Accurate Decisions Research Articles

"The eyes are the window to the representation": Linking gaze to memory precision and decision weights in object discrimination tasks.

Humans selectively attend to task-relevant information in order to make accurate decisions. However, selective attention incurs consequences if the learning environment changes unexpectedly. This trade-off has been underscored by studies that compare learning behaviors between adults and young children: broad sampling during learning comes with a breadth of information in memory, often allowing children to notice details of the environment that are missed by their more selective adult counterparts. The current work extends the exemplar-similarity account of object discrimination to consider both the intentional and consequential aspects of selective attention when predicting choice. In a novel direct input approach, we used trial-level eye-tracking data from training and test to replace the otherwise freely estimated attention dynamics of the model. We demonstrate that only a model imbued with gaze correlates of memory precision in addition to decision weights can accurately predict key behaviors associated with (a) selective attention to a relevant dimension, (b) distributed attention across dimensions, and (c) flexibly shifting strategies between tasks. Although humans engage in selective attention with the intention of being accurate in the moment, our findings suggest that its consequences on memory constrain the information that is available for making decisions in the future. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Novel Learning-Based Trajectory Generation Strategy for a Quadrotor.

In this article, a learning-based trajectory generation framework is proposed for quadrotors, which guarantees real-time, efficient, and practice-reliable navigation by online making human-like decisions via reinforcement learning (RL) and imitation learning (IL). Specifically, inspired by human driving behavior and the perception range of sensors, a real-time local planner is designed by combining learning and optimization techniques, where the smooth and flexible trajectories are online planned efficiently in the observable area. In particular, the key problems in the framework, temporal optimality (time allocation), and spatial optimality (trajectory distribution) are solved by designing an RL policy, which provides human-like commands in real-time (e.g., slower or faster) to achieve better navigation, instead of generating traditional low-level motions. In this manner, real-time trajectories are calculated using convex optimization according to the efficient and accurate decisions of the RL policy. In addition, to improve generalization performance and to accelerate the training, an expert policy and IL are employed in the framework. Compared with existing works, the kernel contribution is to design a real-time practice-oriented intelligent trajectory generation framework for quadrotors, where human-like decision-making and model-based optimization are integrated to plan high-quality trajectories. The results of comparative experiments in known and unknown environments illustrate the superior performance of the proposed trajectory generation strategy in terms of efficiency, smoothness, and flexibility.

Growth Monitoring of Greenhouse Tomatoes Based on Context Recognition

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.

Effectiveness of Leaflet Media in Health Education to Improve Maternal Knowledge on Implant Contraception

Introduction: Health education is crucial in increasing public knowledge about reproductive health, including Family Planning. The leaflet serves as a medium for conveying information, the effectiveness of which is measured by how well the leaflet helps in better understanding implant contraception, including its benefits, risks, installation procedure, and maintenance. This increase in knowledge is then expected to encourage more accurate decision-making regarding the use of implant contraceptives. Objective: This study aims to determine the effectiveness of leaflet media in health education on increasing mothers' knowledge about implant contraception at the Kurra Health Center in Tana Toraja Regency. Method: The research design used is a pre-experiment with a one-group pre-post test design. The research population consists of all Women of Childbearing Age (WUS) in Lembang Rante Limbong, the working area of Puskesmas Kurra, totaling 131 individuals. A sample of 57 individuals was selected using a purposive sampling technique. Data were analyzed using a paired t-test or Wilcoxon test, depending on the data distribution. Result: The research results show a p-value of 0.000, which is smaller than the α value of 0.05. Conclusion: There is an effectiveness of leaflet media in health education toward increasing mothers' knowledge about implant contraception at the Kurra Health Center, Tana Toraja Regency. This increase in knowledge is expected to help WUS make wiser decisions regarding family planning and choose contraceptive methods that align with their needs and preferences.

