事实与计算：信息与解决方案空间中网络结构与性能的实验研究
Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces
摘要：
使用数据从一个大型实验室实验中的问题解决，我们改变了16人网络的结构，我们调查如何组织的网络结构可以构建在复杂的解决问题的任务，以优化性能。解决问题既包括寻找信息，也包括寻找理论来理解这些信息。我们发现，网络结构的效果是相反的这两个同样重要的搜索形式。密集聚类鼓励网络成员产生更多样化的信息，但它也有能力阻止多样化理论的产生：聚类促进了信息空间的探索，但减少了解决方案空间的探索。以前的研究往往只关注这两个空间中的一个，对网络聚类的价值得出了不一致的结论。通过采用一个实验平台，在该平台上，信息与解决方案分开测量，我们能够调和过去的矛盾，并澄清网络聚类对性能的影响。这一发现既为组织知识工作的结构化提供了更清晰的工具，也揭示了操纵网络结构以提高绩效所固有的挑战，因为有助于解决问题的一个方面的沟通结构可能会损害另一个方面。
英文摘要：
Using data from a large laboratory experiment on problem solving in which we varied the structure of 16-person networks we investigate how an organization's network structure may be constructed to optimize performance in complex problem-solving tasks. Problem solving involves both search for information and search for theories to make sense of that information. We show that the effect of network structure is opposite for these two equally important forms of search. Dense clustering encourages members of a network to generate more diverse information, but it also has the power to discourage the generation of diverse theories: clustering promotes exploration in information space, but decreases exploration in solution space. Previous research, tending to focus on only one of those two spaces, had produced inconsistent conclusions about the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we were able to reconcile past contradictions and clarify the effects of network clustering on performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals the challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one aspect of problem solving may harm the other.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
