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Critical realism and complexity theory: Building a nonconstructivist systems research framework for effective governance analysis

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  • Yi Yang

Abstract

Complexity theory (CT) intends to reveal public management's unpredictable side but its empirical applications are scarce due to prevailing constructivist approaches that collapse larger systematic outcomes into micro agent actions and conflate them into movements of co‐constitution and co‐evolution, precluding effective analysis. How can we capture emergent properties and outcomes if we cannot delineate objects and subjects? How can we attribute causes and effects without fixed, stable entities? To address this significant weakness of theorization for a more effective CT framework, Margaret Archer's critical realist model solves this constructivist conflation dilemma—agents and systems, though interrelated, are distinct and stable entities—public management and governance process is thus the result of emergence from a learning‐to‐control process for rule‐makers, with structurally unpredictable inputs from the ruled whereby systematic features predate and condition (Time 1) individual actions, which can maintain or reproduce them (Time 3) during agent–system interactive processes (Time 2).

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  • Yi Yang, 2021. "Critical realism and complexity theory: Building a nonconstructivist systems research framework for effective governance analysis," Systems Research and Behavioral Science, Wiley Blackwell, vol. 38(1), pages 177-183, January.
    • Handle: RePEc:bla:srbeha:v:38:y:2021:i:1:p:177-183
    DOI: 10.1002/sres.2662
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    References listed on IDEAS

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    1. Darren Nel & Araz Taeihagh, 2024. "The soft underbelly of complexity science adoption in policymaking: towards addressing frequently overlooked non-technical challenges," Policy Sciences, Springer;Society of Policy Sciences, vol. 57(2), pages 403-436, June.

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