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on Economic Design |
| By: | Dirk Bergemann; Tibor Heumann; Stephen Morris |
| Abstract: | We develop an integrated framework for information design and mechanism design in screening environments with quasilinear utility. Using the tools of majorization theory and quantile functions, we show that both information design and mechanism design problems reduce to maximizing linear functionals subject to majorization constraints. For mechanism design, the designer chooses allocations weakly majorized by the exogenous inventory. For information design, the designer chooses information structures that are majorized by the prior distribution. When the designer can choose both the mechanism and the information structure simultaneously, then the joint optimization problem becomes bilinear with two majorization constraints. We show that pooling of values and associated allocations is always optimal in this case. Our approach unifies classic results in auction theory and screening, extends them to information design settings, and provides new insights into the welfare effects of jointly optimizing allocation and information. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.17267 |
| By: | Zihao Li; Xuandong Chen |
| Abstract: | We study the design of optimal allocation mechanisms in an environment where agents and goods arrive stochastically. Agents have private types that determine the principal payoff. Either agents or goods can be held in a queue at a flow cost until allocation. The principal cannot use monetary transfers, but can verify agents types at a cost. We characterize the optimal mechanism at the steady state of the system. It is a dynamic threshold mechanism in which the principal sets type thresholds for agent admission and goods allocation. These thresholds depend on the current state of the mechanism. The model applies to public programs such as public housing and grant allocation, and to allocation problems within organizations such as capital budgeting. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.20728 |
| By: | Quitz\'e Valenzuela-Stookey |
| Abstract: | A principal must allocate a set of heterogeneous tasks (or objects) among multiple agents. The principal has preferences over the allocation. Each agent has preferences over which tasks they are assigned, which are their private information. The principal is constrained by the fact that each agent has the right to demand some status-quo task assignment. I characterize the conditions under which the principal can gain by delegating some control over the assignment to the agents. Within a large class of delegation mechanisms, I then characterize those that are obviously strategy-proof (OSP), and provide guidance for choosing among OSP mechanisms. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.20035 |
| By: | Yaonan Jin; Yingkai Li |
| Abstract: | We study revenue maximization when a seller offers $k$ identical units to ex ante heterogeneous, unit-demand buyers. While anonymous pricing can be $\Theta(\log k)$ worse than optimal in general multi-unit environments, we show that this pessimism disappears in large markets, where no single buyer accounts for a non-negligible share of optimal revenue. Under (quasi-)regularity, anonymous pricing achieves a $2+O(1/\sqrt{k})$ approximation to the optimal mechanism; the worst-case ratio is maximized at about $2.47$ when $k=1$ and converges to $2$ as $k$ grows. This indicates that the gains from third-degree price discrimination are mild in large markets. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.16488 |
| By: | Shipra Agrawal; Yiding Feng; Wei Tang |
| Abstract: | Quality information on online platforms is often conveyed through simple, percentile-based badges and tiers that remain stable across different market environments. Motivated by this empirical evidence, we study robust quality disclosure in a market where a platform commits to a public disclosure policy mapping the seller's product quality into a signal, and the seller subsequently sets a downstream monopoly price. Buyers have heterogeneous private types and valuations that are linear in quality. We evaluate a disclosure policy via a minimax competitive ratio: its worst-case revenue relative to the Bayesian-optimal disclosure-and-pricing benchmark, uniformly over all prior quality distributions, type distributions, and admissible valuations. Our main results provide a sharp theoretical justification for quantile-partition disclosure. For K-quantile partition policies, we fully characterize the robust optimum: the optimal worst-case ratio is pinned down by a one-dimensional fixed-point equation and the optimal thresholds follow a backward recursion. We also give an explicit formula for the robust ratio of any quantile partition as a simple "max-over-bins" expression, which explains why the robust-optimal partition allocates finer resolution to upper quantiles and yields tight guarantees such as 1 + 1/K for uniform percentile buckets. In contrast, we show a robustness limit for finite-signal monotone (quality-threshold) partitions, which cannot beat a factor-2 approximation. Technically, our analysis reduces the robust quality disclosure to a robust disclosure design program by establishing a tight functional characterization of all feasible indirect revenue functions. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.01066 |
| By: | Chonghuan Wang |
