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Multilevel organisational learning concerns an interplay of different types of learning at individual, team, and organisational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.

Aggregation of developed individual mental models to obtain shared mental models for the organisation is a crucial process for organisational learning. This aggregation process usually does not only depend on the mental models used as input for it, but also on several context factors that may vary over circumstances and time. This means that for computational modeling of organisational learning the aggregation process better can be modeled as an adaptive dynamical process where adaptation is used to obtain a context-sensitive outcome of the aggregation. In this chapter it is explored how Boolean functions of these context factors can be used to model this form of adaptation. Using self-modeling networks, mental model adaptation by learning, formation or aggregation can be modeled in an appropriate manner at a first-order self-model level, whereas the control over such processes can be modeled at a second-order self-model level. Therefore, for adaptation of aggregation of mental models in particular, a second-order adaptive self-modeling network model for organisational learning can be used. In this chapter it is shown how in such a network model at the second-order self-model level, Boolean functions can be used to express logical combinations of context factors and based on this can exert context-sensitive control over the mental model aggregation process. Thus, a computational network model of organisational learning is presented in which the process of aggregation of individual mental models to form shared mental models takes place in an adaptive context-dependent manner based on any Boolean combinations of context factors.

In this chapter, key findings presented in this volume are summarised and evaluated to demonstrate the usefulness and great potential of the adaptive dynamical system approach based on self-modeling networks in providing a useful structure to formalise, analyse and simulate multilevel organisational learning processes. Moreover, future perspectives are discussed for further development and application based on what already has been achieved.

This chapter describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control.

This chapter addresses formalisation and computational modeling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modeling network model is introduced and an example simulation for the case study is discussed.

In this chapter, equilibrium analysis for network models is addressed and applied to a network model of multilevel organisational learning. Equilibrium analysis can consider both properties of aggregation characteristics and properties of connectivity characteristics of a network. For connectivity characteristics, it is shown by introducing a form of stratification how for acyclic networks the equilibrium values of all nodes can be directly computed (by any functions used for aggregation) from those of the (independent) nodes without incoming connections. Moreover, by introducing a form of stratification for the network’s strongly connected components, it is also shown for any type of connectivity, similar equilibrium analysis results can be obtained relating equilibrium values in any component to equilibrium values in (independent) components without incoming connections. For aggregation characteristics, it is shown how certain classes of nonlinear functions used for aggregation in network models enable equilibrium analysis of the emerging dynamics within the network like linear functions do. All these results are illustrated by applying them to an example network model for organisational learning.

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.

Within organisational learning, aggregation of developed individual mental models to obtain shared mental models for the organisation is a crucial process. This aggregation process usually does not only depend on the mental models used as input for it, but also on several context factors that may vary over circumstances and time. This means that for computational modeling of organisational learning by adaptive networks, where the formation of a shared mental model is a form of network adaptation, the underlying aggregation process better can be controlled by a second-order adaptive dynamical process to obtain a context-sensitive way of aggregation. In this chapter it is explored how context factors can be used to model this form of second-order network adaptation. Indeed, using self-modeling networks, mental model adaptation by learning, formation or aggregation can be modeled in an appropriate manner at a first-order adaptive self-model level, whereas the control over such processes can be modeled at a second-order adaptive self-model level. In this chapter it is shown how by a second-order self-model in such a network, context-rich knowledge can be specified that is used to control the aggregation with the first-order self-model in a context-sensitive manner. This is illustrated for heuristic knowledge. Based on this control knowledge such an adaptive network model can exert context-sensitive control over the mental model aggregation process. Thus, a computational network modeling approach of organisational learning is presented in which the process of aggregation of individual mental models to form shared mental models is controlled in an adaptive context-dependent manner based on heuristic knowledge.

This chapter describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organisational learning in team-related performances. The chapter describes the value of using shared mental models to illustrate the concept of organisational 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 chapter discusses potential elaborations of the different studied concepts, as well as implications of the research in the domain of teamwork and team performance, and in terms of organisational learning.

This chapter addresses formalisation and computational modeling of multilevel organisational learning, which is one of the major challenges for the area of organisational learning. It is discussed how various conceptual mechanisms in multilevel organisational learning as identified in the literature, can be formalised by computational mechanisms which provide mathematical formalisations that enable computer simulation. The formalisations have been expressed using a self-modeling network modeling approach.

Using a proper mental model during mental processes is often crucial. Such a mental model has to be learnt and maintained; this involves mental model adaptation. Metacognition is applied to control use and adaptating in a context-sensitive manner. In this chapter, a second-order adaptive network model for handling mental models, covering their use, adaptation and control, is discussed and used to illustrate these processes.