Культурный трансфер в семиотической модели перевода (на примере художественного текста Дж. Хеллера)

The goal of the work is to identify the influence of temporary discursive practices on specific translation decisions. From this goal follow specific tasks in this work: to present discursive and interpretive models of translation; correlate the provisions of these concepts with real translation practice; use factual material to show translation decisions determined by temporary discursive practice. The article also explores ways to convey culturally significant information, provides successful and controversial cases of solving translation tasks through linguistic means. Translation problems, including literary translation, in contemporary philological and cultural studies are interdisciplinary. Only at the intersection of semiotic concepts and literary approaches it is possible to determine the accuracy and completeness of translation decisions in the practice of literary translation. In turn, the study of the issue of cultural transfer in this case, based on various translations of J. Heller’s novel, allows us to overcome the limiting framework of purely philological approach in translation studies. Comparing the work of translators with respect to the same text - in our case, “Catch-22” fills the-abstract concept of the “translator’s figure” with real content, especially when considering the socio-cultural environment that influenced the specificity of translation decisions. The article represents an analysis of current issues in the theory and practice of literary translation. The work offers a review of modern translation models, primarily a discursive model of translation, interpretative concept and the perception of translation as an interpretation of the source text in the existing discursive practice. This research is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23485752).

Securing the Data Using an Efficient Machine Learning Technique

More accessible data and the rise of advanced data analysis contribute to using complex models in decision-making across various fields. Nevertheless, protecting people’s privacy is vital. Medical predictions often employ decision trees due to their simplicity; however, they may also be a source of privacy violations. We will apply differential privacy to this end, a mathematical framework that adds random values to the data to provide secure confidentiality while maintaining accuracy. Our novel method Dual Noise Integrated Privacy Preservation (DNIPP) focuses on building decision forests to achieve privacy. DNIPP provides more protection against breaches in deep sections of the tree, thereby reducing noise in final predictions. We combine multiple trees into one forest using a method that considers each tree’s accuracy. Furthermore, we expedite this procedure by employing an iterative approach. Experiments demonstrate that DNIPP outperforms other approaches on real datasets. This means that DNIPP offers a promising approach to reconciling accuracy and privacy during sensitive tasks. In DNIPP, the strategic allocation of privacy budgets provides a beneficial compromise between privacy and utility. DNIPP protects privacy by prioritizing privacy concerns at lower, more vulnerable nodes, resulting in accurate and private decision forests. Furthermore, the selective aggregation technique guarantees the privacy of a forest by combining multiple data points. DNIPP provides a robust structure for decision-making in delicate situations, ensuring the model's effectiveness while safeguarding personal privacy.

Analysis of decentralized drone control model and interception trajectory calculation

Subject matter: This article is devoted to the study of applying the innovative Cascade DataHub method for optimizing the management of automated mobile systems, especially unmanned aerial vehicles. The work analyzes both theoretical and practical aspects of implementing this method across various application sectors. Goal: The objective of the study is to conduct a comprehensive analysis of contemporary models and methods for managing a group of drones, focusing on decentralized approaches. Additionally, the study aims to develop effective algorithms for optimizing the interception trajectory, with the goal of enhancing the accuracy and reliability of managing complex automated systems through increased real-time data integration. The research is directed towards identifying the potential advantages of this method in reducing system response times and improving decision-making accuracy. Tasks: The main tasks of the research include the development of comprehensive algorithms for rapid processing and analysis of large volumes of data from various sources, creating reliable communication protocols to ensure connection stability under extreme conditions. Another important task is the integration of these developments into practical applications, which will increase their effectiveness in real operational conditions. Methods: To achieve the set goals, advanced techniques of mathematical modeling, statistical analysis, machine learning, and deep learning are used. The application of these techniques ensures high accuracy and reliability of the management systems. Results: During the research, it was found that the Cascade DataHub method significantly reduces the response time of systems to commands, increases the accuracy of task execution, and decreases data loss during their transmission. The implementation of this method also contributes to a more efficient distribution of resources among automated units, which is critically important for missions requiring high coordination and time synchronization. Conclusions: A comprehensive analysis of contemporary models and methods for managing a group of drones with a focus on decentralized approaches has been conducted. Effective algorithms for optimizing the interception trajectory have been developed, aimed at enhancing the accuracy and reliability of managing complex automated systems through real-time data integration. The study revealed the potential advantages of the proposed method in reducing system response times and improving decision-making accuracy, contributing to the more efficient functioning of automated systems.

Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

Vehicular Edge Computing (VEC) provides a flexible distributed computing paradigm for offloading computations to the vehicular network, which can effectively solve the problem of limited vehicle computing resources and meet the on-vehicle computing requests of users. However, the conflict of interest between vehicle users and service providers leads to the need to consider a variety of conflict optimization goals for computing offloading, and the dynamic nature of vehicle networks, such as vehicle mobility and time-varying network conditions, make the offloading effectiveness of vehicle computing requests and the adaptability to complex VEC scenarios challenging. To address these challenges, this paper proposes a multi-objective optimization model suitable for computational offloading of dynamic heterogeneous VEC networks. By formulating the dynamic multi-objective computational offloading problem as a multi-objective Markov Decision Process (MOMDP), this paper designs a novel multi-objective reinforcement learning algorithm EMOTO, which aims to minimize the average task execution delay and average vehicle energy consumption, and maximize the revenue of service providers. In this paper, a preference priority sampling module is proposed, and a model-augmented environment estimator is introduced to learn the environmental model for multi-objective optimization, so as to solve the problem that the agent is difficult to learn steadily caused by the highly dynamic change of VEC environment, thus to effectively realize the joint optimization of multiple objectives and improve the decision-making accuracy and efficiency of the algorithm. Experiments show that EMOTO has superior performance on multiple optimization objectives compared with advanced multi-objective reinforcement learning algorithms. In addition, the algorithm shows robustness when applied to different environmental settings and better adapting to highly dynamic environments, and balancing the conflict of interest between vehicle users and service providers.

Improving efficiency evaluation in the presence of ratio data: inverse non-radial Enhanced Russell Models

Abstract Accepted by: Ali Emrouznejad In many real-world scenarios, decision makers often rely on data available in ratio form. Under the data envelopment analysis (DEA) framework, radial (R) models, such as DEA-R, do consider ratio data for the efficiency evaluation of decision-making units. Nevertheless, the omission of the slack values over the evaluation process may lead to inaccurate results. Hence, this paper introduces non-radial Enhanced Russell Models (ERM) with ratio data for more precise and reliable assessments. Furthermore, we develop new inverse non-linear ERM formulations to determine the optimal levels of inputs and outputs for preset ratio-efficiency scores. The validity of the proposed models is demonstrated through illustrative examples and a real-world case study, highlighting their practical relevance across diverse organizational contexts. Our research contributes novel insights and methodologies to the field of efficiency assessment, offering managers robust tools for more accurate decision-making.

The Impact of Information Distortions on Decision-Making: A Case Study in Land–Sea Transport Chain Planning

Management based on reliable, complete, and up-to-date information is key to increasing a transport chain’s effectiveness and sustainability. The instability of the business environment and the increase in competition have contributed to the growth of challenges faced by managers of transport and logistics companies who make decisions using low-value information exposed to significant distortions. This article aims to investigate the impact of information distortions on decision-making quality and to determine the probability of making trustworthy decisions in freight land–sea transport chain planning. The research was carried out in several stages, which included the formulation of rules for detecting information distortions, as well as distortion clustering and evaluation of their impact on decision quality. A methodology to assess the probability of making trustworthy decisions was developed. It was shown that information value directly impacts the quality of decisions related to transport chain planning for both traditional and unique/occasional freight. In the case of significant information distortion, the manager’s ability to assess available information considerably increases, especially in crises, when the lack of time to verify information threatens the accuracy of decisions. This study’s results may be useful for transport and logistics companies’ managers, who make decisions using information obtained from various sources.

Smart grid enterprise decision-making and economic benefit analysis based on LSTM-GAN and edge computing algorithm

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications.

Impact of Prehospital Ultrasound Training on Simulated Paramedic Clinical Decision-Making.

