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Found 5 papers, 4 papers with code
A dynamic network model of societal complexity and resilience inspired by Tainter's theory of collapse
In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies.
Physics and Society Populations and Evolution
Deep reinforcement learning in World-Earth system models to discover sustainable management strategies
Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions.
Deterministic limit of temporal difference reinforcement learning for stochastic games
Reinforcement learning in multi-agent systems has been studied in the fields of economic game theory, artificial intelligence and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory).
Multiagent Systems
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series.
Data Analysis, Statistics and Probability Atmospheric and Oceanic Physics
Macroscopic description of complex adaptive networks co-evolving with dynamic node states
In many real-world complex systems, the time-evolution of the network's structure and the dynamic state of its nodes are closely entangled.
Physics and Society
