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We study a duel game in which each player has incomplete knowledge of the game parameters. We present a simple, heuristically motivated and easily implemented algorithm by which, in the course of repeated plays, each player estimates the missing parameters and consequently learns his optimal...
Persistent link: https://ebvufind01.dmz1.zbw.eu/10015271702
affiliation-motivated individuals engaged in game-play. The first model captures learning by motivated agents during strategic …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011384060
We propose a framework in order to econometrically estimate case-based learning and apply it to empirical data from … twelve 2 × 2 mixed strategy equilibria experiments. Case-based learning allows agents to explicitly incorporate information …-based learning to other learning models (reinforcement learning and self-tuned experience weighted attraction learning) while using …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10012432206
local interactions. Amongst others, we discuss best reply learning in a global- and in a local- interaction framework and … best reply learning in multiple location models and in a network formation context. Further, we discuss imitation learning …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010369364
In this paper, we provide a theoretical prediction of the way in which adaptive players behave in the long run in normal form games with strict Nash equilibria. In the model, each player assigns subjective payoff assessments to his own actions, where the assessment of each action is a weighted...
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010369376
This paper describes the 'Bounded Memory, Inertia, Sampling and Weighting' (BI-SAW) model, which won the http://sites.google.com/site/gpredcomp/Market Entry Prediction Competition in 2010. The BI-SAW model refines the I-SAW Model (Erev et al. [1]) by adding the assumption of limited memory span....
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010369381
We submitted three models to the competition which were based on the I-SAW model. The models introduced four new assumptions. In the first model an adjustment process was introduced through which the tendency for exploration was higher at the beginning and decreased over time in the exploration...
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010369415
Our study analyzes theories of learning for strategic interactions in networks. Participants played two of the 2 × 2 …, payoff-sampling equilibrium, and impulse balance equilibrium) which represent the long-run equilibrium of a learning process …. Secondly, we relate our results to four different learning models (impulse-matching learning, action-sampling learning, self …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011709833
affiliation-motivated individuals engaged in game-play. The first model captures learning by motivated agents during strategic …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011709868
cooperation. We model agents' learning when they imitate successful players over similar games, but lack any information about the …
Persistent link: https://ebvufind01.dmz1.zbw.eu/10012227750