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Learning in public goods game: alternative model performance
Grohmannová, Klára ; Červinka, Michal (advisor) ; Gregor, Martin (referee)
Over the last fifty years the research field of the public goods game experi- ment became increasingly more convoluted. Especially in the area of learning models, which attempted to explain the experimental data, many models with different mechanisms were proposed and studied. However, when it comes to assessing the performance of the models in relation to each other, few conclu- sions were reached. This thesis attempts to further the comparative analysis conducted in Cotla (2015). Using R software simulations, three directional learning models are assessed on their ability to accurately predict experimental data. Among the considered models, K-strong equilibrium model is found to be the best predicting model for all of public goods game experiments considered in this thesis. JEL Classification C71, C73, C87, C92, D64, D90 Keywords public goods game, behavioral game theory, learning Title Learning in public goods game: alternative model performance
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Cooperation with incomplete monitoring
Caisl, Jakub ; Bauer, Michal (advisor) ; Červinka, Michal (referee)
We approach the topic of provision of public goods in an experimental economic study using the Public Goods Game setting. We allow subjects to punish each other but vary the structure and amount of information they can base their decisions upon. We try to add to the current literature on reciprocity and study whether people are willing to punish under such incomplete information. Since punishment under uncertain conditions can be quite destructive in terms of welfare, this closely relates to whether people are able to provide public good themselves or whether they need some external assistance. In terms of public policy, this can add to the debate on when should government provide certain services and when it should be left to individuals. In terms of behaviour inside of an enterprise, our study could be well applied to problems that arise when teams work on a common project but some people free-ride on effort of others.
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