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Decision Making Under Uncertainty

Decision Making Under Uncertainty
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批准号：
1000230057-2013
负责人：
Delage, Erick
金额：
$7.29万
依托单位：
HEC Montréal
依托单位国家：
加拿大
项目类别：
Canada Research Chairs
财政年份：
2017
资助国家：
加拿大
项目状态：
已结题
起止时间：
2017-01-01 至 2018-12-31
来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=642429
关键词：
DecisionMakingUnderUncertainty

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中文摘要
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英文摘要
Taking adequate managerial decisions in today's highly complex and interconnected world has become increasingly difficult. Considering the recent financial crisis and the still unresolved effects of climate changes, it has become apparent that determinant decisions must now seriously account for imperfect knowledge about our future. This research focuses on the development and dissemination of optimization tools that provide support or act autonomously in context of large scale problems where uncertainty plays a key role. Such tools will efficiently extract from large datasets the information that is most pertinent to identify decisions that optimally compromise between long term risks and returns.
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Decision making under uncertainty
  • 批准号：
    CRC-2018-00105
  • 项目类别：
    Canada Research Chairs
  • 资助金额：
    $7.29万
  • 财政年份：
    2022
  • 负责人：
    Delage, Erick
  • 依托单位：
    HEC Montréal
Optimizing risk averse decisions in data-driven problems
  • 批准号：
    RGPIN-2022-05261
  • 项目类别：
    Discovery Grants Program - Individual
  • 资助金额：
    $3.13万
  • 财政年份：
    2022
  • 负责人：
    Delage, Erick
  • 依托单位：
    HEC Montréal
Optimizing decisions in situations with unresolved ambiguity
  • 批准号：
    RGPIN-2016-05208
  • 项目类别：
    Discovery Grants Program - Individual
  • 资助金额：
    $2.26万
  • 财政年份：
    2021
  • 负责人：
    Delage, Erick
  • 依托单位：
    HEC Montréal
Decision Making Under Uncertainty
  • 批准号：
    CRC-2018-00105
  • 项目类别：
    Canada Research Chairs
  • 资助金额：
    $7.29万
  • 财政年份：
    2021
  • 负责人：
    Delage, Erick
  • 依托单位：
    HEC Montréal
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis