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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 7h

arXiv📈🤖 Learned Causal Method Prediction By

For a given causal question, it is important to efficiently decide which
causal inference method to use for a given dataset. This is challenging because
causal methods typically rely on complex and difficult-to-verify assumptions,
and cross-validation is not applicable since ground truth causal quantities are
unobserved.In this work, we propose CAusal Method Predictor (CAMP), a framework
for predicting the best method for a given dataset. To this end, we generate
datasets from a diverse set of synthetic causal models, score the candidate
methods, and train a model to directly predict the highest-scoring method for
that dataset. Next, by formulating a self-supervised pre-training objective
centered on dataset assumptions relevant for causal inference, we significantly
reduce the need for costly labeled data and enhance training efficiency. Our
strategy learns to map implicit dataset properties to the best method in a
data-driven manner. In our experiments, we focus on method prediction for
causal discovery. CAMP outperforms selecting any individual candidate method
and demonstrates promising generalization to unseen semi-synthetic and
real-world benchmarks.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 12h

arXiv📈🤖 UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters By

Estimating the number of clusters k while clustering the data is a
challenging task. An incorrect cluster assumption indicates that the number of
clusters k gets wrongly estimated. Consequently, the model fitting becomes less
important. In this work, we focus on the concept of unimodality and propose a
flexible cluster definition called locally unimodal cluster. A locally unimodal
cluster extends for as long as unimodality is locally preserved across pairs of
subclusters of the data. Then, we propose the UniForCE method for locally
unimodal clustering. The method starts with an initial overclustering of the
data and relies on the unimodality graph that connects subclusters forming
unimodal pairs. Such pairs are identified using an appropriate statistical
test. UniForCE identifies maximal locally unimodal clusters by computing a
spanning forest in the unimodality graph. Experimental results on both real and
synthetic datasets illustrate that the proposed methodology is particularly
flexible and robust in discovering regular and highly complex cluster shapes.
Most importantly, it automatically provides an adequate estimation of the
number of clusters.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 11/10/2026

arXiv📈🤖 Constrained Gaussian Random Fields with Continuous Linear Boundary Restrictions for Physics-informed Modeling of States By Ma, Chkrebtii, Niezgoda

Boundary constraints in physical, environmental and engineering models restrict smooth states such as temperature to follow known physical laws at the edges of their spatio-temporal domain. Examples include fixed-state or fixed-derivative (insulated) boundary conditions, and constraints that relate the state and the derivatives, such as in models of heat transfer. Despite their flexibility as prior models over system states, Gaussian random fields do not in general enable exact enforcement of such constraints. This work develops a new general framework for constructing linearly boundary-constrained Gaussian random fields from unconstrained Gaussian random fields over multi-dimensional, convex domains. This new class of models provides flexible priors for modeling smooth states with known physical mechanisms acting at the domain boundaries. Simulation studies illustrate how such physics-informed probability models yield improved predictive performance and more realistic uncertainty quantification in applications including probabilistic numerics, data-driven discovery of dynamical systems, and boundary-constrained state estimation, as compared to unconstrained alternatives.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 11/10/2026

arXiv📈🤖 Random Fourier Signature Features By

arXiv:2311.12214v2 Announce Type: replace-cross
Abstract: Tensor algebras give rise to one of the most powerful measures of similarity for sequences of arbitrary length called the signature kernel accompanied with attractive theoretical guarantees from stochastic analysis. Previous algorithms to compute the signature kernel scale quadratically in terms of the length and the number of the sequences. To mitigate this severe computational bottleneck, we develop a random Fourier feature-based acceleration of the signature kernel acting on the inherently non-Euclidean domain of sequences. We show uniform approximation guarantees for the proposed unbiased estimator of the signature kernel, while keeping its computation linear in the sequence length and number. In addition, combined with recent advances on tensor projections, we derive two even more scalable time series features with favourable concentration properties and computational complexity both in time and memory. Our empirical results show that the reduction in computational cost comes at a negligible price in terms of accuracy on moderate-sized datasets, and it enables one to scale to large datasets up to a million time series.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 10/10/2026

arXiv📈🤖 Multiple Randomization Designs: Estimation and Inference with Interference By Masoero, Vijaykumar, Richardson et al

Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limited value for answering standard questions in settings, common in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we discuss new experimental designs and corresponding estimands to account for and capture these complex spillovers. We derive the finite-sample properties of tractable estimators for main effects, direct effects, and spillovers, and present associated central limit theorems.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 10/10/2026

arXiv📈🤖 Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso By Hu\'e, Laurent, Aiounou et al

Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso. Its use in a standard application of economic growth sheds new light on the hypothesis of convergence from poor to rich economies.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 10/10/2026

arXiv📈🤖 Estimating Treatment Effects Under Bounded Heterogeneity By Kwon, Sun

$Researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects. When treatment effects are heterogeneous, however, such specifications generally fail to recover this average effect. Augmenting these specifications with interaction terms between demeaned covariates and treatment eliminates this bias, but often leads to imprecise estimates and becomes infeasible under limited overlap. We propose a generalized ridge regression estimator, $\texttt{regulaTE}$, that penalizes the coefficients on the interaction terms to achieve an optimal trade-off between worst-case bias and variance in estimating the average effect under limited treatment effect heterogeneity. Building on this estimator, we construct confidence intervals that remain valid under limited overlap and can also be used to assess sensitivity to violations of the constant effects assumption. We illustrate the method in empirical applications under unconfoundedness and staggered adoption, providing a practical approach to inference under limited overlap.$

