Sequential Selection of Correlated Ads by POMDPs

Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines

Advertising, Entry Deterrence, and Industry Innovation

This paper studies how advertising influences firms’ incentives to invest in R&D. The link between advertising and industry innovation is important, not only because advertising can spur R&D by spreading product knowledge, but also because advertising can discourage new innovative firms from entering the industry. This paper finds that a worse advertising technology can result in local improvements in industry innovation rates. Globally, however, a complete ban on advertising always reduce industry growth. This result is significant because industry advertising spending is quantitatively significant and there are potential connections between public policy towards advertising and R&D. This paper presents a variant of the Grossman and Helpman (1991) quality ladder model. The key difference is that the model in this paper allows advertising to gradually spread product awareness among consumers. This model differs from the entry deterrence literature by assuming perfect price discrimination. Technically, this assumption allows a fully tractable model and analytical characterization of a stationary equilibrium in a dynamic setting, which is not previously available. In terms of economic analysis, this assumption eliminates the extra profit incentives for new firms to enter early, and makes incumbent firms more inclined to use advertising as a deterrent.Advertising, Entry Deterrence, Innovation

Estimation of cost synergies from mergers without cost data: Application to U.S. radio

This paper develops a new way to estimate cost synergies from mergers without using actual data on cost. The estimator uses a structural model in which companies play a dynamic game with endogenous mergers and product repositioning decisions. Such a formulation has several benefits over the widespread static merger analysis. In particular, it corrects for sample selection of more profitable mergers and captures follow-up mergers and post-merger product repositioning. The framework is applied to estimate cost efficiencies after the deregulation of U.S. radio in 1996. The procedure uses the data on radio station characteristics and numerous acquisitions, without explicit need for cost data. It turns out that between 1996 and 2006 additional ownership concentration generated $2.5b per-year cost savings, which is about 10% of total industry revenue.

Habits, Market Power, and Policy Selection

This paper examines monopolistic behavior in a framework with habit formation and consumer commitment. We show that time consistent output and pricing policies yield di®erent market outcomes. Policy selection determines the strategic properties of the producer's intra-personal game: current and future quantities are strategic com- plements, while current and future prices are strategic substitutes. In both a simple two-period model and an in¯nite-horizon model, we ¯nd that pricing policies allow the monopolist to attain higher equilibrium pro¯ts.Habit persistence, monopoly, time consistency

Monopoly Pricing of Experience Goods

We develop a dynamic model of experience goods pricing with independent private valuations. We show that the optimal paths of sales and prices can be described in terms of a simple dichotomy. In a mass market, prices are declining over time. In a niche market, the optimal prices are initially low followed by higher prices that extract surplus from the buyers with a high willingness to pay. We consider extensions of the model to integrate elements of social rather than private learning and turnover among buyers.Monopoly, dynamic pricing, learning, experience goods, continuous time, Markov perfect equilibrium

Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach

This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain. Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product