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在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为智能手机提供了支持，而智能手机又与社交媒体协同作用，推动了新型软件的开发。与此同时，通过对单一技术的长期观察，可以看到性能的不断改进。与第一代相比，目前的智能手机存储更多的照片，并且无需充电即可使用更长的时间。EARLY概念探索性研究奖（EAGER）支持基础研究，以创建描述技术性能演变的数学理论，同时，技术之间的生态协同作用。创建的理论模型将具有足够的保真度和因果指标，以便人们可以预测和影响创新。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，通过了解新技术的影响和预测新技术的演变，决策者可以为关键的研究举措做出合理的投资和供资决定。加深了解还将有助于公共政策官员为今后的技术发展制定适当的规章和激励结构。 采用广义Lotka-Volterra（L-V）种群模型建立了技术及其相互作用的技术生态系统模型。这项工作的智力影响包括对L-V方程的一般格式进行扩展，将它们与微尺度技术性能改进（单一技术的改进性能）联系起来。微尺度技术演化模型将遵循S曲线趋势，但实际上是用L-V方程建立的。这个技术进化的一般模型将使用来自发明问题解决理论（TIPS）的理论进行扩展。假设是，这种多尺度，层次，L-V模型将能够捕捉生态系统类型的工程产品和技术的相互作用，以及性能的演变。