This dissertation studies three topics in information economics. In Chapter One, “Monopoly and Competition in the Markets for Information”, I analyze the generation and provision of information products, and the implications of competition in these markets. In the model, buyers face a decision problem with uncertainty about the states of the world. A buyer can purchase experiments to augment his private information, therefore the value of an experiment depends on his private information. To generate these experiments, sellers have to make an investment, which determines the most informative experiment a seller can provide. Sellers then post menus of experiments and prices. I first characterize the optimal menu given any investment level and derive the optimal investment. When two sellers compete with investment, I find an equilibrium in which two sellers split the market: one seller only serves to high belief buyers and the other serves to low beliefs buyers. Each seller specializes in generating a more informative signal about one state. Monopoly seller always provides more informative experiments, and to more buyers, than the case of duopoly competition.

In Chapter Two, “Preferential Attachment as an Information Cascade in Emerging Networks”, I study the preferential attachment observed in real-world social networks as a social learning problem. Networks grown via preferential attachment exhibit the "rich-get-richer" phenomena; nodes with higher connectivity degree are more likely to acquire more connections. This chapter develops a Bayesian social learning model in which agents arriving sequentially to a network judge the qualities of predecessor agents based on their own private signals, and on public signals inferred from the observed network structure. It shows that preferential attachment emerges endogenously as a sequentially equilibrium of the social learning process, where agents may engage in a rational herd behavior. The condensed preferential attachment, in which one agent gets all the future links, emerges with probability one when the private signals are bounded.

In Chapter Three, “Information Design in Contests”, I consider the information disclosure problem in contests. The designer of a contest has an informational advantage over agents’ ability. There is a strong agent (res. weak agent) who will has a higher probability of being a high ability (res. low ability) player. In the optimal information disclosure policy, the designer discriminates two types of agents. When the weak agent has a disadvantage in abilities, the designer will partially disclose the state to him privately. Compared with the no-disclosure benchmark, the optimal policy increases the total effort level. On the other hand, committing to a public message disclosure can not improve the equilibrium of the no-disclosure benchmark.