The explanatory power of network models was further compared with classical biodiversity-ecosystem functioning models, which explained pathogen invasion success and disease spread as a function of diversity (continuous) or the presence-absence of each species.
We treat dynamic behavior such as periodic oscillations, bifurcations, chaos, and spatial patterns and also the status of mechanisms and mathematical models, which explain the dynamics.
Venkatesh et al. (2003) developed the unified model through reviewing eight models which explain ICT usage, namely TRA, TAM, the motivational model, TPB, a model combining TAM and TPB, the model of PC utilization, DOI, and the social cognitive theory.
However, there are no models which explain (1) how threads compete among themselves to receive attraction from users and/or (2) which features are the most appealing to users when posting a message in a pool of candidate threads.
Through the detailed reading of 193 papers and reports we identified 28 different models which explained all or part of the knowledge transfer process.
A recent technical investigation of alternatives to the SCRalyze GLM has revealed that most models which explain more variance in the observed SCR data have worse sensitivity (Bach et al., 2013), despite their ability to better explain the data.
Despite the uncertainty in the completeness of the PKN and the uncertainty in the experimental data, we are able to find relatively simple mechanistic models which explain the data.
In our opinion, there is little hope that realistic multi-scale spatiotemporal cGP models which explain high-dimensional phenotypic variation associated with the emergence of biological structure, organisation and function through differentiation, can be expressed in a simple way.
An analytical model, which explains spring in a partition wall, is presented.
A mechanistic model, which explains this surface roughening behavior, is presented.
