nep-des New Economics Papers
on Economic Design
Issue of 2023‒01‒23
eight papers chosen by
Alex Teytelboym
University of Oxford

  1. Screening with Persuasion By Dirk Bergemann; Tibor Heumann; Stephen Morris
  2. School Choice with Farsighted Students By Ata Atay; Ana Mauleon; Vincent Vannetelbosch
  3. Optimal Robust Mechanism in Bilateral Trading By Komal Malik
  4. Limited Farsightedness in Priority-Based Matching By Ata Atay; Ana Mauleon; Vincent Vannetelbosch
  5. A Characterization of Maximum Nash Welfare for Indivisible Goods By Warut Suksompong
  6. Cross-game Learning and Cognitive Ability in Auctions By Giebe, Thomas; Ivanova-Stenzel, Radosveta; Kocher, Martin G.; Schudy, Simeon
  7. Optimal Refund Mechanism By Qianjun Lyu
  8. Approximate Bayesian Implementation and Exact Maxmin Implementation: An Equivalence By Song, Yangwei

  1. By: Dirk Bergemann; Tibor Heumann; Stephen Morris
    Abstract: We consider a general nonlinear pricing environment with private information. The seller can control both the signal that the buyers receive about their value and the selling mechanism. We characterize the optimal menu and information structure that jointly maximize the seller's profits. The optimal screening mechanism has finitely many items even with a continuum of values. We identify sufficient conditions under which the optimal mechanism has a single item. Thus the seller decreases the variety of items below the efficient level as a by-product of reducing the information rents of the buyer.
    Date: 2022–12
  2. By: Ata Atay; Ana Mauleon; Vincent Vannetelbosch
    Abstract: We consider priority-based school choice problems with farsighted students. We show that a singleton set consisting of the matching obtained from the Top Trading Cycles (TTC) mechanism is a farsighted stable set. However, the matching obtained from the Deferred Acceptance (DA) mechanism may not belong to any farsighted stable set. Hence, the TTC mechanism provides an assignment that is not only Pareto efficient but also farsightedly stable. Moreover, looking forward three steps ahead is already sufficient for stabilizing the matching obtained from the TTC.
    Date: 2022–12
  3. By: Komal Malik
    Abstract: We consider a model of bilateral trade with private values. The value of the buyer and the cost of the seller are jointly distributed. The true joint distribution is unknown to the designer, however, the marginal distributions of the value and the cost are known to the designer. The designer wants to find a trading mechanism that is robustly Bayesian incentive compatible, robustly individually rational, budget-balanced and maximizes the expected gains from trade over all such mechanisms. We refer to such a mechanism as an optimal robust mechanism. We establish equivalence between Bayesian incentive compatible mechanisms (BIC) and dominant strategy mechanisms (DSIC). We characterise the worst distribution for a given mechanism and use this characterisation to find an optimal robust mechanism. We show that there is an optimal robust mechanism that is deterministic (posted-price), dominant strategy incentive compatible, and ex-post individually rational. We also derive an explicit expression of the posted-price of such an optimal robust mechanism. We also show the equivalence between the efficiency gains from the optimal robust mechanism (max-min problem) and guaranteed efficiency gains if the designer could choose the mechanism after observing the true joint distribution (min-max problem).
    Date: 2022–12
  4. By: Ata Atay; Ana Mauleon; Vincent Vannetelbosch
    Abstract: We consider priority-based matching problems with limited farsightedness. We show that, once agents are sufficiently farsighted, the matching obtained from the Top Trading Cycles (TTC) algorithm becomes stable: a singleton set consisting of the TTC matching is a horizon-$k$ vNM stable set if the degree of farsightedness is greater than three times the number of agents in the largest cycle of the TTC. On the contrary, the matching obtained from the Deferred Acceptance (DA) algorithm may not belong to any horizon-$k$ vNM stable set for $k$ large enough.
    Date: 2022–12
  5. By: Warut Suksompong
    Abstract: In the allocation of indivisible goods, the maximum Nash welfare (MNW) rule, which chooses an allocation maximizing the product of the agents' utilities, has received substantial attention for its fairness. We characterize MNW as the only additive welfarist rule that satisfies envy-freeness up to one good. Our characterization holds in the simplest setting of two agents.
    Date: 2022–12
  6. By: Giebe, Thomas (Linnaeus University); Ivanova-Stenzel, Radosveta (TU Berlin); Kocher, Martin G. (University of Vienna, CESifo and University of Gothenburg); Schudy, Simeon (LMU Munich and CESifo)
    Abstract: Overbidding in sealed-bid second-price auctions (SPAs) has been shown to be persistent and associated with cognitive ability. We study experimentally to what extent cross-game learning can reduce overbidding in SPAs, taking into account cognitive skills. Employing an order-balanced design, we use first-price auctions (FPAs) to expose participants to an auction format in which losses from high bids are more salient than in SPAs. Experience in FPAs causes substantial cross-game learning for cognitively less able participants but does not affect overbidding for the cognitively more able. Vice versa, experiencing SPAs before bidding in an FPA does not substantially affect bidding behavior by the cognitively less able but, somewhat surprisingly, reduces bid shading by cognitively more able participants, resulting in lower profits in FPAs. Thus, 'cross-game learning' may rather be understood as 'cross-game transfer', as it has the potential to benefit bidders with lower cognitive ability whereas it has little or even adverse effects for higher-ability bidders.
    Keywords: cognitive ability; cross-game learning; cross-game transfer; experiment; auction; heuristics; first-price auctions; second-price auctions;
    JEL: C72 C91 D44 D83
    Date: 2022–12–27
  7. By: Qianjun Lyu (Institute for Microeconomics, University of Bonn)
    Abstract: This paper studies the optimal refund mechanism when an uninformed buyer can privately acquire information about his valuation over time. In principle, a refund mechanism can specify the odds that the seller requires the product returned while issuing a (partial) refund, which we call stochastic return. It guarantees the seller a strictly positive minimum revenue and facilitates intermediate buyer learning. In the benchmark model, stochastic return is sub-optimal. The optimal refund mechanism takes simple forms: the seller either deters learning via a well-designed non-refundable price or encourages full learning and escalates price discrimination via free return. This result is robust to both good news and bad news framework.
    Keywords: buyer learning, refund contract, information design, implementable mechanism
    JEL: D82 D86 L15
    Date: 2022–12
  8. By: Song, Yangwei (HU Berlin)
    Abstract: This paper provides a micro-foundation for approximate incentive compatibility using ambiguity aversion. In particular, we propose a novel notion of approximate interim incentive compatibility, approximate local incentive compatibility, and establish an equivalence between approximate local incentive compatibility in a Bayesian environment and exact interim incentive compatibility in the presence of a small degree of ambiguity. We then apply our result to the implementation of efficient allocations. In particular, we identify three economic settings—including ones in which approximately efficient allocations are implementable, ones in which agents are informationally small, and large double auctions—in which efficient allocations are approximately locally implementable when agents are Bayesian. Applying our result to those settings, we conclude that efficient allocations are exactly implementable when agents perceive a small degree of ambiguity.
    Keywords: approximate local incentive compatibility; ambiguity aversion; efficiency; informational size; modified VCG mechanism; double auction;
    Date: 2022–12–28

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