, underscoring its practical applicability in complex aerial systems. This study advances control technologies for morphing-wing UAV formation and offers new insights into multi-agent cooperative control, with substantial potential for real-world applications.

Information accumulation on the item versus source test of source monitoring: Insights from diffusion modeling.

Source monitoring involves attributing previous experiences (e.g., studied words as items) to their origins (e.g., screen positions as sources). The present study aimed toward a better understanding of temporal aspects of item and source processing. Participants made source decisions for recognized items either in succession (i.e., the standard format) or in separate test blocks providing independent measures of item and source decision speed. Comparable speeds of item and source decision across the test formats would suggest a full separation between item and source processing, whereas different speeds would imply their (partial) temporal overlap. To test these alternatives, we used the drift rate parameter of the diffusion model (Ratcliff, Psychological Review, 85, 59-108, 1978). We examined whether the drift rates, together with the other parameters, assessed separately for the item and source decision varied as a function of the test format. Threshold separation and nondecision time differed between the test formats, but item and source decision speeds represented by drift rates did not change significantly. Thus, despite facilitation on the source decision when the item decision was immediately followed by a test for source memory than when item and source were tested in separate blocks, findings did not suggest that source information already begins accumulating in the item test in the standard format. We discuss the temporal sequence of item and source processing in light of different assumptions about the contribution of familiarity and recollection.

The effect of juggling on the proprioceptive and attentional abilities among older women.

Age-related changes in attentional abilities can lead to a decline in body segment awareness in space. However, studies have reported that physical activity can improve proprioception among older adults, although proven activities with this potential are limited. Juggling is a promising activity for enhancing proprioception, as it requires high levels of attention and sensory precision. The first hypothesis posited that a juggling intervention would positively impact ipsilateral and contralateral elbow joint position matching without visual input. The second hypothesis suggested a correlation between cognitive abilities and joint position sense efficiency. A total of 20 older women (mean age: 69.95 ± 4.58) participated in a repeated-measures study using a Latin square design. Measurements were taken at three time points (baseline, post-juggling, and control). Ipsilateral and contralateral elbow joint position matchings without visual or verbal feedback of accuracy were used to assess proprioception. Attention and reaction time variables were measured using the Vienna Test System protocols. Although significant changes were observed between baseline and subsequent time points in joint position sense accuracy, no specific effect of juggling was detected. Low and medium correlations were found between decision time and the variability of choice reaction time with contralateral accuracy. For ipsilateral accuracy, a relationship was observed only with handedness. No correlations were found between attention test scores and joint position sense accuracy. The study did not demonstrate a significant effect of juggling on position-matching ability. However, cognitive abilities such as decision speed and the stability of choice reaction time may play a role in enhancing position-matching in older women. ClinicalTrials.gov, identifier NCT06108713.

Word Type and Frequency Effects on Lexical Decisions Are Process-dependent and Start Early.

When encountering letter strings, we rapidly determine whether they are words. The speed of such lexical decisions (LDs) is affected by word frequency. Apart from influencing late, decision-related, processing stages, frequency has also been shown to affect very early stages, and even the processing of nonwords. We developed a detailed account of the different frequency effects involved in LDs by (1) dividing LDs into processing stages using a combination of hidden semi-Markov models and multivariate pattern analysis applied to EEG data and (2) using generalized additive mixed models to investigate how the effect of continuous word and nonword frequency differs between these stages. We discovered six stages shared between word types, with the fifth stage consisting of two substages for pseudowords only. In the earliest stages, visual processing was completed faster for frequent words, but took longer for word-like nonwords. Later stages involved an orthographic familiarity assessment followed by an elaborate decision process, both affected differently by frequency. We therefore conclude that frequency indeed affects all processes involved in LDs and that the magnitude and direction of these effects differ both by process and word type.

