Abstract: We consider boundedly rational learning processes in which players have a priori limited set of behavior rules. A behavior rule is a function from information to a stage-game action, which reflects the available information and one’s reasoning about how others act. Commonly used behavior rules include the adaptive rule and the conservative rule (inertia). Sophisticated players may use iterative best responses, called forward-looking behavior rules. The feasible behavior rules set the framework and limitations of learning processes. We investigate a general relationship between the set of feasible behavior rules and the properties of the long-run outcomes of any learning process restricted to use the feasible behavior rules. From v...
Fudenberg and Kreps consider adaptive learning processes, in the spirit of fictitious play, for inf...
This paper provides a genera1 framework to analyze rational learning in strategic situations where t...
We generalize results of earlier work on learning in Bayesian games by allowing players to make deci...
The present study focuses on a class of games with reinforcement-learning agents that adaptively cho...
Do boundedly rational players learn to choose equilibrium strategies as they play a game repeatedly?...
In this note, we consider repeated play of a finite game using learning rules whose period-by-period...
This paper investigates a class of population-learning dynamics. In every period agents either adopt...
In imperfect-information games, a common assumption is that players can perfectly model the strategi...
The paper studies infinite repetition of finite strategic form games. Players use a learning behavio...
We study learning in Bayesian games (or games with differential information) with an arbitrary numbe...
This paper summarizes recent work of Foster and Young (2001), which shows that some games are unlear...
The paper studies the highly prototypical Fictitious Play (FP) algorithm, as well as a broad class o...
This paper presents and tests a new learning model of boundedly rational players interacting with na...
ADInternational audienceConsider a two-player normal-form game repeated over time. We introduce an a...
This paper considers a learning rules for environments in which little prior in-formation is availab...
Fudenberg and Kreps consider adaptive learning processes, in the spirit of fictitious play, for inf...
This paper provides a genera1 framework to analyze rational learning in strategic situations where t...
We generalize results of earlier work on learning in Bayesian games by allowing players to make deci...
The present study focuses on a class of games with reinforcement-learning agents that adaptively cho...
Do boundedly rational players learn to choose equilibrium strategies as they play a game repeatedly?...
In this note, we consider repeated play of a finite game using learning rules whose period-by-period...
This paper investigates a class of population-learning dynamics. In every period agents either adopt...
In imperfect-information games, a common assumption is that players can perfectly model the strategi...
The paper studies infinite repetition of finite strategic form games. Players use a learning behavio...
We study learning in Bayesian games (or games with differential information) with an arbitrary numbe...
This paper summarizes recent work of Foster and Young (2001), which shows that some games are unlear...
The paper studies the highly prototypical Fictitious Play (FP) algorithm, as well as a broad class o...
This paper presents and tests a new learning model of boundedly rational players interacting with na...
ADInternational audienceConsider a two-player normal-form game repeated over time. We introduce an a...
This paper considers a learning rules for environments in which little prior in-formation is availab...
Fudenberg and Kreps consider adaptive learning processes, in the spirit of fictitious play, for inf...
This paper provides a genera1 framework to analyze rational learning in strategic situations where t...
We generalize results of earlier work on learning in Bayesian games by allowing players to make deci...