theoretical issues are discussed. Special emphasis is put on aspiration adaptation theory. Further remarks concern basic models of … decision behavior (like learning and expectation formation), reasoning, and the connection between bounded rationality and …

learning procedures. We consider the Stochastic Gradient-Algorithm as a boundedly rational learning procedure in an univariate … influence of the forecast feedback is limited the learning agents cannot destabilize the model and learn to form rational …

Adaptive learning and punishment are highly prominent competing explanations for ultimatum game behavior. We report on … an experiment that considers each theory in stand-alone form, so that one does not rely on the other in any substantial … way. Our data exhibits patterns for which punishment can account but learning by itself cannot. Initial play varies …

equilibrium of the game does not organize the individual data, two individual learning models are studied: one following a … predictive value of such learning models is found to be poor. …

Recently there has been much theoretical and experimental work on learning in games. However, learning usually means … "learning about the strategic behavior of opponents" rather than "learning about the game" as such. In contrast, here we report …

number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious … play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those … and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was …

In this note we study a very simple trial & error learning process in the context of a Cournot oligopoly. Without any …

theory of melioration is to exclusively play A, while the Nash equilibrium is to almost exclusively play B. The predictive … learning processes exhibited by players. …