Decision Speed Research Articles

MODERN PRINCIPLES AND MEANS OF AUTOMATION IN ACCOUNTING, MANAGEMENT, AND MARKETING

Automation of auxiliary business processes (specifically accounting, management, and marketing) has long ceased to be an experiment and has become necessary. This necessity is a direct result of the impact of using automated devices and technical means, as well as computing technology based on mathematical methods, on the speed of decision-making and task execution, labor cost levels, resource utilization efficiency, the number of errors, and the quality of products or services. The article outlines the modern principles and tools of automation in accounting, management, and marketing. The research highlights important aspects of business process automation and identifies some key points. Based on the results obtained, a deep analysis of the interaction between humans and machines in automated accounting, management, and marketing systems can be conducted.

Automatic versus manual investing: Role of past performance

Using unique data from a leading peer-to-peer (P2P) lending platform, we investigate the link between past investment performance and choice of auto-investing tool. Our results suggest that investors who experience fewer defaults in the manual mode are more inclined to switch to automatic investment. Several factors account for this relationship, including investor inattention, decision speed, investment delegation, and experience. Regarding the latter, our results suggest that experienced investors are more likely to continue self-directed bidding, even if they have faced defaults in manual investments in the past. These investors may attribute their previous mistakes to their own actions rather than the limitations of the self-directed bids. Our results are robust to alternative specifications.

Best-Practice Biomarker Testing of Oesophago-Gastric Cancer in the UK: Expert Consensus Recommendations Developed Using a Modified Delphi

AimsOesophago-gastric cancers (OGCs) are amongst the most commonly diagnosed malignancies worldwide and are associated with high disease-related mortality. Predictive biomarkers are molecules that can be objectively measured and used to indicate a likely response to therapeutic intervention, thus facilitating individualised cancer therapy. However, there remains variation in uptake and implementation of biomarker testing across the UK. Materials and methodsWe conducted a modified Delphi study to formulate consensus recommendations for best-practice biomarker testing of OGC in the UK. We employed two rounds of online questionnaires followed by a virtual consensus meeting. Biomarkers for discussion included HER2, MSI/MMR, and PD-L1. Topics comprised the overall biomarker pathway, pre-analytical, analytical, and post-analytical considerations, including challenges in current practice. ResultsTwenty-six and eighteen participants completed the first and second round Delphi questionnaire, respectively, with an even split of pathologists and oncologists from across the UK. There was consensus (>80% agreement) across several topics, including the requirements for standardisation of the pathway, which must include coordination throughout the tissue journey, requirements for a quality-assured process to ensure accuracy and validity of testing, plus the need for clear, detailed information on the pathology report to support treatment decisions. There was consensus amongst oncologists regarding reflex testing of all biomarkers depending on histology; however, concerns over capacity in relation to workload and availability of pathologists were evident among the pathologists. Overall, participants were in the opinion that reflex testing improves the speed of treatment decisions and improves patient care. ConclusionThe recommendations reflect best-practices and should be implemented to support rapid multidisciplinary team decision-making within oesophago-gastric cancer. Results reflect the need for standardisation and demonstrate the challenges faced in clinical practice by those requesting and testing biomarkers for oesophago-gastric cancer, suggesting significant concerns relating to pathologist capacity.

Research and application of rough set attribute reduction algorithm based on genetic algorithm in pipeline network

Abstract Explosion accidents often occur in urban underground pipeline network systems, which has caused safety issues in public life and industrial production. GIS technology can visually manage pipeline equipment and solve the problem of valve closure analysis in the event of current accidents, avoiding the escalation of the situation, but it cannot make predictions. Attribute reduction is one of the core contents of rough set theory and is an NP-hard problem. This article proposes a custom attribute reduction algorithm based on an algorithm and uses the algorithm in the historical burst pipeline dataset of the water supply pipeline industry. The results have shown that this method combined with a decision tree can make predictions before water pipe leakage. The algorithm provides the best result of attribute reduction for decision tree methods, which will improve the speed and accuracy of decision-making.

