Collaborative Research: MOD and TLS: A Predictive Simulation Model of Competitive Dynamics in Innovation

合作研究：MOD 和 TLS：创新竞争动态的预测模拟模型

• 批准号: 0915236
• 负责人: Riitta Katila
• 金额: $ 18.7万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915236&HistoricalAwards=false
• 关键词: Collaborative; Research; MOD; TLS; Predictive

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). How does competition influence innovation? This project takes a unique approach to understand this question: It develops an evidence-based simulation platform to understand how competitive dynamics influences innovation. This approach contributes new knowledge to technology strategy and a transformative new tool for science and technology policy. Intellectual Merit: There are three main contributions. First, a computational multi-agent search theory is developed and evaluated for environments with complex interactions of competing agents. The results help understand search for innovation when multiple agents search simultaneously, and develop computational representations of it. Second, qualitative observations on innovation and competition are made through structured interviews in a real technology-based industry. The goal is to provide theoretically informed answers to an enduring question in technology strategy: What are the most effective firm-level innovation search strategies in competitive markets? Third, a software research tool called ASaP is developed by integrating insights from the computational and strategy components of this project. This simulation tool makes it possible to perform "live" simulations of industry data, similar to physical simulations in engineering. Scholars and public policy makers can use this tool to understand the ways in which their recommendations are likely to play out and therefore help design effective policies to manage competition and simultaneously promote innovation.Broader Impacts: The results of the project can be used not only by academics to analyze competition and innovation rigorously but also by business and policy analysts to improve innovativeness of firms and industries, and thus ultimately advance economic productivity. To foster such progress, the ASaP tool is made publicly available to scholars and practitioners through a website, thus lowering the barrier of entry to computational analysis in general, and predictive analysis of business data in particular. During the current difficult economic times, it is more important than ever to remain innovative, both to resist deeper downturn and to bring our technology-based economy back to a growth trajectory. The ASaP tool serves as a live model of the data, and thus can be used to identify the sequence of events that generated the data, and modified to find out what would have happened if some factors (such as supporting the livelihood of certain players in the industry over others) had been different. In essence, ASaP makes it possible to study the archival data interactively in laboratory-like experiments, and it can therefore lead to insights on science and technology policy that are not possible to obtain otherwise.

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。竞争如何影响创新？这个项目采用了一种独特的方法来理解这个问题：它开发了一个基于证据的模拟平台，以了解竞争动态如何影响创新。这种方法为技术战略提供了新的知识，为科学技术政策提供了一种变革性的新工具。智力价值：有三个主要贡献。首先，发展了一种计算多智能体搜索理论，并对竞争智能体之间存在复杂交互作用的环境进行了评估。结果有助于理解当多个代理同时搜索时搜索创新，并开发它的计算表示。其次，对创新和竞争的定性观察是通过在一个真实的以技术为基础的行业中进行的结构化访谈进行的。其目标是为技术战略中的一个长期存在的问题提供理论上明智的答案：在竞争激烈的市场中，什么是最有效的企业层面的创新搜索战略？第三，通过整合该项目的计算和策略部分的见解，开发了一个名为ASAP的软件研究工具。该模拟工具使执行行业数据的“实时”模拟成为可能，类似于工程中的物理模拟。学者和公共政策制定者可以使用这个工具来了解他们的建议可能会如何发挥作用，从而帮助设计有效的政策来管理竞争，同时促进创新。广泛的影响：该项目的结果不仅可以被学者用来严格分析竞争和创新，也可以被商业和政策分析师用来提高公司和行业的创新能力，从而最终提高经济生产率。为了促进这一进展，通过网站向学者和从业人员公开提供ASAP工具，从而降低了一般计算分析，特别是商业数据预测性分析的准入门槛。在当前的经济困难时期，保持创新比以往任何时候都更加重要，既要抵御更深层次的衰退，也要让我们以科技为基础的经济重新回到增长轨道。ASAP工具充当数据的实时模型，因此可以用来识别生成数据的事件序列，并进行修改以找出如果某些因素(如支持行业中某些参与者的生计)不同会发生什么。从本质上讲，ASAP使在实验室般的实验中以交互方式研究档案数据成为可能，因此它可以导致对科学和技术政策的洞察，而这是以其他方式无法获得的。