Abstract
Within organisational learning literature, mental models are considered a vehicle for both individual learning and organisational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organisation is created, which after its formation can then be adopted by individuals. This provides mechanisms for organisational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Argyris Ch., Schön D.A.: Organizational Learning: A Theory of Action Perspective. Addison-Wesley, Reading, MA. (1978)
Bogenrieder, I.: Social architecture as a prerequisite for organizational learning. Manag. Learn. 33(2), 197–216 (2002)
Bazerman, M.H., Giuliano, T., Appelman, A.: Escalation of commitment in individual and group decision making. Organizational Behaviour and Human Performance 33, 141–152 (1984)
Bhalwankar, R., Treur, J.: A Second-Order Adaptive Network Model for Learner-Controlled Mental Model Learning Processes. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds), Proc. of the 9th International Conference on Complex Networks and their Applications. Studies in Computational Intelligence, vol 944, pp. 245–259. Springer Nature Switzerland AG. (2021a)
Bhalwankar, R., Treur, J.: Modeling Learner-Controlled Mental Model Learning Processes by a Second-Order Adaptive Network Model. PLoS ONE 16(8), e0255503 (2021b)
Burthscher, M.J., Kolbe, M., Wacker, J.: Interaction of team mental models and monitoring behaviors predict team performance in simulated anesthesia inductions. J. Exp. Psychol. Appl. 17(3), 257–269 (2011)
Canbaloğlu, G., Treur, J.: Modeling Context-Sensitive Metacognitive Control of Focusing on a Mental Model During a Mental Process. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds), Data Science and Intelligent Systems. Proc. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231, pp 992–1009 Springer Nature (2021)
Canbaloğlu, G., Treur, J., Roelofsma, P.H.M.P.: Computational Modeling of Organisational Learning by Self-Modeling Networks. Cogn. Syst. Res. 73, 51–64 (2022)
Canbaloğlu, G., Treur, J., Wiewiora, A. (eds.), (2023). Computational Modeling of Multilevel Organisational Learning and its Control Using Self-Modeling Network Models (this volume). Springer Nature
Craik, K.J.W.: The nature of explanation. University Press, Cambridge, MA (1943)
Crossan, M.M., Lane, H.W., White, R.E.: An organizational learning framework: From intuition to institution. Acad. Manag. Rev. 24, 522–537 (1999)
DeChurch, L.A., Mesmer-Magnus, J.R.: Measuring shared team mental models. A meta-analysis. In Group Dynamics: Theory, Research, and Practice 14(1), 1–14. (2010)
De Kleer, J., Brown, J.: Assumptions and ambiguities in mechanistic mental models. D. Gentner, A. Stevens (eds.), Mental models pp. 155–190. Hillsdale NJ: Lawrence Erlbaum Associates. (1983)
Dionne, S.D., Sayama, H., Hao, C., Bush, B.J.: The role of leadership in shared mental model convergence and team performance improvement: An agent-based computational model. Leadersh. q. 21, 1035–1049 (2010)
Doyle, J.K., Ford, D.N.: Mental models concepts for system dynamics research. Syst. Dyn. Rev. 14(1), 3–29 (1998)
Fischhof, B., Johnson, S.: Organizational Decision Making. Cambridge University Press, Cambridge (1997)
Furlough, C.S., Gillan, D.J.: Mental models: structural differences and the role of experience. J. Cogn. Eng. Decis. Mak. 12(4), 269–287 (2018)
Gentner, D., Stevens, A.L.: Mental models. Erlbaum, Hillsdale NJ (1983)
Hebb, D.O.: The organisation of behavior: A neuropsychological theory. John Wiley and Sons, New York (1949)
Hendrikse, S.C.F., Treur, J., Koole, S.L.: Modeling Emerging Interpersonal Synchrony and its Related Adaptive Short-Term Affiliation and Long-Term Bonding: A Second-Order Multi-Adaptive Neural Agent Model. International Journal of Neural Systems. https://doi.org/10.1142/S0129065723500387 (2023)
Higgs, A., McGrath, B.A., Goddard, C., Rangasami, J., Suntharalingam, G., Gale, R., Cook, T.M.: Guidelines for the management of tracheal intubation of critically ill adults. Br. J. Anaesth. 120(2), 323–352 (2018)
Hogan, K.E., Pressley, M.E.: Scaffolding student learning: Instructional approaches and issues. Brookline Books. (1997)
Isenberg, D.J.: Group polarisation: a critical review and meta-analysis. J. Pers. Soc. Psychol. 50, 1141–1151 (1986)
Janis, I.L.: Victims of Groupthink. Houghton MiZin, Boston (1972)
Johnson-Laird, P.N.: Mental models: Towards a cognitive science of language, inference, and consciousness. Harvard University Press. (1983)
Jones, P.E., Roelofsma, P.H.M.P.: The potential for social contextual and group biases in team decision making: biases, conditions and psychological mechanisms. Ergonomics 43(8), 1129–1152 (2000)
Kim, D.H.: The Link Between Individual and Organizational Learning. Sloan Management Review, Fall 1993, pp. 37-50. Also in: Klein, D.A. (ed.), The Strategic Management of Intellectual Capital. Routledge-Butterworth-Heinemann, Oxford. (1993)
Kleindorfer, P.R., Kunreuther, H.C., Schoemaker, P.J.H.: Decision Sciences: An Integrated Perspective. Cambridge University Press, Cambridge (1993)
