title: Learning with repeated-game strategies.
abstract: We use the self-tuning Experience Weighted Attraction model with repeated-game strategies as a computer testbed to examine the relative frequency, speed of convergence and progression of a set of repeated-game strategies in four symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. In the Prisoner's Dilemma game, we find that the strategy with the most occurrences is the "Grim-Trigger." In the Battle of the Sexes game, a cooperative pair that alternates between the two pure-strategy Nash equilibria emerges as the one with the most occurrences. In the Stag-Hunt and Chicken games, the "Win-Stay, Lose-Shift" and "Grim-Trigger" strategies are the ones with the most occurrences. Overall, the pairs that converged quickly ended up at the cooperative outcomes, whereas the ones that were extremely slow to reach convergence ended up at non-cooperative outcomes.
authors.affiliation: Department of Economics, University of Southampton Southampton, UK.
authors.forename: Christos A
authors.initials: CA
authors.lastname: Ioannou
authors.affiliation: Department of Economics, Krannert School of Management, Purdue University Lafayette, IN, USA.
authors.forename: Julian
authors.initials: J
authors.lastname: Romero
keywords: adaptive models; experience weighted attraction model; finite automata