Outcome Selection with Algorithmic Learners

Mateus Hiro Nagata, Francesco Giordano

Abstract: We define a new procedure to nudge the selection of desirable outcomes in games played by algorithms. We consider the case where agents use a learning algorithm to play a repeated game. The innovative feature is to introduce a correlation device: decision makers update the values assigned to each action given the past actions performance and a payoff irrelevant message. Messages, which can be either public or private, are correlated among players. The probability distribution over messages is either fixed or time-varying according to some welfare criterion. We ask the following questions: do algorithms learn desirable correlated equilibria? Does information improves welfare and fairness when algorithms compete? We give a partial answer to the above questions based on simulations.