nep-des New Economics Papers
on Economic Design
Issue of 2022‒03‒28
three papers chosen by
Alex Teytelboym
University of Oxford

  1. Market Design and Walrasian Equilibrium By Faruk R. Gul; Wolfgang Pesendorfer; Mu Zhang
  2. Information Design in Concave Games By Alex Smolin; Takuro Yamashita
  3. Inverse Selection By Markus Brunnermeier; Rohit Lamba; Carlos Segura-Rodriguez

  1. By: Faruk R. Gul (Princeton University); Wolfgang Pesendorfer (Princeton University); Mu Zhang (Princeton University)
    Abstract: We establish the existence of Walrasian equilibrium for economies with many discrete goods and possibly one divisible good. Our goal is not only to study Walrasian equilibria in new settings but also to facilitate the use of market mechanisms in resource allocation problems such as school choice or course selection. We consider all economies with quasilinear gross substitutes preferences but allow agents to have limited quantities of the divisible good (limited transfers economies). We also consider economies without adivisible good (nontransferable utility economies). We show the existence and efficiency of Walrasian equilibrium in limited transfers economies and the existence and efficiency of strong (Walrasian) equilibrium in nontransferable utility economies. Finally, we show that various constraints on minimum and maximum levels of consumption and aggregate constraints of the kind that are relevant for school choice/course selection problems can be accommodated by either incorporating these constraints into individual preferences or by incorporating a suitable production technology into nontransferable utility economies
    Keywords: Walrasian equilibrium
    JEL: D50 D59
    Date: 2020–06
  2. By: Alex Smolin; Takuro Yamashita
    Abstract: We study information design in games with a continuum of actions such that the players' payoffs are concave in their own actions. A designer chooses an information structure--a joint distribution of a state and a private signal of each player. The information structure induces a Bayesian game and is evaluated according to the expected designer's payoff under the equilibrium play. We develop a method that facilitates the search for an optimal information structure, i.e., one that cannot be outperformed by any other information structure, however complex. We show an information structure is optimal whenever it induces the strategies that can be implemented by an incentive contract in a dual, principal-agent problem which aggregates marginal payoffs of the players in the original game. We use this result to establish the optimality of Gaussian information structures in settings with quadratic payoffs and a multivariate normally distributed state. We analyze the details of optimal structures in a differentiated Bertrand competition and in a prediction game.
    Date: 2022–02
  3. By: Markus Brunnermeier (Princeton University); Rohit Lamba (Pennsylvania State University); Carlos Segura-Rodriguez (Banco Central de Costa Rica)
    Abstract: Big data, machine learning and AI inverts adverse selection problems. It allows insurers to infer statistical information and thereby reverses information advantage from the insuree to the insurer. In a setting with two-dimensional type space whose correlation can be inferred with big data we derive three results: First, a novel tradeoff between a belief gap and price discrimination emerges. The insurer tries to protect its statistical information by offering only a few screening contracts. Second, we show that forcing the insurance company to reveal its statistical information can be welfare improving. Third, we show in a setting with naive agents that do not perfectly infer statistical information from the price of offered contracts, price discrimination significantly boosts insurer’s profits. We also discuss the significance our analysis through three stylized facts: the rise of data brokers, the importance of consumer activism and regulatory forbearance, and merits of a public data repository.
    Keywords: Insurance, Big Data, Informed Principal, Belief Gap, Price Discrimination
    JEL: G22 D82 D86 C55
    Date: 2020–04

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