Previous studies of learning in certain classes of games appear to lead to inconsistent conclusions. Studies that focus on the observed sequential changes in behavior support models that imply high-action inertia and allow for different parameters in different games. Studies that use simulation-based analysis and focus on the prediction of behavior in new games support models that imply little-action inertia, and demonstrate the value of models that assume general parameters over certain classes of games. We show that this apparent inconsistency emerges even when analyzing a large data set with a single model. We then show that the inconsistency between the two analyses can be a product of the tendency by subjects to repeat past choices.
%0 Journal Article
%1 citeulike:347919
%A Erev, Ido
%A Haruvy, Ernan
%D 2005
%J Journal of Mathematical Psychology
%K descriptive learning
%N 5
%P 357--371
%R 10.1016/j.jmp.2005.06.009
%T Generality, repetition, and the role of descriptive learning models
%U http://dx.doi.org/10.1016/j.jmp.2005.06.009
%V 49
%X Previous studies of learning in certain classes of games appear to lead to inconsistent conclusions. Studies that focus on the observed sequential changes in behavior support models that imply high-action inertia and allow for different parameters in different games. Studies that use simulation-based analysis and focus on the prediction of behavior in new games support models that imply little-action inertia, and demonstrate the value of models that assume general parameters over certain classes of games. We show that this apparent inconsistency emerges even when analyzing a large data set with a single model. We then show that the inconsistency between the two analyses can be a product of the tendency by subjects to repeat past choices.
@article{citeulike:347919,
abstract = {Previous studies of learning in certain classes of games appear to lead to inconsistent conclusions. Studies that focus on the observed sequential changes in behavior support models that imply high-action inertia and allow for different parameters in different games. Studies that use simulation-based analysis and focus on the prediction of behavior in new games support models that imply little-action inertia, and demonstrate the value of models that assume general parameters over certain classes of games. We show that this apparent inconsistency emerges even when analyzing a large data set with a single model. We then show that the inconsistency between the two analyses can be a product of the tendency by subjects to repeat past choices.},
added-at = {2007-08-18T13:22:24.000+0200},
author = {Erev, Ido and Haruvy, Ernan},
biburl = {https://www.bibsonomy.org/bibtex/2a846565350d8ce0bfae42017c2a06112/a_olympia},
citeulike-article-id = {347919},
description = {citeulike},
doi = {10.1016/j.jmp.2005.06.009},
interhash = {479a2aad5b700ccea763f7e5e030ab88},
intrahash = {a846565350d8ce0bfae42017c2a06112},
journal = {Journal of Mathematical Psychology},
keywords = {descriptive learning},
month = {October},
number = 5,
pages = {357--371},
priority = {2},
timestamp = {2007-08-18T13:22:40.000+0200},
title = {Generality, repetition, and the role of descriptive learning models},
url = {http://dx.doi.org/10.1016/j.jmp.2005.06.009},
volume = 49,
year = 2005
}