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拟议的研究是一个更广泛的研究计划的一部分，其目标是汇集以前关于（1）建模和理解集体现象和（2）因果关系本体论的工作，以发展集体因果关系的一般计算理论，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。最近与Matt Duckham博士和其他人的联合工作，从与鱼类运动有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2012年FOIS国际会议上提出。这一理论的一个显著特点是，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似因果关系的性质和功能可以关键地取决于它适用于这些类型的项目中的哪一个--因此，例如，一个离散事件导致另一个这样的事件的情况，以及一个正在进行的过程使另一个这样的过程永久化的情况。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这一点，并进一步开发数据挖掘方法，使用额外的数据集和更详细的理论分析。我们特别感兴趣的是开发技术来识别数据中真正的因果效应的签名，从而将它们与机会相关性和其他类型的关系区分开来。这种技术，如果可用的话，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涵盖形成集体的大型个体群体的数据集，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）先前开发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描述，通过这些层次，我们可以区分集体作为一个整体的运动，被视为一个统一的点状实体，集体结构的变化，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可能由集体表现出来的不同种类的因果互动上;特别是，对集体中个人运动的因果影响可能来自个人自身的内部动力学，来自集体中的其他个人，来自集体整体（例如，它可能施加某种压力以符合群体规范），以及来自集体之外。因此，个人的因果关系可能会在一个粒度级别或级别之间的行为，和拟议的研究的另一部分是整合到数据挖掘技术的三个层次的分析，使我们能够辨别因果关系在不同级别的粒度对应于不同类型的定性描述的集体行为。

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.