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Efficient nonparametric estimators of discriminationmeasures with censored survival data By

arXiv:2409.05632v1 Announce Type: new
Abstract: Discrimination measures such as concordance statistics (e.g. the c-index or the concordance probability) and the cumulative-dynamic time-dependent area under the ROC-curve (AUC) are widely used in the medical literature for evaluating the predictive accuracy of a scoring rule which relates a set of prognostic markers to the risk of experiencing a particular event. Often the scoring rule being evaluated in terms of discriminatory ability is the linear predictor of a survival regression model such as the Cox proportional hazards model. This has the undesirable feature that the scoring rule depends on the censoring distribution when the model is misspecified. In this work we focus on linear scoring rules where the coefficient vector is a nonparametric estimand defined in the setting where there is no censoring. We propose so-called debiased estimators of the aforementioned discrimination measures for this class of scoring rules. The proposed estimators make efficient use of the data and minimize bias by allowing for the use of data-adaptive methods for model fitting. Moreover, the estimators do not rely on correct specification of the censoring model to produce consistent estimation. We compare the estimators to existing methods in a simulation study, and we illustrate the method by an application to a brain cancer study.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Justifiable Priority Violations By Ortega, Arribillaga

Making Deferred Acceptance more efficient requires priority violations, but which ones are justifiable? We argue that a priority can be justifiably violated if the affected student either (i) directly benefits from the improvement that caused said violation, or (ii) is unimprovable under any assignment that Pareto-dominates DA. We construct a ``just-below-cutoffs'' mechanism that always finds a justifiable DA-improvement whenever DA's outcome is inefficient, and build on it to construct an algorithm that expands justifiable improvements iteratively, converging to a matching that cannot be Pareto-improved by any justifiable outcome. Finally, we prove that both justifiability and consent frameworks have important limitations in reaching Pareto-efficient outcomes, and use data and simulations to quantify how binding these constraints are in practice.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Correlated Perfect Equilibrium By Kline, Huang, Man

We propose a refinement of correlated equilibrium based on common perturbations of the mediator's recommendation distribution, which we call correlated perfect equilibrium (CPE). In finite games, the set of CPEs is nonempty and forms a finite union of convex polyhedra. Like perfect equilibrium, a CPE never assigns positive probability to any weakly dominated strategy. We provide a dual representation of CPE, which yields an iterative procedure for identifying CPEs. These perturbations provide a common source of off-equilibrium beliefs, a feature that distinguishes CPE from two existing refinements of correlated equilibrium---acceptable correlated equilibrium (Myerson, 1986) and perfect direct correlated equilibrium (Dhillon and Mertens, 1996)---as we demonstrate through examples.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Delphos: A reinforcement learning framework for assisting discrete choice model specification By Nova, Hess, Cranenburgh

$We introduce Delphos, a deep reinforcement learning framework for assisting discrete choice model specification process. Delphos aims to support the modeller by providing automated, data-driven suggestions for model specifications, thereby reducing the effort required to develop and refine utility functions. Delphos conceptualises model specification as a sequential decision-making problem, inspired by the way human choice modellers iteratively construct models through a series of reasoned specification decisions. In this setting, an agent learns to specify candidate model specifications by choosing a sequence of modelling actions, such as adding alternative specific constants, accommodating both generic and alternative-specific taste parameters, applying non-linear transformations to attributes, and including interactions with covariates. Each resulting candidate model is estimated and evaluated using a reward function defined by the modeller, which can reflect statistical model fit as well as behavioural expectations. Specifically, Delphos uses a Deep Q-Network to learn how individual specification decisions contribute to the eventual quality of the resulting model and, in turn, which sequences of modelling decisions tend to produce well-performing candidates. We evaluate Delphos on both simulated and empirical datasets using alternative reward functions. In simulated cases, learning curves, Q-value patterns, and performance metrics show that Delphos learns effective specification strategies while exploring only a small fraction of the feasible modelling space. We further apply the framework to two empirical datasets to benchmark and demonstrate its practical use. These experiments illustrate the ability of Delphos to generate competitive, behaviourally plausible models and highlight the potential of this adaptive, learning-based framework to assist the model specification process.$

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Learning about informativeness By Huang

We study whether individuals can learn the informativeness of their information technology through social learning. As in the classic sequential social learning model, rational agents arrive in order and make decisions based on the past actions of others and their private signals. There is uncertainty regarding the informativeness of the common signal-generating process. We show that in this setting asymptotic learning about informativeness is not guaranteed and depends crucially on the relative tail distributions of private beliefs induced by uninformative and informative signals. We identify the phenomenon of perpetual disagreement as the cause of learning and characterize learning in the canonical Gaussian environment.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Protecting Losers Undermines Auction Credibility By Nakamura, Okamoto

A second-price auction must determine its price but need not learn how far losing values lie below it. We show that protecting this information from the seller conflicts with auction credibility. In deterministic protocols with private communication, lower-tail privacy forces the first informative question to reveal whether a bidder has the highest possible value. The seller can then manipulate the winner's payment without any bidder detecting the deviation, increasing revenue at some valuation profiles and never reducing it. A descending protocol conceals losing values below the price but permits manipulation; with mandatory sale, a credible ascending protocol reveals them.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Microfinance Competition in the Presence of Moneylenders: Theory and Evidence By Chowdhury, Chowdhury, Smits

