JDec: Joint decision models for citizens, crops, and environment

这个项目将使决策理论工具适用于农业环境管理，这是一个数学方法服务不足的领域。农业环境下的决策过程是复杂的，因为它跨越了多个相互依存的阶段，并且在此过程中涉及许多风险。决策——何时施用农药、施用多少、何时修剪、何时浇水，甚至何时收获——都能对农产品的数量和质量产生至关重要的影响，从而影响企业的短期成功。这些决定还将决定环境危害的程度，这一点一直很难界定，生态系统提供的“服务”的价值也是如此。为了促进将它们纳入决策，我们开发了比运筹学中的常见框架更灵活、更全面的模型。首先，需要用综合反映成本和收益的效用函数来评估结果。除其他事项外，它们需要沿着决策轨迹进行评估，包括适当的记忆和预见水平，以及在此过程中的相互依赖性。例如，一种除草剂只有在其对拜访杂草的野生传粉者的间接影响被忽略时才看起来有效，而这些传粉者的损失将需要用新的成本来补偿。其次，在农业环境背景下，决策不是由人类单独做出的。一种建模方法着眼于农民、作物和环境三者共同做出的决策，为处理相互作用提供了所需的灵活性。我们进一步考虑了更高水平的不确定性，因为这些因素的影响本身可能取决于随机事件。第三，我们的模型承认时间维度和潜在的资源配置约束。在一个大型的、相互联系的、多阶段的土地和资源管理系统中，过去的行为影响着未来的决定。加上快速变化的环境，极端天气事件的频率增加，害虫和传粉者范围的变化以及资源的枯竭，我们需要在模型开发中考虑对鲁棒近似解的需求。换句话说，必须在“实时的现实世界”中做出决策的挑战需要一个“足够好”决策的范例，以及它与最佳解决方案之间的差距的概念化。我们的主要目标是建立涵盖这三个方面的决策建模的数学和统计框架。我们的工作通过为不确定性和相互依赖性建立更灵活的机制来扩展现有方法。行为科学的关键思想将使我们超越狭隘的理性框架。根据数据的可用性，我们的理论将适用于小型和大型景观尺度。我们将在两个案例研究中探讨这些想法。第一个是苹果园里的野生传粉者系统，这是一个特别合适的试验场，可以用来了解管理土地及其周围景观之间边界的间接影响。第二个案例研究探讨了决策模型在一个有4种作物和多种干预方法的大型农场规模实验中的应用。它为比较决策策略提供了丰富的数据集。我们的工作可以直接惠及许多公民：不仅是作物科学家和土地管理者，还包括生态学家、保护主义者、地方当局、慈善机构和决策者。我们正在设计的工具将向科学领域开放，这种方法已经在其他各种学科中取得了巨大成功。有了更好的工具，比如我们提出的工具，在不断变化的气候条件下，为养活不断增长的人口而采取的环保行动可以是动态的、适应性强的和可持续的。

This project will adapt decision-theoretic tools to agri-environmental management, a domain that has been underserved by mathematical methodology. The process of decision-making within an agricultural context is complex, because it spans multiple interdependent stages, and involves many risks along the way. Decisions - when to apply pesticides, how much to apply, when to prune, when to water, even when to harvest - can affect crucially the produce quantity and quality, and hence the short-term success of the enterprise. The decisions will also determine the extent of environmental harm, which have been challenging to define, as is the value of "services" provided by the ecosystem. To facilitate their inclusion in decision-making we develop models that are more flexible and more holistic than common frameworks in operational research. First, outcomes need to be valued by utility functions that reflect costs and benefits comprehensively. Among other things, they need to be evaluated along decision trajectories, including appropriate levels of memory and foresight, and interdependencies along the way. For example, a herbicide treatment may look effective only as long as its indirect effect is ignored on the wild pollinators that had visited the weeds, and whose loss will need to be compensated with new costs.Second, in an agricultural-environmental context, decisions are not taken by humans alone. A modelling approach looking at decisions being taken jointly by all three --- the farmer, the crop and the environment --- opens the flexibility needed to deal with interactions. We further allow for a higher level of uncertainty, in that the influence each of these agents has may itself depend on random events.Third, our models acknowledge the temporal dimension and potential resource allocation constraints. In a large, interconnected, multi-stage system of land and resource management, past actions influence future decisions. Adding rapidly changing environment, with extreme weather events increasing in frequency, shifting pest and pollinator ranges, and resource depletion, we need to take account of the need for robust approximate solutions in model development. In other words, the challenges of having to make decisions in the "real world in real time" requires a paradigm for "good enough" decision-making, and a conceptualisation of the gap it has to optimal solutions. Our major objective is to build the mathematical and statistical framework for decision modelling that covers these three aspects. Our work extends existing approaches by building in more flexible mechanisms for uncertainty and interdependencies. Key ideas from behavioural sciences will move us beyond a narrow rationality framework. Subject to data availability, our resulting theory will be applicable to both small and large landscape scales.We will explore these ideas in two case studies. The first is a system of wild pollinators in apple orchards, a particularly suitable testing ground for understanding indirect effects at the frontier between managed land and its surrounding landscape. The second case study explores the use of decision modelling in a large farm scale experiment with 4 crops and multiple intervention methods. It provides a rich data set for comparing decision strategies. Our work can directly benefit many citizens: not only crop scientists and land managers, but also ecologists, conservationists, local authorities, charities and policy-makers. The tools we are designing will open up to the field sciences an approach that has been used with great success in a variety of other disciplines. With better tools, such as the ones we are proposing, environmentally-conscious actions taken to feed a growing population in a changing climate can be dynamic, adaptable, and sustainable.