OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
We introduce the algorithmic learning equations, a set of ordinary differential equations which characterizes the finite-time and asymptotic behavior of the stochastic interaction between state-dependent learning algorithms in dynamic games. Our framework allows for a variety of information and memory structures, including noisy, perfect, private, and public monitoring and for the possibility that players use distinct learning algorithms. We prove that play converges to a correlated equilibrium for a family of algorithms under correlated private signals. Finally, we apply our methodology in a repeated 2×2 prisoner’s dilemma game with perfect monitoring. We show that algorithms can learn a reward-punishment mechanism to sustain tacit collusion. Additionally, we find that algorithms can also learn to coordinate in cycles of cooperation and defection.
