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

该项目的主要目标是解释人类生物生态系统--特别是森林覆盖地区--的长期、复杂的变化过程。我们将开发基于主体的模型，以考察在一个层级(家庭)做出的土地使用决定如何影响该层级以及层级嵌套系统中的几个较高和较低级别的结果。我们开发了两个基于主体的模型来解释美国中西部边境和边境后以及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巨大的、看似不经济的速度砍伐木材，最终导致土地剥落，导致大规模侵蚀和土壤流失，并导致大量农场被遗弃？以及(2)当如此多的公共政策基于支离破碎的私有地块注定永远不会有显著的森林再生的假设时，为什么森林在私有土地上如此广泛地再生？第二个模型将解释过去三十年亚马逊森林砍伐的空间和时间模式。我们在这两个模型中所做的假设将经过经验检验，并以严格的实验室实验为基础。还将对照从地面真实卫星数据、航空照片、土地调查、人口普查数据、家庭访谈、对森林斑块样本进行的森林测量以及关于木材和农业价格、投入成本和土地价值的档案数据，测试任何时间点的土地利用模式和变化过程。在进一步开发和测试之后，这两个模型将被用来推断未来，并评估不同的公共政策可能如何影响总体上的土地利用，特别是这些地区的森林变化。该项目将涉及三个重要的顶峰活动：基于主体的生物复杂性模型研讨会，将从研讨会衍生的合成卷，以及夏季研究所。这项研究将产生多重影响。通过实现对不同政策制度下个别家庭的土地使用决定的经验性验证的理解，这项研究将产生评估替代公共政策的有用工具。查明公共诱因、税收和限制如何影响森林变化率，有助于全球努力寻找有效的方法来刺激重新造林，从而封存碳以抵消释放到大气中的碳。这项研究还解决了与在复杂、动态的环境中审查投资决策组合时使用的适当的人类行为模式相关的基本问题。因此，这项研究对于为土地利用和森林砍伐以外的一系列决策情况提供经验性验证的基础具有重要意义。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组合和扩展，并在出版物、研讨会和培训机构中传播。这项研究活动是2000财年生物复杂性特别竞赛的一部分。

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.