1,721,533 research outputs found
A modeling environment for reified temporal-causal network models
Full text linkThe introduced multilevel reified (temporal-causal) network architecture is the basis of the implementation of a dedicated software environment developed by the author in Matlab. The environment includes a combination function library and a generic computational reified network engine. It uses role matrices specifying the characteristics for the designed network model as input. Based on this input, the computational reified network engine can be used to generate simulations for the network model, thereby using combination functions from the library. In this chapter, this software environment is described in more detail.</p
A Controlled Adaptive Self-modeling Network Model of Multilevel Organisational Learning for Individuals, Teams or Projects, and Organisation
Full text linkMultilevel 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.Safety and Security Scienc
Using Self-modeling Networks to Model Organisational Learning
Full text linkWithin 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.Safety and Security Scienc
With a Little Help: A Modeling Environment for Self-modeling Network Models
Full text linkThe concept of self-modeling network architecture is the basis of the implementation of a dedicated software environment developed by the author in MATLAB. The environment includes a combination function library and a generic computational self-modeling network engine. It uses as input role matrices specifying in a standardised and compact form the characteristics for the designed network model. Based on this input, the computational self-modeling network engine can be used to generate simulations for the network model, thereby using combination functions from the library. In this chapter, this software environment and how to use it is described in more detail.</p
Ins and outs of network-oriented modeling
Full text linkNetwork-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this chapter, it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. Thus main characteristics for a network structure are obtained: Connectivity in terms of the connections and their weights, Aggregation of multiple incoming connections in terms of combination functions, and Timing in terms of speed factors. The basics and the scope of applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, and Mental Network models for cognitive and affective processes. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure.</p
On the universal combination function and the universal difference equation for reified temporal-causal network models
Full text linkThe universal differential and difference equation form an important basis for reified temporal-causal networks and their implementation. In this chapter, a more in depth analysis is presented of the universal differential and difference equation. It is shown how these equations can be derived in a direct manner and they are illustrated by some examples. Due to the existence of these universal difference and differential equation, the class of temporal-causal networks is closed under reification: by them it can be guaranteed that any reification of a temporal-causal network is itself also a temporal-causal network. That means that dedicated modeling and analysis methods for temporal-causal networks can also be applied to reified temporal-causal networks. In particular, it guarantees that reification can be done iteratively in order to obtain multilevel reified network models that are very useful to model multiple orders of adaptation. Moreover, as shown in Chap. 9, the universal difference equation enables that software of a very compact form can be developed, as all reification levels are handled by one computational reified network engine in the same manner. Alternatively, it is shown how the universal difference or differential equation can be used for compilation by multiple substitution for all states, which leads to another form of implementation. The background of these issues is discussed in the current chapter.</p
A unified approach to represent network adaptation principles by network reification
Full text linkIn this chapter, the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. This is explained for temporal-causal networks where connection weights, combination functions, and speed factors represent the characteristics for Connectivity, Aggregation, and Timing describing the network structure. Having the network structure represented in an explicit manner within the extended network enables to model the adaptation of the base network by dynamics within the reified network: an adaptive network is represented by a non-adaptive network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated for a number of well-known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks.</p
Using multilevel network reification to model second-order adaptive bonding by homophily
Full text linkThe concept of multilevel network reification introduced in the previous chapters enables representation within a network not only of first-order adaptation principles, but also of second-order adaptation principles expressing change of characteristics of first-order adaptation principles. In the current chapter, this approach is illustrated for an adaptive Social Network. This involves a first-order adaptation principle for bonding by homophily represented at the first reification level, and a second-order adaptation principle describing change of characteristics of this first-order adaptation principle, and represented at the second reification level. The second-order adaptation addresses adaptive change of two of the characteristics of the first-order adaptation, specifically similarity tipping point and connection adaptation speed factor.</p
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