Diversity, Network Structure, and the Effectiveness of Collective Design and Innovation

多样性、网络结构以及集体设计和创新的有效性

• 批准号: 1734147
• 负责人: Hiroki Sayama
• 金额: $ 44.1万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734147&HistoricalAwards=false
• 关键词: Diversity; Network; Structure; Effectiveness; Collective

This project will investigate how the diverse knowledge, expertise and behaviors of people and their social networks can make large-scale collaborative design processes more effective and innovative. This has become a very important issue in modern society where developing successful solutions for complex problems requires collaboration among many individuals with diverse knowledge, expertise and behaviors, and where the interactions of those individuals are often complex and dynamic. The project will use multiple research methods to address this issue, combining computer simulations for idea and hypothesis generation and online experiments with human participants for idea and hypothesis testing. The project will expand knowledge about the linkage between individuals' interactions in various organizational configurations and their performance in innovation, which will be applicable to product, service, concept, policy, and/or aesthetic design tasks in a wide variety of domains.As the complexity of products and services has skyrocketed over the last several decades, collective design and innovation processes have become a necessity for the development of successful solutions for real-world problems. Such large-scale design processes typically involve individuals with diverse knowledge, expertise and behaviors, and the organizational structures under which the collective design takes place are often complex and dynamic with temporally changing non-trivial network properties. This project aims to investigate, both theoretically and experimentally, how the diversity of knowledge, expertise, and behaviors of individual members and the topological properties of organizational network structures will affect the effectiveness of design and innovation processes at collective levels. This will be accomplished through (a) theoretical agent-based network modeling and simulation, and (b) model evaluation through online laboratory experiments involving student participants with diverse majors. Modeling and simulation will investigate potential interactions between task-related diversity and large-scale organizational network structures. The predictions produced by the simulations will be evaluated through online laboratory experiments, in which participants will collaborate on several open-ended collective design tasks through a computer-mediated collaboration platform. In these experiments, the participants? task-related diversity will be measured and its distribution in the organizational network will be manipulated. The topologies of the network will also be monitored and manipulated dynamically. The successful outcome of the project will be a new "guiding principle" for configuring and manipulating task-related diversity and organizational network structures to promote effective collective design and innovation. This will bear multidisciplinary impacts on organizational science, management science, systems science, systems engineering, operations research, and network science.

这个项目将调查人们及其社交网络的多样化知识、专业知识和行为如何使大规模协同设计过程更有效和更具创新性。这在现代社会已经成为一个非常重要的问题，在现代社会，为复杂问题制定成功的解决方案需要具有不同知识、专业知识和行为的许多个人之间的合作，这些个人之间的互动往往是复杂和动态的。该项目将使用多种研究方法来解决这个问题，将计算机模拟用于生成想法和假设，并与人类参与者进行在线实验以进行想法和假设检验。该项目将扩大关于个人在各种组织结构中的互动与他们在创新中的表现之间的联系的知识，这将适用于各种领域的产品、服务、概念、政策和/或美学设计任务。随着产品和服务的复杂性在过去几十年中飙升，集体设计和创新过程已经成为为现实世界问题开发成功解决方案的必要条件。这种大规模的设计过程通常涉及具有不同知识、专业知识和行为的个人，在其中进行集体设计的组织结构通常是复杂和动态的，具有随时间变化的非平凡网络属性。本项目旨在从理论和实验两个方面研究个体成员的知识、专业知识和行为的多样性以及组织网络结构的拓扑特性如何影响集体层面的设计和创新过程的有效性。这将通过(A)基于理论代理的网络建模和仿真，以及(B)通过涉及不同专业的学生参与者的在线实验室实验进行模型评估来实现。建模和模拟将调查与任务相关的多样性和大规模组织网络结构之间的潜在交互作用。模拟产生的预测将通过在线实验室实验进行评估，参与者将通过计算机中介的协作平台就几个开放式集体设计任务进行协作。在这些实验中，参与者是谁？将衡量与任务相关的多样性，并对其在组织网络中的分布进行操纵。网络的拓扑结构也将被动态监控和操纵。该项目的成功成果将是一个新的“指导原则”，用于配置和操纵与任务有关的多样性和组织网络结构，以促进有效的集体设计和创新。这将对组织科学、管理科学、系统科学、系统工程、运筹学和网络科学产生多学科的影响。

项目成果

Detecting Dynamic States of Temporal Networks Using Connection Series Tensors

• DOI: 10.1155/2020/9649310
• 发表时间: 2020-07
• 期刊: Complex.
• 作者: Shun Cao;Hiroki Sayama

Mutual-Information-based Feature Selection for Facial Emotion Recognition on Light-Weight Devices

轻型设备上基于互信息的特征选择面部情绪识别

• DOI: 10.1109/ssci44817.2019.9003050
• 发表时间: 2019
• 期刊: 2019 IEEE Symposium Series on Computational Intelligence (SSCI
• 作者: Dong, Yingjun;Sayama, Hiroki
• 通讯作者: Sayama, Hiroki

Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations

• DOI: 10.1155/2019/7591072
• 发表时间: 2013-11
• 期刊: Complex.
• 作者: Shelley D. Dionne;Hiroki Sayama;F. Yammarino

Capturing the Production of Innovative Ideas: An Online Social Network Experiment and “Idea Geography” Visualization

捕捉创新想法的产生：在线社交网络实验和“想法地理”可视化

• DOI: 10.1007/978-3-030-77517-9_20
• 发表时间: 2021
• 期刊: Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas
• 作者: Cao, Yiding;Dong, Yingjun;Kim, Minjun;MacLaren, Neil G.;Kulkarni, Ankita;Dionne, Shelley D.;Yammarino, Francis J.;Sayama, Hiroki
• 通讯作者: Sayama, Hiroki