In this paper, I introduce a profit-maximizing centralized marketplace into a decentralized market with frictions. I analyze the equilibria of a market choice game where agents choose between the centralized marketplace and decentralized bilateral trade. I use a mechanism design approach to characterize the optimal rules of the profit-maximizing marketplace in this game. In the unique equilibrium, the centralized marketplace and decentralized trade coexist. The thickness of the centralized marketplace is not affected by the decentralized trade. The equilibrium profit of the marketplace is at least half of the baseline profit, independent of the distribution of valuations. The decrease in the profit as a result of decentralized trade only depends on the efficiency of the decentralized market and not on the distribution. Under some conditions, this equilibrium results in higher welfare than either institution on its own.
In financial markets as well as online marketplaces, each user can be a buyer or a seller depending on the market conditions and their endowments. Here, I consider the problem of designing a marketplace for such a market with a divisible good to maximize profit. I first focus on Dominant-Strategy Implementable mechanisms and invoke the revelation principle. I show that the designer's profit is the expected virtual surplus. Then, I describe the optimal allocation through an algorithm. After finding the optimal Dominant Strategy Implementable mechanism, I argue that this mechanism is in fact optimal within the class of Bayesian Implementable mechanisms as well. Finally, I consider an extension where the marketplace itself can own some endowments and illustrate the type of inefficiency this can lead.
In this paper, I show that the existence of a solution to a market design problem can be obtained as long as the designer's and the agents' preferences satisfy any sufficiently well-behaved abstract convexity, using `convex' price orders that rank bundles instead of price vectors. Walrasian Equilibrium is obtained as a special case.
In this paper, I provide two characterizations for the existence of stable matchings in this environment. Moreover, if `part-time' contracts are allowed, I show that there is always a stable matching. Finally, I introduce a measure of instability in the market, measured as the amount of subsidy needed to `stabilize' an efficient outcome.
Here, we study the Non-Bayesian Persuasion problems where the agents' non-Bayesian belief updates make some posteriors infeasible. Here we show that the standard tool used by most of the information design literature, concavification, may not be feasible. However, it is possible to modify the concavification approach.
We study how a seller can optimally conceal the available quantity to maximize the revenue. We show that introducing any uncertainty increases the expected revenue (compared to the case the quantity available is known with certainty). Then, we find the optimal belief the designer would like the buyers to have. Lastly, we show that the designer cannot improve the revenue in a classical Bayesian persuasion game.
In this paper, we study the problem of revenue-maximization where buyers can first choose how much they want to learn about their valuations. Single-buyer version of this problem has been studied in the literature: There, the buyer optimally balances the costs of knowing too much and too little to be exploited by the seller. However, with multiple buyers, knowing `less than' the other buyers is itself a disadvantage. We study several selling mechanisms and show that in certain cases, obtaining full information is an equilibrium.