Doctor of Philosophy (PhD)



Document Type



The first chapter provides a way of evaluating a player's contribution to their team and relates their effort to their market values. We extend the work of Ballester et al. (2006) by incorporating a network outcome component in the players' payoff functions. As an illustration of the theory, we create a unique data set from the UEFA Euro 2008 tournament. To capture the interaction between players, we create the passing network of each team. This all allows us to identify the key player and key groups of players for both teams in each game. We then use our measure to explain player ratings by experts and their market values. Our measure is significant in explaining expert ratings. We also find that players having higher intercentrality measures, regardless of their field position have significantly higher market values. The second chapter characterizes efficient networks in player and partner heterogeneity models for both the one-way flow and the two-way flow models. Player (partner) dependent network formation allows benefits and costs to be player (partner) heterogeneous which is an important extension for modeling social networks in the real world. Employing widely used assumptions, I show that efficient networks in the two-way flow model are minimally connected and have star or derivative of star type architectures, whereas efficient networks in the one way flow model have wheel architectures. The third chapter considers a non-cooperative network formation game where identity is introduced as a single dimension to capture the player characteristics. Each player is allowed to choose their commitment level to their identities. The cost of link formation decreases as the players forming the link share the same identity and higher commitment levels. We then introduce link and node imperfections to the model. Each existing link in the network successfully transmits information with a probability. We consider two cases for reliability probability of existing links: a homogenous probability, p and heterogeneous probability. We characterize the Nash networks and we find that the set of Nash networks are either singletons with no links formed or separated blocks or components with mixed blocks or connected.



Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Sarangi, Sudipta

Included in

Economics Commons