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Overseas travel grant - Melbourne

海外旅行补助金 - 墨尔本

基本信息

批准号：
EP/M012921/1
负责人：
Antony Galton
金额：
$ 2.07万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM012921%2F1
关键词：
Overseas travel grant Melbourne

项目摘要

The proposed research forms part of a wider research programme, the goal of which is to bring together previous work on (1) modelling and understanding collective phenomena and (2) the ontology of causal relations, in order to develop a general computational theory of collective causality which can be applied to the analysis of large spatio-temporal data-sets, such as are becoming increasingly available in biology, medicine, and the social sciences. Recent joint work with Dr Matt Duckham and others on mining candidate causal relationships from a data-set relating to fish movement made use of parts of the theory of causal relations developed by the PI and presented at the international conference FOIS 2012. A notable feature of this theory is that it takes seriously the distinction between events, processes, and states, recognising that the nature and functioning of a causal or causal-like relation can depend critically on which of these types of item it applies to - so that, for example, there is an important difference between the case in which one discrete event causes another such event, and the case in which one on-going process perpetuates another such process. The work with Duckham et al did not make use of the full range of causal relations identified in the FOIS paper, and in particular neglected process-process interactions and the effect of granularity on the descriptions of causes. One of the goals of the proposed research is to make good on this and develop the data-mining methods further with the use of additional data-sets and more detailed theoretical analysis. We are particularly interested in developing techniques to identify the signatures of genuine causal effects in the data and thereby discriminate them from chance correlations and other kinds of relationships. Such techniques, if available, would have an enormous impact on our ability to interpret data-sets collected from large, dynamic populations, whether of humans or other animals.This work will apply to data-sets which typically cover large groups of individuals forming collectives which may exhibit varying degrees of coherence. According to the Three-Level Analysis of collective motion previously developed by the PI together with Dr Zena Wood (Exeter), the motion of such collectives can be described at three different levels of granularity, by which we may distinguish the movement of the collective as a whole, considered as a unified point-like entity, the changes in configuration of the collective, as determined by the relative positions of its members, and the movements of the individuals constituting the collective. Distinguishing these levels helps to focus attention on different kinds of causal interaction that may be manifested by the collective; in particular the causal influences on the motion of an individual within the collective might arise from the internal dynamics of the individual itself, from amongst other individuals in the collective, from the collective as a whole (which might, for example, exert some kind of pressure to conform to a group norm), and from outside the collective. Thus individual causal relations may act within one granularity level or between levels, and another part of the proposed research is to integrate the three-level analysis into the data-mining techniques to enable us to discern causal relations operating at different levels of granularity corresponding to different types of qualitative description of the collective behaviour.

拟议的研究构成了更广泛研究计划的一部分，其目标是将先前关于（1）集体现象建模和理解以及（2）因果关系本体论的工作结合起来，以开发集体因果关系的通用计算理论，该理论可应用于大型时空数据集的分析，例如在生物学、医学和社会科学中越来越多的数据集。最近与 Matt Duckham 博士和其他人合作，从与鱼类运动相关的数据集中挖掘候选因果关系，利用了 PI 开发的因果关系理论的一部分，并在 FOIS 2012 国际会议上提出。该理论的一个显着特点是它认真对待事件、过程和状态之间的区别，认识到因果关系或类因果关系的性质和功能可能严重依赖于这些类型的项目中的哪一种。适用于 - 因此，例如，一个离散事件导致另一个此类事件的情况与一个正在进行的过程使另一个此类过程永久存在的情况之间存在重要区别。 Duckham 等人的工作没有充分利用 FOIS 论文中确定的全部因果关系，特别是忽略了过程与过程的相互作用以及粒度对原因描述的影响。拟议研究的目标之一是利用额外的数据集和更详细的理论分析来充分利用这一点并进一步开发数据挖掘方法。我们特别感兴趣的是开发技术来识别数据中真正因果效应的特征，从而将它们与机会相关性和其他类型的关系区分开来。这些技术如果可用，将对我们解释从大型动态群体（无论是人类还是其他动物）收集的数据集的能力产生巨大影响。这项工作将适用于通常覆盖大群体个体形成的集体的数据集，这些群体可能表现出不同程度的一致性。根据PI与Zena Wood博士（埃克塞特）先前提出的集体运动的三层次分析，这种集体的运动可以在三个不同的粒度层次上进行描述，通过这种分析，我们可以区分集体作为一个整体的运动（被视为统一的点状实体）、集体配置的变化（由其成员的相对位置决定）以及构成集体的个体的运动。区分这些层次有助于将注意力集中在集体可能表现出的不同类型的因果相互作用上；特别是，对集体内个体运动的因果影响可能来自个体本身的内部动态，来自集体中的其他个体，来自整个集体（例如，可能施加某种压力以遵守群体规范），以及来自集体外部。因此，个体因果关系可能在一个粒度级别内或级别之间起作用，所提出的研究的另一部分是将三级分析集成到数据挖掘技术中，使我们能够辨别与集体行为的不同类型的定性描述相对应的不同粒度级别上的因果关系。