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This dissertation mainly focuses on revenue management problems that focus on demand learning and sharing economy. It includes three major chapters. In Chapter 1, we consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions but does not directly control the transaction prices, which are endogenously determined. Buyers and sellers are divided into types, and we represent the compatibility between different types using a bipartite network. Traders are heterogeneous in terms of their valuations, and different types have possibly different value distributions. Buyers may have additional value for trading with some seller types. The platform chooses commissions/subscriptions to maximize its revenues. Two salient features of most online platforms are that they do not dictate the transaction prices, and use commissions/subscriptions for extracting revenues. We shed light on how these commissions/subscriptions should be set in networked markets. Using tools from convex optimization and combinatorial optimization, we obtain tractable methods for computing the optimal commissions/subscriptions and provide insights into the platform's revenues, buyer/seller surplus, and welfare. We provide a tractable convex optimization formulation to obtain the revenue-maximizing commissions/subscriptions, and establish that, typically, different types should be charged different commissions/subscriptions depending on their network positions. In Chapter 2, we consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces an uncertainty about the customers' forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod sales season, the firm sequentially marks down the product's price and makes demand observations to learn the underlying demand model. Because forward-looking customers create an intertemporal dependency, we identify that the keys to achieving good profit performance are: (i) judiciously accumulating information on the demand model, (ii) preserving the market size in early sales periods, and (iii) limiting the impact of the firm's learning on the forward-looking customers. Based on these, we construct and analyze markdown policies that exhibit near-optimal performance under a wide variety of forward-looking customer behaviors. In Chapter 3, we consider a platform in which multiple sellers offer their products for sale over a time horizon of $T$ periods. Each seller sets its own price. The platform collects a fraction of the sales revenue, and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller's product is a function of all sellers' prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales observations: each seller observes its own sales, whereas the platform observes all sellers' sales as well as the customer feature information. In this setting, the platform faces a trade-off between exploiting its informational advantage and revealing information to facilitate demand learning. Measuring the platform's performance by comparing its expected revenue with the full-information optimal revenue, we design policies that enable the platform to judiciously manage information revelation and price-setting incentives.




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