The Contender’s Momentum? COVID-19 and IO Relations in the Regime Complex of Financial Assistance

Abstract How do crises affect authority structures in international regime complexes? The international order is characterized by regime complexes consisting of ever more overlapping institutions competing for authority. Most complexes consist of established institutions facing a growing number of more recently created institutional contenders. We argue that crises can alter the competitive structure of regime complexes. From the perspective of states, crisis situations can provide newly created contenders—designed as institutional alternatives—with functional advantages over established institutions. Long-established institutions tend to be highly institutionalized, decreasing flexibility and speed of decision-making. Thus, during crises, states may shift their cooperative activities away from inert institutions and to newly created, more flexible alternatives that are less encompassing. We test the plausibility of our claim by studying the effects of the COVID-19 crisis on the regime complex of financial assistance (FA). The results of a qualitative process analysis of institutional crisis responses combined with a comparative network analysis of the FA complex support our argument. The COVID-19 pandemic provided an opportunity for the Asian Infrastructure Investment Bank and the New Development Bank to position themselves as crisis-proof alternatives for developing countries to established institutions like the World Bank.

Autonomous driving test system under hybrid reality: The role of digital twin technology

Autonomous vehicles have attracted attention as a result of enhancements in artificial intelligence, the Internet of Things, and communication technologies. There is a priority for new testing frameworks to keep up with the increasing complexity of decision-making, connectivity, data interchange, and data transmission speed. Traditional vehicle testing tools and methods cannot meet the new testing requirements imposed by the upgrading of autonomous driving (AD) technology. They are expensive, time-intensive, and present safety hazards during testing, as well as cannot simulate hybrid real-world situations and manage real-time data efficiently. Therefore, in terms of test efficiency, cost, and safety, a smart vehicle testing in hybrid reality and evaluation method based on a digital twin (DT) is presented to speed up the development and testing of AD functions. Our model uses three-dimensional coordinate mapping, a collision detection model, and virtual scene registration to plot the AD information in the actual environment to the virtual scenario. In addition, the consistent mixed reality-based AD test model is constructed at the same time. Using this model, we demonstrated that our proposal allows for better performance of an AD test. Furthermore, the collision test demonstrates that the mixed reality system has interactive features. The performance of the system under the sampling rate of 50 ms, 200 ms, and 800 ms is compared and analyzed. Also, the experiments show that the algorithm described in this paper works better when the sampling frequency is 200 ms or more.

Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.

The tortoise in the urban development network: How social relations affect decision-making speed in a road expansion project in Kristiansand, Norway

ABSTRACT Why do urban development projects take a long time to complete? This question is of paramount importance, both practically and academically. Practically, understanding the reasons for delays in these projects can help improve project efficiency, reduce costs, and enhance stakeholder satisfaction. Academically, the study contributes to the field of urbanization by applying social network analysis (SNA) to explore the interactions and relationships among decision-makers in urban development projects. This study draws upon original data from a road extension project in Kristiansand to illustrate and explain why the implementation of large development projects suffers from convoluted and slow decision-making processes after an overall agreement on project aims and goals has been established. This study draws on a sample of 33 actors who participated in the decisions concerning the project. I collected the data through a combination of interviews and document reviews and subsequently analyzed them using social network structural metrics and visualization techniques. The results show that the actors are connected in less dense, more centralized, and clustered networks with subgroups and low reciprocity which have hampered efficient communication, resulting in delays. The findings imply that the pattern of relationships among decision-making actors can affect the amount of time taken to reach conclusions in urban development initiatives.

Hierarchical deep reinforcement learning for self-adaptive economic dispatch

It is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility of renewable energy. Deep reinforcement learning (DRL) algorithms are introduced as a solution to avoid modeling the complex uncertainties and to adapt the fluctuation of uncertainty by interacting with the environment and using feedback to continuously improve their strategies. However, the large-scale nature and uncertainty of the system lead to the sparse reward problem and high-dimensional space issue in DRL. A hierarchical deep reinforcement learning (HDRL) scheme is designed to decompose the process of solving this problem into two stages, using the reinforcement learning (RL) agent in the global stage and the heuristic algorithm in the local stage to find optimal dispatching decisions for power systems under uncertainty. Simulation studies have shown that the proposed HDRL scheme is efficient in solving power system economic dispatch problems under both deterministic and uncertain scenarios thanks to its adaptation system uncertainty, and coping with the volatility of uncertain factors while significantly improving the speed of online decision-making.