After decades of microfinance expansion and despite charging higher interest rates, moneylenders continue to exist alongside microfinance institutions (MFIs). We develop a dynamic model with ex post moral hazard, in which borrowers use MFI loans for production and can rely on moneylenders for bridge loans after shocks to repay MFIs and preserve future access to MFI credit. We then study how MFI competition shapes the coexistence between MFIs and moneylenders. The effect of competition on MFI credit is ambiguous: it may weaken repayment incentives or replace moneylenders as bridge lenders. By contrast, competition unambiguously reduces moneylender credit. In a village-level randomized expansion in Bangladesh, stratified by baseline MFI presence, one additional MFI reduces the number of moneylender loans by roughly 30-40% two to three years later, with no detectable effect on MFI or overall borrowing. This suggests that, in markets where MFIs rely primarily on the threat of exclusion to induce repayment and moneylenders provide emergency finance, gains from additional MFI entry may be greater in less saturated areas.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 The Myth of Restrictive Working-Time Agreements: Firms' Working-Time Adjustment after Exit from Collective Bargaining By Pyka, Rieder

Employers in Germany argue that collective agreements restrict their flexibility concerning working hours. Using data from the IAB Establishment Panel for the years 2008 to 2024, we investigate working-time adjustments after exit from collective bargaining. We find that exit is associated with only a modest increase in weekly working hours of around 0.3 to 0.6 per cent using fixed-effects and event-study models, corresponding to eight to thirteen additional minutes per week. This adjustment occurs shortly after leaving collective bargaining coverage. We further find no evidence that other margins of working-time adjustment, such as Saturday or Sunday work, are used more extensively after exit. In contrast to previous survey evidence, our findings call into question that working-hours constraints are one of the main drivers of exit from collective bargaining coverage.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Dictatorial profiles: a proof of Arrow's theorem By Bonifacio

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Delegate Pricing to Algorithms: When Slow and Steady Wins the Race By Ashkenazi-Golan, Plumb, Possnig et al

We study the strategic design of learning algorithms in a canonical continuous-time pricing game. We introduce a meta-game in which firms select learning rates for a gradient dynamic, evaluating payoffs as the discounted sum of profits accrued along the entire learning path. We uncover a fundamental dichotomy driven by stage-game incentives: if the stage game exhibits strategic substitutability, firms unambiguously prefer the fastest possible algorithms to rapidly exploit a gradually adjusting opponent. Under strategic complementarity, however, an excessively fast algorithm accelerates the rival's competitive response, destroying transitional profit margins. Even though the competitive price is a strictly dominant action in the underlying stage game, we prove that firms optimally design sluggish algorithms, extracting surplus during a prolonged convergence to the competitive equilibrium.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Singular Equilibrium and Selection of Narratives By Jin

An agent learns from data that many parameters of her model explain equally well. Berk's theorem states that her belief concentrates on the parameters that best fit the data, but does not select between them. We show that, with a continuum of parameters, the posterior concentrates on the best-fitting parameters with the smallest local learning coefficient. We propose a definition of narratives, subsets of the best-fitting parameters on which the learning coefficient is constant. Narratives are characterized by the number of coincidences they require (complexity) and how exactly each must hold (robustness). When the agent's actions affect her data, every long-run action is a singular equilibrium, a best reply to a belief supported on the least complex and most robust narratives. Singular equilibrium refines Berk-Nash and self-confirming equilibrium by restricting overly complex or knife-edge off-path beliefs, and its strict uniform version characterizes uniformly stable actions.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Who Leads and Who Collects:Algorithmic Collusion in Markets of Heterogeneous Language Models By Lee

Evidence that pricing algorithms collude comes from markets in which every seller runs the same algorithm. We ask what happens when they do not. Four language models from four providers, each at its cheapest tier, price in a four-firm logit Bertrand market without communication, in every homogeneous, two-by-two and fully mixed composi- tion (11 cells, 20 runs, 200 periods). Collusion is a property of the model: Claude and Gemini markets reach 72 to 79 percent of the monopoly rent, DeepSeek markets 24 percent, and GPT markets none, though GPT prices drift above the monopoly level rather than toward competition. Mixing does not reduce collusion by itself. Markets containing Gemini, which opens at the highest price and settles highest, are more col- lusive than the homogeneous markets they are built from; markets containing Claude, which opens lower and follows its rivals down, are less so; and only the fully mixed market is significantly less collusive than the average homogeneous one. Stability de- pends on the least stable participant: two GPT firms suffice to keep any market from converging. Inside mixed markets the rent is shared in a transitive order, DeepSeek over Claude over Gemini over GPT, that inverts the anchor ranking. The model that raises the price collects the least of the rent, as the price-leadership model predicts.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Information Divergence and Endogenous Evolution of Asymmetric Information By Columbia), Technologies), Inc)

Standard theories trace information asymmetry to privately held information, heterogeneous signals, or costly information acquisition. We identify a distinct mechanism: otherwise identical rational agents observing a common Markov state at asynchronous times can hold different predictive distributions because their information differs in age. When the transition kernel separates information ages, independent Bernoulli observation arrivals make such divergence recur infinitely often almost surely, even with identical arrival probabilities. When agents instead choose costly updating intensities, independent update realizations can regenerate heterogeneous information ages among ex ante identical agents. In a stationary Gaussian AR(1) environment, the consequences depend on common staleness as well as the age gap: holding the gap fixed, older information raises individual uncertainty while reducing expected squared forecast disagreement and the value of certifying relative freshness. This generates stale consensus: low disagreement despite high uncertainty. With multiple ordered ages, costly certification supports partial-unraveling equilibria in which fresher types certify and sufficiently stale types pool. Under geometric information ages, more frequent updating weakly lowers the smallest and largest equilibrium disclosure cutoffs wherever positive-certification equilibria exist. The framework has implications for credit, insurance, financial markets, and multi-agent AI.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Dynamic Mechanism and Information Design in AI-Agent Environments: A General Analytical Framework By Tian

