Thursday 08 Feb 2018Dynamics of Learning in Game Theory

Sofia Castro - University of Porto

LSI seminar room A 13:30-14:30

In Game Theory, decisions are made in order to optimise payoff. When a sequence of decisions occurs over time, these can change according to what is considered more favourable at a given decision time. The change comes from an update of beliefs and constitutes a learning mechanism which can be modelled by a dynamical system. Depending on the update procedure, different learning mechanisms are constructed.

I shall focus on two classic learning mechanisms in continuous time: replicator dynamics (RD) and best-response dynamics (BRD). It is known that the Nash equilibria, describing the outcome of the game, are the same for these two learning mechanisms. However, non-equilibrium solutions (along which learning is involved) can behave quite differently under RD and BRD. I shall address the relation between the solutions under RD and BRD from two points of view: those starting from the same initial state and those reaching the same limit set.

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