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The paper proposes Ignorant Influence Diagrams (IIDs), enabling reasoning under incomplete information and improving decision accuracy as available information grows. IIDs explicitly represent missing information without losing standard ID capabilities, and assess decision reliability via a single analytical measure instead of full sensitivity analysis.",{"@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/ignorant-influence-diagrams-a-paper-introduces-a-new-class-of-influence-diagrams/665384/",{"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/ignorant-influence-diagrams-a-paper-introduces-a-new-class-of-influence-diagrams/665384.png","ImageObject",300,407,{"name":92,"@type":93},"dbqoiu","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem do Influence Diagrams face in real decision making?","Question",{"text":112,"@type":113},"They require a large amount of point-valued probabilistic information, including conditional probabilities and priors, which is often not available to the decision maker.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are Ignorant Influence Diagrams (IIDs)?",{"text":117,"@type":113},"IIDs are a new class of influence diagrams that reason with incomplete information and improve decision accuracy as more information becomes available.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the paper propose to assess the reliability of IID decisions?",{"text":121,"@type":113},"It replaces traditional sensitivity analysis with a single analytical measure to evaluate decision reliability.","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},665384,1793301895,{"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":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"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":143},3985747862462,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Marco RamoniCognitive Studies in MedicineMcGill Cognitive Science CentreMcGill University,Montreal,Canada  \n# Abstract\n\nInfluence Diagrams(IDs)are a graphic formal-ism able to provide a compact representationof decision problems.IDs are based on the ax-ioms of probability and decision theory,andthey define a normative framework to modeldecision making.Unfortunately,IDs require alarge amount of information that is not alwaysavailable to the decision maker.This paper in-troduces a new class of IDs,called Ignorant In-fuence Diagrams(IIDs),able to reason on thebasis of incomplete information and to improvethe accuracy of their decisions as a monotoni-cally increasing function of the available infor-mation.IIDs represent a net gain with respectto the traditional IDs,since they are able to ex-plicitly represent lack of information,withoutloosing any capability of traditional IDs whenthe required information is available.Further-more,IIDs provide a new method to assess thereliability of the decisions by replacing the tra-ditional sensitivity analysis with a single ana-lytical measure.  \n## 1 Introduction\n\nBayesian Belief Networks(BBNs)[Pearl,1988]are a well-known formalism to reason under uncertainty and theyhave been successfully applied to a variety of problemsin different domains.A BBN is a direct acyclic graph inwhich nodes represent stochastic variables and arcs rep-resent conditional dependencies among variables.Froma probabilistic point of view,they provide a straightfor-ward way to represent independence assumptions amongvariables,thus making easy the representation and theacquisition of knowledge.BBNs are particularly appeal-ing since they are based on a sound probabilistic seman-tics and they easily extend into a complete decision the-oretic formalism,called Influence Diagrams(IDs).IDs[Horvitz et al.,1988]provide a compact representationfor decision problems and their sound probabilistic se-mantics guarantees the normative character of their de-cisions.IDs are an appealing complement to more tradi-tional methods for representing decision problems,suchas tables of joint probability distributions or decision  \nIgnorant Influence Diagrams  \ntrees,because they exploit the ability of BBNs to ex-press conditional independence assumptions in graphicalterms,thus dramatically reducing the amount of infor-mation needed to specify a decision problem.  \nNonetheless,a BBN still requires a fixed and poten-tially very large amount of probabilistic information,that is not always available to the decision maker:thenumber of conditional probabilities needed to specifya conditional dependency grows exponentially with thenumber of its parent variables.Current propagation al-gorithms require that all the conditionals probabilitiesdefining a conditional dependency among variables haveto be known,as well as all prior probabilities for thestates of the root variables,before any reasoning processcan start.Furthermore,these probability measures haveto be assessed as point-valued probabilities,even whenthe decision maker is not completely sure about them.This requirement,called Credal Uniqueness [Stiling andMorrel,1991],is one of the most controversial pointsof Bayesian probability and decision theory [Levi,1980;Kyburg,1983],and it is the reason why BBNs requiresuch a large amount of probabilistic information:in or-der to specify a unique probability distribution over thestochastic variables of a BBN,we need to know the con-ditional probabilities relating it with all its parents inthe network.  \nThis limitation becomes even more apparent in the de-velopment of an ID:when the decision maker is not ableto specify a single probability,he is nonetheless forcedto provide point-valued probability measures,and thento perform a costly and tiring analysis to assess the sen-sitivity of the resulting decisions to all the possible com-binations of his imprecise assessment.  \nTo overcome this limitation,and maintain the ap-pealing features of probabilistic soundn","cbCaie8jkoxOym2j","https://ap.wps.com/l/cbCaie8jkoxOym2j","pdf",5399560,"English","# Abstract\n# 1 Introduction\n# 2 Ignorant Belief Networks","[{\"question\":\"What problem do Influence Diagrams face in real decision making?\",\"answer\":\"They require a large amount of point-valued probabilistic information, including conditional probabilities and priors, which is often not available to the decision maker.\"},{\"question\":\"What are Ignorant Influence Diagrams (IIDs)?\",\"answer\":\"IIDs are a new class of influence diagrams that reason with incomplete information and improve decision accuracy as more information becomes available.\"},{\"question\":\"How does the paper propose to assess the reliability of IID decisions?\",\"answer\":\"It replaces traditional sensitivity analysis with a single analytical measure to evaluate decision reliability.\"}]","Ignorant Influence Diagrams - A paper introduces a new class of influence diagrams | PDF",18]