Strategic decision-making under non-probabilistic uncertainty

非概率不确定性下的战略决策

• 批准号: 2890417
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890417
• 关键词: Strategic; decision; making; under; non

The study of decisions made by rational agents in multi-agent systems is a key foundation in the field of game theory. Naturally, the question arises of how agents reason in situations with uncertainty or randomness; this is a frequent topic in the literature but traditionally has been modelled with probability, allowing for exact quantification of uncertainty. However, probability may not necessarily capture all the aspects of uncertainty in a given situation: the common simplifying assumption that all agents have perfect knowledge is unrealistic, and probability can be in a sense too precise to truly capture situations without this assumption. As an example, an uncertainty theory like possibility theory may be a more suitable tool for modelling incomplete information or in systems where uncertainty is somewhat ordinal in nature (i.e. the plausibility of outcomes can only really be qualified against each other). These ideas motivate the proposed PhD: the goal is to explore decision making of agents under uncertainty, especially in such cases that warrant alternatives to probability, and how other methods of modelling uncertainty may impact the outcome of decisions. This will involve investigation of the mathematical basis of systems modelling the reasoning of agents under uncertainty, as well as the computational properties of aspects of these systems. Furthermore, the potential applications of these systems will be looked at.

多主体系统中理性主体决策的研究是博弈论领域的重要基础。自然，问题出现了代理人如何在不确定性或随机性的情况下进行推理;这是文献中常见的话题，但传统上是用概率建模的，允许精确量化不确定性。然而，概率可能不一定能捕捉到给定情境中不确定性的所有方面：所有主体都具有完美知识的常见简化假设是不现实的，在某种意义上，概率可能过于精确，无法真正捕捉到没有这个假设的情境。作为一个例子，不确定性理论，如可能性理论，可能是一个更合适的工具，用于建模不完整的信息或在系统中的不确定性在本质上是有序的（即结果的可验证性只能相互比较）。 这些想法激发了拟议的博士学位：目标是探索决策的代理下的不确定性，特别是在这种情况下，保证替代概率，以及其他方法的建模不确定性可能会影响决策的结果。这将涉及调查的数学基础的系统建模的推理代理下的不确定性，以及这些系统的方面的计算特性。此外，这些系统的潜在应用将被视为。