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on Economic Design |
| By: | Federico Echenique; Yu-Ting Ho |
| Abstract: | We analyze auctions with interdependent values and horizontally differentiated bidders, where good news for one bidder is bad news for the other bidders. This generates a winner's bliss, reversing the well-known winner's curse: winning means buying an object that is more valuable than a na\"{\i}ve estimate suggests. Our main result is about the English auction. We introduce the ``own effect property, '' generalizing the single-crossing condition, to prove the existence of an efficient ex-post equilibrium. Regarding revenue, we show that with affiliated signals, the English auction yields no more revenue than the second-price auction, while independent signals yield the standard revenue equivalence. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25576 |
| By: | Barış Kaymak |
| Abstract: | This paper compares equilibrium bidding strategies in a first-price sealed-bid auction with two asymmetric bidders who have dependent private valuations. Modeling bidder strength via a stochastic asymmetry condition – where one bidder is systematically more likely to hold the maximum valuation – I show that the globally weak bidder, facing a competitive disadvantage at all top valuation levels, bids more aggressively at every valuation. Conversely, the stronger bidder wins more frequently, generalizing Maskin and Riley (2000) to dependent private-value environments. When the stochastic asymmetry flips once, so that each bidder is stronger over a different range, the bid distributions cross once, with each bidder bidding more aggressively where their competitive disadvantage is most acute. In both environments, allocative inefficiency is an inherent feature of the equilibrium, driven by the stronger bidder's heightened incentives to shade bids relative to their valuation. The results translate to procurement auctions and wage competition between employers, and extend to interdependent-value auctions and the war of attrition under modified conditions. |
| Keywords: | asymmetric auctions; bid shading; first price auctions; dependent valuations |
| JEL: | D44 C72 D82 |
| Date: | 2026–10–07 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedcwq:103917 |
| By: | Mingyu Guo; Jiayuan Liu; Vincent Conitzer |
| Abstract: | We aim to design revenue-maximizing single-item auctions that are deterministic, strategy-proof, and ex post individually rational --- the quintessential fundamental model for optimal mechanism design. While Myerson's seminal work solved this model for independent bidders, straightforward extensions to correlated settings often result in non-monotonic, and therefore non-strategy-proof allocations. Critically, optimal allocation design for correlated bidders is NP-hard to approximate, rendering theoretical optimality guarantees in this domain computationally intractable. We establish a new standard of empirical rigor for this theoretically hard model by proposing an empirical pipeline. We synthesize neural interpolation of Myerson's greedy allocation based on marginal profits (the correlated variant of virtual valuation), formal neural verification to enforce exact strategy-proofness, and a constructive repair procedure. Empirically, our method is highly effective, consistently achieves near-optimal revenue across a wide range of distributions, including synthetic, adversarial, and real-world (consumer, financial, and industrial) datasets. Compared to existing manual and neural baselines, our approach shows substantial improvement, often reducing the revenue gap by an order of magnitude. Our study is also the first to introduce (unattainable) greedy revenue as a rigorous upper bound for empirical benchmarking, providing a definitive quantitative performance measure where true optima remain theoretically elusive. We demonstrate the generality of our approach by extending it to multi-unit auctions with unit demand and integrating our verification techniques into RegretNet to achieve exact strategy-proofness. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24240 |
| By: | Linchen Zhang |
| Abstract: | I study the coarse revenue guarantee (CRG) of the single-unit first-price auction with common values. The CRG is the minimum expected revenue over all information structures and all Bayes coarse correlated equilibria (BCCE), the equilibrium notion to which no-regret learning leads. Identical play, in which all bidders submit the same bid, attains the worst-case revenue, and this reduction yields a closed-form characterization. The coarse guarantee is positive and increases with the number of bidders $N$. It lies strictly below the revenue guarantee of Bergemann, Brooks and Morris (2017) at every finite $n$, and the gap between the two closes at rate $\log N / N$. I extend the analysis to reserve prices, symmetric and asymmetric priors, other standard auctions, and an unknown value distribution. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24901 |
| By: | Paola Manasero; Pablo Neme |
| Abstract: | We study two-sided many-to-many matching markets where agents' preferences satisfy strong substitutability and the law of aggregate demand (LAD). In this framework, the set of stable matchings forms a finite distributive lattice, and quantile stable matchings exist and satisfy the polarization of interests. Building on the cycle-reduction machinery of Bonifacio et al. (2022), we provide a purely combinatorial characterization of quantile stable matchings formulated in terms of preference cycles and their induced precedence order. Specifically, we define an order-theoretic counting function on cycles and prove that each quantile stable matching corresponds to the elimination of an endogenously determined closed set of cycles starting from the firm-optimal stable matching. This characterization extends the rotation-based framework of Cheng (2008) from one-to-one markets to many-to-many markets and bypasses the need for exhaustive outcome enumeration or partner sorting across the entire stable set. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.23282 |