Krueger, J.: On the perception of social consensus. In: Zanna, M.P. (ed.) Advances in experimental social psychology, vol. 30, pp. 163–240. Academic Press, New York (1998)
Lamm, H., Myers, D.G.: Group induced polarisation of attitudes and behaviour. In: Berkowitz, L. (ed.) Advances in experimental social psychology, vol. 11, pp. 145–195. Academic Press, New York (1978)
Langan-Fox, J., Code, S., Langfield-Smith, K.: Team mental models. Techniques, methods, and analytic approaches. Human factors 42(2), 242–271. (2000)
McShane, S.L., von Glinow, M.A.: Organizational Behavior. McGraw-Hill, Boston (2010)
Mathieu, J.E., Hefner, T.S., Goodwin, G.F., Salas, E., Cannon-Bowers, J.A.: The influence of shared mental models on team process and performance. J. of Applied Psychology 85(2), 273–283 (2000)
Nini, M.: All on the same page: How Team Mental Models (TMM) increase team performance. CQ Net (2019) https://www.ckju.net/en/dossier/team-mental-models-increase-team-performance. (2019)
Outland, N.B.: A computational cognitive architecture for exploring team mental models. College of Science and Health Theses and Dissertations. 289. https://via.library.depaul.edu/csh_etd/289 (2019)
Ross, L., Greene, D., House, P.: The ‘false consensus effect’: an egocentric bias in social perception and attribution process. J. Exp. Soc. Psychol. 13, 279–301 (1977)
Seo, S., Kennedy-Metz, L.R., Zenati, M.A., Shah, J.A., Dias, R.D., Unhelkar, V.V.: Towards an AI coach to infer team mental model allignment in healthcare. Rice University Houston TX, USA, Department of Computer Science (2021)
Shih, Y.F., Alessi, S.M.: Mental models and transfer of learning in computer programming. J. Res. Comput. Educ. 26(2), 154–175 (1993)
Stelmaszczyk, M.: Relationship between individual and organizational learning: mediating role of team learning. J. Econ. Manag. 26(4), 1732–1947. https://doi.org/10.22367/jem.2016.26.06 (2016)
Todd, J.: Audit of compliance with WHO surgical safety checklist and building a shared mental model in the operating theatre. BJM Leader 2(1), 32–135 (2018)
Treur, J.: Modeling higher-order adaptivity of a network by multilevel network reification. Network Science 8, S110–S144 (2020a)
Treur, J.: Network-oriented modeling for adaptive networks: designing higher-order adaptive biological, mental and social network models. Springer Nature, Cham (2020b)
Treur, J., Van Ments, L. (eds.) : Mental Models and their Dynamics, Adaptation, and Control: a Self-Modeling Network Modeling Approach. Springer Nature. (2022)
Turi, J.A., Sorooshian, S.: The impact of organizational structure on organizational learning. Middle East J. Management. 6(2), 204–232 (2019)
Van Gog, T., Paas, F., Marcus, N., Ayres, P., Sweller, J.: The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educ. Psychol. Rev. 21(1), 21–30 (2009)
Van Ments, L., Treur, J.: Reflections on dynamics, adaptation and control: a cognitive architecture for mental models. Cogn. Syst. Res. 70, 1–9 (2021)
Van Ments, L., Treur, J., Roelofsma, P.H.M.P.: A Temporal-Causal Network Model for the Relation Between Religion and Human Empathy. Proc. of the 5th International Workshop on Complex Networks and their Applications. Lecture Notes in Computer Science, 693, pp. 55–67. Springer Publications (2016)
Van Ments, L., Treur, J., Roelofsma, P.H.M.P.: Modeling the effect of religion on human empathy based on an adaptive temporal-causal network model. Comput. Soc. Netw. 5(1) (2018)
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: Proceedings of the 14th International Conference on Brain Informatics, BI'21. Lecture Notes in AI, 12960, pp. 67–78. Springer Nature. (2021a)
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., et al. (eds.), Proceedings of the 13th International Conference on Computational Collective Intelligence, ICCCI'21. Lecture Notes in AI, 12876, pp. 126–140. Springer Nature (2021b)
Wilson, A.: Creating and applying shared mental models in the operating room. J. Perioper. Nurs. 32(3), 33–36 (2019)
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 organisation. Eur. Manag. Rev. 16, 93–115 (2019)
Williams, D.: The mind as a predictive modelling engine: generative models, structural similarity, and mental representation. Ph.D. Thesis, University of Cambridge, UK (2018)
Yi, M.Y., Davis, F.D.: Developing and validating an observational learning model of computer software training and skill acquisition. Inf. Syst. Res. 14(2), 146–169 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Canbaloğlu, G., Treur, J., Roelofsma, P.H.M.P. (2023). Using Self-modeling Networks to Model Organisational Learning. In: Canbaloğlu, G., Treur, J., Wiewiora, A. (eds) Computational Modeling of Multilevel Organisational Learning and Its Control Using Self-modeling Network Models. Studies in Systems, Decision and Control, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-28735-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-28735-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28734-3
Online ISBN: 978-3-031-28735-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)