Biocomplexity Research: Emergence of Cooperation from Human-Environmental Interactions

生物复杂性研究：人类与环境相互作用中合作的出现

• 批准号: 0083524
• 负责人: John Lansing
• 金额: $ 100万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-15 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0083524&HistoricalAwards=false
• 关键词: Biocomplexity; Research; Emergence; Cooperation; Human

A key question in the social and evolutionary sciences is the explanation of cooperation among human groups. Current models explain cooperation either as a result of fitness benefits to kin or as a consequence of reciprocal altruism among individuals in small groups. Both explanations are limited to small groups that are united by kinship ties' or reciprocal exchanges. The purpose of this Biocomplexity research project is to improve general understandings of the role of the environment in the formation and dissolution of large-scale cooperative groups. To accomplish this goal, an adaptive-agent simulation model will be created that can predict variation in cooperation as a consequence of processes that occur over historical time. Among the changes to be examined are changes in population density, kinship relationships, and ecological feedback. The model will also predict statistical patterns of biological kinship relationships among individuals and conflicts between social groups. These patterns will be tested against observed data on conflict and cooperation among Balinese farming communities.This project builds on prior NSF-funded research that showed how stable cooperative networks encompassing tens of thousands of farmers in Balinese watersheds can be explained through a model of self-organization through which ecological feedback rewarded cooperation with high harvest yields and reduced variance in yields. In computer simulations, agents coalesced into cooperative groups as a consequence of ecological feedback, and these groups often coalesced into higher-level agents. Calculated optimal structural patterns of cooperation bear a very close resemblance to observed patterns among the subaks. But despite the ecological rewards for cooperation, it frequently breaks down. Because the existing ecological model always predicts that cooperation will be rewarded, it cannot account for such failures to cooperate. Detailed observations of 15 villages over the past four years suggest that cultural rules of kinship and patterns of demographic change strongly influence both the emergence and dissolution of cooperative bonds among farmers. The proposed adaptive-agent simulation model will enable simulation of these historical processes. The model will predict specific patterns of demography, kinship relationships, and levels of cooperation for particular villages. These predictions will be tested against observed data. The model will be constructed and tested using data from Bali, but it will be designed to facilitate a search for similar processes and dynamics in other societies.

社会和进化科学中的一个关键问题是解释人类群体之间的合作。 目前的模型解释合作，无论是作为一个结果，健身的好处，亲属或作为一个结果，互惠利他主义的个人之间的小团体。 这两种解释都局限于通过亲属关系或互惠交换而团结在一起的小团体。 这个生物复杂性研究项目的目的是提高对环境在大规模合作群体的形成和解散中的作用的普遍理解。 为了实现这一目标，将创建一个自适应代理仿真模型，该模型可以预测随着历史时间的推移而发生的过程所导致的合作变化。 在这些变化中，需要研究的是人口密度、亲属关系和生态反馈的变化。 该模型还将预测个人之间的生物亲属关系和社会群体之间的冲突的统计模式。 这些模式将根据观察到的巴厘岛农业社区之间的冲突和合作的数据进行测试。该项目建立在以前的NSF资助的研究基础上，该研究表明，如何通过自组织模型来解释巴厘岛流域数万名农民的稳定合作网络，通过自组织模型，生态反馈奖励合作，获得高收获产量和减少产量差异。 在计算机模拟中，作为生态反馈的结果，代理人合并为合作团体，这些团体通常合并为更高级别的代理人。 计算出的最佳结构模式的合作承担一个非常密切的相似之处，观察到的模式之间的subaks。 但是，尽管合作的生态回报，它经常崩溃。 因为现有的生态模型总是预测合作会得到回报，所以它无法解释这种合作失败。 过去四年对15个村庄的详细观察表明，亲属关系的文化规则和人口变化模式对农民之间合作关系的出现和解散产生了强烈影响。 所提出的自适应代理仿真模型将使这些历史过程的模拟。 该模型将预测特定村庄的人口统计、亲属关系和合作水平的具体模式。 这些预测将根据观测数据进行检验。 将利用巴厘岛的数据来构建和测试该模型，但其设计目的将是便利在其他社会中寻找类似的进程和动态。