主观不确定性下的决策

F. Campos, André M. M. Neves, F. D. Souza
{"title":"主观不确定性下的决策","authors":"F. Campos, André M. M. Neves, F. D. Souza","doi":"10.1109/MCDM.2007.369421","DOIUrl":null,"url":null,"abstract":"The uncertainty may be classified into two major groups, \"objective uncertainty\" and \"subjective uncertainty\". The subject of this article is the decision making under subjective uncertainty. One of the formal models that deal with subjective uncertainty, the mathematical theory of evidence, is extended and its counter-intuitive behavior corrected, allowing the making of correct decisions in a wider range of situations than the original model. The mathematical theory of evidence, or Dempster-Shafer theory, is a popular formalism to model someone's degrees of belief. This theory provides a method for combining evidence from different sources without prior knowledge of their distributions, it is also possible to assign probability values to sets of possibilities rather than to single events only, and it is unnecessary to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. However, it has some pitfalls caused by the non-natural embodiment of the uncertainty in the results. In this paper we present a method of automatic embodiment of the uncertainty that overcomes the aforementioned pitfalls, allowing the combination of evidence with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypotheses. This is accomplished by means of a new rule of combination of bodies of evidence that embodies in the numeric results the unknown belief and conflict among the evidence, naturally modeling the epistemic reasoning","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Decision Making under Subjective Uncertainty\",\"authors\":\"F. Campos, André M. M. Neves, F. D. Souza\",\"doi\":\"10.1109/MCDM.2007.369421\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The uncertainty may be classified into two major groups, \\\"objective uncertainty\\\" and \\\"subjective uncertainty\\\". The subject of this article is the decision making under subjective uncertainty. One of the formal models that deal with subjective uncertainty, the mathematical theory of evidence, is extended and its counter-intuitive behavior corrected, allowing the making of correct decisions in a wider range of situations than the original model. The mathematical theory of evidence, or Dempster-Shafer theory, is a popular formalism to model someone's degrees of belief. This theory provides a method for combining evidence from different sources without prior knowledge of their distributions, it is also possible to assign probability values to sets of possibilities rather than to single events only, and it is unnecessary to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. However, it has some pitfalls caused by the non-natural embodiment of the uncertainty in the results. In this paper we present a method of automatic embodiment of the uncertainty that overcomes the aforementioned pitfalls, allowing the combination of evidence with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypotheses. This is accomplished by means of a new rule of combination of bodies of evidence that embodies in the numeric results the unknown belief and conflict among the evidence, naturally modeling the epistemic reasoning\",\"PeriodicalId\":306422,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MCDM.2007.369421\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCDM.2007.369421","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

不确定性可分为两大类:“客观不确定性”和“主观不确定性”。本文的研究主题是主观不确定性下的决策问题。其中一个处理主观不确定性的正式模型,即证据的数学理论,得到了扩展,并纠正了它的反直觉行为,允许在比原始模型更广泛的情况下做出正确的决策。证据的数学理论,或登普斯特-谢弗理论,是一种流行的形式主义,用来模拟某人的信仰程度。该理论提供了一种方法来组合来自不同来源的证据,而不需要事先知道它们的分布,也可以将概率值分配给可能性集而不是单个事件,并且没有必要在事件之间划分所有的概率值,一旦剩余的概率应该分配给环境而不是剩余的事件,从而更自然地建模某些类别的问题。然而,由于不确定性在结果中的非自然体现,它也存在一些缺陷。在本文中,我们提出了一种克服上述陷阱的不确定性的自动体现方法,允许将证据与更高程度的冲突相结合,并避免过度倾向于其他不相交假设的共同可能性。这是通过一种新的证据体组合规则来实现的,该规则在数字结果中体现了未知的信念和证据之间的冲突,自然地模拟了认知推理
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Decision Making under Subjective Uncertainty
The uncertainty may be classified into two major groups, "objective uncertainty" and "subjective uncertainty". The subject of this article is the decision making under subjective uncertainty. One of the formal models that deal with subjective uncertainty, the mathematical theory of evidence, is extended and its counter-intuitive behavior corrected, allowing the making of correct decisions in a wider range of situations than the original model. The mathematical theory of evidence, or Dempster-Shafer theory, is a popular formalism to model someone's degrees of belief. This theory provides a method for combining evidence from different sources without prior knowledge of their distributions, it is also possible to assign probability values to sets of possibilities rather than to single events only, and it is unnecessary to divide all the probability values among the events, once the remaining probability should be assigned to the environment and not to the remaining events, thus modeling more naturally certain classes of problems. However, it has some pitfalls caused by the non-natural embodiment of the uncertainty in the results. In this paper we present a method of automatic embodiment of the uncertainty that overcomes the aforementioned pitfalls, allowing the combination of evidence with higher degrees of conflict, and avoiding the excessive tendency toward the common possibility of otherwise disjoint hypotheses. This is accomplished by means of a new rule of combination of bodies of evidence that embodies in the numeric results the unknown belief and conflict among the evidence, naturally modeling the epistemic reasoning
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Multi-criteria Set Partitioning for Portfolio Management: A Visual Interactive Method Exploring Robustness of Plans for Simulation-Based Course of Action Planning: A Framework and an Example On the Convergence of Multi-Objective Descent Algorithms Prediction of Stock Price Movements Based on Concept Map Information Interactive Utility Maximization in Multi-Objective Vehicle Routing Problems: A "Decision Maker in the Loop"-Approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1