Abstract
In this paper, it is shown how, in contrast to often held beliefs, certain classes of nonlinear functions used for aggregation in network models enable analysis of the emerging within-network dynamics like linear functions do. In addition, two specific classes of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are introduced. Focusing on them in particular, it is illustrated in detail how methods for equilibrium analysis (based on a symbolic linear equation solver), can be applied to predict the state values in equilibria for such nonlinear cases as well.
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Treur, J. (2021). Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_8
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DOI: https://doi.org/10.1007/978-3-030-88081-1_8
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