{"title":"利用游戏进行模拟分析的 BI,促进决策和知识管理的发展","authors":"Jie Liu , Shan Ding","doi":"10.1016/j.entcom.2024.100811","DOIUrl":null,"url":null,"abstract":"<div><p>Increasing technology advancements have led to a number of problems with modern corporate decision-making, which is a challenging occurrence in the absence of business intelligence and machine learning (ML). Because effective decision-making is impossible without ML, integration of ML with business intelligence (BI) is essential to both corporate decision-making and business intelligence as a whole. Only once they have learned anything again may machines assist in your educational process. This study suggests a fresh approach to knowledge building in company management decision-making through the use of gaming and machine learning models. Using a game model that involves decision-making, knowledge analysis based on business management is conducted. Subsequently, quantum reinforcement reward neural networks build knowledge. The accuracy, precision, recall, F_1 score, MSE, NSE of business management modelling with knowledge growth are all assessed by simulation. The student’s gender had no bearing on the income they were offered throughout the job placement process or their MBA specialisations in Marketing and Finance (Mkt & Fin) or Marketing and Human Resource (Mkt & HR), according to a statistical <em>t</em>-test with a significance threshold of 0.05 (p > 0.05).</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"52 ","pages":"Article 100811"},"PeriodicalIF":2.8000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BI in simulation analysis with gaming for decision making and development of knowledge management\",\"authors\":\"Jie Liu , Shan Ding\",\"doi\":\"10.1016/j.entcom.2024.100811\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Increasing technology advancements have led to a number of problems with modern corporate decision-making, which is a challenging occurrence in the absence of business intelligence and machine learning (ML). Because effective decision-making is impossible without ML, integration of ML with business intelligence (BI) is essential to both corporate decision-making and business intelligence as a whole. Only once they have learned anything again may machines assist in your educational process. This study suggests a fresh approach to knowledge building in company management decision-making through the use of gaming and machine learning models. Using a game model that involves decision-making, knowledge analysis based on business management is conducted. Subsequently, quantum reinforcement reward neural networks build knowledge. The accuracy, precision, recall, F_1 score, MSE, NSE of business management modelling with knowledge growth are all assessed by simulation. The student’s gender had no bearing on the income they were offered throughout the job placement process or their MBA specialisations in Marketing and Finance (Mkt & Fin) or Marketing and Human Resource (Mkt & HR), according to a statistical <em>t</em>-test with a significance threshold of 0.05 (p > 0.05).</p></div>\",\"PeriodicalId\":55997,\"journal\":{\"name\":\"Entertainment Computing\",\"volume\":\"52 \",\"pages\":\"Article 100811\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Entertainment Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1875952124001794\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Entertainment Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1875952124001794","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
摘要
技术的不断进步导致现代企业决策中出现了许多问题,在缺乏商业智能和机器学习(ML)的情况下,企业决策面临着巨大挑战。因为没有 ML 就不可能实现有效决策,所以 ML 与商业智能 (BI) 的整合对于企业决策和整个商业智能都至关重要。只有当他们再次学习到任何知识后,机器才有可能在您的教育过程中提供帮助。本研究提出了一种全新的方法,即通过使用游戏和机器学习模型来构建公司管理决策中的知识。利用涉及决策的游戏模型,进行基于企业管理的知识分析。随后,量子强化奖励神经网络构建知识。通过仿真评估了带有知识增长的企业管理建模的准确度、精确度、召回率、F_1 分数、MSE、NSE。根据显著性临界值为 0.05 的统计 t 检验(p > 0.05),学生的性别对他们在整个就业安置过程中获得的收入或他们的 MBA 专业市场营销与金融(Mkt & Fin)或市场营销与人力资源(Mkt & HR)没有影响。
BI in simulation analysis with gaming for decision making and development of knowledge management
Increasing technology advancements have led to a number of problems with modern corporate decision-making, which is a challenging occurrence in the absence of business intelligence and machine learning (ML). Because effective decision-making is impossible without ML, integration of ML with business intelligence (BI) is essential to both corporate decision-making and business intelligence as a whole. Only once they have learned anything again may machines assist in your educational process. This study suggests a fresh approach to knowledge building in company management decision-making through the use of gaming and machine learning models. Using a game model that involves decision-making, knowledge analysis based on business management is conducted. Subsequently, quantum reinforcement reward neural networks build knowledge. The accuracy, precision, recall, F_1 score, MSE, NSE of business management modelling with knowledge growth are all assessed by simulation. The student’s gender had no bearing on the income they were offered throughout the job placement process or their MBA specialisations in Marketing and Finance (Mkt & Fin) or Marketing and Human Resource (Mkt & HR), according to a statistical t-test with a significance threshold of 0.05 (p > 0.05).
期刊介绍:
Entertainment Computing publishes original, peer-reviewed research articles and serves as a forum for stimulating and disseminating innovative research ideas, emerging technologies, empirical investigations, state-of-the-art methods and tools in all aspects of digital entertainment, new media, entertainment computing, gaming, robotics, toys and applications among researchers, engineers, social scientists, artists and practitioners. Theoretical, technical, empirical, survey articles and case studies are all appropriate to the journal.