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 Python API, 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.

This talk will introduce some fundamental concepts in game theory using PyGambit, explaining how the package can be used to create and visualise non-cooperative games, and compute their Nash equilibria (where game players have no incentive to deviate their strategies). The talk will also 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.