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By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission. Although simulations of social systems received substantial attention in the mid-1970s after the Club of Rome published reports predicting that policies promoting exponential economic growth would eventually bring global environmental catastrophe, the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific. Hoping to avoid the same fate, many social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level. However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.

By the late 1960s and early 1970s, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. These models used differential equations to predict population distributions as holistic functions of other systematic factors such as inventory control, urban traffic, migration, and disease transmission. Although simulations of social systems received substantial attention in the mid-1970s after the Club of Rome published reports predicting that policies promoting exponential economic growth would eventually bring global environmental catastrophe, the inconvenient conclusions led many authors to seek to discredit the models, attempting to make the researchers themselves appear unscientific. Hoping to avoid the same fate, many social scientists turned their attention toward micro-simulation models to make forecasts and study policy effects by modeling aggregate changes in state of individual-level entities rather than the changes in distribution at the population level. However, these micro-simulation models did not permit individuals to interact or adapt and were not intended for basic theoretical research.

截至20世纪60年代末70年代初，社会科学家越来越多地使用已有的计算技术，在组织、工业、城市和全球人口中进行控制和反馈过程的'''宏观模拟 Macrosimulation''' 。这些模型使用微分方程作为其他系统因素的整体函数来预测人口分布，这些系统因素包括库存控制、城市交通、人口迁移和疾病传播等。20世纪70年代中期，'''罗马俱乐部 Club of Rome''' 发表报告预测，促进指数式经济增长的政策最终将导致全球环境灾难，这个悲观的结论导致许多研究者试图反驳这些模型，并试图让研究显得不那么科学。

截至20世纪60年代末70年代初，社会科学家越来越多地使用已有的计算技术，<s>在</s><font color='blue'>对</font>组织、工业、城市和全球人口<s>中</s>进行<font color='blue'>包含</font>控制和反馈过程的'''宏观模拟 Macrosimulation''' 。这些模型使用微分方程作为其他系统因素的整体函数来预测人口分布，这些系统因素包括<font color='red'>库存</font><font color='blue'> 财产</font>控制、城市交通、人口迁移和疾病传播等。<font color='red'>20世纪70年代中期，'''罗马俱乐部 Club of Rome''' 发表报告预测，促进指数式经济增长的政策最终将导致全球环境灾难，这个悲观的结论导致许多研究者试图反驳这些模型，并试图让研究显得不那么科学。</font>

   （--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 根据意思，inconvenient译为悲观的，因为这个结论预示了悲观的未来，所以令指出这一点的科学家们感到inconvenient）

   （--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 根据意思，inconvenient译为悲观的，因为这个结论预示了悲观的未来，所以令指出这一点的科学家们感到inconvenient）

为了避免同样的情况，许多社会科学家将注意力转向'''微观模拟 Microsimulation'''模型。这些模型通过模拟个体状态的总体变化而不是总体人口级别的变化来进行预测和研究政策的效果。然而，这些微观模拟模型并不允许个体相互作用或适应、变化（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]）变化是根据适应（adapt）加上去的，不知道是否合理），研究者也不打算将它们用于基础理论研究。

为了避免同样的情况，许多社会科学家将注意力转向'''微观模拟 Microsimulation'''模型。这些模型通过模拟个体状态的总体变化而不是总体人口级别的变化来进行预测和研究政策的效果。然而，这些微观模拟模型并不允许个体相互作用或适应、变化（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]）变化是根据适应（adapt）加上去的，不知道是否合理），研究者也不打算将它们用于基础理论研究。