Organisational learning is often considered to concern mental models as a vehicle for individual, team, and organizational learning. By learning individual mental models, a basis for formation of shared team mental models is created, and based on the different shared team mental models, a shared organisation mental model can be obtained. This pathway is indicated by feed-forward learning. In addition, feedback learning follows the opposite pathway: shared team mental models can be learned from a shared organisation mental model and individual mental models can be learned from shared team mental models. These pathways and their interactions provide complex dynamic and adaptive mechanisms that together constitute organizational learning. These mechanisms have been used as a basis for an adaptive computational network model for organisational learning. The model is illustrated by a not too complex but realistic case study.

This paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modelling network model is introduced and an example simulation for the case study is discussed., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Safety and Security Science

This paper addresses formalization and computational modeling of multilevel organizational learning, which is one of the major challenges for the area of organizational learning. It is discussed how various conceptual mechanisms in multilevel organizational learning as identified in the literature, can be formalized by computational mechanisms which provide mathematical formalizations that enable computer simulation. The formalizations have been expressed using a self-modeling network modeling approach.

This research addresses the influence of leadership and communication on learning within an organisation by direct mutual interactions in dyads. This is done in combination with multilevel organizational learning as an alternative route, which includes feed forward and feedback learning. The results show that effective communication (triggered by the active team leader, and/or by natural, informal communication), leads to a faster learning process within an organization compared to the longer route via feed forward and feedback formal organisational learning. However, this more direct form of bilateral learning in general may take more of the employee's time, as a quadratic number of dyadic interactions in general is less efficient than a linear number of interactions needed for feed forward and feedback organisational learning., Safety and Security Science

This research addresses the influence of leadership and communication on learning within an organisation by direct mutual interactions in dyads. This is done in combination with multilevel organizational learning as an alternative route, which includes feed forward and feedback learning. The results show that effective communication (triggered by the active team leader, and/or by natural, informal communication), leads to a faster learning process within an organization compared to the longer route via feed forward and feedback formal organisational learning. However, this more direct form of bilateral learning in general may take more of the employee's time, as a quadratic number of dyadic interactions in general is less efficient than a linear number of interactions needed for feed forward and feedback organisational learning., NA

In this paper, equilibrium analysis for network models is addressed and applied in particular to a network model of multilevel organisational learning. The equilibrium analysis addresses properties of aggregation characteristics and connectivity characteristics of a network. For aggregation characteristics, it is shown how certain classes of nonlinear functions enable equilibrium analysis of the emerging dynamics within the network like linear functions do. For connectivity characteristics, by using a form of stratification for the network's strongly connected components, it is shown how equilibrium analysis results can be obtained relating equilibrium values in any component to equilibrium values in (independent) components without incoming connections. In addition, concerning aggregation characteristics, two specific types of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are analysed. It is illustrated in detail how by using certain function transformations also methods for equilibrium analysis based on a symbolic linear equation solver, can be applied to make predictions about equilibrium values for them. All these results are applied to a network model for organisational learning. Finally, it is analysed in some depth how the function transformations applied can be described by the more general notion of function conjugate relation, also often used for coordinate transformations., Safety and Security Science

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.

Organisational learning processes often exploit developed individual mental models in order to obtain shared mental models for the organisation by some form of unification or aggregation. The focus in this paper is on this aggregation process, which may depend on a number of contextual factors. It is shown how a second-order adaptive network model for organisation learning can be used to model this process of aggregation of individual mental models in a context-dependent manner.

Aggregation of developed individual mental models to obtain shared mental models for the organization is a crucial process for organizational learning. This aggregation process usually does not only depend on the mental models used as input for it, but also on several context factors that may vary over circumstances and time. This means that for computational modeling of organizational learning the aggregation process better can be modeled as an adaptive dynamical process where adaptation is used to obtain a context-sensitive outcome of the aggregation. In this paper it is explored how Boolean functions of these context factors can be used to model this form of adaptation. Using self-modeling networks, mental model adaptation by learning, formation or aggregation can be modeled in an appropriate manner at a first-order self-model level, whereas the control over such processes can be modeled at a second-order self-model level. Therefore, for adaptation of aggregation of mental models in particular, a second-order adaptive self-modeling network model for organizational learning can be used. In this paper it is shown how in such a network model at the second-order self-model level, Boolean functions can be used to express logical combinations of context factors and based on this can exert context-sensitive control over the mental model aggregation process. Thus, a computational network model of organizational learning is presented in which the process of aggregation of individual mental models to form shared mental models takes place in an adaptive context-dependent manner based on any Boolean combinations of context factors.

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational 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., NA

Focusing on a proper mental model during mental processes is often crucial. Metacognition is used to control such focusing in a context-sensitive manner. In this paper, a second-order adaptive mental network model is introduced for this form of metacognitive control. The second-order adaptive network model obtained is illustrated by a case scenario concerning social interaction.

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational 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., Safety and Security Science