Simulation models of science are an excellent basis for studying innovation processes. There are a number of reasons for this. First, science exemplifies innovation as a social and collaborative process. Not only are scientific advances the product of teams of researchers in laboratories, workshops, libraries and in the field, but also contributions build upon the past work of other scientists, ‘standing on the shoulders of giants’, as Newton once put it, but also on the shoulders of vast numbers of more modestly talented workers. Second, much scientific work leaves data trails, through which we can seek to trace its processes. The data includes the existence of institutions, such as industrial laboratories, universities and research centres, but the two most useful sources of data are academic publications and patents. Both publication and patent data can record multiple authors, indicating who collaborated with whom, and citations to previous publications or patents, indicating intellectual debts.
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