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·ï̾ WS Robert Veszteg»á
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¥¿¥¤¥È¥ë¡§Learning and playing best response in the hide-and-seek game

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Robert Veszteg¹Ö»Õ¤Ï¥Þ¥É¥ê¥Ã¥É¡¢¥«¥ë¥í¥¹Âè»°Âç³Ø¤Î´ü¸ÂÉÕ¤­¤ÎÀìǤ¹Ö»Õ¤Ç¤¢¤ê¡¢¸½ºß¡¢ÂçºåÂç³Ø¤Î¼Ò²ñ·ÐºÑ¸¦µæ½ê¤Ë¸¦µæ°÷¤È¤·¤ÆÂںߤ·¤Æ¤¤¤ë¡£¥²¡¼¥àÍýÏÀ¤òÍѤ¤¤¿À©ÅÙ¹½ÃÛ¤ÎÍýÏÀ¤È¼Â¸³¤ÎÀìÌç²È¤Ç¤¢¤ê¡¢GLOPE£²¤ÎËÜ¥×¥í¥¸¥§¥¯¥È¤Î¸¦µæ¤È¤â´ØÏ¢¤¬¿¼¤¤¡£
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We study short and long-run behavior in hide-and-seek games in
which treatments vary with symmetric and asymmetric labeling and with
framing. We find that long-run behavior is not affectd by the
treatment variables. In particular, we do not find that subjects fall
prey to the usual focal point bias. We also study to what extent
level-k reasoning, Nash equilibrium, quantal response equilibrium
(QRE), fictitious play, reinforcement learning, and
experience-weighted attraction (EWA) learning organize the data. All
analyzed versions of these models predict the same outcome for this
game. For short-run behavior, our data can not be well organized by
level-k reasoning. It can be considered as the results of random play.
For long-run behavior and behavior over time, the learning models
outperform the static equilibrium solutions for hiders and seekers
both jointly and separately. Our results suggest that experimental
subjects start the game using the available actions randomly and they
adjust their strategies to the opponent¡Çs observed behavior.
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