Agent-based modelling and assessment of human-environment interactions

更好地了解人与环境的相互作用，特别是土地使用者的决策过程，是探索应对气候变化的可能策略和改善土地利用规划的关键条件。到目前为止，土地系统模型主要以土地面积为主要分析单位，并没有明确地反映土地使用者的决策。因此，在第二代土地系统模型中，注意力将从像素转向代理。当建立所谓的多主体模型时，挑战就变成了真实地代表个体土地利用决策，特别是对气候变化的适应。需要考虑的因素包括技术采用、学习和风险应对以及空间互动的选择。另一个挑战是多智能体模型与陆地-地表-大气模型，特别是在详细空间尺度上的作物生长模型的整合，这一点目前尚未实现。本项目的主要科学目标是开发基于agent的整体土地系统模型组件。该项目在个体农场层面的微观经济模型和景观层面的生物物理模型之间发挥了联系作用。

A better understanding of human-environment interactions and especially of land users’ decision- making processes is a key requisite for exploring possible coping strategies to climate change and for improving land use planning. So far, land system models have focused on land areas as the main unit of analysis and have not represented the decision-making of land users explicitly. Within the second generation of land system models, attention will therefore shift from pixels to agents. When building so-called multi-agent models, the challenge becomes to realistically represent individual land-use decisions and especially the adaptation to climate change. Factors to be considered are options for technology adoption, learning and risk-coping, and spatial interaction. Another challenge is the integration of multi-agent models with land-surfaceatmosphere models and especially crop growth models at detailed spatial scales, which has not yet been achieved. The major scientific goal of this project is to develop the agent-based model component for the overall land system model. The project fulfills a linking role between the microeconomic modelling at the individual farm level and the biophysical modelling at the landscape level.