CHS: Small: Distributed Analogical Innovation

CHS：小型：分布式类比创新

• 批准号: 1526665
• 负责人: Aniket Kittur
• 金额: $ 49.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526665&HistoricalAwards=false
• 关键词: CHS; Small; Distributed; Analogical; Innovation

This social computing research will build a scientific foundation for distributed analogical innovation that integrates cognitive science, social science, design, and computer science. The results will contribute back to theory in these source disciplines as well, including theories of analogy, coordination, distributed innovation, scientific discovery, crowdsourcing, and collective intelligence. Innovation in science and technology is often driven by analogy, and opportunities for finding fruitful analogies are exploding with the increased online availability of repositories of ideas ranging from scientific papers to product ideas to the diverse text and video resources. However, our ability to process this deluge of information to find and use analogies is severely bottlenecked by individual cognitive limits, as the speed and capacity with which individuals can learn and explore new domains have not kept up with the rapid growth in online information from which analogies can be discovered. Furthermore, people are hampered by cognitive biases that prevent them from seeing and applying important parallels to solve comparable problems in different domains. Instead of relying on a single individual to find and apply an analogy, this research will develop methods for distributing analogical processing across multiple individuals. The development of improved innovation processes could accelerate technological innovation and scientific discovery, with corresponding benefits to the economy and to science.The general approach will be to conduct empirical studies to rigorously explore and characterize the benefits and limitations of distributed analogical innovation, and then to generalize these findings in the context of two real-world innovation communities. Distributing innovation tasks to different individuals could have a number of significant advantages. First, increasing the number of people involved could increase the capacity to find and use analogies across many domains. Second, changing the representations of problems and solutions could help innovators see the relevance of solutions from distant domains and reduce problems with fixation, in which people are overly influenced by surface features. Third, by distributing the steps involved in analogy to different people, alternative innovation paths could be explored effectively in parallel. Fourth, disaggregating the innovation pipeline opens up participation to many more people; one need not be an expert in a problem domain, for example, to find analog solutions in remote domains. Fifth, in contrast to innovation contests that reward only a small number of contest winners, the sequential method to be used in this research allows people to build on each other's work and doesn't waste the labor of the vast majority of participants.

这项社会计算研究将为融合认知科学、社会科学、设计和计算机科学的分布式类比创新奠定科学基础。这些成果也将为这些来源学科的理论做出贡献，包括类比、协调、分布式创新、科学发现、众包和集体智力理论。科学和技术的创新往往是由类比推动的，随着从科学论文到产品想法到各种文本和视频资源的在线可获得性增加，找到富有成效的类比的机会正在爆炸式增长。然而，我们处理这些泛滥的信息以发现和使用类比的能力受到个人认知能力的严重限制，因为个人学习和探索新领域的速度和能力没有跟上可以发现类比的在线信息的快速增长。此外，人们受到认知偏见的阻碍，这种偏见使他们无法看到和应用重要的相似之处来解决不同领域中的可比较问题。这项研究不是依赖单个个体来寻找和应用类比，而是开发出将类比处理分布在多个个体中的方法。改进的创新过程的发展可以加速技术创新和科学发现，对经济和科学都有相应的好处。一般的方法是进行实证研究，严格探索和表征分布式类比创新的好处和局限性，然后在两个现实世界创新社区的背景下推广这些发现。将创新任务分配给不同的个人可能会有许多显著的优势。首先，增加参与人员的数量可以提高在许多领域寻找和使用类比的能力。其次，改变问题和解决方案的表现形式可以帮助创新者看到来自遥远领域的解决方案的相关性，并减少人们过度受表面特征影响的固恋问题。第三，通过将类比所涉及的步骤分配给不同的人，可以并行有效地探索替代创新路径。第四，分解创新渠道让更多的人参与进来；例如，人们不必是某个问题领域的专家，就可以在偏远领域找到类似的解决方案。第五，与只奖励少数获胜者的创新竞赛不同，这项研究中使用的顺序方法允许人们在彼此的工作基础上发展，而不会浪费绝大多数参与者的劳动。