Abstract
Progress in theoretical physics is often made by the investigation of toy models, the model organisms of physics, which provide benchmarks for new methodologies. For complex systems, one such model is the adaptive voter model. Despite its simplicity, the model is hard to analyze. Only inaccurate results are obtained from well-established approximation schemes that work well on closely-related models. We use the adaptive voter model to illustrate a new approach that combines (a) the use of a heterogeneous moment expansion to approximate the network model by an infinite system of ordinary differential equations (ODEs), (b) generating functions to map the ODE system to a two-dimensional partial differential equation (PDE), and (c) solution of this partial differential equation by the tools of PDE-theory. Beyond the adaptive voter models, the proposed approach establishes a connection between network science and the theory of PDEs and is widely applicable to the dynamics of networks with discrete node-states.
Highlights
A core challenge in statistical physics is to understand emergence far from equilibrium
In this paper we have proposed an approach for the investigation of network dynamics that combines heterogeneous expansions, generating functions, and solution of the resulting partial differential equation (PDE)
Using this mathematical triple jump, analytic solutions for heterogeneous expansions can be obtained, which we demonstrated on the example of the adaptive voter model
Summary
Nottingham University, Business School, Nottingham, UK. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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