Evaluation and parameterisation of individual-based models of animal populations

生态系统是由自主的、适应性强的个体组成的，每个个体都有自己实现目标的方法。人们普遍希望，控制这种复杂系统的一般原理最终将从计算机模拟的分析中得到理解，这些模拟被统称为基于个体的模型（ibm）。ibm是一个动态系统，包含许多自主的相互作用的代理，这些代理广泛地用于已知影响单个代理行为的因素，但兴趣集中在群体水平上发生的事情。人口会增加还是减少？反应会有多快？在需要对生态系统进行实际管理的地方，许多人认为这只能通过ibm来实现。例子包括自然保护区和贝壳渔业的养护管理、评估风力发电场和高速公路等建筑建议的环境影响、鱼类资源的管理以及评估用于控制农业害虫的新化学品对非目标生物的影响。科学期刊上的文章指出，在包括经济分析在内的多个领域，ibm是唯一可行的前进道路。在经济分析领域，使用ibm模型或许可以避免最近的全球“信贷紧缩”。因此，ibm是对许多复杂系统建模的唯一可行方法，其中预测对所有人都至关重要。尽管ibm的重要性得到了广泛的认可，但是对这些非常复杂的系统的评估仍然有很多需要改进的地方。很明显，模型的目的是解释我们周围的世界。从统计学的角度来看，我们希望使模型“适合”数据。我们该怎么做呢？统计理论的最新进展，被称为近似贝叶斯计算（ABC），向我们展示了如何做到这一点。ABC的实现需要开发实用的方法，这些方法将允许用户以有效的方式使他们的IBM模型适应实际数据。这种贝叶斯方法应该允许计算ibm中可能参数值的分布、给定的观察值，以及评估一个模型是否优于另一个模型。在这个项目中，我们设计了实用的方法，允许ibm的所有制造商通过参考相关数据来正确地验证他们的模型。如果我们要在做出有关环境影响、自然保护和控制农业害虫的新化学品许可的关键决策时拥有强大而可靠的基础，那么提供这些方法是至关重要的。

Ecosystems are populated by autonomous, adaptive individuals, each figuring out its own ways of achieving its goals. It is a widely shared hope that the general principles governing such complex systems will eventually be understood from analysis of computer simulations known collectively as individual-based models (IBMs). IBMs are dynamical systems containing many autonomous interacting agents which are used where, broadly, the factors influencing the behaviour of individual agents are known, but interest centres on what happens at the population level. Will the population increase or decrease? How fast will be the response? Where practical management of ecosystems is required, many consider this can only be realistically performed with IBMs. Examples include conservation management of nature reserves and shell fisheries, assessment of environmental impacts of building proposals including wind farms and highways, management of fish stocks and assessment of the effects on non-target organisms of new chemicals for the control of agricultural pests. Articles in scientific journals have suggested IBMs are the only realistic way forward in diverse fields including economic analysis where the recent global 'credit crunch' might have been avoided with the use of such models. Thus IBMs are the only practicable method of modelling many complex systems where prediction is of vital importance to all. Despite the widely-appreciated importance of IBMs, the evaluation of these very complex systems still leaves much to be desired. Clearly, the purpose of a model is to explain the world that we see around us. From a statistical point of view we wish to 'fit' the model to data. How can we do this? Recent advances in statistical theory, known as Approximate Bayesian Computation, ABC, suggest how this might be done. Implementation of ABC requires development of practical methods that will allow users to fit their IBM models to real data in an efficient manner. This Bayesian approach should allow calculation of distributions of possible parameter values in IBMs, given observations, and evaluation of whether one model is better than another. In this project we devise practical methods that will allow all makers of IBMs to validate their models properly by reference to relevant data. Provision of such methods is crucial if we are to have robust and reliable bases for making crucial decisions about environmental impacts, nature conservation, and the licensing of new chemicals for the control of agricultural pests.