Dynamic modeling based on a temporal–causal network modeling approach

Treur, Jan

doi:10.1016/j.bica.2016.02.002

Cited by 52 publications

(55 citation statements)

References 38 publications

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“…To illustrate these notions, consider the example from [17], with conceptual representation depicted here in Fig. 1, and an example simulation shown in Fig.…”

Section: How To Verify a Temporal-causal Network Model By Mathematicamentioning

confidence: 99%

“…For more details, see [17]. Combination functions used in this simple example are the scaled sum function and the identity function, and all connections have weight 1, except the connections to ps a , which have weight 0.5.…”

Section: How To Verify a Temporal-causal Network Model By Mathematicamentioning

confidence: 99%

“…Moreover, if all connection weights are 1, except that ω responding = 0.5 and ω amplifying = 0.5, as in the example simulation shown in [17], Section 2.4.1, the values become:…”

Section: Mathematical Analysis For Equilibrium States: An Examplementioning

confidence: 99%

“…This is illustrated in Sect. 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 then kept forever, until the stimulus disappears: an equilibrium state.…”

Section: Introductionmentioning

confidence: 99%

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Verification of temporal-causal network models by mathematical analysis

Treur¹

2016

Vietnam J Comput Sci

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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:• whether some values for the variables exist for which no change occurs (stationary points or equilibria), and how such values may depend on the values of the parameters of the model and/or the initial values for the variables • whether certain variables in the model converge to some limit value (equilibria) and how this may depend on the values of the parameters of the model and/or the initial values for the variables • whether or not certain variables will show monotonically increasing or decreasing values over time (monotonicity) • how fast a convergence to a limit value takes place (convergence speed) • whether situations occur in which no convergence takes place but in the end a specific sequence of values is repeated all the time (limit cycle)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 B Jan Treur j.treur@vu.nl 1 VU University Amsterdam, Behavioral Informatics Group, Amsterdam, The Netherlands 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.

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“…To illustrate these notions, consider the example from [17], with conceptual representation depicted here in Fig. 1, and an example simulation shown in Fig.…”

Section: How To Verify a Temporal-causal Network Model By Mathematicamentioning

confidence: 99%

Section: How To Verify a Temporal-causal Network Model By Mathematicamentioning

confidence: 99%

“…Moreover, if all connection weights are 1, except that ω responding = 0.5 and ω amplifying = 0.5, as in the example simulation shown in [17], Section 2.4.1, the values become:…”

Section: Mathematical Analysis For Equilibrium States: An Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Verification of temporal-causal network models by mathematical analysis

Treur¹

2016

Vietnam J Comput Sci

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“…In recent years, within Cognitive Science, dynamics has been recognized and emphasized as a central issue in describing cognitive processes; for example, ; see also (Treur 2016). As has been discussed in more detail there, the notion that lies at the heart of this dynamical perspective is the notion of a state-determined system.…”

Section: Introductionmentioning

confidence: 99%

Transactions on Computational Collective Intelligence XXIII

Nguyên¹,

Kowalczyk²,

Mercik³

2016

Lecture Notes in Computer Science

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Abstract.A novel approach to a human centric support of a consensus reaching process in a group of agents who present their testimonies as individual fuzzy preference relations is proposed. The concept of a degree of consensus is used which is meant as the degree to which, for instance, most of important agents agree as to almost all of relevant options. The fuzzy majorities are equated with linguistic quantifiers and Zadeh's calculus of linguistically quantified propositions is used. The new concepts of a consensory and dissensory agent is introduced. The authors' approach of using linguistic data summaries for a comprehensive summarization of how the agents' current testimonies look like is then employed for the consensory and dissensory agents to obtain suggestions to the agents on changes of specific preferences that could lead to a higher degree of consensus. An explicit inclusion of opinions of the consensory and dissensory agents is shown to be an important step towards a fairness type attitude of the moderator as opinions of all agents are accounted for.

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Network-Oriented Modeling of Multi-criteria Homophily and Opinion Dynamics in Social Media

Козырева

Pechina

Treur

2018

Lecture Notes in Computer Science

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Dynamic modeling based on a temporal–causal network modeling approach

Cited by 52 publications

References 38 publications

Verification of temporal-causal network models by mathematical analysis

Verification of temporal-causal network models by mathematical analysis

Transactions on Computational Collective Intelligence XXIII

Network-Oriented Modeling of Multi-criteria Homophily and Opinion Dynamics in Social Media

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