EAGER: A Quantitative Theory for Technology Evolution and Innovation

EAGER：技术进化与创新的定量理论

• 批准号: 1550002
• 负责人: Daniel McAdams
• 金额: $ 14.82万
• 依托单位: Texas A&M Engineering Experiment Station
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550002&HistoricalAwards=false
• 关键词: EAGER; Quantitative; Theory; Technology; Evolution

Technology innovation is critical to the social welfare, health, economy, and security of the USA and the world. A fundamental understating of innovation is elusive. It can be observed, but it is hard to predict, and even harder to execute. The difficulty in understanding and executing innovation rests in part on the complexity of the technology ecosystem. For example, the invention and development of Corning's Gorilla Glass supports the smartphone which in turn synergizes with social media to drive new types of software development. At the same time, observation of a single technology through time shows an evolutionary improvement in performance. Compared to the first generation, current smartphones store more photographs and operate longer without the need for charging. This EArly-Concept Grant for Exploratory Research (EAGER) award supports fundamental research to create a mathematical theory that describes technology performance evolution and, simultaneously, ecological synergies between technologies. The created theoretical model will have sufficient fidelity and causal indicators such that one can predict and effect innovation. This new theory will inform individual and enterprise-scale technology innovation. Also, by knowing the impact and by predicting the evolution of new technologies, policy makers can make sound investment and funding decisions for key research initiatives. The enhanced understanding will also help public policy officials develop appropriate regulations and incentive structures for future technology development. The technology ecosystem model of technologies and their interaction is modeled using generalized Lotka-Volterra (L-V) population models. The intellectual impact of this work includes the extensions made to the general format of L-V equations to connect them to microscale technology performance improvement (the improved performance of a single technology). The microscale technology evolution model will follow S-curve trends, but in fact be created with L-V equations. This general model of technology evolution will be extended using theories from The Theory of Inventive Problem Solving (TIPS). The hypothesis is that this multiscale, hierarchical, L-V model will be able to capture the ecosystem type interaction of engineered products and technologies as well as performance evolution.

技术创新对于美国和世界的社会福利、健康、经济和安全至关重要。对创新的基本理解是难以捉摸的。它可以被观察到，但很难预测，更难执行。理解和执行创新的困难部分取决于技术生态系统的复杂性。例如，康宁大猩猩玻璃的发明和开发支持了智能手机，而智能手机又与社交媒体协同作用，推动新型软件的开发。与此同时，随着时间的推移对单一技术的观察显示出性能的进化改进。与第一代相比，当前的智能手机可以存储更多照片，并且无需充电即可运行更长时间。这项探索性研究早期概念资助 (EAGER) 奖项支持基础研究，以创建描述技术性能演变以及同时技术之间的生态协同作用的数学理论。创建的理论模型将具有足够的保真度和因果指标，以便人们能够预测和影响创新。这一新理论将为个人和企业规模的技术创新提供信息。此外，通过了解影响并预测新技术的演变，政策制定者可以为关键研究计划做出合理的投资和资助决策。加深的理解还将帮助公共政策官员为未来的技术发展制定适当的法规和激励结构。 技术及其相互作用的技术生态系统模型使用广义 Lotka-Volterra (L-V) 群体模型进行建模。这项工作的智力影响包括对 L-V 方程一般格式的扩展，以将它们与微尺度技术性能改进（单一技术的性能改进）联系起来。微观技术演化模型将遵循S曲线趋势，但实际上是用L-V方程创建的。这种技术进化的一般模型将使用创造性问题解决理论（TIPS）中的理论进行扩展。假设这种多尺度、分层、L-V 模型将能够捕获工程产品和技术的生态系统类型交互以及性能演变。