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State Space Truncation: How Algorithmic Control Collapses the Behavioural Repertoire in Organisational Networks Cover

State Space Truncation: How Algorithmic Control Collapses the Behavioural Repertoire in Organisational Networks

Open Access
|Jul 2026

References

  1. Abdul Nabi, M., Elsayegh, A., & El-adaway, I. H. (2023). Understanding Collaboration Requirements for Modular Construction and Their Cascading Failure Impact on Project Performance. Journal of Management in Engineering, 39.
  2. Albarracin, M., Hipolito, I., Raffa, M., & Kinghorn, P. (2024). Modeling Sustainable Resource Management using Active Inference. arXiv preprint arXiv:2406.07593.
  3. Burns, A. J., Posey, C., & Roberts, T. L. (2021). Insiders’ Adaptations to Security-Based Demands in the Workplace: An Examination of Security Behavioral Complexity. Information Systems Frontiers, 23(2), 343-360.
  4. Fox, B. C., Simsek, Z., & Heavey, C. (2022). Top Management Team Experiential Variety, Competitive Repertoires, and Firm Performance: Examining the Law of Requisite Variety in the 3D Printing Industry (1986–2017). Academy of Management Journal, 65(2).
  5. Fox, S. (2021). Active inference: Applicability to different types of social organization explained through reference to industrial engineering and quality management. Entropy, 23(2), 198.
  6. Heins, R. C., Millidge, B., Demekas, D., & Tschantz, A. (2022). pymdp: A Python library for active inference in discrete state spaces. Journal of Open Source Software, 7(73), 4098.
  7. Heins, R. C., Millidge, B., Da Costa, L., & Couzin, I. D. (2024). Collective behavior from surprise minimization. Proceedings of the National Academy of Sciences, 121.
  8. Ionescu, Ș., Delcea, C., Chiriță, N., & Nica, I. (2024). Exploring the Use of Artificial Intelligence in Agent-Based Modeling Applications: A Bibliometric Study. Algorithms, 17(1), 21.
  9. Michel, F., & Siegle, M. (2025). Formal error bounds for the state space reduction of Markov chains. Performance Evaluation, 167, 102464.
  10. Miller, M., Albarracin, M., Pitliya, R. J., & Ramstead, M. J. D. (2022). Resilience and active inference. Frontiers in Psychology, 13, 1059117.
  11. Muller, E., Neukam, M., Martins-Nourry, L., Gnamm, M.-B., Djuricic, K., Raffin, D., & Burger-Helmchen, T. (2025). Requisite Resilience: Towards a Definition. evoREG Research Note #50. Fraunhofer ISI.
  12. Parr, T., Pezzulo, G., & Friston, K. J. (2022). Active Inference: The Free Energy Principle in Mind, Brain and Behavior. MIT Press.
  13. Rani, U., Pesole, A., & Gonzalez Vazquez, I. (2024). Algorithmic Management practices in regular workplaces: case studies in logistics and healthcare. Publications Office of the European Union.
  14. Sedlak, B., Casamayor Pujol, V., Morichetta, A., Donta, P. K., & Dustdar, S. (2024). Adaptive Stream Processing on Edge Devices through Active Inference. arXiv preprint arXiv:2409.17937.
  15. Shastry, V., Reeves, D. C., Willems, N., & Rai, V. (2022). Policy evaluation using agent-based modeling for shock events. PLOS One, 17(1), e0262172.
  16. Wood, A. J. (2021). Algorithmic management consequences for work organisation and working conditions. Joint Research Centre Working Papers Series on Labour, Education and Technology, 2021/07.
Language: English
Page range: 6159 - 6172
Published on: Jul 23, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Alin-Marius MATEI, Augustin SEMENESCU, Alexandru Dorian FĂINĂ, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.