Dialectical Synthesis of Laboratory and Policy Simulator in the Light of Actor-Network Theory: Reciprocal Calibration and Black Box Formation in the Governance of Complex Adaptive Systems

Dalir, Ramin; Hassani Ahangar, Ali; Aghayani, Mohammadamin; Nasirzadeh, Mohammad; Miri Zarandi, Mojtaba

Dialectical Synthesis of Laboratory and Policy Simulator in the Light of Actor-Network Theory: Reciprocal Calibration and Black Box Formation in the Governance of Complex Adaptive Systems

Articles in Press

Document Type : Research Articles

Authors

Ramin Dalir ¹

Ali Hassani Ahangar ²

Mohammadamin Aghayani ³

Mohammad Nasirzadeh ⁴

Mojtaba Miri Zarandi ⁵

¹ Artificial Intelligence Center, Faculty of Artificial Intelligence and Cognitive Sciences, Imam Hussein (AS) University

² Faculty of Management, Imam Hossein University (IHU), Tehran, Iran

³ Faculty of Management, Imam Sadiq University (ISU), Tehran, Iran

⁴ Faculty of Literature and Humanities, Kharazmi University (KHU)

⁵ Faculty of Governance, University of Tehran (UT), Tehran, Iran

Abstract

The root of governance dysfunction lies in the epistemological rupture between the linear logic of traditional decision models and the nonlinear, feedback-driven nature of complex adaptive systems. This gap predisposes policy laboratories to environmental reductionism and simulators to algorithmic rigidity. The “policy laboratory–simulator” model is proposed as an optimal decision-support mechanism for adapting to such systems. The central question is how methodological synthesis between the behavioral capacities of the laboratory and the computational power of the simulator can yield an integrated governance framework for radical uncertainty. Drawing on Actor–Network Theory, this study tests the hypothesis that dialectical integration through reciprocal calibration generates an adaptive decision-making ecosystem. By addressing the blind spots of both approaches—environmental reductionism and algorithmic rigidity—this ecosystem enables the observation of emergent properties and the prediction of systemic breakdown points. The findings indicate that this methodological synergy stabilizes the agency of computational models alongside human actors, bridging the gap between mathematical abstraction and concrete governance reality. Consequently, the proposed model facilitates a transition from technocratic management to wisdom-based governance in complex systems.

Keywords

Complex Adaptive Systems

Policy Laboratory

Policy Simulator

Actor-Network Theory

Reciprocal Calibration

Subjects