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; 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《美国复苏与再投资法》（公法111-5）资助的。竞争如何影响创新？该项目采用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仿真平台，以了解竞争动态如何影响创新。这种方法为技术战略和科学技术政策的变革性新工具提供了新的知识。智力优点：有三个主要贡献。首先，开发和评估了与竞争代理相互作用复杂的环境的计算多代理搜索理论。当多个代理同时搜索并开发其计算表示时，结果有助于了解搜索创新。其次，关于创新和竞争的定性观察是通过在真正的基于技术的行业中的结构化访谈做出的。目的是为技术策略中持久问题提供理论上知情的答案：竞争市场中最有效的公司级创新搜索策略是什么？第三，通过整合该项目的计算和策略组成部分的洞察力来开发一种称为ASAP的软件研究工具。该模拟工具使对行业数据进行“实时”模拟，类似于工程中的物理模拟。 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.为了促进此类进展，ASAP工具通过网站公开向学者和从业人员公开使用，从而降低了一般计算分析的进入障碍，并尤其是对业务数据的预测分析。在当前艰难的经济时期，保持创新性比以往任何时候都更为重要，既可以抵抗更深层的衰退并将我们的技术经济重新回到增长轨迹。 ASAP工具是数据的实时模型，因此可以用来识别生成数据的事件的顺序，并进行了修改以找出如果某些因素（例如支持某些因素（例如支持该行业中某些参与者而不是其他因素））的情况有所不同。从本质上讲，尽快可以在类似实验室的实验中进行交互性研究档案数据，因此它可以导致对科学技术政策的见解，而这些政策是无法获得的。