This paper develops a general framework for dynamic mechanism and information design with AI agents, encompassing optimal contracting and integrating evolving hidden information and hidden action with partial verification, technological authorization, and multi-agent interaction. It nests conventional dynamic screening, hidden-action contracting, and conditional dynamic information design, while allowing reporting, action, and information-use decisions to interact through a common continuation mechanism. We establish a dynamic revelation principle and a recursive characterization of implementability in which truthful reporting and post-report obedience are jointly determined through continuation incentives. In quasilinear optimal-contracting environments, dynamic envelope and integral-monotonicity methods yield a verification-adjusted dynamic virtual-surplus representation and identify three distinct margins: dynamic information rents, verification rents, and the implementation cost of hidden action. These margins generate a branched hierarchy separating conventional dynamic screening, hidden-action contracting, and their joint problem. We also study the joint design of monitoring, information use, and incentives: more informative monitoring capacity expands the designer's opportunity set, but full disclosure need not be optimal, and information use can itself alter hidden-action incentives. With multiple agents, correlated information, peer discipline, coupled incentives, joint feasibility, and continuation opportunities create additional strategic interactions. Although motivated by AI-agent environments, the framework applies more broadly to dynamic agency settings with similar economic features.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Risk Ceilings and Development Deadlines: Pacing AI under Uncertain Safety Productivity By Gan

Can a regulator promise both a risk ceiling and a development deadline when safety productivity is unknown? A ceiling below the final model's unprotected hazard requires a minimum stock of safety knowledge, so both promises hold only if weak research can be ruled out. Learning first and then replaying development, with spare compute in safety, comes close to that minimum. In a calibration with only state risk, constant safety yield, and full knowledge transfer, it needs only 3.3 percent more productivity than the necessary bound. Customer services pay for the guarantee. Rules that fix compute allocation fix dates, not risk.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Decision Noise in Threshold Selection By Lu, Smits

Can noisier threshold selection produce a group with higher mean true value? We answer this question by varying the scale of a fixed assessment error. Holding the selection rate fixed, less noise yields a first-order stochastically dominant distribution of selected values. At a fixed threshold, however, more noise can raise the selected mean even as selection loss, which weights selection mistakes by the distance of true value from the threshold, increases. Under joint Gaussianity, we characterize when the selected mean rises or falls with noise. In treatment selection, noise alone can generate subgroup differences in participants' average gains despite identical population gain distributions.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 Climate Cooperation and Spatial Industrial Relocation: Stability, Emissions, and Regional Policy Incentives By Martiradonna

Climate policy changes production costs and can reshape the geography of production. We study this spatial channel in a focused model where mobile industrial activity responds to local attractiveness, agglomeration strengthens attractiveness, and carbon policy lowers the profitability of emissions-intensive locations. The model yields an explicit carbon-policy threshold separating spatial instability from stability. When emissions intensity rises with local industrial density, spatial concentration raises aggregate emissions even with unchanged total industrial activity. Embedding these dynamics in a two-jurisdiction policy game creates a wedge between regional and collective incentives: each government values the industrial activity located within its own territory, while aggregate welfare internalizes the effect of policy on economy-wide concentration. With transferable utility, cooperation can select a policy on the stable side of the threshold while the non-cooperative policy lies on the unstable side. A numerical exercise illustrates this regime change. European regional evidence, based on industrial emissions and gross value added at NUTS2 level, documents a positive emissions-activity association and identifies the empirical quantities needed to measure relocation and concentration effects. The analysis links climate cooperation to the spatial organization of production and establishes a benchmark for policy-induced industrial relocation.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 The Gendered Impacts of Perceived Skin Tone: Evidence from African American Siblings in 1870-1940 By Abramitzky, Conway, Mill et al

We study differences in economic outcomes by perceived skin tone among African Americans using full-count U.S. decennial census data from the late-19th and early-20th centuries. Comparing children coded as "Black" or "Mulatto" by census enumerators and linking these children across population censuses, we first document large gaps in educational attainment and income among African Americans with darker and lighter perceived skin tones. To disentangle the drivers of these gaps, we identify all 35,992 families in which enumerators assigned same-gender siblings different Black/Mulatto classifications. Relative to sisters coded as Mulatto, sisters coded as Black had lower educational attainment, were less likely to marry, and had less-educated, likely lower-earning husbands. These patterns are consistent with more severe contemporaneous discrimination against African American women with darker perceived skin tones. In contrast, we find similar educational attainment, marital outcomes, and incomes among differently-classified brothers. Men perceived as African Americans of any skin tone faced similar contemporaneous discrimination, consistent with the "one-drop" racial classification rule that grouped together individuals with any known Black ancestry. Lower incomes for African American men perceived as having darker skin tone in the general population were driven by differences in opportunities and resources that varied across families, likely reflecting the impacts of historical or family-level discrimination.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 09/10/2026

arXiv📈🤖 How trace plots help interpret meta-analysis results By

The trace plot is seldom used in meta-analysis, yet it is a very informative
plot. In this article we define and illustrate what the trace plot is, and
discuss why it is important. The Bayesian version of the plot combines the
posterior density of tau, the between-study standard deviation, and the
shrunken estimates of the study effects as a function of tau. With a small or
moderate number of studies, tau is not estimated with much precision, and
parameter estimates and shrunken study effect estimates can vary widely
depending on the correct value of tau. The trace plot allows visualization of
the sensitivity to tau along with a plot that shows which values of tau are
plausible and which are implausible. A comparable frequentist or empirical
Bayes version provides similar results. The concepts are illustrated using
examples in meta-analysis and meta-regression; implementaton in R is
facilitated in a Bayesian or frequentist framework using the bayesmeta and
metafor packages, respectively.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 A distance-based theory of lottery complexity By Principi

