Abstract
Usually dynamic properties of models can be analysed by conducting simulation experiments. But sometimes, as a kind of prediction properties can also be found by calculations in a mathematical manner, without performing simulations. Examples of properties that can be explored in such a manner are: Such properties found in an analytic mathematical manner can be used for verification of the model by checking them for the values observed in simulation experiments. If one of these properties is not fulfilled, then there will be some error in the implementation of the model. In this paper some methods to analyse such properties of dynamical models will be described and illustrated for the Hebbian learning model, and for dynamic connection strengths in social networks. The properties analysed by the methods discussed cover equilibria, increasing or decreasing trends, recurring patterns (limit cycles), and speed of convergence to equilibria.
Highlights
Dynamic properties of dynamic models can be analysed by conducting simulation experiments
Whether situations occur in which no convergence takes place but in the end a specific sequence of values is repeated all the time. Such properties found in an analytic mathematical manner can be used for verification of the model by checking them for the values observed in simulation experiments
Vietnam J Comput Sci (2016) 3:207–221 wss sensing sss representing srss responding psa executing esa amplifying srse predicting erties found in an analytic mathematical manner can be used for verification of the model by checking them for the values observed in simulation experiments
Summary
Dynamic properties of dynamic models can be analysed by conducting simulation experiments. Erties found in an analytic mathematical manner can be used for verification of the model by checking them for the values observed in simulation experiments If one of these properties is not fulfilled, there will be some error in the implementation of the model. 3 by an analysis of a simple example as discussed in [17], Section 2.4.1, using sum and identity combination functions In simulations it is observed for this example model that when a constant stimulus level occurs in the world, for each state its activation value increases from 0 to some value that is kept forever, until the stimulus disappears: an equilibrium state. In subsequent sections three more general examples of this type of analysis for which equilibrium states occur are addressed: for a scaled sum combination function
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