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Critical realism and complexity theory: Building a nonconstructivist systems research framework for effective governance analysis
Author(s) -
Yang Yi
Publication year - 2019
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2662
Subject(s) - conflation , corporate governance , epistemology , dilemma , critical realism (philosophy of perception) , constitution , computer science , process (computing) , realism , sociology , management science , positive economics , economics , political science , management , law , philosophy , operating system
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).