2026-07-21 –, Room 1.19 (Ground Floor, Shannon)
This talk will demonstrate how the PyGambit package for game theory can be used to construct games, and compute their Nash equilibria. Secondly, the talk will demonstrate the DrawTree package and challenges associated with drawing game trees, given the constraints of visualising “information sets” in game theory. Finally, the talk will highlight how PyGambit fits into the broader open-source scientific computing ecosystem for research on games via interoperability with the OpenSpiel framework, which is used for reinforcement learning.
The “Gambit” project for computation in game theory has been through multiple phases of development, dating back to the 1980s. Game theory as a field & methodology emerged from economics, but increasingly has applications in cybersecurity, multi-agent systems research and AI. Gambit is used across these fields for both teaching purposes, and as a suite of software tools for scientific computing.
Recent Gambit development has been carried out at The Alan Turing Institute and has involved a modernisation of the PyGambit package, with a particular focus on improving the user experience, including clear user tutorials and documentation. This in turn has helped to guide the prioritisation of features in recent package releases, as well as the development of a new package called DrawTree, which creates (TeX/TikZ) game visuals for games constructed in PyGambit (working in Jupyter via the Jupyter-TikZ dependency).
This talk will introduce some fundamental concepts in game theory using PyGambit, explaining how the package can be used to create non-cooperative games, and compute their Nash equilibria (where game players have no incentive to deviate their strategies). Secondly, the talk will demonstrate DrawTree and the challenges associated with drawing game trees, given the constraints of visualising “information sets” in game theory. Finally, the talk will highlight how PyGambit fits into the broader open-source scientific computing ecosystem for research on games via interoperability with the OpenSpiel framework, which is used for reinforcement learning.
Research Data Scientist and Software Engineer at The Alan Turing Institute in London. I have worked for over a decade in scientific computing, in fields ranging from computational biology, environmental sciences, digital humanities and more.
I have also worked on a lot of Python packages!