Players are rational if they always choose best replies given their beliefs. They are good predictors if the difference between their beliefs and the distribution of the others' actual strategies goes to zero over time. Learning is deterministic if beliefs are fully determined by the initial conditions and the observed data. (Bayesian updating is particular example). If players are rational, good predictors, and learn deterministically, there are many games for which neither beliefs nor actions coverage to a Nash equilibrium. We introduce an alternative approach to learning called prospecting in which players are rational and good predictors, but beliefs have a small random component. In any finite game, and from any initial conditions, pro...
We add the assumption that players know their opponents' payoff functions and rationality to a model...
We outline a mathematical model of rational decision-making based on standard game-theoretical assum...
We propose a new classification for multi-agent learning algorithms, with each league of players cha...
This paper summarizes recent work of Foster and Young (2001), which shows that some games are unlear...
Although there exist rules that converge to Nash equilibrium for special classes of games (like fict...
Although there exist learning processes for which the empirical distribution of play comes close to ...
We argue that a Bayesian explanation of strategic choices in games requires introducing a p...
This paper uses experimental data to examine the existence of a teaching strategy among boundedly ra...
A foundational assumption in economics is that people are rational-- they choose optimal plans of ac...
A foundational assumption in economics is that people are rational--they choose optimal plans of act...
This paper uses experimental data to examine the existence of a teaching strategy among bounded rati...
We relax the assumption that priors are common knowledge, in the stan-dard model of games of incompl...
Consider a finite stage game G that is repeated infinitely often. At each time, the players have hyp...
This paper uses experimental data to examine the existence of a teaching strat-egy among bounded rat...
Consider a finite stage game G that is repeated infinitely often. At each time, the players have hyp...
We add the assumption that players know their opponents' payoff functions and rationality to a model...
We outline a mathematical model of rational decision-making based on standard game-theoretical assum...
We propose a new classification for multi-agent learning algorithms, with each league of players cha...
This paper summarizes recent work of Foster and Young (2001), which shows that some games are unlear...
Although there exist rules that converge to Nash equilibrium for special classes of games (like fict...
Although there exist learning processes for which the empirical distribution of play comes close to ...
We argue that a Bayesian explanation of strategic choices in games requires introducing a p...
This paper uses experimental data to examine the existence of a teaching strategy among boundedly ra...
A foundational assumption in economics is that people are rational-- they choose optimal plans of ac...
A foundational assumption in economics is that people are rational--they choose optimal plans of act...
This paper uses experimental data to examine the existence of a teaching strategy among bounded rati...
We relax the assumption that priors are common knowledge, in the stan-dard model of games of incompl...
Consider a finite stage game G that is repeated infinitely often. At each time, the players have hyp...
This paper uses experimental data to examine the existence of a teaching strat-egy among bounded rat...
Consider a finite stage game G that is repeated infinitely often. At each time, the players have hyp...
We add the assumption that players know their opponents' payoff functions and rationality to a model...
We outline a mathematical model of rational decision-making based on standard game-theoretical assum...
We propose a new classification for multi-agent learning algorithms, with each league of players cha...