Collaborative Research: Improving the Validity and Reliability of Creativity Ratings in Engineering Design

没有创造力，就没有创新的潜力。因此，创造力被视为工程设计的重要组成部分。已经开发了许多创造力指标来评估人们产生的设计的创造力。这些指标对于评估商业和工程实践非常重要，这是提高美国创新和经济竞争力的关键部分。然而，现有的指标通常分散在不同的专业领域，很难验证它们准确衡量所产生的各种工程解决方案的创造性的能力。此外，过去的研究提供了有限的指导，如何使用和验证一个给定的设计问题的创造性指标。这导致了普遍应用的指标，而没有系统地评估在何处以及何时给定的指标适合于给定的任务。该奖项支持基础研究如何评估不同类型产品或服务的不同创造力指标的有效性。该项目通过统一统计模型来实现这一点，这些模型可以通过实验验证不同的创造性指标在设计领域中的表现。因此，这项工作将通过提供一种可验证的方法来确定什么能提高创造力，什么不能提高创造力，从而对社会产生影响。由于创造力和创新是经济成功的驱动力，因此工作有机会推动设计创新，并作为一种双产品，有助于刺激经济。此外，作为研究的一部分开发的统一统计框架将通过提供这种方法测量准确性和精度的实用性的证据来推进工程设计和应用数学领域。该研究涉及多个学科，包括工程学，心理学和应用数学。多学科方法将有助于扩大代表性不足的群体对研究的参与。该项目的技术目标是：（1）通过开发一个统一的统计框架来评估创造力指标的有效性，该框架结合了两种技术--极大极小条件熵原理和Lovasz-Bregman分歧--比较给定问题不同指标的准确性和精确度;以及（2）通过Lovasz-Bregman分歧的方差和置信度测量，实验性地验证用于验证跨不同设计域的创造性度量的传递以识别跨设计和系统工程内的不同应用鲁棒的度量的方法。该项目的成果将通过开发一种统一的方法来测量和比较创造力的数学，计算和人类判断模型来推进工程领域。这些新知识将提供一个严格的基础，在此基础上建立和验证各种设计学科的设计创造力方法（例如，艺术和建筑、心理学、工程学、商业）。

Without creativity, there is no potential for innovation. Consequently, creativity is seen as an essential component of engineering design. Numerous creativity metrics have been developed to assess the creativity of designs people produce. Such metrics are important for assessing business and engineering practices, a key part of improving US innovation and economic competitiveness. However, existing metrics are often scattered across different, specialized domains and it is difficult to validate their ability to accurately measure the creativity of the wide variety of engineered solutions that are produced. In addition, past research provides limited guidance on how to use and validate creativity metrics for a given design problem. This has led to universally applying metrics without systematic assessment of where and when a given metric is appropriate for a given task. This award supports fundamental research into how to evaluate the effectiveness of different creativity metrics for different types of products or services. This project does so by unifying statistical models that can experimentally validate how well different creativity metrics perform across design domains. Thus, the work will impact society by providing a verifiable method for identifying what does and does not improve creativity. Because creativity and innovation are the drivers of economic success, the work has the opportunity to drive design innovation and, as a bi-product, help stimulate the economy. In addition, the unified statistical framework developed as part of the research will advance the field of engineering design and applied mathematics by providing evidence on the utility of this approach for measuring accuracy and precision. The research involves several disciplines including engineering, psychology and applied mathematics. The multidisciplinary approach will help broaden participation of underrepresented groups in research.The technical objectives of this project are to: (1) evaluate the effectiveness of creativity metrics through the development of a unified statistical framework that combines two techniques--the minimax conditional entropy principle and the Lovasz-Bregman Divergence--to compare the accuracy and precision of different metrics for a given problem; and (2) experimentally validate a methodology for validating the transfer of creativity metrics across different design domains to identify metrics that are robust across different applications within design and systems engineering via variance and confidence measures of the Lovasz-Bregman Divergence. The results from this project will advance the field of engineering by developing a unified method of measuring and comparing mathematical, computational, and human-judgment models of creativity. This new knowledge will provide a rigorous foundation upon which to build and verify methods of design creativity across a wide variety of design disciplines (e.g., arts and architecture, psychology, engineering, business).