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.@en

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.@en

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

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.@en

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.@en

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.

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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.@en