This dissertation studies a variety of topics related to information manipulation, such as the manipulation of reviews in online rating platforms, or the act of misreporting one's preferences in matching mechanisms; and how those manipulations affect the overall allocation in the economy.

Chapter 1 analyses the incentives that drive some sellers to fake reviews in online rating platforms, such as Amazon and Yelp. Among other things, I find that sellers' optimal investment in fake reviews is not a monotone function of their reputation. More precisely, sellers that currently possess a very good or very bad history of past reviews have less incentives to solicit fake reviews praising their own products, the intuition being that, for sellers with very bad reputation, it is too costly to pretend that they are high quality sellers; while sellers that have already accumulated a very good reputation do not need to spend much effort in convincing buyers that they are high quality sellers.

Moreover, in order to maximize the impact from each fake review, sellers tend to concentrate review manipulation at the initial stages after they have entered the market.

Chapter 2 develops a theoretical model aimed at explaining the observed polarization on agents' beliefs regarding topics that have objective truths (e.g., such as whether or not global warming is a hoax). The main premises surrounding the model are that rational agents seek to learn the truth about a certain state of the world, but the acquisition of information is costly, and the available information channels are biased and imprecise. The paper vies to understand how the level of bias from those channels affect opinion polarization overall.

Chapter 3 analyses agents' incentives to misreport their preferences or vacancies in large stable matches. I find that, under certain assumptions, those incentives vanish for sufficiently large markets, suggesting that stable matching mechanisms are effectively strategy-proof for sufficiently thick markets.