Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 0704.2533

Multi-Agent Approach to the Self-Organization of Networks

Frank Schweitzer
View original source ↗Read source document ↗Export metadata ↓

Abstract

Is it possible to link a set of nodes without using preexisting positional information or any kind of long-range attraction of the nodes? Can the process of generating positional information, i.e. the detection of ``unknown'' nodes and the estabishment of chemical gradients, \emph{and} the process of network formation, i.e. the establishment of links between nodes, occur in parallel, on a comparable time scale, as a process of co-evolution? The paper discusses a model where the generation of relevant information for establishing the links between nodes results from the interaction of many \emph{agents}, i.e. subunits of the system that are capable of performing some activities. Their collective interaction is based on (indirect) communication, which also includes memory effects and the dissemination of information in the system. The relevant (``pragmatic'') information that leads to the establishment of the links then emerges from an evolutionary interplay of selection and reamplification.

Explore related subjects

nlin.AOphysics.bio-phq-bio.QM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Frank Schweitzer. 2007-04-19. Multi-Agent Approach to the Self-Organization of Networks. https://arxiv.org/abs/0704.2533

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Beyond 11/16: dense Kuramoto networks that resist synchrony

How densely must a network of identical Kuramoto oscillators be wired before synchrony is its only stable state? The answer is a critical connectivity $μ_c$ (the minimum degree divided by $n-1$), known to satisfy $11/16\leμ_c<3/4$. We raise the lower bound from $11/16=0.6875$ to $μ_c\ge(4+2q^*)/(7+q^*)=0.6929077\ldots$, where $q^*\approx0.6506$ is a root of an explicit polynomial of degree 8. The networks approaching this bound carry stable states that hug the four-cluster state: three neighboring clusters each split into two slightly separated halves, and the four crossed links between neighboring halves (those whose phase gap deviates most from a quarter turn) have densities approaching $q^*$. We build these networks from a coarse-grained cluster model, verify stability with interval arithmetic, and blow the model up into large networks with an exact minimum degree. The states are fragile and easily missed by numerical searches that impose stability margins.

nlin.AO↗
arXiv2026

Cooperation Dynamics with Environmental Feedback: Steady States and Transitions

Cooperative behavior and its regulatory mechanisms are central to complex systems and evolutionary game theory. In natural populations, strategy updates and endogenous payoff environments interact bidirectionally, shaping cooperation. We propose a strategy-payoff environmental-feedback model for four canonical games: Prisoner's Dilemma, Snowdrift, Stag-Hunt, and Harmony. Mathematical derivation reduces the high-dimensional coupled system to an analytically equivalent canonical replicator equation. Bifurcation diagrams, phase portraits, and simulations show how initial cooperation, temptation to defect, and punishment jointly regulate cooperation. Stag-Hunt and Harmony display bistability and initial-state dependence; Prisoner's Dilemma exhibits robust dynamics with stable mixed equilibria. Temptation and punishment drive sequential cross-type game transitions at critical thresholds. Sufficient punishment suppresses defection, shifts bifurcation thresholds, delays destabilization of cooperative steady states, and reshapes these transitions. Numerical tests confirm equivalence and stability switching. Spatial lattice simulations agree with theoretical predictions, supporting robustness. This work reveals regulatory mechanisms from endogenous payoff feedback and initial-condition sensitivity, providing a basis for understanding and optimizing collective prosocial behavior.

nlin.AO↗
arXiv2026

Data-driven reconstruction of spatiotemporal phase dynamics for traveling and oscillating patterns via Bayesian inference

Building on the phase reduction theory formulated for reaction-diffusion systems with spatial translational symmetry, we develop a data-driven method that reconstructs the spatiotemporal phase dynamics of traveling and oscillating patterns. Spatiotemporal phase dynamics are described by spatial and temporal phases that represent the position and oscillation of the pattern, respectively. Using Bayesian inference, our method directly reconstructs phase equations from time-series data. When tested on simulation data from coupled Gray-Scott models exhibiting traveling breathers, the method accurately reconstructs the deterministic part of the phase equations in the weak-noise regime, in which the phase dynamics converge to a linearly stable fixed point.

nlin.AO↗