[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665480-105":59,"doc-detail-665480-en":122},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":115,"head_meta":117,"extra_data":119,"updated_unix":121},105,"en","modeling-uncertain-situations-in-decision-making-with-influence-diagrams","Modeling Uncertain Situations in Decision-Making with Influence Diagrams","","The paper investigates modern scenario analysis and forecasting methods represented by tree-like graphs, focusing on the theoretical foundations of influence diagrams. It presents a mathematical basis using Bayesian networks, combining graphical structure of decision processes with probabilistic behavior. Influence diagrams model uncertain events in complex decision-making, capturing key factors and their relationships clearly and compactly, enabling real-time scenario analysis and supporting probabilistic inference. Numerical examples demonstrate practical applications to improve decision quality under uncertainty.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/modeling-uncertain-situations-in-decision-making-with-influence-diagrams/665480/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/modeling-uncertain-situations-in-decision-making-with-influence-diagrams/665480.png","ImageObject",300,407,{"name":92,"@type":93},"Nguyễn Văn Học","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"What is the difference between partially defined and completely defined influence diagrams?","Question",{"text":112,"@type":113},"A partially defined influence diagram contains only the graph structure without all needed information. To enable decision analysis, each node must be supplemented with relevant data such as alternative options for decision nodes, probability information for chance nodes, and outcome/value assessments for value nodes, producing a completely defined diagram.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},665480,1793304410,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":121,"read_time":135},1374402739827,"https://ap-avatar.wpscdn.com/avatar/14000c97e7351f1a627?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787885694763230660","Modeling Uncertain Situations in Decision-Making with  \nInfluence Diagrams  \nIgor Kovalenko1[0000-0003-2655-6667], Yevhen Davydenko1[0000-0002-0547-3689] and Alyona Shved 1[0000-0003-4372-7472]  \n1Petro Mohyla Black Sea National University, Mykolaiv, Ukraine {ihor.kovalenko,davydenko,[avshved}@chmnu.edu.ua](avshved}@chmnu.edu.ua)  \nAbstract. The modern methods of scenario analysis and forecasting, represented by tree-like graphs, are investigated in this paper. The basic theoretical positions of influence diagrams are considered. Its mathematical apparatus is based on Bayesian networks and supported a combination of graphical representation of analyzed process and its probabilistic behavior. The influence diagrams are an effective tool for modeling uncertain events in a complex decision-making process, which reflect decision-making situations in a more compact form. Influence diagrams also reflect all possible factors of the problem and their relationship ina clear and understandable way; provide all relevant information and implementation of the most appropriate decision-making procedure; allow to perform realtime analysis of possible scenarios. The numerical calculations that illustrate the possibilities of practical application of influence diagrams in complex decisionmaking problems under uncertainty are provided. The results obtained are aimed at improving the quality and effectiveness of decision-making in scenario analysis process with a large number of interrelated factors.  \nKeywords: Decision-Making, Influence Diagram, Scenario analysis, Uncertainty.  \n1 Introduction  \nThe initial situation in decision-making tasks is often characterized by complexity and uncertainty, and various combinations of uncertain events can influence the outcomes of decisions made. These events are known as scenarios [1] . Therefore, there was a need to construct models that should not only reflect real situations, but also be used to formally solve probabilistic inference problems. Which here means calculating the probabilities of scenarios or events based on known probabilities of other related events implementation [1, 2, 4, 10] .  \nThe probability trees and decision trees are some of the most widely known methods for modeling uncertain situations, which are successfully used for a small number of uncertain events (n≤4-5) . With the increasing number of uncertain events, probability  \ntrees are characterized by a large dimension, which makes it extremely difficult to display the information that they contain (a set of alternative solutions, systems of uncertain events and outcomes of alternative solutions) [4] .  \nIn this regard, a new “tool” called the “influence diagram”(ID) appeared to model uncertain events [6-9] . The advantage of IDs in relation to decision trees is especially noticeable for complex decision-making problems, when they are caused by a large number of interrelated factors. In addition, the IDs allow reflecting decision-making situations in a more compact form [1] . At the same time, it should be noted that IDs are currently known only to a fairly narrow circle of specialists and have not received such widespread application as decision trees.  \nThe purpose of the paper is to present the basic theoretical principles of the influence diagrams and propose an example of their practical application in scenarios analysis tasks with a large number of interrelated factors.  \n2 The Influence Diagram Basics  \nLet us consider the basics of IDs that reveal the essence and features of them in accordance with the works [1, 4] . The ID is an oriented acyclic graph, the vertices (nodes) of which reflect the set (system) of factors: decisions, events and outcomes. All relevant information is not as closely related to the structure ofthe ID as is the case for decision trees.  \nThe following types of nodes are distinguished on any ID. The decision node displays a variety of alternative solutions, which are graphically depicted in the fo","cbCaigGMSCLTPN9a","https://ap.wps.com/l/cbCaigGMSCLTPN9a","pdf",751308,"English","# Introduction\n# The Influence Diagram Basics","[{\"question\":\"What is the difference between partially defined and completely defined influence diagrams?\",\"answer\":\"A partially defined influence diagram contains only the graph structure without all needed information. To enable decision analysis, each node must be supplemented with relevant data such as alternative options for decision nodes, probability information for chance nodes, and outcome/value assessments for value nodes, producing a completely defined diagram.\"}]","Modeling Uncertain Situations in Decision-Making with Influence Diagrams | PDF",25]