| By: | Xiaoming Cai; Pieter A. Gautier |
| Abstract: | How does buyers’ search for bundles generate sorting among heterogeneous sellers? We study competitive search in which buyers assemble bundles through separate transactions, where prices direct search and determine congestion. With two seller types, buyers choose only same-type bundles below a single threshold in cross-type value and cross-type bundles above it. The threshold lies strictly between geometric and arithmetic means of same-type values, so search produces cross-type matching even when types are complements. With a continuum of types, log-supermodularity and submodularity yield unique stable equilibria with positive and negative assortative matching. Positive sorting extends to any fixed number of visits. |
| Keywords: | competitive search, search for bundles, assortative matching, seller heterogeneity |
| JEL: | C78 D83 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13058 |
| By: | Hexiao Liu; Lijian Lu; Mingzheng Wang |
| Abstract: | Digital marketplaces increasingly earn revenue from transaction commissions and seller-facing data services. Sellers may purchase data to increase revenue or avoid competing with an informed rival. We examine how consumer disclosure rights shape these motives and the platform's incentive to provide data. We develop a game-theoretic model of a commissioned platform, two differentiated sellers, and privacy-sensitive consumers. Data generates value through bilateral sales-revenue gains and competition threats arising from asymmetric access. Under mandatory collection, the platform can monetize defensive adoption, even when bilateral personalization reduces seller revenue. Under voluntary disclosure, anonymous purchasing and strictly positive privacy costs make unilateral disclosure unattractive in the baseline. Exclusive access thus yields no usable information, restores sellers' no-data rejection payoffs, and eliminates defensive fees. Profitable provision therefore requires nonnegative bilateral revenue gains. Under full fee extraction, sellers retain their no-data net profits. This revenue alignment does not guarantee welfare gains: additional transactions may benefit consumers, whereas segmentation in an already covered market can raise anonymous prices and reduce welfare. Capping anonymous offers at their no-data benchmark protects individual consumers and limits positive-mass provision to the transaction-creation region. Extensions consider access choice, heterogeneous privacy costs, imperfect identification, and limited contractual credibility. Platforms should evaluate data services based on both bilateral-use and seller-rejection outcomes, and sellers should distinguish revenue-enhancing from defensive investments. Regulators should distinguish equal-access rules from effective consumer control and monitor pricing in anonymous channels. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.22864 |
| By: | Lukyanov, Georgy; Zolotarev, Mikhail |
| Abstract: | Public support for early adoption can affect what later users learn from observed choices. An adopter may have favourable private information, or may have received a rebate. We study a government that randomly assigns eligibility for a fixed rebate and chooses the program’s coverage and reporting rule. We characterize when disclosing individual eligibility changes optimal coverage from zero to a positive rate under general convex program costs. This requires both a favourable marginal return to a disclosed program and a bound on the direct benefit available from a large opaque intervention. In the benchmark, disclosure allows the government to increase early adoption and welfare while leaving expected total adoption unchanged. We then allow imperfect compliance and private information for the late user. An eligibility label can be strictly informative without changing anyone’s decision, in which case disclosure has no welfare value. The policy reversal survives when both extensions are present. The results identify when an eligibility record is a useful part of the design of an adoption program. |
| Keywords: | social learning; adoption rebates; policy disclosure; early adoption; public program design |
| JEL: | D83 H23 D62 |
| Date: | 2026–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:132288 |
| By: | Yichen Huang |
| Abstract: | We study simple auctions for one additive buyer whose values for $m$ items are independent. Write $\mathrm{REV}$ for optimal revenue, $\mathrm{SREV}$ and $\mathrm{BREV}$ for the revenues from separate selling and grand bundling, and $\mathrm{BSPA}_2$ for grand-bundle second-price revenue with two independent bidders. For independent regular items, we prove $\mathrm{REV}\le\mathrm{BSPA}_2\le2\mathrm{BREV}$. This establishes tight approximation and competition guarantees for bundling under regularity and answers an open question from Beyhaghi et al. (EC 2025). For general independent items, we prove $\mathrm{REV} \le \mathrm{SREV} + \frac{\pi^2}{6}\mathrm{BREV} \le 2.645 \max\{\mathrm{SREV}, \mathrm{BREV}\}$. This improves the previous approximation factors of $6$ from Babaioff, Immorlica, Lucier, and Weinberg (FOCS 2014, JACM 2020) and $5.2$ from Ma and Simchi-Levi (AISTATS 2021). On the lower bound side, we construct an instance in which prices for three disjoint bundles earn more than $2.11\max\{\mathrm{SREV}, \mathrm{BREV}\}$, improving the previous lower bound of $2$ from Rubinstein (ITCS 2016). We also show that exact recovery of one buyer's optimal revenue by the better of separate selling and bundling requires $\Theta(\log m)$ bidders in the worst case. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24973 |
| By: | Florian Brandl (University of Bonn); Wanying Huang (Monash University); Atulya Jain (University of Bonn) |
