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Risk-sensitive decision making under inclomplete information

综合信息下的风险敏感决策

基本信息

批准号：
339441241
负责人：
Professor Dr. Klaus Obermayer
金额：
--
依托单位：
Institut für Softwaretechnik und Theoretische Informatik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2022-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/339441241?language=en
关键词：
Risk sensitive decision making under

项目摘要

Daily decisions have to be made in the face of risk that arises from incomplete information. For instance, a firefighter in a smoky burning house may be uncertain about whether she spotted a trapped victim and whether the rescue operation involves danger. Two different sources of uncertainty, from which risk arises, can be identified: (1) The incomplete knowledge of states of the world and (2) the uncertain consequences of future events at those states. We call them perceptual risk and economic risk. Both types of risk have to be taken into account by a decision maker.Decision making under risk has been a topic of research in a wide range of disciplines, such as finance, machine learning, control, operations research, behavioral economics, and cognitive neuroscience. However, both types of risk are usually investigated separately in two strands of research, and no integrated computational framework that incorporates both types of risk yet exists. The main goals of this research project are to develop an integrated computational framework for sequential decision making problems in face of both perceptual and economic risk and to derive computationally tractable algorithms to solve the corresponding optimization problems. The research is strongly driven by theory, and aims in its core at the extension of risk-sensitive reinforcement learning to partially observable Markov decision processes, which has never been done for a general setting.In order to test the applicability of the theoretical framework and the derived algorithms, we will evaluate both for optimal risk-sensitive decision making in stock market trading.

日常决策必须面对信息不完整带来的风险。例如，一名消防员在烟雾弥漫的燃烧的房子里可能不确定她是否发现了被困的受害者，以及救援行动是否涉及危险。风险产生的不确定性有两种不同的来源：（1）对世界状态的不完全了解;（2）这些状态下未来事件的不确定后果。我们称之为感知风险和经济风险。风险下的决策一直是金融、机器学习、控制、运筹学、行为经济学和认知神经科学等众多学科的研究课题。然而，这两种类型的风险通常在两个研究领域中分别进行研究，并且还不存在将这两种类型的风险结合起来的综合计算框架。本研究的主要目标是开发一个综合的计算框架，面对感性和经济风险的顺序决策问题，并推导出计算上易于处理的算法来解决相应的优化问题。该研究是强烈的理论驱动，并在其核心的扩展风险敏感的强化学习部分可观察的马尔可夫决策过程，这是从来没有做过的一般setting. To测试的理论框架和衍生算法的适用性，我们将评估两者的最佳风险敏感的决策在股票市场交易。