ArticleiScience2023
EGTtools: Evolutionary game dynamics in Python.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Evolution favours positively biased reasoning in sequential interactions with high future gains.Journal of the Royal Society, Interface · 2025Article
- An evolutionary model of rhythmic accelerando in animal vocal signalling.PLoS computational biology · 2025Article
- A new electoral bottom-up model of institutional governance.Scientific reports · 2025Article
- Article
- Committing to the wrong artificial delegate in a collective-risk dilemma is better than directly committing mistakes.Scientific reports · 2024Article
- Evolution of a theory of mind.iScience · 2024Article
- Article
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3 authors.
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Abstract
Evolutionary Game Theory (EGT) provides an important framework to study collective behavior. It combines ideas from evolutionary biology and population dynamics with the game theoretical modeling of strategic interactions. Its importance is highlighted by the numerous high level publications that have enriched different fields, ranging from biology to social sciences, in many decades. Nevertheless, there has been no open source library that provided easy, and efficient, access to these methods and models. Here, we introduce EGTtools, an efficient hybrid C++/Python library which provides fast implementations of both analytical and numerical EGT methods. EGTtools is able to analytically evaluate a system based on the replicator dynamics. It is also able to evaluate any EGT problem resorting to finite populations and large-scale Markov processes. Finally, it resorts to C++ and MonteCarlo simulations to estimate many important indicators, such as stationary or strategy distributions. We illustrate all these methodologies with concrete examples and analysis.
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