A Conceptual Framework for Cognitive Digital Twins in Urban Waste Management

Authors

  • Hamidreza Zahedi * School of Management, Economics, and Progress Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. https://orcid.org/0009-0007-8232-4234
  • Rahim Khanizad School of Management, Economics, and Progress Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.

https://doi.org/10.22105/raise.v3i1.86

Abstract

Urban waste management remains a persistent challenge for smart city initiatives, which largely rely on reactive and correlation-based decision support systems. This study proposes a conceptual framework for Cognitive Digital Twins (CDT) in urban waste management that aims to support causal reasoning and adaptive governance. The framework integrates sensing, simulation, causal inference, learning, and human oversight within a unified architecture designed to represent urban waste dynamics as an evolving system. The proposed architecture consists of five functional layers: a sensor and actuator layer for real-time data acquisition, a dynamic simulation layer representing the urban waste system, a causal reasoning layer for diagnosis and counterfactual analysis, a learning layer based on multi-agent reinforcement learning for adaptive policy exploration, and a human interaction layer ensuring transparency and supervisory control. Within this framework, non-parametric Bayesian networks can be applied to identify latent socioeconomic and temporal drivers of waste generation and to construct a structural causal representation of urban waste processes. The learning layer is intended to explore cooperative and adaptive collection strategies under multiple objectives related to efficiency, equity, and service reliability. While empirical validation remains a future step, conceptual analysis and reference to prior literature suggest that the framework has the potential to improve operational understanding and support more adaptive management strategies. Beyond methodological considerations, the framework highlights key governance aspects, including interpretability of algorithmic decisions, alignment of optimization objectives with public values, and the role of human supervision in autonomous urban systems. The findings suggest that CDTs can provide a structured approach for integrating causal modeling and adaptive learning into urban waste management while supporting accountable and participatory governance.

Keywords:

Cognitive digital twin, Causal artificial intelligence, Multi-agent reinforcement learning, Urban waste management, Structural causal model

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Published

2026-03-13

Issue

Vol. 3 No. 1 (2026): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Zahedi, H. ., & Khanizad, R. . (2026). A Conceptual Framework for Cognitive Digital Twins in Urban Waste Management. Research Annals of Industrial and Systems Engineering, 3(1), 39-55. https://doi.org/10.22105/raise.v3i1.86

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