PSA2016: The 25th Biennial Meeting of the Philosophy of Science Association

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Detecting and Explaining Innovations in Science with Big-Data Computational Methods and Modeling

In this paper we develop a conceptual framework for analyzing the history scientific innovations that combines an extended evolution theory with network analysis and signal detection and agent based modeling applied to a number of case studies in the history of modern biomedical research. These formal techniques are embedded within a conceptual framework that analyzes evolutionary processes as transformations of extended regulatory network structures, and is designed to apply to a whole range of phenomena, from genome and biological to cultural and technological evolution.

Author Information:

Manfred Laubichler    
Arizona State University and Santa Fe Institute


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