When used appropriately, focused limited-scope ultrasound exams could potentially provide paramedics with accurate and actionable diagnostic information to guide prehospital decision-making. In this study we aimed to investigate the impact of a 13-hour prehospital ultrasound training course on the simulated clinical decision-making of paramedics as well as their ultrasound skills, knowledge, and self-confidence. We evaluated the ultrasound competence of 31 participants using post-course written and practical assessments. Written clinical decision scenarios were administered pre- and post-training. Post-training scenarios included an uninterpreted ultrasound clip to aid decision-making. Scenarios included extended focused assessment with sonography in trauma, pulmonary exam, and focused echocardiography combined with carotid pulse check exams. Correct answers to scenarios were defined as those selected by a veteran emergency physician. Participants also indicated their confidence in each of their decisions using a Likert scale. Training yielded a statistically significant increase in both mean scenario score (35.5% absolute increase) and mean participant self-confidence (15.8% relative increase), across all exam/decision types assessed (P ≤ 0.001). The focused pulmonary exam yielded the largest increase in both mean score improvement (59.7% absolute increase) and paramedic confidence in their decisions (28.6% increase). Trained paramedics can perform focused ultrasound exams and accurately interpret and apply actionable exam findings in the context of written scenarios. Analysis through our model characterized the theoretical clinical yield of each prehospital ultrasound exam and demonstrated how each exam may provide improved decision accuracy in several specific simulated clinical contexts. These results provide support for growing evidence that focused limited-scope ultrasound may be an effective prehospital diagnostic tool in the hands of trained paramedics.

A Hybrid Approach for the Multi-Criteria-Based Optimization of Sequence-Dependent Setup-Based Flow Shop Scheduling

A key challenge in production management and operational research is the flow shop scheduling problem, characterized by its complexity in manufacturing processes. Traditional models often assume deterministic conditions, overlooking real-world uncertainties like fluctuating demand, variable processing times, and equipment failures, significantly impacting productivity and efficiency. The increasing demand for more adaptive and robust scheduling frameworks that can handle these uncertainties effectively drives the need for research in this area. Existing methods do not adequately capture modern manufacturing environments’ dynamic and unpredictable nature, resulting in inefficiencies and higher operational costs; they do not employ a fuzzy approach to benefit from human intuition. This study successfully demonstrates the application of Hexagonal Type-2 Fuzzy Sets (HT2FS) for the accurate modeling of the importance of jobs, thereby advancing fuzzy logic applications in scheduling problems. Additionally, it employs a novel Multi-Criteria Decision-Making (MCDM) approach employing Proportional Picture Fuzzy AHP (PPF-AHP) for group decision-making in a flow shop scheduling context. The research outlines the methodology involving three stages: group weight assessment through a PPF-AHP for the objectives, weight determination using HT2FS for the jobs, and optimization via Genetic Algorithm (GA), a method that gave us the optimal solution. This study contributes significantly to operational research and production scheduling by proposing a sophisticated, hybrid model that adeptly navigates the complexities of flow shop scheduling. The integration of HT2FS and MCDM techniques, particularly PPF-AHP, offers a novel approach that enhances decision-making accuracy and paves the way for future advancements in manufacturing optimization.

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.

Strengthening the Role of Artificial Intelligence Technology in the Role of Court Judges in Indonesia

This research explores the strengthening role of artificial intelligence (AI) technology in supporting the duties and responsibilities of court judges in Indonesia. In the digital era, the integration of AI in the judicial system is necessary to improve efficiency, accuracy, and fairness in legal decision-making. This research uses a qualitative method with literature studies, in-depth interviews, and case analysis from other countries that have implemented AI in their justice systems. The results show that AI can reduce judges' workload, speed up case resolution, and minimize human error and bias. However, there are challenges such as ethical issues, data privacy, and public trust in AI decisions. This research emphasizes the need for a clear regulatory framework and specialized training for judges to ensure the effective and responsible use of AI. In addition, the importance of collaboration between governments, legal institutions, and technology developers to create AI solutions that comply with the principles of fairness and transparency was expressed. In conclusion, although AI has great potential to strengthen the role of judges in Indonesia, its implementation must be done carefully to maintain the principles of fairness.

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.

