Developing statistical models to explain and forecast joint decision making

项目摘要（简要概述-一段）：虽然在过去的二十年里，在许多学科中，数学模型在解释和预测人类行为方面取得了快速发展，但受到关注最少的过程之一是由多个代理人共同做出的决策。联合决策以及社会影响与生活的大多数方面相关，从家庭管理到疾病情况下的治疗选择;虽然在博弈论领域已经开发了先进的理论模型，但对真实的数据集的适用性落后。近年来，机器学习已经成为代表决策的关键分析工具，并且在捕获代理和联合决策之间的交互方面，可以说比传统的统计方法取得了更大的进步。然而，与选择建模或博弈论不同，机器学习缺乏计量经济学和心理学基础-输出不能用于福利分析，并且除了能够预测结果之外，对行为过程的了解很少。本博士项目旨在解决这一领域的一些重要的研究空白。首先，候选人将对联合和集体决策的数学模型的研究进行审查，这在方法和应用方面都是稀疏领域急需的贡献。该项目的核心内容将围绕着制定一个新的统计框架，该框架既能容纳联合决策，又能确保行为的可解释性。这一框架的应用领域可以从健康到交通决策，并将取决于候选人的愿望和背景以及数据的可用性。然后，将开发的方法的预测能力和福利影响与机器学习和人工智能等其他技术进行比较，产生一项工作，不仅评估不同技术的优势和劣势，还反映了它们如何相互作用，以分析联合决策的复杂过程。

Project Summary (brief overview - one paragraph):While the past two decades have seen rapid development in mathematical models to interpret and forecast human behaviour in many disciplines, one of the processes that has received the least attention is that of decisions made jointly by multiple agents. Joint decisions as well as social influences are relevant to most aspects of life, from household management to therapy choices in the case of illness; and while advanced theoretical models have been developed in the field of Game Theory, applicability to real datasets lags behind. In recent years, Machine Learning has emerged as a key analytical tool for representing decision making, and has arguably made greater strides than traditional statistical approaches when it comes to capturing interactions between agents and joint decision making. However, unlike Choice Modelling or Game Theory, Machine Learning lacks an econometric and psychological foundation - the outputs cannot be used for welfare analysis and little is learned about the behavioural processes beyond being able to predict outcomes. The present PhD project aims to address some important research gaps in this area. First of all, the candidate will conduct a review of the studies of mathematical models of joint and collective decision making, a much needed contribution in a sparse field both in terms of methods and applications. The core element of the project will revolve around the development of a new statistical framework which can accommodate joint decision making while ensuring behavioural interpretability. The areas of applications of this framework can range from health to transport decisions, and will depend on the aspirations and background of the candidate and data availability. The forecasting ability and welfare implications of the developed methods will then be compared to other techniques such as machine learning and AI, producing a piece of work that does not only assess strengths and weaknesses of the different techniques but also reflects on how they can interact to analyse the complex process of joint decision making.