This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network. Players only have incomplete information on the network structure. The first part of this thesis studies how players' beliefs over the network they belong to affect game-theoretic outcomes, and develops a natural model for players' beliefs. The second part of this thesis focuses on learning in games. An intuitive learning model is introduced, and the predictions of this model are analyzed. Furthermore, learning in a class of congestion games is studied from different perspectives.
|Qualification||Doctor of Philosophy|
|Award date||9 Apr 2008|
|Place of Publication||Tilburg|
|Publication status||Published - 2008|