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A Second-Order Adaptive Network Model for Organizational Learning and Usage of Mental Models for a Team of Match Officials

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Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

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Abstract

This paper describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organizational learning in team-related performances. The paper describes the value of using shared mental models to illustrate the concept of organizational learning, and factors that influence team performances by using the analogy of a team of match officials during a game of football and show their behavior in a simulation of the shared mental model. The paper discusses potential elaborations of the different studied concepts, as well as implications of the paper in the domain of teamwork and team performance, and in terms of organizational learning.

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References

  • Abraham, W.C., Bear, M.F.: Metaplasticity: the plasticity of synaptic plasticity. Trends Neurosci. 19(4), 126–130 (1996)

    Article  Google Scholar 

  • Boyko, R.H., Boyko, A.R., Boyko, M.G.: Referee bias contributes to home advantage in English premiership football. J. Sports Sci. 25(11), 1185–1194 (2007)

    Google Scholar 

  • Canbaloğlu, G., Treur, J.: Context-sensitive mental model aggregation in a second-order adaptive network model for organisational learning. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds.) COMPLEX NETWORKS 2021. SCI, vol. 1015, pp. 411–423. Springer, Cham (2022a). https://doi.org/10.1007/978-3-030-93409-5_35

  • Canbaloğlu, G., Treur, J.: Using boolean functions of context factors for adaptive mental model aggregation in organisational learning. In: Klimov, V.V., Kelley, D.J. (eds.) BICA 2021. SCI, vol. 1032, pp. 54–68. Springer, Cham (2022b). https://doi.org/10.1007/978-3-030-96993-6_5

    Chapter  Google Scholar 

  • Canbaloğlu, G., Treur, J., Roelofsma, P.H.M.P.: Computational modeling of organisational learning by self-modeling networks. Cognit. Syst. Res. J. 73, 51–64 (2022a)

    Google Scholar 

  • Canbaloğlu, G., Treur, J., Roelofsma, P.: An adaptive self-modeling network model for multilevel organizational learning. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds.) ICICT 2022. LNNS, vol. 448, pp. 179–191 (2022b). Springer, Singapore. https://doi.org/10.1007/978-981-19-1610-6_16

  • Canbaloğlu, G., Treur, J., Wiewiora, A.: Computational modeling of multilevel organisational learning: from conceptual to computational mechanisms. In: Proceedings of Computational Intelligence: Automate Your World. The Second International Conference on Information Technology, InCITe 2022. Lecture Notes in Electrical Engineering, Springer (2022c)

    Google Scholar 

  • Craik, K.J.W.: The nature of explanation. CUP Archive, vol. 445 (1952)

    Google Scholar 

  • Crossan, M.M., Lane, H.W., White, R.E.: An organizational learning framework: From intuition to institution. Acad. Manag. Rev. 24, 522–537 (1999)

    Article  Google Scholar 

  • Gomez-Carmona, C., Pino-Ortega, J.: Kinematic and physiological analysis of the performance of the referee football and its relationship with decision making. J. Hum. Sport Exerc. 11(4), 397–414 (2016)

    Google Scholar 

  • Heil, J.: Philosophy of Mind. Routledge (1998)

    Google Scholar 

  • Katz-Navon, T.Y., Erez, M.: When collective-and self-efficacy affect team performance: the role of task interdependence. Small Group Res. 36(4), 437–465 (2005)

    Google Scholar 

  • Kim, D.H.: The link between individual and organizational learning. Sloan Manag. Rev. 33(1), 37–50 (1993)

    Google Scholar 

  • Mathieu, J.E., et al.: The influence of shared mental models on team process and performance. J. Appl. Psychol. 85(2), 273 (2000)

    Google Scholar 

  • Salas, E., Cooke, N.J., Rosen, M.A.: On teams, teamwork, and team performance: discoveries and developments. Hum. Factors 50(3), 540–547 (2008)

    Google Scholar 

  • Treur, J.: A modeling environment for reified temporal-causal networks: modeling plasticity and metaplasticity in cognitive agent models. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds.) PRIMA 2019. LNCS, vol. 11873, pp. 487–495. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33792-6_33

  • Treur, J.: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31445-3

  • Treur, J.: On the dynamics and adaptivity of mental processes: relating adaptive dynamical systems and self-modeling network models by mathematical analysis. Cogn. Syst. Res. 70, 93–100 (2021)

    Article  Google Scholar 

  • Van Ments, L., Treur, J., Klein, J., Roelofsma, P.H.M.P.: A computational network model for shared mental models in hospital operation rooms. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS, vol. 12960, pp. 67–78. Springer, Cham (2021a). https://doi.org/10.1007/978-3-030-86993-9_7

  • Van Ments, L., Treur, J., Klein, J., Roelofsma, P.H.M.P.: A second-order adaptive network model for shared mental models in hospital teamwork. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds.) ICCCI 2021. LNCS (LNAI), vol. 12876, pp. 126–140. Springer, Cham (2021b). https://doi.org/10.1007/978-3-030-88081-1_10

  • Treur, J., Van Ments, L. (eds.): Mental Models and their Dynamics, Adaptation, and Control: a Self-modeling Network Modeling Approach. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-85821-6

  • Van Ments, L., Treur, J.: Reflections on dynamics, adaptation, and control: a cognitive architecture for mental models. Cogn. Syst. Res. 70, 1–9 (2021)

    Article  MATH  Google Scholar 

  • Wiewiora, A., Smidt, M., Chang, A.: The ‘how’ of multilevel learning dynamics: a systematic literature review exploring how mechanisms bridge learning between individuals, teams/projects and the organization. Eur. Manag. Rev. 16, 93–115 (2019)

    Article  Google Scholar 

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Correspondence to Jan Treur .

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Kuilboer, S., Sieraad, W., Canbaloğlu, G., van Ments, L., Treur, J. (2022). A Second-Order Adaptive Network Model for Organizational Learning and Usage of Mental Models for a Team of Match Officials. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_55

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_55

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