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Software developed in the COPAN project.

Here we provide access to computational models and other software tools we develop for our research. COPAN strives to make software publicly available as open source code latest upon publication of the related research articles.


The Exploit model is an agent-based adaptive network model, conceptualizing planetary social-ecological coevolution. Each of the N agents harvests its individual resource according to either a sustainable or non-sustainble strategy reflecting either long-term sustainable yields or short-term profit maximization. These strategies get updated via a preference formation process covering two key schemes of social interaction on an adaptive network: imitation and homophily.


BEHAVE is a model of the coevolution of individual decision making or opinion formation and adaptive social network dynamics under external political pressure. The model incorporates realistic assumptions on human cognitive parameters such as the maximum number of social relationships that can be effectively maintained (Dunbar number).

pymofa: Python modeling framework

A collection of simple functions to run and evaluate computer models systematically.

pyunicorn: Python modules for complex network and nonlinear time series analysis

pyunicorn logo


In our group we are currently developing the high performance, object oriented package pyunicorn for analyzing general (spatially embedded) networks, climate networks, recurrence plots and recurrence networks using the scripting language Python. Particularly, the libraries implement the algorithms and measures described in our publications on climate and recurrence networks.

Event coincidence analysis

We have developed the method of event coincidence analysis (ECA) for quantifying the strength, lag and possible directionality of statistical interrelationships between event time series (considered as realizations of unmarked point processes). A package in the statistical scripting language R is available (developed by Jonatan F. Siegmund) that implements ECA:

EvoMine algorithm

Complex networks like social networks are ever-changing. New links are formed, existing ties are broken, individuals change their attitudes. In this project, we aim at microscopic descriptions of the processes that govern network evolution by mining frequently occurring graph evolution rules. These rules formally characterize the evolution of a dynamic network, help domain experts analyze the underlying processes, and allow to build data-driven models for friendship and opinion dynamics. In collaboration with Erik Scharwächter and the Knowledge Discovery and Data Mining group of Prof. Dr. Emmanuel Müller at Hasso-Plattner-Institute Potsdam.

pyregimeshifts: Python scripts for detecting regime shifts in paleoclimate time series

Scripts for reproducing the analysis reported in:

The analysis scripts provided in this package provide a general toolkit for detecting regime shifts in multiple (paleo-) climate time series. They should, hence, prove useful for diverse studies on Earth system dynamics beyond the work reported in the original paper. First, the methodology developed in the original paper can be applied to a broad range of data sets of interest. Second, the methodology can be easily generalized by making full use of the capabilities of the pyunicorn package. For example, other measures for detecting regime shifts from recurrence analysis such determinism or laminarity could be used or visibility graph analysis could be applied instead of recurrence networks.

Development version and download of pyregimeshifts:

pycopanpbcc: Python scripts for modelling collateral transgression of planetary boundaries

Python scripts for analyzing Planetary Boundaries in a conceptual model of the Earth's Carbon Cycle including geoengineering by terrestrial carbon dioxide removal for reproducing the analysis reported in:

Development version and download of pycopanpbcc:

  • Original software release along with paper publication: DOI

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