Quantum simulation of strategic decision-making: A mechanism for rapid response to optimal situational awareness in the business ecosystem

Document Type : Research Paper

Authors
1 Assistant Professor, Department of Business Strategy and Policy, School of Management, University of Tehran, Tehran, Iran.
2 PhD in Business Policy Management, University of Tehran, Tehran, Iran.
3 PhD student in Business Administration, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
4 Master's degree student in Software Engineering, Imam Khomeini International University, Qazvin, Iran.
Abstract
The accelerating pace of change in business ecosystems has exposed the limitations of conventional strategic decision-making models. This article rethinks strategic decision-making by proposing a cognitive–quantum simulation framework that conceptualizes decisions not as instantaneous events but as dynamic, gradual, and path-dependent processes. The study assumes that pre-decisional states—particularly non-decision—play an active and constitutive role in shaping decisions and cannot be reduced to simple delay or information scarcity. Methodologically, the research employs adaptive computational simulation to compare classical Markovian dynamics with cognitive–quantum dynamics within a shared analytical framework. Decision-making is modeled as the system’s gradual transition out of a state of non-decision, enabling explicit examination of decision timing, cognitive evidence evolution, and path dependence. The findings show that although both frameworks lead to decision outcomes, their temporal logics differ fundamentally. Markovian models display uniform, memoryless decision behavior, whereas cognitive–quantum dynamics generate decisions through the gradual stabilization of cognitive states following periods of oscillation and suspension. These results highlight the importance of pre-decisional dynamics in strategic decision-making and strategy selection under high uncertainty.
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  • Receive Date 27 January 2026
  • Revise Date 16 February 2026
  • Accept Date 18 February 2026

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