Abstract

Across much of social science, quantitative methods involving linear regression reign supreme. This article makes the case for studying co-evolving systems, as a form of non-linear dynamics. If such investigations are to be fruitful, they must not only be elaborated theoretically, they must also be applied to empirical datasets. This article considers how this can be done, with what sorts of data sets and what forms of data analysis. It takes as its specific example the international datasets on patents, as revealing processes and patterns of technological innovation. It shows how such an approach can illuminate scholarly debates and develop indicators for policy makers. Finally, it offers an agenda for research into dynamic co-evolving systems across other empirical areas.
Original languageEnglish
Number of pages24
Publication statusSubmitted - 21 Feb 2019

Cite this

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title = "THE EMPIRICAL INVESTIGATION OF NON-LINEAR DYNAMICS IN THE SOCIAL WORLD – ONTOLOGY, METHODOLOGY AND DATA",
abstract = "Across much of social science, quantitative methods involving linear regression reign supreme. This article makes the case for studying co-evolving systems, as a form of non-linear dynamics. If such investigations are to be fruitful, they must not only be elaborated theoretically, they must also be applied to empirical datasets. This article considers how this can be done, with what sorts of data sets and what forms of data analysis. It takes as its specific example the international datasets on patents, as revealing processes and patterns of technological innovation. It shows how such an approach can illuminate scholarly debates and develop indicators for policy makers. Finally, it offers an agenda for research into dynamic co-evolving systems across other empirical areas.",
author = "Graham Room",
year = "2019",
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day = "21",
language = "English",

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AB - Across much of social science, quantitative methods involving linear regression reign supreme. This article makes the case for studying co-evolving systems, as a form of non-linear dynamics. If such investigations are to be fruitful, they must not only be elaborated theoretically, they must also be applied to empirical datasets. This article considers how this can be done, with what sorts of data sets and what forms of data analysis. It takes as its specific example the international datasets on patents, as revealing processes and patterns of technological innovation. It shows how such an approach can illuminate scholarly debates and develop indicators for policy makers. Finally, it offers an agenda for research into dynamic co-evolving systems across other empirical areas.

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