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On this webpage you will find information on my research on learning across games and categorization. This research was partly funded by the EU under the Horizon 2020 programme. Funding is gratefully acknowleged.
Publications:
- Extrapolation and Structural Similarity in Games (joint with E. Sciubba), Economics Letters 125(3), (2014), 381-385. [Replication Data]
Abstract: We conduct a laboratory experiment and provide evidence of learning spillovers within and across equivalence classes of “structurally similar” games. These spillovers are inconsistent with existing theories of learning in games.
- An Experiment on Learning in a Multiple Games Environment,(joint with V. Grimm), Journal of Economic Theory 147(6), (2012), 2220-2259. [Replication Data]
Abstract: We study how players learn to make decisions if they face many different games. Games are drawn randomly from a set of either two or six games in each of 100 rounds. If either there are few games or if extensive summary information is provided (or both) convergence to the unique Nash equilibrium generally occurs. Otherwise this is not the case. We demonstrate that there are learning spillovers across games but participants learn to play strategically equivalent games in the same way. Our design and analysis allow us to distinguish between different sources of complexity and theoretical models of categorization
- On the Evolution of Coarse Categories, Journal of Theoretical Biology, 307 (2012), 117-124.
Abstract: We compare the evolutionary fitness of different cultures (or populations), where we think of culture as partitioning a set of decision situations into categories of situations treated the same. Information about optimal behavior in each category is passed on via a process of noisy cultural transmission. We show that coarse partitions (distinguishing less situations) can provide higher evolutionary fitness even if there are no explicit costs to holding finer partitions.
- Learning Across Games, Games and Economic Behavior, 74(2), (2012), 601-619.
Abstract: This paper studies the learning process carried out by two agents who are involved in many games. As distinguishing all games can be too costly (require too much reasoning resources) agents might partition the set of all games into categories. Partitions of higher cardinality are more costly. A process of simultaneous learning of actions and partitions is presented and equilibrium partitions and action choices characterized. Learning across games can destabilize strict Nash equilibria even for arbitrarily small reasoning costs and even if players distinguish all the games at the stable point. The model is also able to explain experimental findings from the travelerʼs dilemma and deviations from subgame perfection in bargaining games.
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