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的研究计划的一部分，其目的是将以前的工作汇总为（1）建模和理解集体现象以及（2）因果关系的本体论，以发展集体因果关系的一般计算理论，该理论可以应用于大型时空数据集的分析，例如在生物学中逐渐获得的大型时空数据集的分析。 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适用于 - 这样，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这样的过程持续的情况之间存在重要的区别。与Duckham等人的工作没有利用FOIS论文中确定的全部因果关系，特别是被忽视的过程过程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通过使用其他数据集和更详细的理论分析来对此做好并进一步开发数据挖掘方法。我们特别有兴趣开发技术来确定数据中真正因果关系的签名，从而将它们与偶然的相关性和其他类型的关系区分开来。这样的技术（如果有）如果有的话，将对我们解释从大型人群（无论人类还是其他动物）收集的数据集的能力产生巨大影响。这项工作将适用于数据集，这些数据集通常涵盖通常涵盖可能表现出不同程度相干性的大量个人。根据PI先前与Zena Wood博士（埃克塞特）对集体运动的三级分析，可以在三种不同级别的粒度上描述此类集体的运动，通过该运动，我们可以区分整个集体的运动，被认为是统一的点状实体，集体的构型在集体群体中所确定的，其成员的成员和成员的成员和成员的成员和群体的成员和成员的成员的变化。区分这些水平有助于将注意力集中在可能由集体表现出来的各种因果相互作用上。特别是因果关系对集体内部的运动的影响可能是由于个人本身的内部动力学，来自集体中的其他个体，整个集体（例如，这可能施加某种压力以符合群体规范），以及集体外部。因此，个体因果关系可以在一个粒度水平或水平之间起作用，而拟议的研究的另一部分是将三级分析整合到数据挖掘技术中，以使我们能够识别以不同级别的粒度性的因果关系，对应于集体行为的不同类型的定性描述。