3 research outputs found

    An open reproducible framework for the study of the iterated prisoner's dilemma

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    The Axelrod library is an open source Python package that allows for reproducible game theoretic research into the Iterated Prisoner's Dilemma. This area of research began in the 1980s but suffers from a lack of documentation and test code. The goal of the library is to provide such a resource, with facilities for the design of new strategies and interactions between them, as well as conducting tournaments and ecological simulations for populations of strategies. With a growing collection of 139 strategies, the library is a also a platform for an original tournament that, in itself, is of interest to the game theoretic community. This paper describes the Iterated Prisoner's Dilemma, the Axelrod library and its development, and insights gained from some novel research.Comment: 11 pages, Journal of Open Research Software 4.1 (2016

    Relaxation as treatment for chronic musculoskeletal pain - a systematic review of randomised controlled studies

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    v1.5.0, 2016-07-19 New tournament type, new strategy, seeding, dev tools, docs + minor/bug fixes User facing: Spatial tournaments: https://github.com/Axelrod-Python/Axelrod/pull/654 New strategy, slow tit for tat: https://github.com/Axelrod-Python/Axelrod/pull/659 Seed the library: https://github.com/Axelrod-Python/Axelrod/pull/653 More uniform strategy transformer behaviour: https://github.com/Axelrod-Python/Axelrod/pull/657 Results can be calculated with non default game: https://github.com/Axelrod-Python/Axelrod/pull/656 Documentation: A community page: https://github.com/Axelrod-Python/Axelrod/pull/656 An overall results page that replaces the payoff matrix page: https://github.com/Axelrod-Python/Axelrod/pull/660 Development: A git hook script for commit messages: https://github.com/Axelrod-Python/Axelrod/pull/648 Caching of hypothesis database on travis: https://github.com/Axelrod-Python/Axelrod/pull/658 Here are all the commits for this PR: https://github.com/Axelrod-Python/Axelrod/compare/v1.4.0...v1.5.
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