EAGER: A Quantitative Theory for Technology Evolution and Innovation

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。对创新的根本轻描淡写是难以捉摸的。这是可以观察到的，但很难预测，更难执行。理解和实施创新的困难在一定程度上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智能手机，智能手机反过来又与社交媒体协同推动新型软件开发。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渐进式改进。与第一代相比，目前的智能手机存储的照片更多，续航时间更长，不需要充电。这一早期概念探索性研究(AGER)奖支持基础研究，以创建一种描述技术性能演变的数学理论，同时描述技术之间的生态协同效应。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实施创新。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，通过了解影响和预测新技术的发展，政策制定者可以为关键研究项目做出合理的投资和资金决策。加深了解还将有助于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。利用推广的洛特卡-沃尔特拉(L-V)种群模型对技术及其相互作用的技术生态系统模型进行了建模。这项工作的智力影响包括对L-V方程的一般格式进行的扩展，以将它们与微尺度技术性能改进(单一技术性能的改进)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方程创建的。这一技术进化的一般模型将使用发明问题解决理论(TIPS)中的理论进行扩展。假设这个多尺度、分层的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。

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.