Return to search

Essays on Network Formation and Attention

This dissertation tackles two important developing topics in economics: network formation and the allocation of attention. First, it examine the idea that the timing of entry into the network is a crucial determinant of a node’s final centrality. We propose a model of strategic network growth which makes novel predictions about the forward-looking behaviors of players. In particular, the model predicts that agents entering the network at specific times will become central “vie for dominance”. In a laboratory experiment, we find that players do exhibit “vying for dominance” behavior, but do not always do so at the predicted critical times. A model of heterogeneous risk aversion best fits the observed deviations from initial predictions. Timing determines whether players have the opportunity to become attempt to become dominant, but individual characteristics determine whether players exploit that opportunity. This dissertation also examines models of rational inattention, in which decision-makers rationally evaluate the trade-off between the costs and the benefits of information acquisition. We provide results on recovering the implicit attention cost function by looking at the relationship between incentives and performance. We conduct laboratory experiments consisting of simple perceptual tasks with fine-grained variation in the level of potential rewards. We find that most subjects exhibit monotonicity in performance with respect to potential rewards, and there is mixed evidence on continuity and convexity of costs. We also perform a model selection exercise and find that subjects’ behavior is generally most consistent with a small but diverse subset of cost functions commonly assumed in the literature.

Identiferoai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8N603HZ
Date January 2018
CreatorsNeligh, Nate Leigh
Source SetsColumbia University
LanguageEnglish
Detected LanguageEnglish
TypeTheses

Page generated in 0.0018 seconds