 Complex evolving social systems: unending, imperfect learningProfessor Peter Allen Chapter 2 in Handbook of Research Methods in Complexity Science, 2018, pp 18-44 from Edward Elgar Publishing Abstract: A methodology implies a purpose which here is to reveal and understand what patterns and structures exist in social systems and how, why and when they occur. In the natural sciences, we can perform repeatable experiments that allow us to find robust general laws by induction and make predictions about specific behaviour by deduction. In social systems, however, agents inhabiting a situation are really in co-evolution with each other and their environment, hence constantly changing over time. This makes induction for general laws much harder and predictions on the basis of deduction questionable. Complexity Science provides a ‘scientific’ basis for evolutionary, qualitative changes, revealing the impossibility of guaranteed prediction. We use several examples to show how complexity and evolution involve changing systems of changing elements – both qualitative and quantitative. Our interpretive frameworks (models) do not make predictions about the world but about themselves thus making, through reflexivity, evasive action more likely. Keywords: Business and Management; Geography; Innovations and Technology; Politics and Public Policy Research Methods; Urban and Regional Studies (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:16937_2 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in Chapters from Edward Elgar Publishing |
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