[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665383-105":59,"doc-detail-665383-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","myopic-value-of-information-in-influence-diagrams-paper-method","Myopic Value of Information in Influence Diagrams - Paper - Method","","We present a calculation method for the myopic value of information in influence diagrams using the strong junction tree framework. Influence diagrams encode an observation-and-decision order through node marginalization in corresponding junction trees. This order can be changed via table expansion and control structures, enabling expected value of information computation across multiple information scenarios within a shared junction tree. Expanded strong junction trees facilitate value-of-information evaluation between scenarios with different observation-decision orderings.",{"@graph":69,"@context":122},[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/myopic-value-of-information-in-influence-diagrams-paper-method/665383/",{"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/myopic-value-of-information-in-influence-diagrams-paper-method/665383.png","ImageObject",300,407,{"name":92,"@type":93},"lugxt","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address in influence diagrams?","Question",{"text":112,"@type":113},"It addresses efficient calculation of the myopic value of information for influence diagrams under uncertainty and decision making.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is observation-decision order handled in the proposed method?",{"text":117,"@type":113},"The order is reflected in junction trees by the marginalization order of nodes, and can be altered using table expansion and control structures.",{"name":119,"@type":110,"acceptedAnswer":120},"What computational framework does the method rely on?",{"text":121,"@type":113},"It uses the strong junction tree framework and builds on ideas for solving influence diagrams efficiently.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},665383,1793301894,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":46},3985747862600,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Myopic Value of Information in In􀀂uence Diagrams  \nS􀀂ren L􀀃 Dittmer  \nDINA Skejby The Danish Agricultural Advisory Centre Udk􀀂rsvej 􀀃􀀄􀀅 Skejby  \n􀀊  \nDK 􀀆 􀀇􀀈􀀉􀀉 Arhus N􀀅 Denmark  \nE􀀋mail􀀌 dittmer􀀂lr􀀃dk  \nFinn V􀀃 Jensen  \nDepartment of Computer Science Aalborg University Fredrik Bajers Vej 􀀍EDK 􀀆 Aalborg 􀀎st􀀅 Denmark  \nE􀀋mail􀀌 fvj􀀂cs􀀃auc􀀃dk  \nAbstract  \nWe present a method for calculation of my􀀋 opic value of information in in􀀏uence dia􀀋 grams 􀀐Howard 􀀑 Matheson􀀅 􀀃􀀒􀀇􀀃 􀀓 based on the strong junction tree framework 􀀐Jensen et al􀀂􀀅 􀀃􀀒􀀒􀀔 􀀓 􀀕  \nAn in􀀏uence diagram speci􀀖es a certain or􀀋 der of observations and decisions through its structure􀀕 This order is re􀀏ected in the corre􀀋 sponding junction trees by the order in which the nodes are marginalized􀀕 This order of marginalization can be changed by table ex􀀋 pansion and use of control structures􀀅 and this facilitates for calculating the expected value of information for di􀀗erent information scenarios within the same junction tree􀀕 Ine􀀗ect􀀅 a strong junction tree with expanded tables may be used for calculating the value of information between several scenarios with di􀀗erent observation􀀋decision order􀀕  \nWe compare our method to other methods for calculating the value of information in in􀀋  \n􀀏uence diagrams􀀕  \nKeywords􀀄 In􀀏uence diagrams􀀅 value of in􀀋 formation􀀅 strong junction tree􀀅 table expan􀀋 