This paper proposes a metric approach to measuring the complexity of lotteries. Starting by observing that degenerate lotteries are the simplest choice alternatives, the complexity of a lottery is evaluated by its distance from the closest degenerate lottery. Equivalently, a lottery is complex when it is difficult to approximate it by a single outcome. Given a metric over outcomes, the complexity index we consider is the minimum average distance between the lottery and one of its best degenerate proxies.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Outsourced Cryptocurrency Wash Trading By Ng

I show that outsourced cryptocurrency wash trading is shaped by the objective assigned to the outside firm. When hired to manufacture volume, wash traders buy and sell nearly equal amounts, repeatedly turning over the same inventory. When also tasked with selling issuer holdings, trading becomes markedly more one-sided, with sales exceeding purchases. Using documented enforcement cases and transaction records, I trace these differences in net selling, two-way trading, and inventory replenishment across controlled wallets. The results show that similar reported trading volume can mask fundamentally different economic activity as contractual objectives shape whether manipulation recycles inventory or liquidates issuer holdings.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Screening with Discrete Types and Discrete Contracts By Francetich, Schipper

We study screening when both the agent's types and the contracts available to the principal are finite: Quantities and transfers are drawn from a finite integer grid, while marginal costs lie on a slightly off-set grid, with integer costs as a limit case. We compare two solution concepts, equilibrium and rationalizability. Since rationalizability does not feature an equilibrium tie-breaking convention, we analyze screening under weak and under strict incentives. We develop a discrete first-order approach and characterize the optimal menus for full-support log-concave beliefs. Optimal quantities need not be unique, but at most two adjacent quantities are optimal and multiplicity is a knife-edge case in beliefs. Our rationalizability solution concept, Delta-O rationalizability, restricts the principal's beliefs over types and requires her best replies to be robust to belief perturbations. Two rounds of elimination suffice, and the rationalizable and the equilibrium outcomes essentially coincide: The predictions of equilibrium survive without a common prior, while avoiding a tie-breaking convention.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Validity Without Ground Truth: What Stated-Preference Economics Offers the Evaluation of Language Models By Alexander, Kling

$Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should choose, how to weigh competing values. Stated-preference economics has faced this problem for decades. It judges survey responses without knowing the true value, through a framework of validity and related concepts: content, construct, and criterion validity, reliability, incentive compatibility, and consequentiality. We argue that this framework is a general method for evaluating language models, and we set out what each concept means for LLM evaluation. We demonstrate the approach using a published water-quality stated preference economic valuation survey (Vossler et al. 2023) administered to six models. In this economic application, the validity tests take the form of predictions from economic theory: demand should slope down, and willingness to pay should respond to the scope of the good and to income. The tests separate the models sharply. Two older models fail the most basic test at a household income level of \$75,000, and the two newest pass every test of theoretical validity we can score, but diverge on convergent validity. Passing validity tests shows that a model's answers are coherent, not that they are correct.$

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Equivalent Behavioural Martingale Measure By Charles-Cadogan

$The paper places the Samuelson--Merton ``util-prob'' construction and preference-based state-price interpretation inside a continuous-time equivalent-martingale-measure framework. The contribution is not the existence of a marginal-utility pricing kernel, but a compensated stochastic-exponential representation of the ``util-prob'' density. Behavioral weighting by marginal utility becomes an equivalent martingale measure only after the drift generated by risk aversion and prudence is compensated. Under positivity, integrability, normalization, and martingale conditions, the behavioral density is generated by the Brownian kernel $-A_t\sigma_t$, where $A_t$ is Arrow--Pratt risk attitude and $\sigma_t$ is state volatility. The required compensator identifies the finite-variation term that must be removed before marginal-utility weighting defines a true martingale density. The framework preserves put--call parity under a common admissible kernel and yields a Cox--Ross risk-premium identity linking option and underlying premia through volatility exposures. Empirically, the paper maps option-implied and physical densities into a signed Arrow--Pratt decomposition, with positive curvature interpreted as risk aversion and negative curvature as risk-seeking or speculative tilt. In a panel of liquid index ETF and single-name options, arbitrage-penalized EBMM has lower pooled holdout squared-error loss than arbitrage-penalized SVI when both models receive the same total-variance positivity and butterfly-density penalties, although slice-level results remain mixed. The exercise is best read as a density-ratio admissibility test rather than a replacement for production volatility-surface estimation.$

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Risk-minimizing GUE-implementations of truthful reporting in a Dynamic Social Choice Problem By Cs\'oka, Viczi\'an

The Guaranteed Utility Mechanism (GUM) was introduced to implement efficiency in Guaranteed Utility Equilibrium (GUE) in dynamic social choice problems. We show that this mechanism also has a previously unknown connection to risk minimization. Fixing an efficient decision policy, we study variance-minimizing budget-balanced transfer rules in the class of GUE-implementations of truthful reporting. A symmetrized version of GUM (sym-GUM) minimizes the sum of the agents' utility variances under truthful reporting when there is no public state. When a public state is present, a slight modification of this rule yields the minimizer for the same objective. Sym-GUM, however, does not minimize transfer risk. The minimizer for that problem is instead the symmetrized version of a closely related GUM-like transfer rule. In both problems, the minimizing truthful total transfers are unique up to agent-specific constants summing to zero and events of probability zero.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 The addicted predator-prey model: How opioid use disorder shapes productivity and growth-cycle dynamics By Chung, Fernandez

