The Empirical Investigation of Non-Linear Dynamics in the Social World. Ontology, Methodology and Data
DOI:
https://doi.org/10.6092/issn.1971-8853/10819Keywords:
Non-linear dynamics, co-evolving systems, autocatalytic sets, patents and technological innovation, contingent historical changeAbstract
Across much of social science, linear models hold sway, but they have significant limitations. This article makes the case for studying social processes as co-evolving systems, involving non-linear dynamics. Co-evolution of species in the natural world is a blind process. In the social world in contrast, purposeful interventions by social actors are omnipresent, in their struggles for positional advantage. The article brings together co-evolving networks and purposeful social action in the "Contingent Historical Model". We seek to apply this model in ways that engage with both scholarly and policy concerns. 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.References
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