IBSS-L: Information Networks and the Evolution of Social Organizations

这一跨学科研究项目将通过提供一种严格的方法来确定一个社会群体的结构在多大程度上受到外部压力(如环境提供的需求和机会)而不是内部压力(如群体成员之间的竞争和错位的激励)的影响，从而解释为什么不同的社会群体表现出特殊的组织形式。该项目将成为社会和行为科学关于组织结构如何限制其处理环境信息的能力的新的研究重点，并将为组织如何在受行动者能力限制的情况下在内部传递信息提供新的见解。这个项目可以帮助社会和行为科学家团结起来，从那些专注于灵长类群体和觅食社会的科学家到那些研究跨国公司结构的科学家。它还将通过建议如何最好地组织现代公司和政府官僚机构来提供切实的回报。该项目将开发和提供具有广泛学术价值的各种过去和现在的社会组织的数据库。从史前社会到商业公司再到军事组织，社会团体的组织方式截然不同，从平等主义的“横向”社会到深层的“垂直”等级制度，从分散的、模块化的团队集合到集中的指挥和控制集合。社会团体是因为这些结构优化了信息交换而展示了他们所做的组织，还是仅仅是因为当地历史上的先例而引导了团体？这个项目将通过使用最近开发的数学技术来解决这个问题，以形式化“集体解决问题”和“认知约束”的概念。这些形式化将被用来调查哪些组织在不同的限制和群体规模下表现最好，以及当成员或成员间的联系被移除或群体面临的压力发生变化时，哪些组织在继续运作方面最“稳健”。然后，调查人员将确定这样的最佳组织是否反映了真正的组织方式(或已经)。从人类学到经济学，最优内部组织问题一直受到各个领域的关注。然而，还没有一个统一的数学框架来研究不同类型的组织之间的关系以及组织面临的压力和约束。这个项目将结合图论和信息论的技术来开发这样一个框架。图论研究社交网络的组织，并为量化组织属性提供了丰富的词汇，如层级、集中化和模块化。信息论的方法可以量化群体成员之间处理或交流的信息比特，将社会群体概念化为一个电信网络，其中网络节点对应于群体成员，网络通道和节点上的带宽限制对应于认知约束。研究人员将确定在不同带宽限制下最佳处理信息的网络结构，并将结果与过去和现在人类组织的经验数据进行比较。该项目通过NSF跨学科行为和社会科学研究(IBSS)竞赛得到支持。

This interdisciplinary research project will explain why different social group exhibits particular forms of organization by providing a rigorous way to determine the extent to which a social group's structure is shaped by external pressures, such as demands and opportunities afforded by the environment, rather than internal pressures, such as competition and misaligned incentives among group members. The project will constitute a new research thrust for the social and behavioral sciences regarding the ways that the structure of organizations constrains their ability to process environmental information, and it will provide new insights as to how organizations internally route information subject to restrictions on actors' capabilities. This project can help unite social and behavioral scientists ranging from those focusing on primate groups and forager societies to those studying the structure of multinational corporations. It will also provide tangible returns by suggesting how to best organize modern firms and governmental bureaucracies. The project will develop and make available databases on a variety of past and present social organizations which will be of broad scholarly value.Social groups ranging from prehistoric societies to business firms to military organizations are organized in dramatically different ways, from egalitarian "horizontal" societies to deep "vertical" hierarchies and from sets of decentralized, modular teams to centralized command-and-control assemblies. Do social groups exhibit the organizations they do because these structures optimize information exchange, or are groups simply channeled by local historical precedent? This project will address this question by using recently developed mathematical techniques to formalize the notions of "collective problem solving" and "cognitive constraints." These formalizations will be employed to investigate which organizations perform best under different constraints and group sizes as well as which are most "robust" in continuing to function when members or inter-member connections are removed or the pressures on the group change. The investigators will then determine whether such optimal organizations reflect the way real groups are (or have been) organized. The problem of optimal internal organization has received attention in various fields, from anthropology to economics. However, there has not been a unifying mathematical framework for studying the relationship between different kinds of organizations and the pressures and constraints on organizations. This project will develop such a framework by combining techniques from graph theory and information theory. Graph theory studies the organization of social networks and provides a rich vocabulary for quantifying organizational properties like hierarchy, centralization, and modularity. Methods from information theory can quantify the bits of information processed or communicated between group members, conceptualizing a social group as a telecommunication network where network nodes correspond to group members and "bandwidth limits" on the network channels and nodes correspond to cognitive constraints. The researchers will identify network structures that best process information under different sets of bandwidth limits and compare the results to empirical data on past and present human organizations. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.