CHS: Small: Distributed Analogical Innovation

这项社会计算研究将为整合认知科学、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。研究结果也将有助于这些源学科的理论，包括类比理论、协调理论、分布式创新理论、科学发现理论、众包理论和集体智慧理论。科学和技术的创新往往是由类比驱动的，随着从科学论文到产品创意再到各种文本和视频资源的在线知识库的增加，寻找富有成效的类比的机会正在爆炸式增长。 然而，我们处理大量信息以发现和使用类比的能力受到个人认知极限的严重制约，因为个人学习和探索新领域的速度和能力跟不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，这使他们无法看到并应用重要的相似之处来解决不同领域的可比问题。这项研究将开发出在多个人之间分配类比处理的方法，而不是依靠单个人来寻找和应用类比。改进的创新过程的发展可以加速技术创新和科学发现，并相应地为经济和科学带来好处。一般的方法是进行实证研究，严格探索和描述分布式类比创新的好处和局限性，然后在两个现实世界的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人可能有许多显著的优势。首先，增加参与的人数可以提高在许多领域发现和使用类比的能力。第二，改变问题和解决方案的表征方式可以帮助创新者从遥远的领域看到解决方案的相关性，并减少固定问题，即人们过度受表面特征的影响。第三，通过将类比所涉及的步骤分配给不同的人，可以并行地有效探索其他创新途径。第四，将创新管道分解，让更多的人参与进来;例如，要在遥远的领域找到模拟解决方案，人们不需要是某个问题领域的专家。第五，与只奖励少数竞赛获胜者的创新竞赛相比，本研究中使用的顺序方法允许人们在彼此的工作基础上建立，而不会浪费绝大多数参与者的劳动。

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.