Biocomplexity Research: Agent-Based Models of Land Use Decisions and Emergent Land Use Patterns

这个项目的主要目标是解释人类生物生态系统，特别是森林地区的长期复杂变化过程。 我们将开发基于代理的模型，以研究如何在一个级别（家庭）的土地利用决策影响结果在该级别和在几个较高和较低的层次嵌套的系统。 我们开发了两个基于代理的模型来解释美国边境和边境后中西部和巴西亚马逊边境的土地利用模式。 第一个模型将解决两个主要的难题：（1）为什么19世纪印第安纳州最初定居者的后代以如此大规模和看似不经济的速度砍伐木材，最终使土地裸露，造成大规模的侵蚀和土壤流失，并导致大量的农场废弃？以及（2）为什么私人拥有的土地上的森林再生如此广泛，而如此多的公共政策是基于这样一种假设，即支离破碎的私人拥有的地块注定永远不会有大量的森林再生？ 第二个模型将解释过去三十年来亚马逊森林砍伐的时空模式。 我们在这两个模型中所做的假设将通过严格的实验室实验进行实证检验和基础。 还将对照从地面实况卫星数据、航空照片、土地调查、人口普查数据、家庭访谈、在森林斑块抽样中进行的森林测定以及关于木材和农产品价格、投入成本和土地价值的档案数据中获得的一套丰富的数据，对任何时间点的土地使用模式和变化过程进行测试。 在进一步开发和测试之后，这两个模型将用于推断未来，并评估不同的公共政策可能如何影响这些地区的一般土地使用和特别是森林变化。 该项目将包括三个重要的顶点活动：生物复杂性的基于主体的模型研讨会，研讨会的综合卷，以及暑期研究所。 通过对不同政策制度下单个家庭的土地使用决定进行经验验证，该研究将为评估替代公共政策提供有用的工具。 确定公共诱因、税收和限制如何影响森林变化率，有助于全世界努力寻找刺激重新造林的有效方法，从而吸收碳以抵消释放到大气中的碳。 该研究还解决了与人类行为的适当模型相关的基本问题，以便在复杂动态环境中检查投资决策组合时使用。 因此，这项研究是相关的实现一个经验验证的基础，一系列的决策情况超出了土地使用和森林砍伐。 来自多个社会，生物和物理科学学科的工具将以独特的方式结合和扩展，并在出版物，研讨会和培训机构中传播。 这项研究活动是作为2000财政年度生物复杂性特别竞赛的一部分资助的。

The primary goal of this project is to explain long-term, complex change processes in human-bioecological systems-especially forested regions. We will develop agent-based models to examine how land-use decisions made at one level (a household) affect outcomes at that level and at several higher and lower levels in a hierarchically nested set of systems. We develop two agent-based models to explain land-use patterns in the frontier and post-frontier Midwest of the United States and the frontier of the Brazilian Amazon. The first model will address two major puzzles: (1) Why did the descendants of the initial settlers in nineteenth-century Indiana cut down timber at such a massive and seemingly uneconomic rate that they eventually denuded the land, causing massive erosion and soil loss, and leading to substantial farm abandonment? and (2) Why have forests regrown so extensively on privately owned land when so many public policies are based on the assumption that fragmented, privately owned parcels are destined never to have significant forest regrowth? The second model will explain the spatial and temporal patterns of deforestation in the Amazon over the last three decades. The assumptions we make in the two models will be empirically tested and grounded by rigorous laboratory experiments. The patterns of land use at any point in time and the processes of change also will be tested against a rich set of data derived from ground-truthed satellite data, aerial photographs, land surveys, census data, household interviews, forest mensuration undertaken in a sample of forest patches, and archival data regarding timber and agricultural prices, input costs, and land values. After further development and testing, both models will be used to extrapolate into the future and assess how diverse public policies are likely to affect land use in general and forest change in particular in these regions. The project will involve three important capstone activities: a Workshop on Agent-Based Models of Biocomplexity, a synthesis volume to be derived from the Workshop, and a Summer Institute.The study will have multiple impacts. By achieving an empirically validated understanding of land-use decisions of individual households under different policy regimes, the study will produce useful tools for evaluating alternative public policies. Ascertaining how public inducements, taxation, and constraints affect rates of forest change contributes to the worldwide effort to find effective methods for stimulating reforestation and thereby sequestering carbon to offset carbon released into the atmosphere. The study also addresses fundamental questions related to the appropriate model of human behavior to use when examining a combination of investment decisions in complex, dynamic environments. Thus, the study is relevant for achieving an empirically validated foundation for an array of decision situations beyond those of land use and deforestation. Tools from multiple social, biological, and physical science disciplines will be combined and expanded in unique ways and disseminated in publications, workshops, and training institutes. This research activity was funded as part of the FY2000 Biocomplexity Special Competition.