| Abstract: | Matching mechanisms play a central role in operations management across diverse fields including education, healthcare, and online platforms. However, experimentally comparing a new matching algorithm against a status quo presents some fundamental challenges due to matching interference, where assigning a unit in one matching may preclude its assignment in the other. In this work, we take a design-based perspective to study the design of randomized experiments to compare two predetermined matching plans on a finite population, without imposing outcome or behavioral models. We introduce the notation of a disagreement set, which captures the difference between the two matching plans, and show that it admits a unique decomposition into disjoint alternating paths and cycles with useful structural properties. Based on these properties, we propose the Alternating Path Randomized Design, which sequentially randomizes along these paths and cycles to effectively manage interference. Within a minimax framework, we optimize the conditional randomization probability and show that, for long paths, the optimal choice converges to $\sqrt{2}-1$, minimizing worst-case variance. We establish the unbiasedness of the Horvitz-Thompson estimator and derive a finite-population Central Limit Theorem that accommodates complex and unstable path and cycle structures as the population grows. Furthermore, we extend the design to many-to-one matchings, where capacity constraints fundamentally alter the structure of the disagreement set. Using graph-theoretic tools, including finding augmenting paths and Euler-tour decomposition on an auxiliary unbalanced directed graph, we construct feasible alternating path and cycle decompositions that allow the design and inference results to carry over. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.21036 |
| By: | Tan Gan; Yingkai Li |
| Abstract: | We study a screening problem in which an agent privately observes a set of feasible technologies and can strategically disclose only a subset to the principal. The principal then takes an action whose payoff consequences for both players are publicly known. Under the assumption that the possible technology sets are ordered by set inclusion, we show that the optimal mechanism promises the agent a utility that is weakly increasing as the reported set expands, and the choice of the principal maximizes her own utility subject to this promised utility constraint. Moreover, the optimal promised utility either coincides with the agent's utility under the complete information benchmark or remains locally constant, with the number of constant segments bounded by the number of downward-sloping segments of the complete information benchmark. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.15580 |
| By: | Mark Whitmeyer |
| Abstract: | I study symmetric competitions in which each player chooses an arbitrary distribution over a one-dimensional performance index, subject to a convex cost. I establish existence of a symmetric equilibrium, document various properties it must possess, and provide a characterization via the first-order approach. Manifold applications--to R&D competition, oligopolistic competition with product design, and rank-order contests--follow. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.22112 |
| By: | Federico Echenique; Mat\'ias N\'u\~nez |
| Abstract: | We propose a solution and a mechanism for two-agent social choice problems with large (infinite) policy spaces. Our solution is an efficient compromise rule between the two agents, built on a common cardinalization of their preferences. Our mechanism, the multimatum, has the two players alternate in proposing sets of alternatives from which the other must choose. Our main result shows that the multimatum fully implements our compromise solution in subgame perfect Nash equilibrium. We demonstrate the power and versatility of this approach through applications to political economy, other-regarding preferences, and facility location. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.21275 |
| By: | Brown, Tarnell |
| Abstract: | Individuals released from jails and prisons face extremely high risks of fatal overdose and reincarceration, yet many jurisdictions continue to underprovide medications for opioid use disorder (MOUD), recovery housing, and supervised consumption services. At the same time, recovery residences and diversion courts are expanding without a clear framework for institutional design. This paper develops a mechanism-design model of harm-reduction policy at the interface of criminal justice and community treatment. A public funder chooses a funding regime and certification rules, diversion judges set the stringency of supervision and treatment conditions, recovery residence providers decide whether to operate abstinence-only or MOUD-inclusive housing, and high-risk individuals choose whether to comply or relapse. The model yields a punitive equilibrium, supported by abstinence-only funding and strict conditions, and a harm-reduction equilibrium under MOUD-inclusive funding and flexible conditions. Using effect sizes from Rhode Island’s statewide corrections MOUD program, Massachusetts’ jail-based MOUD pilots, and recent recovery housing evaluations, we show that the harm-reduction equilibrium is Pareto-superior for funders, judges, providers, and high-severity residents, yet the punitive equilibrium can remain risk-dominant because of political and informational frictions. We then embed the game in a computational social choice framework: stakeholders hold multi-dimensional preferences over policy bundles—combinations of funding rules, certification standards, diversion guidelines, and overdose prevention interventions such as supervised consumption sites—and social choice is constrained by justice-based requirements that rule out policies generating avoidable lethal risk or systematic exclusion of MOUD patients from housing and treatment. The analysis characterizes which harm-reduction mechanisms are implementable as equilibrium outcomes of the institutional game while respecting these constrained social preferences, and it identifies simple instruments—MOUD-inclusive funding commitments, performance-based transparency, and structured diversion defaults—that can move jurisdictions from punitive to harm-reduction equilibria within existing legal constraints. |
