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

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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.