PPB-MCTS: A novel distributed-memory parallel partial-backpropagation Monte Carlo tree search algorithm

Monte-Carlo Tree Search (MCTS) is an adaptive and heuristic tree-search algorithm designed to uncover sub-optimal actions at each decision-making point. This method progressively constructs a search tree by gathering samples throughout its execution. Predominantly applied within the realm of gaming, MCTS has exhibited exceptional achievements. Additionally, it has displayed promising outcomes when employed to solve NP-hard combinatorial optimization problems. MCTS has been adapted for distributed-memory parallel platforms. The primary challenges associated with distributed-memory parallel MCTS are the substantial communication overhead and the necessity to balance the computational load among various processes. In this work, we introduce a novel distributed-memory parallel MCTS algorithm with partial backpropagations, referred to as Parallel Partial-Backpropagation MCTS (PPB-MCTS). Our design approach aims to significantly reduce the communication overhead while maintaining, or even slightly improving, the performance in the context of combinatorial optimization problems. To address the communication overhead challenge, we propose a strategy involving transmitting an additional backpropagation message. This strategy avoids attaching an information table to the communication messages exchanged by the processes, thus reducing the communication overhead. Furthermore, this approach contributes to enhancing the decision-making accuracy during the selection phase. The load balancing issue is also effectively addressed by implementing a shared transposition table among the parallel processes. Furthermore, we introduce two primary methods for managing duplicate states within distributed-memory parallel MCTS, drawing upon techniques utilized in addressing duplicate states within sequential MCTS. Duplicate states can transform the conventional search tree into a Directed Acyclic Graph (DAG). To evaluate the performance of our proposed parallel algorithm, we conduct an extensive series of experiments on solving instances of the Job-Shop Scheduling Problem (JSSP) and the Weighted Set-Cover Problem (WSCP). These problems are recognized for their complexity and classified as NP-hard combinatorial optimization problems with considerable relevance within industrial applications. The experiments are performed on a cluster of computers with many cores. The empirical results highlight the enhanced scalability of our algorithm compared to that of the existing distributed-memory parallel MCTS algorithms. As the number of processes increases, our algorithm demonstrates increased rollout efficiency while maintaining an improved load balance across processes.

Semantic versus perceptual priming: dissecting their impact on intuitive judgments of semantic coherence.

This research explores the mechanisms underlying the intuitive processing of semantic coherence, focusing on the effects of semantic and perceptual priming on semantic coherence detection. Two studies examined how these priming types influence individuals' abilities to discern semantic incoherence. In Study 1, we used solutions to semantically coherent triads as primes, finding that such priming significantly improves participants' accuracy and confidence in identifying incoherent elements within word tetrads. These results corroborate the hypothesis that intuitive judgments in linguistic tasks are closely tied to the processing fluency elicited by semantic connections. In Study 2, we show that perceptual priming does not significantly enhance accuracy, albeit it does increase the confidence with which individuals make their judgments. Distinct effects of semantic and perceptual priming on intuitive judgments highlight the complex interplay between processing fluency and affect in shaping intuitive judgments of semantic coherence. We discuss the nuanced roles of semantic and perceptual factors in influencing the accuracy and confidence of intuitive decisions.

Investigating the Impact of Information Distortions on the Resilience of Land-sea Transport Chains Under Business-environment Instability Conditions

Possessing reliable information is the basis for making accurate decisions when planning land-sea transport chains. Modern transport chains create increasingly complicated networks, which affect the decisions made when planning these chains. It should be noted, however, that information distortions may arise at individual stages of the transport process within complex transport chains. The article aims to examine the sources of hazards and risks occurring in land-sea transport chains, to determine the information distortion indicators that may be processed by transport and logistics industry enterprises under conditions of business environment instability, as well as to determine the levels of these chains' resilience to possible information distortions. The article presents the selected types of managerial decision errors. An approach to assessing the impact of distorted information on the decision-making process has been proposed. Information distortion indicators processed by transport and logistics industry enterprises in conditions of business-environment instability were also identified. An attempt was made to determine the possible levels of resilience of these chains to the symptoms of business-environment instability. It was found that, depending on the degree of distortion of the information used by the manager, their decision may be erroneous to a varying degree, while the degree of managerial decisions' incorrectness can be assessed by the number of decision-making cycles required to make and implement an effective decision.

APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.