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

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

Bayesian methods for hackers: probabilistic programming and Bayesian inference

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects

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

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.

Axelrod: 1.4.0

v1.4.0, 2016-06-22 New strategy. contrite TitForTat: https://github.com/Axelrod-Python/Axelrod/pull/639 Here are all the commits for this PR: https://github.com/Axelrod-Python/Axelrod/compare/v1.3.0...v1.4.