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Learning in Networks—An Experimental Study Using Stationary Concepts

Institute of Economics, Karlsruhe Institute of Technology (KIT), P.O. Box 6980, Karlsruhe D-76049, Germany
Otto-von-Guericke University Magdeburg, Faculty of Economics and Management, P.O. Box 4120, Magdeburg 39016, Germany
Author to whom correspondence should be addressed.
Games 2014, 5(3), 140-159;
Received: 21 January 2014 / Revised: 8 July 2014 / Accepted: 9 July 2014 / Published: 31 July 2014
(This article belongs to the Special Issue Social Networks and Network Formation 2013)
PDF [820 KB, uploaded 31 July 2014]


Our study analyzes theories of learning for strategic interactions in networks. Participants played two of the 2 × 2 games used by Selten and Chmura [1]. Every participant played against four neighbors. As a distinct aspect our experimental design allows players to choose different strategies against each different neighbor. The games were played in two network structures: a lattice and a circle. We analyze our results with respect to three aspects. We first compare our results with the predictions of five different equilibrium concepts (Nash equilibrium, quantal response equilibrium, action-sampling equilibrium, 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-tuning EWA, and reinforcement learning) which are based on the (behavioral) round-by-round learning process. At last, we compare the data with the experimental results of Selten and Chmura [1]. One main result is that the majority of players choose the same strategy against each neighbor. As other results, we observe an order of predictive success for the equilibrium concepts that is different from the order shown by Selten and Chmura and an order of predictive success for the learning models that is only slightly different from the order shown in a recent paper by Chmura, Goerg and Selten [2]. View Full-Text
Keywords: stationary concepts, networks; learning; experimental economics stationary concepts, networks; learning; experimental economics

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Berninghaus, S.K.; Neumann, T.; Vogt, B. Learning in Networks—An Experimental Study Using Stationary Concepts. Games 2014, 5, 140-159.

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