diff --git a/joss.06703/10.21105.joss.06703.crossref.xml b/joss.06703/10.21105.joss.06703.crossref.xml
new file mode 100644
index 0000000000..673698c2f4
--- /dev/null
+++ b/joss.06703/10.21105.joss.06703.crossref.xml
@@ -0,0 +1,150 @@
+
+
+
+ 20241120202150-b2e24e02822bf2987dbab14f62121c610d6fc240
+ 20241120202150
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 11
+ 2024
+
+
+ 9
+
+ 103
+
+
+
+ Evoke: A Python package for evolutionary signalling
+games
+
+
+
+ Stephen Francis
+ Mann
+
+ LOGOS Research Group, Universitat de Barcelona, Spain
+ Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
+
+ https://orcid.org/0000-0002-4136-8595
+
+
+ Manolo
+ Martínez
+
+ LOGOS Research Group, Universitat de Barcelona, Spain
+
+ https://orcid.org/0000-0002-6194-7121
+
+
+
+ 11
+ 20
+ 2024
+
+
+ 6703
+
+
+ 10.21105/joss.06703
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.14185732
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/6703
+
+
+
+ 10.21105/joss.06703
+ https://joss.theoj.org/papers/10.21105/joss.06703
+
+
+ https://joss.theoj.org/papers/10.21105/joss.06703.pdf
+
+
+
+
+
+ Communication and common
+interest
+ Godfrey-Smith
+ PLOS Computational Biology
+ 11
+ 9
+ 10.1371/journal.pcbi.1003282
+ 1553-7358
+ 2013
+ Godfrey-Smith, P., & Martínez, M.
+(2013). Communication and common interest. PLOS Computational Biology,
+9(11), e1003282.
+https://doi.org/10.1371/journal.pcbi.1003282
+
+
+ Signals: Evolution, learning, and
+information
+ Skyrms
+ 10.1093/acprof:oso/9780199580828.001.0001
+ 978-0-19-958082-8
+ 2010
+ Skyrms, B. (2010). Signals:
+Evolution, learning, and information. Oxford University Press.
+https://doi.org/10.1093/acprof:oso/9780199580828.001.0001
+
+
+ EGTTools: Toolbox for evolutionary game
+theory
+ Fernández Domingos
+ GitHub repository
+ 10.5281/zenodo.3687125
+ 2020
+ Fernández Domingos, E. (2020).
+EGTTools: Toolbox for evolutionary game theory. In GitHub repository.
+https://github.com/Socrats/EGTTools; GitHub.
+https://doi.org/10.5281/zenodo.3687125
+
+
+ Nashpy: 0.0.41
+ Nashpy project developers
+ 10.5281/zenodo.10802174
+ 2024
+ Nashpy project developers. (2024).
+Nashpy: 0.0.41.
+https://doi.org/10.5281/zenodo.10802174
+
+
+
+
+
+
diff --git a/joss.06703/10.21105.joss.06703.pdf b/joss.06703/10.21105.joss.06703.pdf
new file mode 100644
index 0000000000..047e1e0198
Binary files /dev/null and b/joss.06703/10.21105.joss.06703.pdf differ
diff --git a/joss.06703/paper.jats/10.21105.joss.06703.jats b/joss.06703/paper.jats/10.21105.joss.06703.jats
new file mode 100644
index 0000000000..5580b9730e
--- /dev/null
+++ b/joss.06703/paper.jats/10.21105.joss.06703.jats
@@ -0,0 +1,221 @@
+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+6703
+10.21105/joss.06703
+
+Evoke: A Python package for evolutionary signalling
+games
+
+
+
+https://orcid.org/0000-0002-4136-8595
+
+Mann
+Stephen Francis
+
+
+
+
+
+https://orcid.org/0000-0002-6194-7121
+
+Martínez
+Manolo
+
+
+*
+
+
+
+LOGOS Research Group, Universitat de Barcelona,
+Spain
+
+
+
+
+Max Planck Institute for Evolutionary Anthropology,
+Leipzig, Germany
+
+
+
+
+* E-mail:
+
+
+12
+8
+2024
+
+9
+103
+6703
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2024
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+Python
+evolutionary game theory
+signalling games
+sender-receiver framework
+evolutionary simulations
+
+
+
+
+
+ Summary
+
Evoke is a Python library for evolutionary simulations
+ of signalling games. It offers a simple and intuitive API that can be
+ used to analyze arbitrary game-theoretic models, and to easily
+ reproduce and customize well-known results and figures from the
+ literature.
+
A signalling game is a special kind of mathematical game, a formal
+ representation of interactions between agents. In a signalling game,
+ the actions available to the players include sending and responding to
+ signals. The agents in games traditionally studied in game theory
+ develop strategies via such dynamics as reinforcement learning. In
+ contrast, evolutionary game theory investigates how strategies change
+ over time in populations undergoing evolutionary change such as
+ natural selection. Signalling games can be studied in the traditional
+ reinforcement-learning paradigm or in the evolutionary paradigm. Evoke
+ offers methods for both kinds of game dynamic. Users are able to
+ create signalling games and simulate the evolution of agents’
+ strategies over time, using a range of game types and evolutionary and
+ learning dynamics.
+
Evoke also allows the user to recreate and customize figures from
+ the signalling game literature. Examples provided with Evoke include
+ figures from Skyrms
+ (2010)
+ and Godfrey-Smith & Martínez
+ (2013).
+ Users can contribute to the library by adding further examples from
+ the literature. This can be a useful way to become familiar with
+ Evoke, while at the same time increasing the benefit to other users.
+ Evoke can therefore serve as an educational tool (encouraging
+ understanding of existing literature) and a research resource
+ (promoting good practice and effective modelling techniques).
+
+
+ Statement of need
+
While there are Python packages devoted to game theory, such as
+ Nashpy
+ (Nashpy
+ project developers, 2024), and evolutionary game theory, such
+ as EGTtools
+ (Fernández
+ Domingos, 2020), to our knowledge there has not yet been a
+ Python package dedicated to the study of signalling games in the
+ context of both evolution and reinforcement learning. That is the gap
+ Evoke is intended to fill.
+
In the evolutionary game theory literature, models and results are
+ often developed with proprietary code. Evaluating and re-running
+ models can be difficult for readers, because custom-made software is
+ often not developed with other users in mind. Sometimes the model code
+ is not available at all.
+
It would be preferable to have a common framework that different
+ users can share. When new results are presented in a research article,
+ readers of that article could run the model and check the results for
+ themselves. Readers could also vary the parameters to obtain results
+ that were not reported in the original article, lending an air of
+ interactivity to published papers.
+
Built-in examples already shipped with Evoke include figures from
+ Skyrms
+ (2010).
+ These examples allow the user to change some of the input parameters
+ to Skyrms’s figures to see how different parameter values yield
+ different results. In a small way, this makes the book “interactive”:
+ in addition to the static figures on the page, the user can play with
+ the models in order to get a sense of the range of outcomes each model
+ can generate.
+
+
+ Acknowledgements
+
Many thanks to the reviewers and editors for their comments. This
+ work was supported by Juan de la Cierva grant FJC2020-044240-I and
+ María de Maeztu grant CEX2021-001169-M funded by
+ MICIU/AEI/10.13039/501100011033.