| Date: | 2026–02–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:wrkj3_v1 |
| By: | Joshua S. Gans |
| Abstract: | Machine learning systems embed preferences either in training losses or through post-processing of calibrated predictions. Applying information design methods from Strack and Yang (2024), this paper provides decision problem agnostic conditions under which separation training preference free and applying preferences ex post is optimal. Unlike prior work that requires specifying downstream objectives, the welfare results here apply uniformly across decision problems. The key primitive is a diminishing-value-of-information condition: relative to a fixed (normalised) preference-free loss, preference embedding makes informativeness less valuable at the margin, inducing a mean-preserving contraction of learned posteriors. Because the value of information is convex in beliefs, preference-free training weakly dominates for any expected utility decision problem. This provides theoretical foundations for modular AI pipelines that learn calibrated probabilities and implement asymmetric costs through downstream decision rules. However, separation requires users to implement optimal decision rules. When cognitive constraints bind, as documented in human AI decision-making, preference embedding can dominate by automating threshold computation. These results provide design guidance: preserve optionality through post-processing when objectives may shift; embed preferences when decision-stage frictions dominate. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.18732 |
| By: | Giovanni Valvassori Bolg\`e |
| Abstract: | In this paper, we characterize the extreme points of a class of multidimensional monotone functions. This result is then applied to large contests, where it provides a useful representation of optimal allocation rules under a broad class of distributional preferences of the contest designer. In contests with complete information, the representation significantly simplifies the characterization of the equilibria. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.19331 |
| By: | Sander Onderstal (University of Amsterdam and Tinbergen Institute); Ruben van Oosten (University of Amsterdam) |
| Abstract: | We analyze vertical integration in auction markets using a symmetric independent private-values model where the auctioneer invests in the auctioned object's quality. We find that the auctioneer invests more after integration. The integrated bidder enjoys a bidding advantage over other bidders. The merging parties benefit from integration, while non-merging bidders are worse off. In a platform setting where the auctioneer is an intermediary and the bidders are sellers on her platform, vertical integration has ambiguous effects on consumer surplus and total welfare. Our results contribute to the ongoing policy debate about platforms self-preferencing, effective competition policy, and digital market regulation. |
| JEL: | D44 G34 |
| Date: | 2025–08–26 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250046 |
| By: | J. Ignacio Conde-Ruiz; Miguel Díaz Salazar; Juan-José Ganuza |
| Abstract: | This paper combines artificial intelligence with economic modeling to design evaluation committees that are both efficient and fair in the presence of gender differences in economic research orientation. We develop a dynamic framework in which research evaluation depends on the thematic similarity between evaluators and researchers. The model shows that while topic balanced committees maximize welfare, this research- neutral-gender allocation is dynamically unstable, leading to the persistent dominance of the group initially overrepresented in evaluation committees. Guided by these predictions, we employ unsupervised machine learning to extract research profiles for male and female researchers from articles published in leading economics journals between 2000 and 2025. We characterize optimal balanced committees within this multidimensional latent topic space and introduce the Gender-Topic Alignment Index (GTAI) to measure the alignment between committee expertise and female-prevalent research areas. Our simulations demonstrate that AI-based committee designs closely approximate the welfare-maximizing benchmark. In contrast, traditional headcount-based quotas often fail to achieve balance and may even disadvantage the groups they intend to support. We conclude that AI-based tools can significantly optimize institutional design for editorial boards, tenure committees, and grant panels. |
| Keywords: | machine learning, artificial intelligence, Topic Modeling, evaluation committees, committee quotas, research orientation |
