This dissertation comprises three papers in industrial organization and econometrics, aiming to understand how firms acquire information under competition, the role of an informational intermediary, and the inclusion of image information in demand estimation.

Chapter 1 examines the strategic decisions firms make regarding consumer information acquisition before engaging in market competition. A two-stage duopoly model is presented to explore how competition affects these information acquisition choices. In the first stage, two symmetric firms decide which consumer characteristics to learn. In the second stage, they use this information for costly advertising. In the monopoly benchmark, the firm never acquires partial information. Under competition, multiple equilibria exist. Drawing an analogy to Hotelling's principle of maximal differentiation, firms in equilibrium coordinate to differentiate their information acquisition choices. Conditions are identified under which firms learn different consumer characteristics in equilibrium. Additionally, an equilibrium path is characterized where the firm's payoff exhibits a hump shape as information costs increase, suggesting that cheaper information can harm firms by making coordination more difficult.

Chapter 2 explores the role of an intermediary that controls information disclosure but lacks pricing power. Specifically, the optimal information design for an intermediary earning commission fees from retained sales is investigated. The impact of disclosed information is twofold: while more informative policies can attract consumers, some level of concealment may lead to higher revenues. Considering these trade-offs, the optimal disclosure policy is characterized. The analysis reveals that the optimal policy employs upper censorship, fully disclosing information below a certain threshold while pooling all valuations exceeding this threshold. Utilizing this optimal signal characterization, a family of three-point mass prior distributions is proposed. Within this family of prior distributions, it is demonstrated that consumers are worse off under an exogenously full information disclosure policy compared to an endogenously determined upper censorship policy implemented by a strategic intermediary.

Chapter 3 investigates the integration of image information into demand estimation. The visualization of products is crucial for consumers making purchasing decisions. However, incorporating visual information into demand analysis presents challenges due to the high dimensionality of image data. A two-stage semi-nonparametric estimation strategy is proposed to estimate demand in differentiated markets using both aggregated data and image data. This strategy builds on the standard framework developed by Berry (1994) and Berry, Levinsohn and Pakes (1995) by incorporating image data into the model. In the first stage, the demand system is transformed into a partial linear form, utilizing a technique recently proposed by Lu, Shi and Tao (2023). In the second stage, a convolutional neural network (CNN) model from machine learning is applied to estimate the "visual utility" function. The estimation is conducted within a semi-nonparametric framework, with sieve estimation results used to establish consistency. A simulation study is included to demonstrate the proposed estimation strategy.

Power allocation across users in two adjacent cells is studied for a code-division multiple access (CDMA) data service. The forward link is considered and cells are modeled as one-dimensional with uniformly distributed users and orthogonal signatures within each cell. Each user is assumed to have a utility function that describes the user’s received utility, or willingness to pay, for a received signal-to-interference-plus-noise ratio (SINR). The objective is to allocate the transmitted power to maximize the total utility summed over all users subject to power constraints in each cell. It is first shown that this optimization can be achieved by a pricing scheme in which each base station announces a price per unit transmitted power to the users, and each user requests power to maximize individual surplus (utility minus cost). Setting prices to maximize total revenue over both cells is also considered, and it is shown that, in general, the solution is different from the one obtained by maximizing total utility. Conditions are given for which independent optimization in each cell, which leads to a Nash equilibrium (NE), is globally optimal. It is shown that, in general, coordination between the two cells is needed to achieve the maximum utility or revenue.

Power and code allocation across two adjacent cells is studied for the downlink of a code-division multiple-access voice network. Each user has a utility function that measures the user's willingness to pay, or utility, as a function of the received signal-to-interference-plus-noise-ratio. The objective is to maximize the total utility over the two cells subject to code and power constraints. When all active users receive the. same utility, the optimal allocation is characterized by a pair of threshold radii for the two cells, where each radius specifies the set of active users in that cell. The behavior of the optimal radii are characterized as a function of load and available resources (power and codes). The corresponding optimal power allocation can be achieved through a pricing scheme, in which each base station announces a price for each resource, and each user responds by requesting the amount of resources that maximizes the user's surplus (utility minus cost). We show that, depending on the load and resource constraints, the two cells may have to coordinate, or exchange information, in order to maximize the total utility.