Features of forming coordination abilities and properties of the nervous system of schoolchildren (aged 11-12) at physical education classes

Introduction. The problem of forming and improving modern adolescents’ coordination abilities is relevant, considering the fact that most professional skills are based on these abilities. A number of studies indicate the relationship between the development of schoolchildren’ coordination abilities and the properties of their nervous system. Thus, to date, these relationship has been quite poorly studied. The purpose of the article is to reveal the features of forming coordination abilities and properties of the nervous system of schoolchildren aged between 11 and 12 years in the process of physical education. Materials and Methods. The methodological approach of this study is N. A. Bernstein’s theory on the physiological basis of movement control. In order to assess the level of schoolchildren’s motor coordination, the ability to kinetic differentiation, maintaining balance (Romberg’s test), and the ability to coordinate movements were assessed. The properties of nervous system were studied using visual-motor reactions. The tests make it possible to determine a subject’s reaction time to a visual stimulus, the stability of the reaction, the speed of decision-making in the choice reaction, and to assess the mobility of the main nervous processes in the central nervous system. Results. The article presents the results of a study of forming coordination abilities and properties of schoolchildren’s nervous system in the process of Physical Education classes. At the beginning of the experiment, schoolchildren, aged 11-12 years, had a below average level of the ability to balance and kinesthetic differentiation and a low level of development of the ability to coordinate movements. The time for a complex visual-motor reaction was significantly longer compared to a simple reaction. There were no significant differences in the indicators of visual-motor reactions of schoolchildren depending on their gender. During Physical Education classes, when they do gymnastics, basketball and track and field athletics, some specially selected exercises were used aimed at developing coordination abilities in middle schoolchildren. At the end of the experiment, schoolchildren aged 11-12 years were determined to have a high level of the ability to balance and kinesthetic differentiation and an above-average level of development of the ability to coordinate movements. According to the results of the experiment, the highest level of ability to kinesthetic differentiation and coordination of movements were shown by schoolchildren. The authors obtained data about the optimization of indicators of the nervous system properties, assessed by the magnitude of simple-motor reaction (SVMR) and complex visual-motor reaction (CVMR), which proves the leading role of the central nervous system in the formation of coordination abilities. Conclusions. The results of the work allow the authors to draw a conclusion about the relationship between the properties of the nervous system and the dynamics of coordination abilities, which determine the speed of the processes of excitation and inhibition, the mobility of nervous processes in the central nervous system, the ability to develop differential inhibition and the accuracy of the motor act in schoolchildren aged 11-12 years during the Physical Education classes.

К вопросу о проблематике отечественной бюрократии: социально-философское рассмотрение

The article is an attempt at an unbiased assessment of the role of the Russian bureaucracy in the context of the socio-historical process. Despite the diversity of the available theoretical approaches to the phenomenon of domestic bureaucracy, the analysis of historical facts shows that only a centrally soldered authoritarian model that ensures the realization of mobilization potential demonstrated effectiveness. In the course of the historical evolution of the state apparatus, a political and managerial tradition has been formed – a historically established, stable and repetitive system of norms and rules that determines the type of relations primarily within the political and administrative sphere. At present, the domestic bureaucracy is one of the main contradictions for the national social system, playing the role of thesis and antithesis at the same time. On the one hand, the established state administrative apparatus is organically connected with society, and its destruction will inevitably lead to the social system into a systemic crisis, or even to a rapid collapse. On the other hand, the destructive influence of bureaucratization is obvious and boils down to the following points: 1) a lack of flexibility and speed in decision-making, which is relevant in today’s rapidly changing world; 2) a tendency to minimise grassroots initiatives, the potential of which is not adequately understood by the majority of the population; 3) alienation between the population and administrative officials, who are increasingly becoming a separate group, working against social unity and integrity.

