Complex dynamics in the Sherrington-Kirkpatrick game

We study the outcome of adaptive learning of a large number of players engaging in sets of two-strategy two-player games.

We are interested in typical games, and generate the payoff matrices at random at the beginning.

The payoff matrices then remain fixed during the learning process.

This provides a game theoretic foundation for the Sherrington-Kirkpatrick (SK) game, recently introduced by Garnier-Brun, Benzaquen and Bouchaud.

The original model by these authors is a special case, with no bias towards any strategy.

We here determine stability of learning for SK games with general random bias, and find that the nature of the stable state is affected by random fields.

We also introduce a grand-canonical version of the SK game, in which players can choose to abstain.

We determine the stability of learning for this game.

Our analysis confirms that complex situations involving many players are frequently unlearnable, even if each player only chooses between two different actions.

The rate with which players lose memory of past payoffs and the competitiveness of the game emerge as key parameters determining whether learning converges to a unique fixed point, whether there are many fixed points, or if the dynamics remains persistently volatile.