Decoding decision delegation to artificial intelligence: A mixed-methods study on the preferences of decision-makers and decision-affected in surrogate decision contexts

Organizations are increasingly opting to use decision-making systems based on artificial intelligence (AI) to increase decision quality and speed and free up resources. However, delegating decisions to AI is challenging, especially in contexts that go beyond classical optimization problems. Current research has so far largely neglected to include the viewpoints of the decision-affected and of surrogate decision-makers in the discussion. To address these gaps, in this study we rely on two experimental designs and complement our findings with qualitative interview data to shed light on both sides: the perspective of those affected by the decision and that of the decision maker in surrogate and ethically complex decision contexts. Findings reveal that the individual decision-making perspective may lead to opposing perceptions. Whereas the willingness to delegate an ethically challenging decision to AI in a surrogate decision context is lower than that in a non-surrogate context (decision-maker perspective), people affected by the decision do not generally prefer humans to AI (decision-affected perspective). By these findings, we contribute to the literature on AI-enabled decision-making and decision delegation to non-human entities.

Methodological foundations of somatoscopic research in automated decision support systems

Automated Decision Support Systems (ADSS) are widely applied in various fields of science and technology, particularly in medicine, where their role in diagnostic, prognostic, and therapeutic processes is undeniable. The use of computational technologies in medical practice has become a necessary stage in the development of the field; however, the increasing volume of data that requires processing, along with heightened demands for accuracy, speed, and reliability of recommendations, significantly complicates decision-making processes. The growing amount of data, combined with the need to minimize the risks of erroneous decisions, drives the scientific community to seek innovative information technologies that can ensure a high level of accuracy in computational operations while minimizing the time required for their execution. Somatoscopic measurements, which are a crucial element in assessing the condition of the musculoskeletal system and overall physical posture of a patient, require precise and systematic image analysis that allows the identification of critically important anatomical markers. This process involves the accurate determination of key points on images of anatomical structures, which serves as the foundation for making precise predictions about the patient's condition and subsequent treatment planning. In the context of continuously increasing volumes of medical data and stricter requirements for the quality of decisions, the implementation of innovative technologies, particularly deep learning, becomes increasingly relevant as it enhances the processes of analysis and diagnosis. The aim of this study is to conduct a comprehensive analysis of the methodological aspects of applying deep learning in the context of automating the diagnosis of the musculoskeletal system and posture assessment. The study emphasizes the use of specialized neural network architectures that enable the identification of key anatomical points on images, thereby facilitating a deeper and more accurate analysis of anatomical structures. This approach significantly improves the efficiency of diagnostic processes by minimizing the likelihood of errors in decision-making and optimizing the performance of medical systems. The results of the conducted research demonstrate the significant potential of deep learning algorithms in medical systems for the automated analysis of images, allowing for substantial improvements in the accuracy and speed of decision-making. Such automation contributes to reducing the risk of subjective errors associated with the human factor, which is particularly important in complex clinical cases. Therefore, the further development of research in this direction is of paramount importance for the medical field, as it opens new opportunities for solving complex diagnostic tasks at an innovative technological level. The integration of deep learning technologies into the processes of extracting somatoscopic data not only enhances diagnostic efficiency but also creates prerequisites for the development of new decision support systems that optimize medical practice.

Development of a software service for stock price forecasting based on sentiment analysis and autoregressive models

This paper addresses the critical need for efficient market analysis tools in the era of big data and artificial intelligence. We present a novel software service that integrates real-time news sentiment analysis with stock market prediction, enhancing the accuracy and speed of trading decisions. The system employs APIs for data collection, FinBERT for sentiment analysis, and MongoDB for data storage, overcoming limitations of existing platforms like Investing.com and MarketWatch. Our methodology combines sentiment analysis with autoregressive models to forecast stock prices for 11 major companies. The experiment utilized 141 observations, applying multiple regression and binary outcome models. Results demonstrate that investor sentiment significantly affects stock prices for 2 out of 11 companies, with Meta showing a 70 % determination coefficient in price direction changes based on sentiment. The study reveals that incorporating both quantitative (previous stock prices) and qualitative (sentiment) data improves forecast accuracy for certain stocks. This research contributes to the field of financial analytics by providing a more comprehensive approach to stock price prediction, integrating ML models and data analytics to support informed decision-making in dynamic financial markets.

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.