sion􀀅 dynamic programming􀀕  \n􀀂 INTRODUCTION  \nIn􀀏uence diagrams were introduced by Howard 􀀑 Matheson 􀀐􀀃􀀒􀀇􀀃􀀓 as a formalism to model decision problems with uncertainty for a single decision maker􀀕  \nAn in􀀏uence diagram can be considered a Bayesian network augmented with decision variables and a util􀀋 ity function􀀕 The decision variables􀀅 D􀀀 􀀅 􀀂 􀀂 􀀂 􀀅 Dn 􀀅 in the in􀀏uence diagrams are partially ordered and the chance variables are divided into information sets􀀅 I􀀂 􀀅  \n􀀂 􀀂 􀀂 􀀅 In 􀀕 The information set Ii 􀀀 􀀀 is observed immedi􀀋  \nately before decision Di is made􀀅 and the information set In consists of the chance variables that are observed later than the n 􀀘th decision is made􀀅 if ever􀀕  \nLet Vi be the set of variables preceding Di􀀅 that is􀀅 Vi contains the past relevant for Di 􀀕 The solution of a decision problem modeled by an in􀀏uence dia􀀋 gram is a sequence of decisions that maximizes the expected utility􀀕 Shachter 􀀐􀀃􀀒􀀇􀀙􀀓 describes a method to solve an in􀀏uence diagram without unfolding it into a decision tree􀀚 rather􀀅 the in􀀏uence diagram is transformed through a series of node􀀋removal and arc􀀋reversal operations􀀕 Shenoy 􀀐􀀃􀀒􀀒􀀈􀀓 describes an􀀋 other approach to the problem of solving in􀀏uence dia􀀋 grams by conversion into valuation networks􀀕 This ap􀀋 proach is slightly more e􀀛cient than that of 􀀐Shachter􀀅􀀃􀀒􀀇􀀙 􀀓 􀀕 􀀐Shachter 􀀑 Ndilikilikesha􀀅 􀀃􀀒􀀒􀀜 􀀓 and 􀀐 Ndiliki􀀋 likesha􀀅 􀀃􀀒􀀒􀀔 􀀓 modi􀀖ed the node􀀋removal􀀝arc􀀋reversal algorithm and achieved a method that is equivalent to  \nthe algorithm presented in 􀀐Shenoy􀀅 􀀃􀀒􀀒􀀈 􀀓 with respect  \nto computational e􀀛ciency􀀕  \nJensen et al􀀂 􀀐􀀃􀀒􀀒􀀔􀀓 describes an e􀀛cient method for solving in􀀏uence diagrams using strong junction trees􀀕 This is an extension to the junction trees used for com􀀋 putation in pure Bayesian decision analysis􀀕 It is on this framework we base the present work􀀕  \nWe are about to choose among a set of k options􀀕 These options are packed into the decision node D 􀀕 We have already received some information e􀀅 and now we can either choose among the options or we can look for more information􀀕 The 􀀘looking for more information􀀘 is to consult some source which will provide the state of a chance variable􀀕 Let the chance variables in ques􀀋 tion be the set 􀀞 fA􀀀 􀀃 􀀂 􀀂 􀀂 􀀃 Am g 􀀕 We want to calcu􀀋 late what we can expect to gain from consulting the information source􀀕 For all the considerations in this paper we deal with the myopic value􀀋of􀀋information question􀀌 At any time􀀅 we can ask for the state of at most one of the variables in 􀀞􀀕  \nAs basis for the considerations we have EU 􀀐 Dje􀀓􀀅 the expected utilities for D given the evidence e 􀀅 and the decisio","cbCailqFdN5LD4Bn","https://ap.wps.com/l/cbCailqFdN5LD4Bn","pdf",159213,"English","# INTRODUCTION\n## SIMPLE SCENARIOS\n## ONE NON-INTERVENING DECISION","[{\"question\":\"What problem does the paper address in influence diagrams?\",\"answer\":\"It addresses efficient calculation of the myopic value of information for influence diagrams under uncertainty and decision making.\"},{\"question\":\"How is observation-decision order handled in the proposed method?\",\"answer\":\"The order is reflected in junction trees by the marginalization order of nodes, and can be altered using table expansion and control structures.\"},{\"question\":\"What computational framework does the method rely on?\",\"answer\":\"It uses the strong junction tree framework and builds on ideas for solving influence diagrams efficiently.\"}]","Myopic Value of Information in Influence Diagrams - Paper - Method | PDF"]