| JEL: | D72 D82 J16 J78 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1554 |
| By: | Zhuo Chen; Yun Liu |
| Abstract: | We study optimal dynamic persuasion in a bandit experimentation model where a principal, unlike in standard settings, has a single-peaked preference over the agent's stopping time. This non-monotonic preference arises because maximizing the agent's effort is not always in the principal's best interest, as it may lead to a dead end. The principal privately observes the agent's payoff upon success and uses the information as the instrument of incentives. We show that the optimal dynamic information policy involves at most two one-shot disclosures: an accelerator before the principal's optimal stopping time, persuading the agent to be optimistic, and a brake after the principal's optimal stopping time, persuading the agent to be pessimistic. A key insight of our analysis is that the optimal disclosure pattern -- whether gradual or one-shot -- depends on how the principal resolves a trade-off between the mean of stopping times and its riskiness. We identify the Arrow-Pratt coefficient of absolute risk aversion as a sufficient statistic for determining the optimal disclosure structure. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13686 |
| By: | Min-Hung Tsay (Academia Sinica); Youngsub Chun (Seoul National University); Rene van den Brink (Vrije Universiteit Amsterdam and Tinbergen Institute); Chun-Hsien Yeh (Academia Sinica) |
| Abstract: | We investigate implications of the consistency principle for the reordering problem, also known as the queueing problem with an initial queue. The consistency principle specifies how an allocation rule should respond when an agent leaves the problem. We introduce four different consistency properties for the reordering problem and characterize three allocations rules, the pairwise equal-splitting rule (Curiel et al., 1989), the maximum price rule and the minimum price rule. Balanced consistency requires that for each pair of agents i and j, the impact on agent i’s net utility when agent j leaves the initial queue and the agents behind her move forward by one position, should be equal to the impact on agent j’s net utility when agent i leaves the initial queue and the agents behind her move forward by one position. Balanced cost reduction requires that if an agent leaves the initial queue and the agents behind her move forward by one position, then the total net utilities of the remaining agents should be reduced by the amount equal to the net utility of the departing agent. Smallest-cost consistency (respectively, largest-cost consistency) requires that if an agent with the smallest (respectively, largest) unit waiting cost leaves the initial queue and the agents behind her move forward by one position, then the net utilities of the remaining agents should not be affected. We show that either balanced consistency or balanced cost reduction, together with the three basic properties of queue-efficiency, budget-balance and Pareto indifference, characterizes the pairwise equal-splitting rule. On the other hand, together with the three basic properties, smallest-cost consistency characterizes the maximum price rule and largest-cost consistency the minimum price rule. |
| Keywords: | Reordering problem, consistency, pairwise equal-splitting rule, maximum price rule, minimum price rule, axiomatic characterization |
| JEL: | D81 D83 D91 |
| Date: | 2025–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250050 |
| By: | Estelle Cantillon; Li Chen; Juan Sebastian Pereyra Barreiro |
| Abstract: | A classic trade-off that school districts face when deciding which matching algorithm to use is that it is not possible to always respect both priorities and preferences. The student-proposing deferred acceptance algorithm (DA) respects priorities but can lead to inefficient allocations. We identify a new condition on school choice markets under which DA is efficient. Our condition generalizes earlier conditions by placing restrictions on how preferences and priorities relate to one another only on the parts that are relevant for the assignment. Whenever there is a unique allocation that respects priorities, our condition captures all the environments for which DA is efficient. We show through stylized examples and simulations that our condition significantly expands the range of known environments for which DA is efficient. We also discuss how our condition sheds light on existing empirical findings. |
| Date: | 2024–09–15 |
| URL: | https://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/378050 |
| By: | Jan Zápal; Clara Ponsatí |
| Abstract: | A finite group of voters must elect the pope from a finite set of candidates. They repeatedly cast ballots (possibly for ever) until one candidate attains at least Q votes. A candidate is electable—if enough voters prefer him to a continuous disagreement—as well as stable—if no other candidate is preferred to him by a sufficient number of voters. We provide a necessary and sufficient condition for the existence of a candidate that is both electable and stable. When there are three candidates and voters are willing to compromise somewhat, the condition requires choice by two-thirds supermajority, which coincides with the procedure that the Catholic Church has used to appoint the pope for almost a millennium. |
| Keywords: | conclave, electable, Pope, repeated ballots, stable, supermajority |
| JEL: | D71 D72 Z12 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1553 |