This paper extends Goodwin's (1967) predator-prey growth-cycle model to incorporate the negative impact of Opioid Use Disorder (OUD) on labor productivity. Using U.S. state-level data for 1998--2019, we first document that deteriorations in the labor share are followed by rising drug-induced mortality. We then build this link into the model through a novel discrete-choice mechanism in which the probability of OUD decreases with the wage share, so when real wages fail to keep pace with productivity growth, opioid use rises. This behavioral health channel feeds back into the economy through a productivity damage function. Applying the existence part of the Andronov-Hopf bifurcation theorem, we show that the resulting three-dimensional nonlinear system admits a limit cycle, as confirmed by our numerical simulations. Along the cycle, rising OUD paradoxically raises the employment rate, as falling productivity requires more labor per unit of output; across steady states, however, higher OUD prevalence is associated with a lower employment rate, consistent with evidence that opioid use disorder reduces labor force participation. Moreover, our numerical experiments show that OUD sensitivity to income widens fluctuations, while productivity sensitivity to OUD counterintuitively compresses them through a self-correcting feedback that speeds the recovery of the wage share. Policies that weaken the link between income and OUD, such as stronger social safety nets, stabilize the resulting dynamics.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Anchor-proofness in Voting with Ranked Ballots By Fioravanti, Terzopoulou

When voters evaluate alternatives sequentially, an anchoring bias makes their ballots depend on the order in which alternatives are presented. For approval ballots, no non-constant voting rule is robust to this dependence. We show that ranked ballots change the picture sharply: a rank-based rule is robust to the presentation order if and only if it is tops-only. Robustness to anchoring is thus available, but only at the price of ignoring everything a voter reports beyond her favourite alternative.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Identifiable Preference Types and Optimal Menu-Choice Experiments By Kumar

A planner wants to elicit partial information about a subject's preference relation. Preferences are grouped into ``types" relevant to the planner's purpose. Can the planner identify the subject's type through menu-choice data or experiment, without distinguishing preferences within the same type? We provide a complete answer by characterizing identifiable type spaces. The characterization uses a novel geometric condition, called face consistency, which governs distinctions within and across square and hexagonal faces of the permutohedron. For every identifiable type space, we explicitly construct the unique irreducible identifying experiment from its boundary. This experiment is optimal among all identifying experiments under any cost criterion for which adding menus cannot reduce cost. Using the equivalence between identifiability and elicitation with acts, as shown in Azrieli et al. (2021), we study symmetric policies requesting unranked shortlists that contain only acceptable products, include every product preferred to a selected one, and are nonempty whenever an acceptable product exists. Only two such policies are elicitable with acts: requesting the favorite acceptable product or the entire acceptable set.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Price Discrimination and Revealed Consumer Welfare By Business, University), Business et al

We take a revealed-preference approach to study the welfare effects of price discrimination. An analyst observes price and quantity data before and after a monopolist segments the market. The analyst knows neither the aggregate demand curve nor the information available to the monopolist. We characterize the set of observable price-quantity pairs that are rationalizable by some market segmentation. We then obtain sharp bounds on the change in consumer surplus arising from some segmentation. Finally, we provide conditions under which the data identifies an unambiguous increase (decrease) in consumer surplus.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Selective Verification in Centralized Admissions: Which Priority Claims Must Be Checked? By Bansal, Ertemel, Kumar et al

Centralized admissions platforms grant priority based on self-reported facts such as residence, sibling status, or family income, and a false claim can cost another applicant her seat. Verifying every claim before matching is expensive, yet checking only admitted applicants afterward is unsafe: a false claim by an applicant who ends up unmatched may already have triggered a chain of rejections. We show how a platform can check far fewer claims without changing any outcome. A claim matters only if it can move its applicant across a school's admission cutoff, and we give an exact, easily computed test for when this happens. Checking only such claims, and only when they could change a school's tentative choice, reproduces every application, rejection, and assignment of deferred acceptance under full verification. When claims are truthful, checking the highest claimed priority first minimizes checks among such policies at each school decision, and the number of checks never exceeds the school's capacity. Truthful reporting of preferences and eligibility is a weakly dominant strategy, so incentives deliver the conditions under which this audit order is optimal. In simulated markets, the policy cuts verification by 76 percent relative to checking every claim upfront and by 36 percent relative to checking each claim when it first reaches a school. The design principle is to verify decisions, not files.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Demand Models for Market-Level Data with Closed-Form Inverses By Monardo, Fosgerau, Palma

We introduce a class of demand models for market-level data. The models can be estimated by linear instrumental variables regression while accommodating substitution patterns far richer than the logit and nested logit models they embed. They are built from closed-form inverse market share functions through a generator analogous to McFadden's generalized extreme value generating function, but acting on market shares. Constructive results allow arbitrary nesting structures, including overlapping nests and partial membership, yielding inverse-share analogs of generalized extreme value models. The class is consistent with utility maximization and strictly larger than the class of regular additive random utility models.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Recovering Weighted Shapley Values from Position Preferences By Ertemel

Weighted Shapley values represent asymmetry through positive weights and ordered priority classes. We give these parameters a preference interpretation in Roth's framework of lotteries over positions in transferable-utility games. Under common calibration, nullity, ordinary risk neutrality, unanimity efficiency, and unanimity positivity, position values in unanimity games are probability equivalents. Luce conditioning of these probabilities characterizes the Kalai-Samet family and is equivalent to partnership consistency. We separate conditioning into transitivity of revealed priority, pairwise determination of support, and invariance of positive odds. Two-player observations admit a weighted Shapley extension exactly when revealed priority is transitive and within-class odds satisfy a product rule. The extension is unique: pairwise comparisons identify the priority classes and weight ratios within each class. Larger unanimity games provide additional restrictions on supports and shares. We give constructive proofs, establish independence of the five axioms, and explain the scope and limits of the elicitation interpretation.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Large player classes and approximately weighted voting By Einy, Haimanko