Using Big Data and analytics in accounting for strategic decisions

The purpose of this paper is to study the potential of using Big Data and analytics in accounting in order to improve strategic decision-making processes at the enterprise. To achieve the goal, the following tasks were performed in the research process: the concepts, advantages and disadvantages of using Big Data and analytics in accounting were considered; directions for using Big Data in accounting in general and for improving strategic decision-making processes in particular were investigated. The use of advanced technologies in accounting can significantly improve the quality and relevance of accounting information, reduce preparation time and ensure availability of data in real time. Studying the basic principles of accounting organization using information technologies, efficiency, accuracy, security and compliance with regulatory requirements were identified as key. The potential impact of Big Data on the accounting profession and its practices is very significant. As the volume of available information grows, new opportunities appear to improve the accuracy and efficiency of traditional accounting approaches. Big Data analysis can reveal financial trends, patterns, and anomalies that were previously difficult to access or invisible through traditional methods. The use of Big Data and analytics in accounting for making strategic decisions can significantly contribute to the achievement of the goals specified in the modern economic paradigm. Simplified acquisition and analysis of large volumes of data makes it possible not only to increase the accuracy and speed of decision-making in financial management, but also to respond more effectively to changes in macroeconomic conditions, such as diversification of production and exports, improvement of living conditions of the population, and reduction of social inequality. The introduction of high technologies, including Big Data analysis, is becoming a key tool for ensuring sustainable economic development, contributing to the growth of productive forces and optimization of production processes in accordance with modern requirements and challenges of the global market.

Sexually differentiated decision-making involves faster recruitment in the early stages for the Tibetan antelopes Pantholops hodgsonii

Abstract Group living is widespread across diverse taxa, and the mechanisms underlying collective decision-making in contexts of variable role division are critical for understanding the dynamics of group stability. While studies on collective behavior in small animals such as fish and insects are well-established, similar research on large wild animals remains challenging due to the limited availability of sufficient and systematic field data. Here, we aimed to explore the collective decision-making pattern and its sexual difference for the dimorphic Tibetan antelopes Pantholops hodgsonii (chiru) in Xizang Autonomous Region, China, by analyzing individual leadership distribution, as well as the joining process, considering factors such as calving stages and joining ranks. The distinct correlations of decision participants’ ratio with group size and decision duration underscore the trade-off between accuracy and speed in decision-making. Male antelopes display a more democratic decision-making pattern, while females exhibit more prompt responses after calving at an early stage. This study uncovers a partially shared decision-making strategy among Tibetan antelopes, suggesting flexible self-organization in group decision processes aligned with animal life cycle progression.

ПРОБЛЕМы КАДРОВОГО МЕНЕДЖМЕНТА В ОРГАНИЗАЦИЯх МАЛОГО И СРЕДНЕГО БИЗНЕСА

The characteristic features of small and medium-sized enterprises (SMEs), which are reflected in the work with personnel, are considered. There is a convergence in this business of the functions of the owner and manager, an increase in the role of the head of the company and each individual employee, speed of decisionmaking, and flexible organization of work. The identified problems in the process of personnel management of small and medium-sized businesses (SMEs), including business instability, the seasonal nature of activities, insufficient funds for the use of modern personnel management tools, difficulties in attracting highly qualified specialists, the predominance of administrative management methods, imperfection of the remuneration system. It is noted that an effective option for mitigating the effect of negative factors on the process of personnel management in SMEs is the cooperation of small and medium-sized businesses with scientific and educational organizations and, first of all, with universities. The obstacles to such cooperation and the main directions for overcoming these obstacles are identified. Among the latter are strengthening support for SMEs collaborating with scientific and educational institutions, intensifying work to create structures that facilitate the commercialization of the results of joint activities of SMEs and universities.

Artificial intelligence capability and organizational performance: unraveling the mediating mechanisms of decision-making processes

PurposeThis study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.Design/methodology/approachDrawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.FindingsThe results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.Originality/valueThis study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

Knowledge Management Model for Urban Flood Emergency Response Based on Multimodal Knowledge Graphs

Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model’s application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management.

Information Technology for the Operational Processing of Military Content for Commanders of Tactical Army Units