| By: | Julius Graf; Thibaut Mastrolia |
| Abstract: | In this work, we investigate the market-making problem on a trading session in which a continuous phase on a limit order book is followed by a closing auction. Whereas standard optimal market-making models typically rely on terminal inventory penalties to manage end-of-day risk, ignoring the significant liquidity events available in closing auctions, we propose a Deep Q-Learning framework that explicitly incorporates this mechanism. We introduce a market-making framework designed to explicitly anticipate the closing auction, continuously refining the projected clearing price as the trading session evolves. We develop a generative stochastic market model to simulate the trading session and to emulate the market. Our theoretical model and Deep Q-Learning method is applied on the generator in two settings: (1) when the mid price follows a rough Heston model with generative data from this stochastic model; and (2) when the mid price corresponds to historical data of assets from the S&P 500 index and the performance of our algorithm is compared with classical benchmarks from optimal market making. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.17247 |
| By: | Ricardo Martinez; Juan D. Moreno-Ternero |
| Abstract: | We study the adjudication of water rights in international rivers. We characterize allocation rules that formalize focal principles to deal with water disputes in a basic model. Central to our analysis is a family of geometric rules that implement concatenated transfers downstream. They can be seen as formalizing Limited Territorial Sovereignty, as suggested in the Rio Declaration on Environment and Development. We apply our rules to the case of the Nile River, with a long history of disputes between downstream and upstream nations |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04150 |
| By: | Viktor Mitevski (Association for Research and Analysis ZMAI, North Macedonia) |
| Abstract: | This paper examines market competition in North Macedonian public procurement using a novel dataset of all public contracts from 2021–2025. We focus on the number of offers (bids) per tender as a key indicator of competition, following models inspired by Fazekas and Kocsis (2017). The analysis explores how institutional factors, particularly the use of electronic procurement tools and the choice of procedure type, influence bidder participation. We find that the average tender in North Macedonia attracts only 2–3 bids, and over one-third of procedures have a single bidder, raising market competition concerns. Using regression analysis with the number of offers as the dependent variable, we show that fully open procedures and e-procurement usage are associated with modestly higher competition, whereas negotiated or restricted procedures reduce the number of bidders. The paper provides descriptive insights (e.g., variation by contracting institution type and by goods/services/works procurement) and discusses implications for public expenditure effectiveness. Our results underscore the importance of transparent, open, and digitalized tendering processes in increasing competition and improving the efficiency of public spending. |
| Keywords: | Public procurement, Competition, Number of bids; E-procurement; North Macedonia |
| JEL: | H57 D44 D73 |
| Date: | 2025–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:aoh:conpro:2025:i:6:p:141-153 |
| By: | Martimort, David; Simons (Semenov), Aggey |
| Abstract: | We study a repeated buyer-seller relationship with persistent adverse selection and one-sided enforcement, where a prepaid seller can breach by taking the money and running. The optimal stationary contract depends on enforcement strength and the discount factor. Three regimes arise. With a strong legal system, penalties deter breach and the optimal static contract can be repeated. With a weak system, the penalty caps transfers, forcing bunching among efficient (low-cost) types. With a very weak system, compliance relies on relational rents, causing large downward distortions. Strengthening public enforcement relaxes both incentive and enforcement constraints, reducing allocative inefficiency. |
| Keywords: | Adverse selection, Limited enforcement, Relational contracts, Contract breach |
| JEL: | D82 D86 K12 O17 |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:131355 |
| By: | Marie Alder (European University Institute); Eva Franzmeyer (European University Institute); Benjamin Hattemer (University of Helsinki & FIT) |
| Abstract: | This study provides new causal evidence on the firm-level effects of reducing free emission permits in emission trading systems. Using a difference-in-differences design, we exploit a reform that altered an eligibility threshold for free permit allocation. Receiving fewer free permits reduced emissions by more than 14 percent relative to firms that retained them. This reduction was accompanied by similar declines in revenue, employment, and assets. We develop a multi-product general equilibrium model that explains these patterns through a novel mechanism linking permit allocation to firms’ decisions. Firms that receive fewer free emission permits terminate their least productive product lines, increasing the market share of the remaining ones. Higher expected profits then encourage earlier adoption of an efficiency-improving technology. |
| Keywords: | Emissions, Emission permits, Emission trading |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:fit:wpaper:42 |