| Abstract: | We study whether a social planner can restore efficient learning, defined as a finite expected number of incorrect actions, in the canonical sequential social learning model. For each agent, the planner chooses what social information to disclose and what action-contingent transfer to offer. For unbounded signal distributions with regular tails, we show that whenever learning is inefficient without intervention, no combination of disclosure and transfers can restore efficiency with a finite budget. In particular, disclosure alone cannot restore efficiency. We identify agents' incentives as the key source of this impossibility. The budget requirement, however, concerns the transfers offered rather than the payments made. We show that even under full disclosure, the planner can achieve efficient learning using transfers while keeping expenditure arbitrarily small. Thus, the need for an unlimited budget does not entail large payments. |
| Keywords: | Social learning; information disclosure; efficient learning; transfers |
| JEL: | D83 D82 C73 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:433 |
| By: | Stéphane Auray; Frédéric Loss (CY Cergy Paris Université, THEMA) |
| Abstract: | Geopolitical tensions, cyber operations, and supply-chain disruptions have made firms’ resilience to hostile shocks a central concern for economic policy. We develop a unified framework in which firms invest in resilience, a strategic attacker decides whether to strike, and a regulator subsidizes resilience under costly public funds. We analyze static and dynamic environments under symmetric and asymmetric information. In the static benchmark, we characterize the optimal subsidy scheme and show how Myersonian virtual costs distort resilience investment downward and increase attack incentives relative to full information. We then extend the model to repeated interactions in which resilience is a depreciating stock. We derive dynamic virtual costs and characterize stationary first-best and second-best resilience paths, showing that information frictions permanently reduce resilience and raise attack intensity. Three additional extensions examine declining defensive effectiveness, endogenous attack technology, and a forward-looking attacker who strategically erodes resilience over time. These settings generate, respectively, persistent underinvestment as defenses lose effectiveness, an arms race between attackers and defenders, and stationary Markov-perfect equilibria. Across all specifications, asymmetric information and costly public funds systematically reduce resilience investment, with persistent consequences for attack intensity, defensive effectiveness, technological adaptation, and attackers’ strategic incentives. |
| Keywords: | resilience, geopolitical risk, cyber security, dynamic mechanism design, virtual costs, regulation, strategic attacker |
| JEL: | D82 D86 H23 H56 L51 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ema:worpap:2026-15 |
| By: | She, Huixin |
| Abstract: | Digital platforms display sellers' prices and collect ad valorem commissions. We study how these roles shape sellers' disclosure choices and a platform's incentives to regulate them. Sellers offer a base good and an avoidable add-on, which some consumers overlook when choosing sellers. Because commissions apply to the base good but not off-platform add-ons, add-on profit is passed through more than one-for-one into lower base-good prices, creating a competition multiplier. Hidden add-on prices can reduce efficiency while raising consumer surplus through this mechanism. A platform regulates disclosure to protect commission revenue, while mandatory transparency can improve efficiency but reduce consumer surplus. |
| Keywords: | Platform governance; add-on pricing; consumer myopia; market transparency;; ad-valorem commissions; shrouding, information disclosure |
| JEL: | D11 D21 D82 L12 L15 L81 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:132263 |
| By: | Sarah Auster (University of Bonn, Department of Economics & CEPR); Helene Mass (Department of Economics & University of Vienna) |
| Abstract: | We study the value of commitment in verifiable disclosure games when the receiver faces non-quantifiable uncertainty about the evidence environment. The sender has stochastic access to hard evidence and may disclose or suppress it; the receiver chooses a decision rule to maximize her payoff guarantee. When ambiguity concerns the payoff state, the receiver has strict incentives to soften her standards ex post after non-disclosure, making commitment strictly valuable. When ambiguity concerns the sender’s expertise, incentives reverse: the receiver prefers a stricter reaction than is ex-ante optimal, so prespecified, hard-wired rules for non-disclosure improve payoffs for both sides. |
| Keywords: | Ambiguity, Commitment |
| JEL: | D81 D82 D86 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:435 |
| By: | Huseyin Yildirim |
| Abstract: | A principal designs ratings for multiple career-concerned agents with correlated talents, mapping outputs into arbitrary finite-variance statistics. Peer output both benchmarks effort and signals talent, making full disclosure suboptimal under confidential ratings. Relaxing Bayes plausibility to linear orthogonality yields a closed-form rating and computable effort bounds for general priors; under normality, the relaxation is exact. Optimal confidential ratings combine the full-disclosure wage with the Fisher score for effort: they are relative-performance when individual output is informative about talent and team-based when it is noisy. Public ratings must satisfy joint Bayes plausibility, weakening incentives; hence, the principal prefers confidential ratings, whereas agents prefer public ratings. A spectral decomposition characterizes public ratings under normality: small cost heterogeneity implies full disclosure regardless of the noise structure, whereas large cost heterogeneity or strong effort spillovers can induce partial disclosure to reallocate incentives. The results bear on performance ratings and pay transparency. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24819 |