In the modern world, the military sphere occupies a very high place in the life of the country. At the same time, this area needs quick and accurate solutions. This decision can greatly affect the unfolding of events on the battlefield and indicate that they must be used carefully, using all possible means. During the war, the speed and importance of decisions are very important, and we note that the relevance of this topic is growing sharply. The purpose of the work is to create a comprehensive information system that facilitates the work of commanders of tactical units, which organizes the visualization and classification of aerial objects in real-time, the classification of objects for radio-technical intelligence, the structuring of military information and facilitates the perception of military information. The object of research/development is a phenomenon that creates a problematic problem, has the presence of slowing factors in the process of command and control, using teams of tactical links, which can slow down decision-making, as well as affect their correctness. The research/development aims to address emerging bottlenecks in the command-and-control process performed by tactical link teams, providing improved visualization, analysis and work with military data. The result of the work is an information system for processing military data to help commanders of tactical units. This system significantly improves on known officer assistance tools, although it includes a set of programs that have been used in parallel on an as-needed basis. Using modern information technologies and ease of use, the system covers problems that may arise for commanders. Also, each program included in the complex information system has its degree of innovation. The information system for structuring military information is distinguished by the possibility of use on any device. The information system for the visualization and clustering of aerial objects and the information system for the classification of objects for radio technical intelligence are distinguished by their component nature. This means that the application can use sources of input information and provides an API to use other processing information. Regarding the information system for integration into information materials, largely unknown terms and abbreviations are defined, so such solutions, cannot integrate the required data into real documents. Therefore, using this comprehensive information system, the command of tactical units will have the opportunity to improve the quality and achieve the command-and-control process.

Survey of driver's functional state change, considering movement through mountain passes

According to traffic accident statistics, the main causes are violations of traffic rules. Based on scientific research, it is clear that the main reason for these events is the drivers' incorrect assessment of the road situation and their inadequate response. Assessing road conditions depends on drivers' qualifications, skills, work experience, reaction time, and other physiological factors. The functional state of drivers is important to consider. It reflects their ability to work reliably and affects both fatigue levels and how fatigue builds up. Factors that impact decision-making speed and quality are crucial when evaluating the risks of the transportation process, particularly those associated with human behavior. This study investigates the safety of freight transportation for drivers, road users, third parties, and the environment. The study focuses on road traffic characteristics in mountainous conditions at high altitudes. The study aims to analyze how drivers' performance is affected while navigating challenging sections of mountain roads in the Ukrainian Carpathians. Four different routes will be examined. The research will provide graphic analysis of the results. It will show the main patterns of changes in drivers' functional state based on the route's complexity. The research aims to analyze indicators of drivers' functional state and their impact on their performance reliability. It allows for a more detailed evaluation of transport routes and planning of truck drivers' work and rest schedules on suburban routes.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING MICROFINANCE PRODUCTIVITY

More than two billion people do not have access to banking institutions, this is where the important role of microfinance distributes small loans to the poor so that they can access the financial industry. This study aimsto determine the role of artificial intelligence in improving the productivity of microfinance institutions. The qualitative research was employed with a library study approach by reviewing 25 selected journals indexed by Scopus and supported by Rank 1 and Rank 2 Science and Technology Index journals, with research published in 2000 – 2022. A sample of papers based on keywords in the publication was analyzed using bibliometric with VOSviewer application. The research results stated that microfinance institutions were established to provide benefits to poor and lowincome communities. The role of artificial intelligence can increase the productivity of the financial industry. Artificial intelligence can be used to analyze the feasibility and risk of default for customers and potential customers. The advantages of artificial intelligence include speed of decision-making, higher levels of automation for credit decisions, and the ability to be used remotely. In addition, artificial intelligence can also be used to generate investment signals that grow exponentially and generate data for future analysis.

Humans can infer social preferences from decision speed alone.

Humans are known to be capable of inferring hidden preferences and beliefs of their conspecifics when observing their decisions. While observational learning based on choices has been explored extensively, the question of how response times (RT) impact our learning of others' social preferences has received little attention. Yet, while observing choices alone can inform us about the direction of preference, they reveal little about the strength of this preference. In contrast, RT provides a continuous measure of strength of preference with faster responses indicating stronger preferences and slower responses signaling hesitation or uncertainty. Here, we outline a preregistered orthogonal design to investigate the involvement of both choices and RT in learning and inferring other's social preferences. Participants observed other people's behavior in a social preferences task (Dictator Game), seeing either their choices, RT, both, or no information. By coupling behavioral analyses with computational modeling, we show that RT is predictive of social preferences and that observers were able to infer those preferences even when receiving only RT information. Based on these findings, we propose a novel observational reinforcement learning model that closely matches participants' inferences in all relevant conditions. In contrast to previous literature suggesting that, from a Bayesian perspective, people should be able to learn equally well from choices and RT, we show that observers' behavior substantially deviates from this prediction. Our study elucidates a hitherto unknown sophistication in human observational learning but also identifies important limitations to this ability.