This dissertation investigates three different aspects of pricing, employing different methodologies (algorithmic decision-making using reinforcement learning and recommendation systems using information design) and in various substantive areas (an e-commerce digital platform setting, online advertising, or the healthcare market). Chapter 1 empirically investigates the impact of firms using learning algorithms for pricing and advertising decisions in an e-commerce digital platform setting. Previous research suggests that when competing firms use algorithms for pricing decisions, the competition often results in tacit collusion—the algorithms learn to settle on higher than competitive prices, which increase firm profits but hurt consumers. However, in real business scenarios, firms need to make their pricing and advertising decisions together, and the outcomes of such two-dimensional decision-making in a competitive environment are unclear ex-ante without simulating the outcomes experimentally. Therefore, we conduct extensive multi-agent reinforcement learning simulation experiments, calibrated to estimates from large-scale keyword search data from Amazon.com. The results show that algorithms can facilitate “beneficial collusion,” resulting in “win-win-win” outcomes for consumers, sellers, and the platform. We collected and analyzed a large-scale high-frequency product keyword search dataset fromAmazon.com, which includes more than two thousand highly searched keywords. The dataset contains more than two million products and is representative of search results containing both sponsored and organic products from all IP addresses in the US. We estimate consumer consideration size, generate an algorithm usage index based on the correlation patterns in prices and find a negative interaction between the estimated consumer search costs and the algorithm usage index, providing empirical evidence of beneficial collusion. Chapter 2 examines pricing in the US healthcare market by empirically analyzing a large-scale nationwide proprietary healthcare insurance claims dataset. We ask how increased price transparency influences consumer shopping behavior and, ultimately, market prices. We find that potential savings from price shopping are expected to be limited after taking into consideration a few realistic factors in the market. This study presents an important setting where reducing information frictions will not necessarily increase consumer price shopping behavior and will not create downward pressure on high prices in the market. Chapter 3 shifts the focus to recommendation algorithms that firms can design to strategically provide information to consumers about products with uncertain matches to their tastes. We investigate scenarios where firms can also adjust their pricing strategies and examine whether algorithms that always recommend matching products are beneficial for the firm. We surprisingly find that firms are sometimes better off demarketing (i.e., not recommending a matching product) their products, and this is the case also for platforms.