$It is known that completeness of the coalitional ($C$-)desirability relation in a simple game does not imply the existence of a weighted-majority representation. We show that weightedness is implied in an approximate sense. Call an $n$-player simple game $\varepsilon$-weighted if, for some weight vector in the $(n-1)$-simplex, any losing coalition weight exceeds any winning coalition weight by at most $\varepsilon$; let $\varepsilon^{\ast}$ be the smallest such $\varepsilon$. We establish an upper bound on $\varepsilon^{\ast}$ in a general $C$-complete game, and deduce that, if all classes of equally desirable players become large asymptotically but the number of classes is bounded, then $\varepsilon^{\ast}=O(1/n_{\min})$ for the minimal player class size $n_{\min}$, and the bound is tight. Importantly, the minimal attainable fraction $\delta$ of coalitions mislabeled by a weighting rule also tends to $0$ when $n_{\min}\rightarrow\infty$ and the number of player classes is bounded, with $\delta=O(1/\sqrt{n_{\min}})$.$

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 A Regulator's Career Option: Revolving Doors, Regulatory Signals, and Firm Tail Risk By Charles-Cadogan

This paper develops a revolving-door model in which a regulator designs policy signals that affect a regulated firm's capital structure while holding an implicit career option to leave public service for the regulated firm or its advisers. The model has three linked mechanisms. First, regulatory signals alter the firm's pricing, leverage, and risk choices by changing the tradeoff between consumer surplus and firm profit. Second, because the regulator's outside payoff is convex in regulated-firm value, the career option rises with firm value, leverage, and signal-induced volatility. Third, when signal-induced volatility raises the option vega of firm equity, the same mechanism can increase tail risk and reduce distance to default. A proof-of-concept calibration illustrates leverage-vega warning regions and shows how a regulator-induced signal can move a firm from a go-leverage region into an early-warning or crash-risk region. The calibration includes maturity comparisons, sensitivity grids over compensation convexity and initial leverage, a bank-like parameter anchor, stochastic signal simulations, and a stopping-horizon diagnostic. Evidence from federal payroll data on post-employment restrictions supports the career-option primitive by showing that limits on revolving-door opportunities affect post-agency pay. The exercises also organize historical regulatory failures: Lehman Brothers as a regulatory-vacuum case, Silicon Valley Bank as a distorted-signal and weak-supervision case, and Signature Bank as a converted-signal case involving post-public-service incentives, lighter oversight, rapid growth, uninsured-deposit exposure, and tail risk. The model does not predict collapse dates; it identifies conditions under which regulatory forbearance, weak debt penalties, and career-option incentives can delay oversight until firms cross a leverage-vega warning threshold.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 08/10/2026

arXiv📈🤖 Score-based change point detection via tracking the best of infinitely many experts By

arXiv:2408.14073v1 Announce Type: cross
Abstract: We suggest a novel algorithm for online change point detection based on sequential score function estimation and tracking the best expert approach. The core of the procedure is a version of the fixed share forecaster for the case of infinite number of experts and quadratic loss functions. The algorithm shows a promising performance in numerical experiments on artificial and real-world data sets. We also derive new upper bounds on the dynamic regret of the fixed share forecaster with varying parameter, which are of independent interest.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios By

Evaluation of intervention in a multi-agent system, e.g., when humans should
intervene in autonomous driving systems and when a player should pass to
teammates for a good shot, is challenging in various engineering and scientific
fields. Estimating the individual treatment effect (ITE) using counterfactual
long-term prediction is practical to evaluate such interventions. However, most
of the conventional frameworks did not consider the time-varying complex
structure of multi-agent relationships and covariate counterfactual prediction.
This may lead to erroneous assessments of ITE and difficulty in interpretation.
Here we propose an interpretable, counterfactual recurrent network in
multi-agent systems to estimate the effect of the intervention. Our model
leverages graph variational recurrent neural networks and theory-based
computation with domain knowledge for the ITE estimation framework based on
long-term prediction of multi-agent covariates and outcomes, which can confirm
the circumstances under which the intervention is effective. On simulated
models of an automated vehicle and biological agents with time-varying
confounders, we show that our methods achieved lower estimation errors in
counterfactual covariates and the most effective treatment timing than the
baselines. Furthermore, using real basketball data, our methods performed
realistic counterfactual predictions and evaluated the counterfactual passes in
shot scenarios.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Reduced Forms: Feasibility, Extremality, Optimality By Andreyanov, Krasikov, Suzdaltsev

We study independent private values auction settings with heterogeneous bidders, revenue nonlinear in interim winning probabilities, and place no ad hoc restrictions on auctions that a seller can use. We show that feasibility of interim winning probabilities is testable along a unidimensional principal curve, constructed in closed form. We then explicitly characterize the extreme points of the feasible set and the allocations that induce them. Combining these results, we solve for the optimal auction under a natural regularity condition. The optimal auction equalizes bidders' marginal revenues along its principal curve and awards the good to the bidder whose type the curve reaches first. Revenue curvature determines whether and how low types are excluded---at the intensive margin, scaling winning probabilities down, or the extensive margin, cutting them off. Applications include the linear model, endogenous valuations from ex ante investments, and non-expected utility preferences, where previous results were largely limited to symmetric environments and allocations or two bidders.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Social networks, confirmation bias and shock elections By Gallo, Langtry

This paper links the increasing prominence of social networks in politics to recent shock election outcomes. In our setup, agents learning on a network suffer from confirmation bias -- the tendency to ignore contrary evidence to ones own belief. Our main result is that confirmation bias slows down learning in a wide class of networks. For any network, consensus fails to emerge when confirmation bias is above a threshold. We explore the implications for elections and media markets. Confirmation bias increases the likelihood of shock elections, and encourages media organizations already at the fringe to take on even more extreme views.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Personalized Recommendations Without Inducing Congestion: Mitigating Disparities in the NYC High School Match By Chiang, Peng, Lichtenstein et al

