Nash Neural Networks: Inferring Utilities from Optimal Behaviour in Epidemics

纳什神经网络：从流行病中的最佳行为推断效用

• 批准号: 2597125
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597125
• 关键词: Nash; Neural; Networks; Inferring; Utilities

The context of the researchThis PhD project is concerned with applying control-theory to epidemiological problems. In this work we will treat individuals as rational agents who seek to maximise their well-being in the face of an infectious disease. The aims and objectives of the researchWe will employ a new methodology for learning hidden preferences for individuals who target a Nash Equilibrium using Machine learning approach similar to recent develops in Physics Informed Machine Learning (e.g Lagrangian Neural Networks).The main objectives of this project include seeking to integrate out the Lagrange multipliers in order to learn the utility soley from observable information (i.e the SIR dynamics of the epidemic) and to consider different forms of the utility for both direct/inverse optimal control problems. There are, however, alternative and potential furhter aims of the research. Such as inferring utilities from open source, opulation-level data. From this we could explore optimal governemtn control of Nash agents and inferring government objectivesm with which to compare against their proposed objectives. Extensions to our epidemics model can also be explored, such as gerneralising to include uncertainty (stochastic optimal control) and utilising different compartments in our data (e.g young and old individuals).The novelty of the research methodologyOur approach for inferring utilities consistent with a Nash equilibrium is completlty novel. In contrast to previous approached we do not need to assume the functional form of the individual preferences. Further aspect of the novelty include the role of saturating health-care systems in pandemics, later in the project the role of uncertainties in the infomraiton available to individuals.The potential impact, applications and benefitsWe believe there is a pressing need for qunatitative policy making tools to help manage future epidemics of infectious diseases. This may also have applications in other social planning problems.Possible extensions to this work include modification of the objective function (utility) to mimic other forms used in the literature or more accurate models. Later in the project we look for applications in related fields, including economics, finance or swarm behaviour.How the research relates to the remitFundamentally our work involves developing a new ML approach, applied to control theory, a field in engineering. The research falls into the Engineering, Global uncertainties and Healthcare technologies research areas.External partner - soft Matter Engineering Lab, Kyoto University - Prof. Ryoichi Yamamoto (Soft Matter Engineering, Department of Chemical Enginerering, Kyoto University). Together with Drs John Molina and Simon Schnyderr they have devleoped novel computational techniques for simulating complex Soft Matter Systems, including colloidal dispersions, celluar tissues, and ploymer melt flows, among others. More recently, in collaboration with Prof. Matthew Turner (PhD supervisor 1), they have worked to establish a theorectical framework for qunatitative poliymaking in a society of rational individuals. In particular, they have studied how taxes and/or government subsidies can be used to align the decison making rational individuals with the socially optimal behaviour during the pandemic, like the ongoing SARS-CoV-2 global pandeimic.The external partner has agreed to provide technical support and access to high-performance computing resources (Tokyo Univerisyt, Wisterria-BDEC 01, NVIDIA A100 GPU cluster) required for the Machine learning applications of this prokect. The partner has agreed to attend bi-weekly (remote) meetings to discuss the modelling and technical aspects of the work. In addition, the partner has agreed to host me in Kyoto for part of my PhD studies.

研究背景该博士项目涉及将控制理论应用于流行病学问题。在这项工作中，我们将把个人视为理性的代理人，他们在面对传染病时力求最大化自己的福祉。研究的目的和目标我们将采用一种新的方法来学习以纳什均衡为目标的个人的隐藏偏好，使用机器学习方法，类似于物理学信息机器学习（例如拉格朗日神经网络）的最新发展。该项目的主要目标包括寻求整合拉格朗日乘数，以便仅从可观察信息（即流行病的 SIR 动态）中学习效用，并考虑不同形式的 对于直接/逆最优控制问题的实用性。然而，这项研究还有其他的和潜在的进一步目标。例如从开源的操作级数据推断实用程序。由此，我们可以探索纳什代理人的最佳政府控制，并推断政府目标，并与他们提出的目标进行比较。还可以探索我们的流行病模型的扩展，例如概括为包括不确定性（随机最优控制）和利用我们的数据中的不同部分（例如年轻人和老年人）。研究方法的新颖性我们推断与纳什均衡一致的效用的方法是完全新颖的。与之前的方法相比，我们不需要假设个人偏好的函数形式。新颖性的进一步方面包括饱和医疗保健系统在流行病中的作用，以及项目后期个人可获得的信息中的不确定性的作用。潜在的影响、应用和好处我们认为，迫切需要定量政策制定工具来帮助管理未来的传染病流行。这也可能在其他社会规划问题中得到应用。这项工作的可能扩展包括修改目标函数（效用）以模仿文献中使用的其他形式或更准确的模型。在项目的后期，我们寻找相关领域的应用，包括经济学、金融或群体行为。研究如何与职权范围相关从根本上说，我们的工作涉及开发一种新的机器学习方法，应用于控制理论（一个工程领域）。该研究属于工程、全球不确定性和医疗保健技术研究领域。外部合作伙伴 - 京都大学软物质工程实验室 - Ryoichi Yamamoto 教授（京都大学化学工程系软物质工程）。他们与 John Molina 博士和 Simon Schnyderr 博士一起开发了用于模拟复杂软物质系统的新颖计算技术，包括胶体分散体、细胞组织和聚合物熔体流动等。最近，他们与Matthew Turner教授（博士生导师1）合作，致力于为理性个体社会中的定量政策制定建立理论框架。特别是，他们研究了如何利用税收和/或政府补贴，在大流行期间使理性个人的决策与社会最佳行为保持一致，例如正在进行的 SARS-CoV-2 全球大流行。外部合作伙伴已同意提供机器学习所需的技术支持和高性能计算资源（东京大学、Wisterria-BDEC 01、NVIDIA A100 GPU 集群）。 该项目的应用。合作伙伴已同意参加每两周一次的（远程）会议，讨论工作的建模和技术方面。此外，合作伙伴还同意接待我在京都进行部分博士研究。