EWA Lite is a one-parameter theory of learning in normal-form games. It approximates the free parameters in an earlier model (EWA) with functions of experience. The theory is tested on seven different games and compared to other learning and equilibrium theories. Either EWA Lite or parameterized EWA predict best, but one kind of reinforcement learning predicts well in games with mixed-strategy equilibrium. Belief learning models fit worst. The economic value of theories is measured by how much more subjects would have earned if they followed theory recommendations. EWA Lite and EWA add the most economic value in every game but one
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible ...
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible ...
The authors examine learning in all experiments they could locate involving one hundred periods or m...
EWA Lite is a one-parameter theory of learning in normal-form games. It approximates the free parame...
Functional experience weighted attraction (fEWA) is a one-parameter theory of learning in games. It...
Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in games. It...
In the last ten years theory (e.g., Fudenberg and Levine, 1998) and empirical data fitting have pro...
In the last ten years theory (e.g., Fudenberg and Levine, 1998) and empirical data fitting have pro...
Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in games. It...
We describe a general model, 'experience-weighted attraction' (EWA) learning, which includes reinfor...
We describe a general model, 'experience-weighted attraction' (EWA) learning, which includes reinfor...
In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial...
In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial...
In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial...
Experience-weighted attraction is the leading model of learning in games. However, it can not obviou...
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible ...
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible ...
The authors examine learning in all experiments they could locate involving one hundred periods or m...
EWA Lite is a one-parameter theory of learning in normal-form games. It approximates the free parame...
Functional experience weighted attraction (fEWA) is a one-parameter theory of learning in games. It...
Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in games. It...
In the last ten years theory (e.g., Fudenberg and Levine, 1998) and empirical data fitting have pro...
In the last ten years theory (e.g., Fudenberg and Levine, 1998) and empirical data fitting have pro...
Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in games. It...
We describe a general model, 'experience-weighted attraction' (EWA) learning, which includes reinfor...
We describe a general model, 'experience-weighted attraction' (EWA) learning, which includes reinfor...
In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial...
In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial...
In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial...
Experience-weighted attraction is the leading model of learning in games. However, it can not obviou...
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible ...
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible ...
The authors examine learning in all experiments they could locate involving one hundred periods or m...