Algorithmic recommendations can help participants navigate large matching markets. For example, recommendations for school and college choices may reduce information frictions and disparities in access to high-performing programs. At scale, however, recommenders in capacity-constrained settings can be self-defeating: if they steer too many users toward the same items, then even users who were originally predicted to have a high chance of matching to an item may not, due to increased competition. In this paper, we formalize this phenomenon of recommendation-induced congestion; motivated by the NYC high school match, we show that naive recommendations can cause sharp decreases in program acceptance rates, most affecting applicants with the fewest nearby options. Next, we propose and theoretically analyze a congestion-aware, bilevel optimize-and-simulate approach to allocate recommendations and improve match outcomes safely, in equilibrium. Finally, we deploy this approach in the 2025-26 admissions cycle of the NYC high school match, aiming to reduce disparities by highlighting personalized lists of nearby, high-performing programs where an applicant has a high predicted offer likelihood. In a randomized controlled trial, we find that 16.4% of treatment applicants ranked a recommended program, versus 10.5% of control applicants who ranked a program they would have been recommended (57% relative increase; $p$=0.011); 5.6% of treatment applicants matched to such a program, versus 3.3% of control applicants (71% relative increase; $p$=0.071); further, no treatment applicant was rejected from a recommended program. Our findings suggest that recommenders should be analyzed and designed as market-shaping interventions.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Understanding Interfirm AI Talent Flow Networks through Online Professional Profiles By Li, Zheng, Prakash et al

Artificial intelligence capabilities are often measured as resources accumulated within firms, yet they also circulate across organizational boundaries through worker mobility. Drawing on a database of approximately 535 million employment records across 58 countries, we reconstruct the inter-firm network of AI talent flows over 2010--2022. We find that AI talent inflows are becoming increasingly concentrated among a small set of leading firms, with stronger preferential attachment than in general labor mobility. Yet the network core remains contestable: central positions in AI networks change hands far more often than in non-AI networks. Network position also carries information that goes beyond workforce size. This network position is economically consequential: AI network centrality is associated with higher enterprise value, after accounting for workforce scale, assets, and fixed effects. Event-study estimates further indicate that sharp improvements in network position are followed by higher enterprise value relative to matched firms with similar pre-event trajectories. Together, these findings suggest that corporate AI advantage depends not only on how much talent firms accumulate, but also on where they sit in its circulation.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 The Noise Is the Signal: Correlated Sampling Error Is Rank-Informative for Proxy Metric Selection By Provenzano

North-star metrics such as customer lifetime value are often too slow and noisy to decide a short A/B test. Teams therefore rely on a proxy metric, commonly chosen by how closely its effects tracked the north star's across past experiments. Validating that choice, or any method for making it, is hard: the only benchmark is the noisy north star, and the number of available past experiments is limited. In addition, proxy and north-star effects are estimated on the same customers, so their sampling errors are correlated. Recent work at major experimentation platforms removes this shared error as contamination, improving estimates of the true-effect covariance. Choosing a proxy, however, is a ranking problem, and a better estimate need not give a better ranking. We measure agreement free of shared error by estimating the two effects on disjoint random halves of each experiment's customers. In an archive of 262 experiments and 69 candidate proxies, the shared error ranks the candidates in a similar order to this agreement (Spearman correlation 0.65): it carries information about proxy quality. The more of it a correction removes, the worse the ranking because removal discards part of the signal but leaves the main sources of ranking noise, the noisy north star and the limited number of experiments, untouched. Archive-calibrated simulations, in which the correct ranking is known, confirm this even when every correction receives the true sampling covariance. Held-out real experiments, evaluated on disjoint customer halves so that shared error cannot bias the comparison, closely reproduce the predicted ordering (Spearman correlation 0.93). Correction can still pay off with more experiments, but the number needed rises steeply with the north star's noise. We map this crossover and give platform teams three inexpensive checks for deciding from their own archive whether and how strongly to correct.

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Rewarding Informed Voting in PoS Governance By Beccuti, Schneider

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ArXiv Paperboy (Econ.GN + Econ.TH) @arxivecongnbot.bsky.social · 07/10/2026

arXiv📈🤖 Verified, not generated: expert-verified AI study materials and the distribution of learning gains in a university course By Dang, Nguyen

Experimental studies of generative AI in education mostly report average effects, yet field evidence shows that AI can narrow attainment gaps or widen them. We argue that the direction depends on the judgement burden, the expertise a learner must supply to screen AI output before learning from it, and that expert verification before release moves this burden from students to an accountable tutor. We test the argument in a two-cohort difference-in-differences design in which one half of a compulsory firstyear university economics course received AI-generated podcasts, FAQs and quiz-based study guides, produced with a source-grounded model and checked by a named graduate teaching assistant (170 students; 340 examination marks). Access was associated with a 2.34-mark advantage on a 50-mark component. The share of marks below the upper-second classification boundary fell by 24.7 percentage points relative to the counterfactual, effects were significant at every threshold from 23 to 31 marks and at none above, and roughly three-quarters of the average originated in the bottom quintile. The threshold estimate is robust to removing the lowest-scoring students from the pre-intervention cohort; the average effect is not. Interviews and feedback from 36 students indicate that the verification label gave students a reason to engage with AI-generated material without ending their scrutiny of it. Evaluations of AI learning resources that report only mean effects cannot detect whether the students the resources are meant to help are the ones who gain.

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