nep-des New Economics Papers
on Economic Design
Issue of 2019‒12‒09
five papers chosen by
Guillaume Haeringer, Baruch College and Alex Teytelboym, University of Oxford

  1. On the Disclosure of Promotion Value in Platforms with Learning Sellers By Yonatan Gur; Gregory Macnamara; Daniela Saban
  2. Information revelation in procurement auctions: an equivalence result By Domenico Colucci; Nicola Doni; Vincenzo Valori
  3. Facility Location Problem with Capacity Constraints: Algorithmic and Mechanism Design Perspectives By Haris Aziz; Hau Chan; Barton E. Lee; Bo Li; Toby Walsh
  4. Guarantees in Fair Division: beyond Divide & Choose and Moving Knifes By Anna bogomolnaia; Herve Moulin; Richard Stong
  5. Bounded Temporal Fairness for FIFO Financial Markets By Vasilios Mavroudis

  1. By: Yonatan Gur; Gregory Macnamara; Daniela Saban
    Abstract: We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage), and the information it reveals about the additional demand associated with being promoted. Identifying effective joint information design and promotion policies for the platform is a challenging dynamic problem as sellers can sequentially learn the promotion "value" from sales observations and update prices accordingly. We introduce the notion of confounding promotion polices, which are designed to prevent a Bayesian seller from learning the promotion value (at the cost of diverting consumers away from the best product offering). Leveraging this notion, we characterize the maximum long-run average consumer surplus that is achievable by the platform when the seller is myopic. We then establish that long-run average optimality can be maintained by optimizing over a class of joint information design and promotion policies under which the platform provides the seller with a (random) information signal at the beginning of the horizon, and then uses the best confounding promotion policy, which prevents the seller from further learning. Additionally, we show that myopic pricing is a best response to such a platform strategy, thereby establishing an approximate Bayesian Nash equilibrium between the platform and the seller. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models and evaluate the impact of the search environment and the design of promotions on consumer surplus.
    Date: 2019–11
  2. By: Domenico Colucci; Nicola Doni; Vincenzo Valori
    Abstract: Procurement auctions often involve quality considerations as a determinant of the final outcome. When the procurer has private information about qualities, various information policies may be used to affect the expected outcome. For auctions with two cost heterogeneous suppliers, this work defines a notion of duality between pairs of policies, and shows that dual policies are revenue equivalent.
    Keywords: procurement, information revelation, discriminatory policy, asymmetric auctions
    JEL: D44 D82 H57
    Date: 2019–11–01
  3. By: Haris Aziz; Hau Chan; Barton E. Lee; Bo Li; Toby Walsh
    Abstract: We consider the facility location problem in the one-dimensional setting where each facility can serve a limited number of agents from the algorithmic and mechanism design perspectives. From the algorithmic perspective, we prove that the corresponding optimization problem, where the goal is to locate facilities to minimize either the total cost to all agents or the maximum cost of any agent is NP-hard. However, we show that the problem is fixed-parameter tractable, and the optimal solution can be computed in polynomial time whenever the number of facilities is bounded, or when all facilities have identical capacities. We then consider the problem from a mechanism design perspective where the agents are strategic and need not reveal their true locations. We show that several natural mechanisms studied in the uncapacitated setting either lose strategyproofness or a bound on the solution quality for the total or maximum cost objective. We then propose new mechanisms that are strategyproof and achieve approximation guarantees that almost match the lower bounds.
    Date: 2019–11
  4. By: Anna bogomolnaia; Herve Moulin; Richard Stong
    Abstract: Divide and Choose among two agents, and the Diminishing Share (DS) and Moving Knife (MK) algorithms among many, elicit parsimonious information to guarantee to each a Fair Share, worth at least 1/n-th of the whole manna. Our n-person Divide and Choose (D&C) rule, unlike DS and MK, works if the manna has subjectively good and bad parts. If utilities are additive over indivisible items, it implements the canonical "Fair Share up to one item" approximation. The D&C rule also offers one interpretation of the Fair Share when utilities are neither additive nor monotonic . Under a mild continuity assumption, it guarantees to each agent her minMax utility: that of her best share in the worst possible partition. This is lower than her Maxmin utility: that of her worst share in the best possible partition. When the manna is unanimously good, or unanimously bad, better guarantees than minMax are feasible. Our Bid & Choose rules fix an additive benchmark measure of shares, and ask agents to bid the smallest size of a share they find acceptable. The resulting Guarantee is between the minMax and Maxmin utilities
    Date: 2019–11
  5. By: Vasilios Mavroudis
    Abstract: Financial exchange operators cater to the needs of their users while simultaneously ensuring compliance with the financial regulations. In this work, we focus on the operators' commitment for fair treatment of all competing participants. We first discuss unbounded temporal fairness and then investigate its implementation and infrastructure requirements for exchanges. We find that these requirements can be fully met only under ideal conditions and argue that unbounded fairness in FIFO markets is unrealistic. To further support this claim, we analyse several real-world incidents and show that subtle implementation inefficiencies and technical optimizations suffice to give unfair advantages to a minority of the participants. We finally introduce, {\epsilon}-fairness, a bounded definition of temporal fairness and discuss how it can be combined with non-continuous market designs to provide equal participant treatment with minimum divergence from the existing market operation.
    Date: 2019–11

This nep-des issue is ©2019 by Guillaume Haeringer and Alex Teytelboym. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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